A - Z Definitions and Protocols 1 (5)

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CONTENTS 3age

B An A-Z of Commonly Used Terms and Protocols

relating to Box Office and Audience Data

BI "bout this guide and how to use it

B Mndex of terms related protocols formulae and worked 5

examples

B (ommonly used terms and their definitions I

BP The related protocols 59

B5 Digest of formulae with worked examples IR





B1 About this guide and how to use it

BI.I This resource is one of the many documents developed for the

"udience Data ,S initiative.

BI. Built as a reference tool it is a cross between a dictionary and an

encyclopaedia of commonly encountered audience data terms and

processes.

BI. The intention in building it was to provide people who use handle or

analyse audience data with a single source of guidance on what

audience data terms should be taken to mean. "t one and the same

time it sets out recommended procedures for

U (alculating figures and numbers relating to arts facilities

U eporting on the findings coming from such analysis

and

U 3resenting these findings in a rigorous and meaningful way.

BI.P The underlying aim here is to provide a universal guide to such terms

and processes $ one that enables greater consistency. (onsistency in

the ways that audience data is collected and assembled. (onsistency

in the ways in that audience data is processed. "nd consistency in the

ways these audience data is interpreted. This in turn potentially allows

for easier comparisons across the arts and cultural sector W between

organisations; between cities and regions; and between the ,S

nations.





BI.5 Yrom the outset it was recognised that arts and cultural organisations

are a diverse group. They inevitably work with different artforms have

different expertise within them and have different needs from data.

Zence this resource has also been created with this in mind. herever

necessary it indicates how the core set of audience data terms and

processes may need to be varied to suit different types of organisation.

BI.6 This intrinsic difference between organisations also affects how you

use this resource. To aid rapid location of a term or process it has four

basic parts. Yor best results they should be used as follows.

U The ]terms and definitions^ section GB3K is a glossary of what a term

should be taken to mean. Mt is made up of short pithy explanations

of what the various terms actually mean together with how they

should be used. =o if you want to check what a term means W or

should be taken to mean $ you should start with this section.

U But you might be looking for more detailed information. Mnformation

that expands on the basic definitions. This could be an explanation

of the recommended way of working something out; the reasons

why a particular definition has been recommended; any

complicating factors and issues it would be as well to be aware of;

or where to look for further information. Mf this is the case not only

should you look up a term^s definitions you should also check the

]protocols^ section GB4 page 59 onwardsK to see if there is more

detailed material available. G"lthough please note that not all of the

definitions have associated protocolsK.

U Then for the more technically minded section B5 Gfrom page IRK

sets out the detailed formulae and worked examples that apply to

some of the protocols.





BI.7 There is something else to watch out for too. Because of the

differences in practice and purposes within the sector there are

freùent occasions when a basic term definition or process will not

ùite fit with the particular demands of your organisation. Yor example

whether an event is ticketed or non ticketed or whether something is a

performing arts event as opposed to a visual arts event. Thus in such

instances the overall broad headline definitions and protocols will be

followed by ]nested^ sub$definitions and sub$protocols that make them

more precise and applicable to specific settings.

BI.R Mt should also be noted that many of the definitions and protocols are

cross referenced with each other. hen this is the case a cross

reference to a definition of a term appears as bold text in Upper and

lower case. " cross$reference in BOLD AND UPPERCASE indicates

another set of protocols or formulae that could usefully be considered.

BI.9 Then lastly to aid rapid location of a particular definition protocol or

formulae section B2 Gstarting on the next pageK provides an index of

the terms protocols and relevant formulae both defined and explained.





B2 Index of terms, related protocols and formulae

B2.1 Index of commonly used terms and their definitions

Term 3age

AGE GROUP or AGE CATEGORIES I

ANCILLARY INCOME IP

ANNOTATIONS, ACCREDITATION and REFERENCING IP

AREA PROFILES or AREA PROFILE REPORTS IP

ASSOCIATE ATTENDER I5

ATTENDER I5

AUDIENCE POTENTIAL I7

AVERAGE I7

AVERAGE DAILY ATTENDANCE IR

AVERAGE RATE OF ATTENDANCE IR

AVERAGE SPEND (PER HEAD) I9

BOOKER I9

BOOKING 0

CAPACITY I

CATCHMENT AREA or CORE CATCHMENT P

CAVEATS and QUALIFICATION 5

CHURN 5

COMMAS (NUMERIC) 6

COMPLIMENTARY or ‘COMP’ 6

CONCESSION 7

CORE CATCHMENT 7







MARKET POTENTIAL R

MARKETING SPEND PER ATTENDER 9

MEAN or ARITHMETIC MEAN 9

MEDIAN 9

MEMBERS or MEMBERSHIP SCHEME P0

MODE P0

MODEL DATA P0

NET INCOME P0

NO SHOW PI

OCCASIONAL PI

OCCUPANCY PI

PARTY P

PARETO EFFECT or PARETO PRINCIPLE P

PENETRATION P

PERCENTAGE P

PERCENTAGE CAPACITY and PERCENTAGE OCCUPANCY PP

PERCENTAGE CHANGE PP

POPULATION P5

POTENTIAL AUDIENCE P5

PROPORTION P6

RATE OF ATTENDANCE P6

REGULAR P7

RESEARCH OBJECTIVE P7

RESERVATION PR

RESPONSE RATE PR





ROBUST PR

SALES PROMOTION P9

SAMPLE P9

SAMPLE SIZE 50

SEGMENTATION 50

SERIES or RUN 50

SOCIAL GRADE 5I

SUBSIDY PER HEAD 5

SUBSCRIBER 5

SUBSCRIPTION 5

TOURIST or VISITOR 5P

TRANSACTION 55

TRANSACTION TOTAL, TOTAL INCOME PER TRANSACTION 56Or TOTAL TRANSACTION VALUE

UNPAID RESERVATION 56

VISITOR 56

YEAR 57

YIELD PER ATTENDER or AVERAGE YIELD PER ATTENDER 57

YOUNG PERSON 5R





B2.2 Index of related protocols

3rotocols relating to 3age

AGE GROUP or AGE CATEGORIES 6I

ANCILLARY INCOME vs CORE INCOME or CORE 6PTURNOVER

ANNOTATIONS, ACCREDITATION and REFERENCING 65

AREA PROFILES or AREA PROFILE REPORTS 70

ASSOCIATE (ATTENDER) 75

ATTENDER 76

AVERAGE R0

AVERAGE DAILY ATTENDANCE R6

AVERAGE PRICE PAID R7

AVERAGE RATE OF ATTENDANCE RR

AVERAGE SPEND PER HEAD 90

BOOKER 9I

CAPACITY 9P

CATCHMENT AREA or CORE CATCHMENT 97

CAVEATS and QUALIFICATIONS I0I

CHURN I0I

COMMAS (NUMERIC) I07

COMPLIMENTARY or ‘COMP.’ I0R

CONCESSION III

CORE INCOME or CORE TURNOVER II

DATA CAPTURE RATE II

DATE or CUSTOMER REFERENCE DATE II5





DISCOUNT IIR

DOOR SALES II9

DRIVETIME or ISOCHRONE I0

ECONOMIC IMPACT I

EVENT I7

FAMILY IR

FINANCIAL CAPACITY II

GROSS INCOME I

INDEX or COMPARATIVE INDEX I5

MARGIN FOR SAMPLING ERROR aka MARGIN FOR ERROR IR

NET INCOME IPP

PERCENTAGE CAPACITY and PERCENTAGE OCCUPANCY IP5

RESEARCH OBJECTIVE IP7

RESPONSE RATE IP9

SAMPLES, SAMPLING and SAMPLE SIZE I5

SEGMENTATION I65

SOCIAL GRADE I6R

SUBSCRIPTION and SUBSCRIPTION DISCOUNT I7I

TOURIST or VISITOR I7

TRANSACTION, TOTAL TRANSACTION and TOTAL I76TRANSACTION VALUE

YOUNG PERSON I7R





B2.3 Index of formulae (with worked examples)

Yormulae relating to 3age

AREA PROFILE REPORTS IR

AVERAGES I9

AVERAGE RATE OF ATTENDANCE I99

CHURN I0I

DISCOUNT & PERCENTAGE DISCOUNT 00

MARGIN FOR SAMPLING ERROR 0I

SAMPLE SIZE 0P





B3 Commonly used terms and their definitions

AGE GROUP or AGE CATEGORIES

"n approach that uses ranges based on ]years of age^ to subdivide the

wider population into bands. The specific age groups used will depend

on any other data the analysis is to be compared with. Zowever use of

the appropriate age bands combined with the ingenious use of

aggregation potentially makes comparison with larger data sets

possible.

The table overleaf shows the age bands used for the Target roup

Mndex TM data together with those used by the :ffice for Aational

=tatistics to summarise Aational (ensus data. Guidance on how to

ask about and create age groups is provided in the relevant protocols

sectionK.





Target roup Mndex Aational =tatistics

0 to P

5 to 9

,nder I5

I0 to IP

I5 to I9I5 to P

0 to P

5 to 9

0 to P

5 to 9

5 to PP

P0 to PPP5 to P9

50 to 5P

55 to 59

P5 to 6P

60 to 6P

65 to 69

70 to 7P

75 to 79R0 to RP

R5 to R9

65 & upwards

90 and over





ANCILLARY INCOME

oney earned by an organisation through an activity that lies outside

its normal core activity and purpose. Zence this can include incomefrom catering merchandising refreshments ice creams and

confectionary and hires of a facility for a purpose other than the sorts

of events that are included in its core programme Geg conferencesK.

ANNOTATIONS, ACCREDITATION and REFERENCING

" part of professional good practice where if someone else^s work is

referred to an appropriate reference Gto enable the reader to trace it

back to sourceK is given. This should be accompanied with an

acknowledgement of this other author^s contribution together with

details of the source used. G=ee also Caveats and Qualifications on

page 5K

AREA PROFILES or AREA PROFILE REPORTS

" detailed summary of model data relating to a catchment area

Gbased on a selected drive$time areaK and the people who live in it.

" typical area profile includes

U a map of the drive$time area being reported on

U a two page overview table showing information on the people living

in that area Gsuch as age social grade car ownership

geodemographic profile likely attendance at arts events etcK





U a postal sectors report giving the data shown in the overview and

broken down by postal sector and

U a postal sector percentage report that shows the relevant data

items expressed as a percentage and compared with the relevant

percentages for the given drive time area.

ASSOCIATE (ATTENDER)

" person who makes an attendance with another person but who was

not personally responsible for making the booking for that attendance.

ATTENDER

=omeone who actually comes to an event or performance.

"n attender could be someone who has made a booking Gie a

]booker ̂K for themselves and then made an actual attendance. Mt could

also be a person who has come to an event or performance but who

had a booking made for them by someone else Gie an ]associate^K.

ùally being an attender does not imply that a booking has been

made or that money has been paid. hat is crucial here is the

essential act of actually coming to an event or performance. Zowever

someone who books a ticket but then does not use it for their own visit

to a facility ("AA:T be classified as an ]attender^.





"ttenders can be sub$divided according to their current status in terms

of the relative frequency of their relationship with the organisation. The

suggested definitions here are shown in the following table.

TERM DEFINITION

(urrent attender =omeone who has been to an

event at the organisation within the

last I months.

egular attender =omeone who has been to an

event at the organisation a

minimum of two times in the last

I months.

Yreùent attender =omeone who has been to an

event at a recurring rate that an

organisation considers to be

higher than average.

apsed or inactive attender =omeone who has a history of

attending the organisation but

who either

U has asked to be removed from

the organisation^s records or

U has not been to anything at the

organisation during a period eùal

to one year 3,= the number of

years the organisation considers

to be the norm for this type of

attender.

evived attender =omeone who had stopped using

the organisation but has restarted

this behaviour.





AUDIENCE POTENTIAL

The total number of people that an estimate of market potential shows

match the geodemographic profile for the sort of people in a givenarea who come to events at an arts facility.

AVERAGE

" measure of a data set^s central tendency which is the single valuethat best typifies the data set.

Three sorts of average are commonly used for differing purposes.

The mean is found by adding together the values for all the

observations made and dividing the resulting total by the number of

observations. Best used when a commonly understood type of average

is reùired. This is especially effective when all the available data

needs to be included in the analysis but can be distorted by any

extreme values in the data.

The mode is the most freùently occurring answer W ie the one that

crops up the most times. ,se this if the answers given were categories

rather than numbers.

Then the median is the mid point of a range of values that have been

put in order of sie. ,se this measure if there are a few very high or

very low values that might distort the mean.





AVERAGE DAILY ATTENDANCE

The mean number of people attending per day.

(alculated as

Total number of attendances in a given period

Aumber of days covered by that period

AVERAGE RATE OF ATTENDANCE

The mean number of times a typical user visits or attends an arts

facility over a particular period. GYor instance a month or I monthsK.

Zence this is a basic measure of the typical freùency of attendanceGeg twice a yearK.

This is calculated by working out

Total number of attendances made over the period in ùestion

Total number of events these attendances refer to

That is the total number of visits made by all attenders during the

period being looked at and then dividing this total by the number of

people whose attendances have been included in the total.

=ince this is a rate of attendance it would be expressed in the form ]

times a year^ or ] visits per year^.





AVERAGE SPEND (PER HEAD)

The mean income generated per person attending an event series or

year^s programme.

This should be calculated as

Total gross income generated in the given period

Total footfall during the given period

BOOKER

" person making an advance reservation Gor reservationsK for a seat at

a ticketed event or performance. hen the organisation with which a

booking is made has a computerised ticketing system or database the

booker is likely to have been recorded by way of a name and address

record.





BOOKING

Both a verb and a noun referring to ticketing transactions made

between a booker and an arts facility or its agents.

hen the term is used as a verb it describes the act of carrying out

one or many of the interactions that $ when considered collectively $

amount to a complete transaction. =o in this sense ]booking^ is a

verb that can encompass both the making of an unpaid reservation

and the confirming of that reservation by paying money for it Gie makinga purchaseK.

hen used as a noun a ]booking^ should be taken to mean an overall

unit for analysis comprising the complete and finalised seùence of

interactions that W when taken together $ contribute to the eventual

transaction total. Yor instance it is perfectly possible for such a

]booking^ to be made up of a seùence of events that includes an

initial unpaid reservation; it^s following confirmation through purchase;

and any associated returns or refunds.





CAPACITY

" range of measures that ùantify the amount of something that ismade available by an arts facility through a discrete event Gor seùence

of eventsK such as a performance series exhibition or workshop.

There are two general forms such measures can take physical Gthe

number of places available to be usedK; and financial Gthe monetary

value of the places available to be usedK.

There are also three different conceptual levels at which these

measures can be applied as follows.

U ]Theoretical^ Gor ]benchmarking^K capacities ùantify the ultimate

total places that a facility could offer for a given discrete event $

or seùence of events. GZere please note that given the process

of calculating financial capacity and its reliance on having prices

set there can be no theoretical financial capacity that it is

meaningful and useful.K

U ]:perational^ capacities ùantify the number of places Gor the

value of these placesK that are available for use once

operational considerations have been taken into account. =uch

]operational^ considerations include removing seats from

audience use because of a sound desk closing a part of the

house or adding places for people to stand in.

U "nd ]achieved^ capacities are the actual measures of how much

of the available resource Gphysical or financialK was really used

when the event or series took place.





(ombining the above considerations and applying them to different art

forms gives the following set of available measures for capacity. Zere

despite the implicit differences between them it is recommended that

the full name of the measure being used be cited in relevant reports.

Yor seated performing arts venues the capacities used and reported

on should be as follows.

Theoretical physical capacity

The total accommodation available at a given event or performance Gie

the total number of designated places for a given event or performance

available to be put to use for occupancy purposesK.

:perational physical capacity

Total number of seats wheel chair places and standing places MA,=

any seats wheel chair places and standing places removed from public

use for operational reasons Gsuch as installation of sound boards or

the closing off of parts of the houseK.

:perational financial capacity

The published face value of the available places multiplied by the

number of physical places that are available for use at the various

published prices. 3lease see the protocols section Gunder YMA"A(M"

("3"(MTj on page IIK for a further explanation of this.

"chieved physical capacity W the number of available places actually

used.

"chieved financial capacity W the amount of money actually generated

from the relevant event.





Yor visual arts facilities the following measures can be applied.

Theoretical physical capacity is the number of admissions that could be

legally allowed to the facility over the period in ùestion.

The operational physical capacity used and reported on should be the

number of persons the ocal or Yire "uthorities have licensed the

space to accommodate. Mf timed entry is offered then the timed

operational physical capacity should be calculated as the number of

admissions that licensing regulations would allow during each hour the

facility is available.

:perational financial capacity eùals the number of admissions that

are permitted under licensing regulations multiplied by the maximum

possible value of each admission.

"chieved physical capacity is the actual number of admissions made to

an event.

"nd "chieved financial capacity eùals the actual number of

admissions made times the price paid for each admission.

ùally for unseated performance spaces outdoor or informal

venues the theoretical and operational physical capacities used and

reported on should be the number of persons the ocal or Yire

"uthorities have licensed the space to accommodate. Zere if the

space is unlicensed Gsuch as for a street performanceK ocal "uthority

or 3olice estimates of the number of people who could watch an event

whilst staying within safety limits should be used to provide the phyical

capacity figure.





Theoretical financial capacity will eùal the theoretical physical

capacity multiplied by the published full price for each admittance and

the operational financial capacity will also eùal the operational

physical capacity multiplied by the published full price for each

admission.

Then the achieved physical capacity will be the actual number of

people who attended the event whilst the achieved financial capacity

will be the actual number of people who attended times the prices

these individuals actually paid.

CATCHMENT AREA or CORE CATCHMENT

The geographic area that is the source of the largest and most

important proportion of actual users for an event series of events

organisation or facility.

enerally speaking it is preferable for an organisation to decide what is

the sie of such an ]important proportion^. But if this is not possible

80% of the attending audience should be used as a default.

here the organisation is a ticketed one a pragmatic approach here is

to base the calculation on postcodes from addresses captured for

bookers Gunless figures for attenders are availableK. But if the analysis

applies to the catchment for bookers this needs to be stated.

Yor non$ticketed events postcodes should be collected from a sample

of users Geg through self$completion surveys ùick and short face to

face interviews or data generated through a competitionK.





CAVEATS and QUALIFICATIONS

" sentence or paragraph in a research report indicating areas of potential weakness in a research finding.

"ny finding claim or assertion made gains enormously in power if it is

not overstated and thus is accompanied by a caveat or a relevant

ùalification.

This can be done Gas relevantK using a paragraph along the lines of

These findings suggest that the number of people attending this sort

of work has increased. However the research here was done over a

Bank Holiday weekend. Since Bank Holidays typically seem to result in

local residents leaving town, and in a greater number of visitors arriving

in the town, this finding may not be representative of normal patterns.

=ee also the protocol for ANNOTATIONS, ACCREDITATION and

REFERENCING starting at page 65.

CHURN

"n analytical procedure for ùantifying the degree of audience

turnover based on evaluating and comparing the rate at which new

audience members are acùired and existing audience members are

retained or lost. The term ]churn^ is also used as a name for the

identified rate of audience turnover.





COMMAS (NUMERIC)

The use of the character ] , ] to separate hundreds thousands and

millions in large numbers. These are particularly useful in increasing

the speed with which such numbers can be read and understood.

Zence they should be used wherever possible. ie I50 60 rather

than I5060.

COMPLIMENTARY or ‘COMP.’

"n admission that has been allowed to a charging space for no charge

that has been issued as a gratuity by the presenting organisation with

the implied loss of income to that organisation.

=uch ]complimentaries^ should also include admissions made at no

charge for direct business reasons Gfor instance for business

development press and 3 as an apology or correction for customer

service errorsK as a reward or an incentive for someone acting as a

group booker or as part of an audience development scheme.

Mf relevant the free portion of a T:Y G]two tickets for the price of

one^K or B::YY G]buy one get one free^K offer should be recorded and reported as a ticket issued at 50 of its published face value.

Therefore these should not be treated as ]comps^. But experts in the

field Gsuch as oger Tomlinson and Tim BakerK point out that

customers expect to be able to identify a free ticket issued as part of

such an offer so the price printed on the ticket should be shown as

0.00.





CONCESSION

" reduction in the cost of admission to a charging facility event or

performance made in recognition of a user^s social backgroundprofile or particular needs.

Mf such a price reduction is offered as part of a marketing initiative or

campaign Gand not as part of the organisation^s usual concessions

policyK this should be referred to and reported as a discount.

CORE CATCHMENT

=ee catchment area on pages P and 97.

CORE INCOME or CORE TURNOVER

onies Gsuch as ticket sales and public subsidiesK that an organisation

generates or receives from activities that are part of its main mission

and purpose. G(ompare with Ancillary Income see page IPK.





DATA CAPTURE RATE

" measure of the number of booking transactions made at an artsfacility for which details have been recorded. This rate is typically

expressed as a percentage ie

GAo. transactions for which details are recordedK I00

Total number of transactions

DATE or CUSTOMER REFERENCE DATE

" record of when a customer or user did certain things in relation to an

arts facility.

Mdeally this should be recorded as both the date and time for the

relevant activity. (learly there may be many dates that could be

recorded for each customer or user such as

U Time and date of first contact

U Time and date of first full documentation of the customer^s details

U Time and date of first booking

U Time and date of payment

U Time and date of the event attended

and

U Time and date of most recent activity Gwhich could well be

something other than the most recent attendance W for example

making a refund sending them some promotional material or

handling an enùiry from this customerK.





:n balance the most important of these would seem to be the time

and date of a customer’s first activity and this should be obtained

and recorded even if the other dates prove unobtainable.

DEMOGRAPHIC PROFILE

" statistical representation of the groupings within a market

community or area classified according to considerations such as age

social grade economic status and life stage. =ee also geoanalysis

geodemography and geodemographic profile Gon page PK .

DISCOUNT

" generic term relating to a reduction in face value made by a charging

facility on the published value of an admission. These can be offered

as part of a marketing initiative or as part of the usual concessions

policy.

Thus such marketing related discounts could take the form of a sales

promotion special offer or a subscription scheme.

DOOR SALE

The purchase of a ticket for an event or performance that is made on

the way into that event or performance.





=ometimes referred to as a ]walk up^ Gbecause the customer ust walks

up and makes the purchaseK it can often be the case that the customer

remains unidentified so that the purchase is not added to that person^s

overall purchase history.

DRIVETIME or ISOCHRONE1

:ne way of defining an organisation^s catchment. This is based on

drawing a line surrounding all the places that are a given drivetime

Gusually in minutesK from the facility.

(are should be taken to use W where available W a drivetime that

reflects the prevailing conditions at the relevant time of day Geg peak

rush hour mid$afternoon etcK. Data on the relevant drivetimes is

available from the "udience Development "gencies licensed to provide

AREA PROFILE REPORTS. (opies of an area profile report $ for any

given area $ can be obtained from the three Aational "udience

Development "gencies that have been commissioned to undertake this

role. These agencies are Arts About Manchester Gtelephone 0I6I R

P500K; AMH Gformerly Arts Marketing Hampshire) Gtel 0I96 RP6 96K;

and Audiences Yorkshire Gtel 0R70 I60 PP00K. Zowever rather than

contacting these agencies direct there can be particular value in

asking your local "udience Development "gency to arrange for therelevant "rea eports to obtained for you. Gspecially since your local

"gency will be able to discuss your particular needs and help you with

the interpretation of the reports once they have been receivedK.

IThe andom Zouse unabridged dictionary I997 gives the following details of this term

“i•so•chrone Pronunciation: (¥'su-krÿn"), a line, as on a map, connecting all points having some property simultaneously, as in having the same delay in receiving a radio signal from a

given source or requiring the same time to be reached by available transportation froma given centre” Gcurrent author^s emphasisK. The derivation of the term is from the "ncientreek ]iso^ and ]chronos^ meaning ]eùal time^.





ECONOMIC IMPACT

The gross financial contribution made by an organisation or facility to

the overall economy of its base area Gfor example its town region or

nationK.

:ne favoured model of this assesses such impact both in terms of

money received by the organisation Gincoming factorsK and money paid

out by the organisation Goutgoing factorsK.

These incoming factors can include elements such as money received

to finance an organisation^s primary business Gie its core turnover K;

ticket sales; ancillary income; engagement fees for an organisation^s

events presented elsewhere; subcontracting fees paid for using an

organisation^s staff; and money paid to hire an organisation^s facilities.

:utgoing factors include staff wages; payments to suppliers; income

tax; Aational Mnsurance contributions; and Halue "dded Tax.

Yreùently such calculations of economic contributions made by an

organisation can relate not only to the organisation itself but also to

any economic transactions that have occurred in the surrounding

economy Gsuch as travel and the purchase of mealsK that would not

have happened if the organisation did not exist and its events were not

taking place.





EVENT

" discrete one$off happening such as an exhibition performance or participative workshop. The plural of event is a series or run. Zowever

there are potential tensions here between this definition and the term

]event^ as it tends to be used by ticketing system suppliers. Yor details

on this please see the protocols section.

FAMILY

" party which enoys an association based on kinship that is made up

of any adult attending or participating in a cultural event with a child

under the age of I6.

FINANCIAL CAPACITY

The total amount of money that could be generated from a discrete

performance or event calculated by multiplying the total number of

places available for occupation with the full face value of those places.

FOOTFALL

The total number of people passing through an arts facility within a

given period eg per day G]daily footfall^K per week G]weekly footfall^K

per month G]monthly footfall^K or per year G]annual footfall^K.





FREQUENCY

The number of times during a given period that a particular sort of event takes place. Yor instance ]annual freùency of attendance^

]freùency of visits^ ]freùency of purchases^.

FREQUENT

"n adective applied to a term which indicates that this happens on a

repeating and rapidly recurring basis. GYor instance ]freùent attender^

]freùent user^K.

This is one of the set of terms which will vary dynamically from

organisation to organisation. Zence a freùent attender should be

considered as being someone who attends more often than theaverage (mean) number of times Gsee sections starting at pages I7R0

and I9K for an organisation^s customer base and user base.





GEOANALYSIS, GEODEMOGRAPHY and

GEODEMOGRAPHIC PROFILE

"n approach to classifying types of customers based on the sorts of

residential areas they have been drawn from.

Typically geodemography classifies an overall population or group of

people into a number of types using a combination of various personal

attributes such as the average household income occupation age of

head of household number of cars owned lifestage and number of

dependent children.

This process is based on a piece of logic that is akin to the notion that

birds of a feather flock together in that it assumes that W more often

than not $ people in a particular residential area will share a number of

common attributes Gsuch as the ones listed aboveK with their

neighbours.

Thus this is a case of you are like where you live. "nd here it should

be remembered that such profiles tell us about the typical flavour of a

neighbourhood rather than about the actual individuals who live there.

" number of geodemographic profiles are commercially available and

need to be selected on their relative merits. Zowever the two most

often used in relation to the arts are ("(M^s "(:A G" (lassification of

esidential AeighbourhoodsK and xperian^s :="M( G:="M( is a

product name and not an acronymK.





GRATUITY

=omething given freely or without recompense Gie a free gift or apresentK. Zence in the arts sector gratuities can sometimes take the

form of a complimentary admission provided by an organisation as a

favour to an attender.

GROSS INCOME

The amount of income received before any deductions are made to

reflect elements such as H"T credit card charges booking fees or any

other chargeable aspects. Yor purposes of simplicity and consistency

wherever possible the use of gross income Grather than net income

here see page P0K should be encouraged.

GROUP

"n organised attendance by a number of people who are coming by

virtue of an intervention made by Gor on behalf ofK an arts facility. =uch

interventions could include the activities of a dedicated ]roup

Bookings :rganiser^ or the customer^s use of a special and dedicated

]group booking rate^. Gsee Party on page PK.





HOTSPOT

" location within an arts facility^s catchment area where a relativelyhigh potential is being realised. Zence a hotspot is a place from which

an organisation is drawing a relatively high number of users.

INDEX or COMPARATIVE INDEX

" variation on the percentage this is a single figure that shows the

result of comparing a specific instance that you are interested in with

the overall average for the whole from which this instance is drawn.

The formula for calculating an index is

GThe specific instance of interestK I00

The overall basis for comparison

Yor example say that your arts facility was attracting 50 of its

audience from a particular social grade compared to the population of

your catchment area which had only 0 of its population in that

social grade. Then in this case the comparative index for the use of the

facility by people having the specific social grade compared with the

presence of people from that social grade in the catchment area as a

whole would be

50 0 I00 q 5 I00 q .5 I00 q 50.

"s can be seen from the logic of this formula and calculation a

resulting index of I00 would mean you have levels of attenders from a





particular social grade that are on a par with their levels in the

catchment as a whole.

"nd by extension an index of 00 indicates something happening at

twice the rate that might be expected from the basis for comparison

whilst an index of 50 shows it happening at half the rate of the basis for

comparison.

ISOCHRONE

" shape or contour drawn on a map connecting all the locations from

which it takes the same time to travel to a central location such as an

arts facility. G=ee drivetime starting on pages 0 and I0K.

LAPSED

" ]lapsed attender^ is someone who has been to an arts facility on at

least one occasion in the past but now appears to have stopped

attending.

vidence that they have stopped attending could be that they have

actually asked to be removed from an organisation^s records.

But they could be viewed as having become inactive because they

have not made another attendance after the average GmeanK period for

users to remain active 3,= I months has passed.





LOCAL RESIDENT and LOCAL COMMUNITY

"n indigenous person or group of people living in the same area as an

arts facility and to which that facility will owe some responsibility byvirtue of geographical association.

=uch local residents and communities are thus to be found within an

organisation^s core catchment area Gsee page PK.

LOYALTY SCHEME

=ee Members or Membership Scheme on page P0.

MARGIN FOR ERROR or MARGIN FOR SAMPLINGERROR

" statistical term representing the degree to which a sample or

analytical finding may be inaccurate due to the sie of the sample

used.

MARKET POTENTIAL

=ee Area Profile Report and Potential Audience





MARKETING SPEND PER ATTENDER

"n indicator of how much investment has been reùired for each

attendance generated.

This calculated as

Total gross marketing spend in relation to an event

Total attendance at that event

MEAN or ARITHMETIC MEAN

The most commonly used measure of central tendency or average

Gsee page I7K. This is found by adding up all the observations under

consideration and dividing them by the number of observations made.

G=ee also median Gbelow and at AVERAGE on page R0K and mode

Gsee on page P0 and again under AVERAGE on page R0KK.

MEDIAN

"n measure of central tendency or average that is the value that W

literally $ sits exactly in the middle of a data set. That is it is the ]mid

point^ in a range of values that have been put in order of sie. ,se this

calculation if there are a few very high or very low values which might

distort the mean^.





MEMBERS or MEMBERSHIP SCHEME

"n initiative run by an organisation or arts facility with the intention of increasing customer and user loyalty by offering rewards and

inducements for loyal behaviour Geg freùent attendance being an

advocate for or supporter of the organisationK.

=uch schemes are usually charged for and may involve associated

costs for the organisation.

MODE

" measure of central tendency or average identified by finding the

most freùently occurring and thus the most popular value in the data

set. That is it is the value that crops up the most times.

MODEL DATA

" source of summarised statistical information that gives a

representation Gor indicationK of the essential nature of an area and the

people who live in it.

NET INCOME

" figure for income received shown after deductions have been made

for something Gsuch as H"T credit card charges or booking feesK.





enerally W and with the exception of accounting statements where this

may be unavoidable W the use of net figures should be avoided.

NO SHOW

" person or persons who has made and paid for a booking Gsee page

0K W either for themselves or for someone else who then does not

make the relevant attendance.

OCCASIONAL

=omething that happens at a lower rate than is the norm Gor is typicalK

for a given set of circumstances.

Zence an occasional attender is someone who W over time W tends to

keep coming to an arts facility but does so at a rate that is lower than

the average rate of attendance Gsee pages IR RR and I99K.

OCCUPANCY

The proportion of units of accommodation Gie seats or spacesK within a

venue that are available for use that has actually been used. ost

freùently calculated as percentage occupancy.

Zence the calculation here for percentage occupancy is





GAumber of people occupying a facility on both a paid

and unpaid basisK I00

Aumber of units of accommodation available for use

PARTY

ore than one user being admitted to a venue or facility who form a

single entity due to some association they hold with each other. Gsee

Group on page 5K.

PARETO EFFECT or PARETO PRINCIPLE

" general principle Gfirst documented by Mtalian conomist Hilfredo

3areto IRPR to I9K which observes that in many situations there is

often a disproportion between inputs and outputs.

Yreùently called the R00 effect the idea here is that R0 of outputs

or results Gfor example incomeK are generated by 0 of inputs or

causes Gfor example customersK.

But the key thing here is the differences in proportions such that these

will not always be exactly R0 and 0. Mndeed ark Zaell

Garketing Director of the Theatre oyal AorwichK who was alerted to

this by oger c(ann points out that a 00I paper by icheaux and

ayet notes that

In practice, the 80:20 rule is more often found to be a ‘15:35:50’ rule,where the top 15 percent [of customers] generate 50 per cent of





revenue, the next 35 per cent generate 35 per cent of the revenue, and

the remaining 50 per cent contribute 15 per cent of revenue or less;

this rule is common in retail, for example

PENETRATION

The extent to which a facility is attracting actual users or attenders from

within its relevant identified markets. ost freùently expressed as a

percentage thus

3enetration q

GTotal actual attenders or users eg from a postcode sectorK I00

stimated number of individuals in the relevant potential market Geg

number of people actually resident in that postcode sectorK

PERCENTAGE

" way of enabling comparisons to be made on a consistent basis.

Thus a percentage is a proportion re$expressed in terms of

]something out of one hundred^. This is done by multiplying the relevant

proportion by I00.

Zence I out P is IP or 0.5 as a proportion. Then when multiplied by

I00 G ie IP I00 q 0.5 I00K it becomes 5.

Andrea Micheaux & Anne Gayet [2001] Turning a marketing database into a relationshipmarketing database in Interactive Marketing Hol. Ao.P 3ages 7$P6 Zenry =tewart3ublications page P5 reference I.





PERCENTAGE CAPACITY and PERCENTAGE

OCCUPANCY

" measure of the extent to which a facility^s accommodation has Gor isK

being used expressed as a percentage of the total accommodation

available.

3ercentage capacity should be thought of as the potential level of

accommodation use that could be achieved. 3ercentage occupancy

relates to the use of the accommodation that has actually been

achieved.

PERCENTAGE CHANGE

" ùantified alteration in something expressed as a percentage.

The terms percentage and percentage change are sometimes

confused with each other. Mf the percentage of people attracted from a

given area rises from I0 to become I it has risen by two units or

two percentage points. But the percentage increase Gor changeK

undergone by the original I0 is 0 Gie the underlying ]unit^ change

is I$I0 q and out of I0 times I00 q I0 I00 q 0K.

Zence care should be taken to use the most appropriate means of

calculating and expressing a change using percentages. The shift from

I0 to I could be expressed as a rise of percentage points or as

a 0 increase. Zowever it is inadvisable to combine the two different

ways of saying this. "s The uardian =tylebook emphasises an





increase from to 5 is a percentage point increase not a

increase

POPULATION

"ll the items in a defined groupP. Zence the entire group of things Gie

people observations cases occurrencesK that share a common

distinguishing aspect. Thus everyone who lives in the ,S everyone

who came to a particular exhibition everyone who booked.

Mt is important to be clear and spell out what exactly you are dealing

with when you are writing or talking about a population. The examples

given above would be referred to as the population of the ,S; the

population visiting this exhibition and the population of bookers.

POTENTIAL AUDIENCE

3eople who are likely to attend an arts facility for an event or

performance.

This likelihood needs to be ustifiable logical and reasonable and not

based on hopes and aspirations.

=uch a potential audience can be ùantified using an area profile

report Gor other profilesK as follows.

Marsh & Marshall [2004] The Guardian Stylebook ondon; uardian Books.P The Economist [1991] The Economist Numbers Guide – the essentials of business

numeracy ondon; The conomist 3ublications





U Yirst the potential audience is specified by assuming that it will be

made up of people who either match the geodemographic profile

of current attenders or have a geodemographic profile that the

organisation has decided to target as a source of attenders.

U Then the area profile report or Gother geodemographic profilesK

can be used to identify how many people in a catchment area

match the desired specification.

PROPORTION

" ùantity expressed as a part of a larger whole. Zence one out of four

Gie IPK people would be represented by the proportion 0.5.

RATE OF ATTENDANCE

The number of times someone attends an arts facility or venue during a

given period Gusually I monthsK. =ee Average Rate of Attendance

Gon pages IR RR and I99K.





REGULAR

" regular attender is someone who comes to an arts facility or venuemore than once and at a rate that is more or less on par with that

organisation^s overall mean rate of attendance. G=ee Average Rate

of Attendance Gon pages IR RR and I99KK.

Mf someone comes at a rate that is higher than the mean rate of

attendance it is legitimate to consider that person as a ]frequent

attender’.

RESEARCH OBJECTIVE

The underlying and over$riding aims and purposes of a research or

analysis exercise W that is what you specifically want to find out. Thus

examples of research obectives include things such as

U ]Mdentifying the profile of people attending a particular exhibition^

U ]Yinding the average age of a regular attender^

U ]ocating key hotspots within a venue^s catchment area^

or

U ]"ssessing the effectiveness of a new marketing tool^.

Being specific about the research obective that applies is vital for from

it flows the type of research survey to be used the approaches to be

taken in analysing the results and how the findings are reported.





RESERVATION

" transaction carried out in advance of an event or a performance

whereby an individual secures the use of a particular seat or space at a

particular time.

Depending upon the type of facility event and transaction reservations

can be paid Gie all monies relating to them have been receivedK or

unpaid Gie some or all of the money relating to them is still to be

receivedK.

RESPONSE RATE

:ne ùantified measure of the success of a marketing or research

exercise. Done by comparing the actual replies achieved with the initial

number of items issued this is conventionally expressed as a

percentage. G=ee page IP9K.

ROBUST

noying the ùalities of strength vigour and reliability. Thus robust

conclusions are assertions that have been developed by using

procedures Gsuch as margin for sampling error K that are technically

irrefutable. obust data is data that provides a technically valid picture

of instance to which it relates.





SALES PROMOTION

The set of marketing techniùes based on the intention of persuading

customers to buy to buy now and to buy more.

Typically such sales promotion devices tend to employ money off

offers or other rewards Gsuch as free giftsK in return for a purchase.

SAMPLE

"n extract from an overall population usually used as a part of a

survey or analysis exercise. There is more than one sort of sample.

" random sample is a sample that is carried out in a way that ensures

every member of the relevant population has an eùal chance of being

selected. Yor instance choosing every fifth telephone booker.

" stratified sample is a sample that has been designed to reflect the

nature of the relevant population. Thus if a particular audience is 60

female and P0 male a stratified sample of that audience would be

composed of 60 women and P0 men.





SAMPLE SIZE

The total number of cases or instances considered as part of aresearch exercise. The robustness of a finding based on a sample will

depend on the sie of the sample used and on the way in which it was

collected.

SEGMENTATION

" key techniùe intended to provide an enhanced focus for marketing

activities and research exercises based on sub$dividing the wider

market into discrete and identifiable sub$sets. riter "lan Tapp I99R

describes this process as Splitting markets into discrete groups to be

treated differently 5.

SERIES or RUN

" multiple seùence of events or performances made up of the same

production event or artist.

5 Alan Tapp [1998] Principles of Direct & Database Marketing ondon Yinancial

Times3itman 3ublishing





SOCIAL GRADE

" classification of individuals according to their socio$economic status.

"s with age groups the social classification system used shoulddepend on any planned use of comparative data.

The tables below and on the next page show the classification

schemes used in three dominant national models. Zowever because

they are based on differing theoretical assumptions about social

structures the different schemes shown are not directly comparable

and only approximate to each other. G=ee also page I6RK.

Commonly used social classification systems

National Readership Survey (JICNAR)

A ,pper middle class

B iddle class

C1 =killed working class

C2 =killed working class

D orking class

E =ubsistence





Commonly used social classification systems (continued)

Registrar General’s Social Classes

I 3rofessionalsII anagerial & technical

IIIN =killed non $manual

IIIM =killed anual

IV 3artly skilled

V ,nskilled

NS-SEC 2001

1 anagerial & professional

2 Mntermediate occupations

3 =mall employers & own account workers

4 ower supervisory and technical

5 =emi$routine & routine

Aever worked & long$term unemployed





SUBSIDY PER HEAD

" core indicator of an organisation^s effectiveness in using public

subsidy.

Typically this is calculated as

Total public subsidy and funding received in a given year

Total number of attendances made during that year

SUBSCRIBER

=omeone who purchases one or more subscriptions.

SUBSCRIPTION

" particular sales promotion techniùe that is freùently used in the

performing arts. This is a scheme that provides significant reductions in

ticket prices in return for the advance bulk purchase of tickets for a

multiple number of events or performances.





TOURIST or VISITOR

The main distinctions between a ]tourist^ and a ]visitor^ are that ]tourists^do not include people making day visits whilst ]visitors^ can include

people making day visits. "lso in theory ]tourists^ can include people

travelling on business purposes whilst ]visitors^ does not.

Zere the orld Tourism :rgansiation^s the nglish Tourist Board^s

and the D(=^s definitions of tourist should generally be used W

especially in the more precise form proposed by urostat.

urostat^s glossary of definitions says that tourists are

persons travelling to and staying in places outside their usual

environment for not more than one consecutive year for leisure,

business and other purposes…There are three elementary forms of

tourism in relation to a given area:

- Domestic tourism (the activities of residents of a given area

travelling only within that area, but outside their usual environment),

- Inbound tourism (the activities of non-residents travelling in a given

area that is outside their usual environment),

- Outbound tourism (the activities of residents of a given area

travelling to and staying in places outside that area and outside their

usual environment K6.

The same resource defines a visitor as any person travelling to a

place other than his / her usual environment for less than twelve

consecutive months and whose main purpose of travel is other than an

the exercise of an activity remunerated from within the place visited ….

6=ource Coded – The Eurostat concepts and Definitions Database accessible online at

httpforum.europa.eu.intircdsiscodedinfodatacodedengl0069R.htm



http://forum.europa.eu.int/irc/dsis/coded/info/data/coded/en/gl006983.htm




The term visitors (domestic and international) comprises tourists and

same-day visitors7. =ee also page I7.

TRANSACTION

Defined by the :xford (ompact Dictionary as an exchange or

interaction between people in arts use this term should be taken to

mean a discrete and self$contained seùence of actions between a

user and an arts organisation that contribute to and complete a

particular outcome.

Thus a transaction takes place whenever a person contacts and

interacts with the organisation. (onseùently the entire process of

booking a ticket and attending an event could be made up of a number

of different transactions. Yor instance

U " reservation transaction

U " purchase transaction

U "n attendance transaction

and

U " refund transaction.

=o each transaction needs to be seen as a one$off happening that is a

component of more extensive pattern of occurrences. That more

extensive or overall pattern of interaction is the total transaction and

the money generated from it is the total transaction value.

7 Mbid.





TRANSACTION TOTAL , TOTAL INCOME PER

TRANSACTION or TOTAL TRANSACTION VALUE

The total gross amount of money that changes hands during a

complete seùence of identified transactions.

UNPAID RESERVATION

" booking that is still to be paid for in full.

VISITOR

"ny person travelling to a place other than his her usual environment

for less than twelve consecutive months and whose main purpose of

travel is other than an the exercise of an activity remunerated from

within the place visited. The term visitors Gdomestic and internationalK

comprises tourists and same$day visitors. =ee also tourist or visitor

Gpages 5P and I7K.





YEAR

" I month period within an arts facility^s operation.

"lthough this could take the form of a calendar year Gie anuary to

DecemberK a financial year Gthe I month period for which an

organisation prepares its accountsK or an artistic year Gie from the first

to last event in an organisation^s event calendarK for purposes of

uniform and direct comparison with patterns of public funding FISCAL

YEAR is recommended here as the basic period to use Gie the I

month period that the ,S overnment uses as the basis for its

budgeting and accounting processes q the Mncome Tax jear q 6th

"pril

00 to 5th

"pril 00IK.

hichever definition type of ]year^ is used reports and commentaries

should be specific on the one being used.

YIELD PER ATTENDER or AVERAGE YIELD PER

ATTENDER

The mean gross income generated per paying attender from an event

or activity for the legal entity holding the financial interest in that event.

Zence this should be calculated as

ross income generated by an event

The number of admissions to that event that have been paid for





(onseùently this calculation should A:T include complimentary

admissions but can include concessions and discounted

admissions.

YOUNG PERSON

=omeone who is not a legal adult and thus should be treated as a

minor.

Different organisations should set their own definitions of the age at

which someone stops being a young person. But an acceptable and

pragmatic default would seem to be at the age that both marks the start

of practical maority and the "rts (ouncil ngland ]Yamily Yriendly^

initiative^s definition of a child. This is 16 years of age or less. G=ee the

related protocol starting on pageI7RK.





B4 Related protocols

BP.I Overview

The erriam ebster (ollegiate Dictionary says that the current

nglish word ]protocol^ comes from the ate reek word protokollon

Gmeaning ]the first sheet of a papyrus roll bearing its data of

manufacture’ K. "pparently this comes from the reek terms prot and

kollan Gmeaning ]to glue together’ K. By extension more modern usages

include a code prescribing strict adherence to correct etiquette and

precedence (as in diplomatic exchange and in the military services)”

and “a set of conventions governing the treatment and especially the

formatting of data in an electronic communications systemR.

This section provides a selected set of protocols recommended for use

with audience data procedures. Zence here ]protocol^ refers to

standard ways of working with and using such data

Mn this sense the following explanations support the definitions given in

section B by providing fuller Gand occasionally more complicatedK

details on the thinking underpinning the earlier definitions.

To aid rapid location of the information reùired the protocols are

arranged according to the same alphabetical scheme used earlier for

the definitions. Then where appropriate each protocol is laid out usingone or more of the considerations shown in Box =ix GoverleafK.

R"ll ̀ uotations here drawn from the erriam ebster (ollegiate Dictionary





Box Six – Topics potentially covered in the protocols

U Mssues and rationale

U elated procedure and protocols to useU Mnstances of when to use this

U esources and references

U =ee also

"nd if appropriate the really technical stuff Gsuch as formulae and

examples of worked calculationsK has been put in the next section GB5

starting from page IRK.

Yor ease of location an index of the following protocols is provided from

page 9 onwards.





BP. The recommended protocols

AGE GROUP or AGE CATEGORIES

Issues and rationale here

:ne of the essential approaches used in targeting customers and

users is based on ]segmenting^ the available markets. riter "lan Tapp

I99R observes that segmentation is about Splitting markets into

discrete groups to be treated differently 9. "nd among the many ways

available of splitting up markets is according to demographics. That isby a customer^s user]s or visitor^s age and life stage. "s a result

recording someone^s age becomes a key reùirement for analysis and

for marketing activity.

jou might want to set your audience data in a wider context. :r maybe

you want to collect and build data in a form that allows it to be married

up with other similar data. hatever your intention here when it comes

to demographics both these reasons imply taking a consistent

approach to classifying ages.

Zowever some of the available large$scale and aggregate data sets

Gsuch as the Target roup Mndex TM data used by "rts (ouncil

ngland and the Aational (ensus data provided by the :ffice for

Aational =tatisticsK use slightly different age ranges as their basis.

=o before devising a survey or conducting an age classification of your

audience it would be as well to know which dataset you want to

compare your data with and use the same set of age ranges

accordingly.

9 Alan Tapp [1998] Principles of Direct & Database Marketing ondon Yinancial

Times3itman 3ublishing





G" table showing the various age ranges used for TM and the :A=

Aational (ensus data is provided in the definitions section on page PIK.

Related procedures and protocols to use here

(learly this is a case where it can be useful W if not vital W to ensure

that the categories used fit with the most precise bands used

elsewhere. Then if more wide$ranging age categories are desired for

simpler reporting purposes they can be combined to form a larger

category.

Yor instance if you are particularly interested in ]middle aged people^

by using the age bands P0 to PP years old P5 to P9 years old 50 to 5P

years old and 55 to 59 years old these can then be combined to give

information for everyone aged between P0 and 59 years of age.

Using precise age ranges allow

s subsequent combination

"ge range (ombined category

to P9 40 to 59

P0 to PP

P5

50 to 5P

55 to 59





,nderstandably broad terms such as ]young people^ ]middle aged

people^ and ]elderly people^ can have different meanings for different

organisations. Zence the recommended approach to dealing with this

is to first determine how your organisation interprets such terms and

then decide which age ranges each term is to cover. hen such an

understanding has been specifically created for an organisation^s use

its particular definition should be spelled out at an early stage.

Instances of when to use

Defined age ranges are of use for exercises such as audience and

vistor surveys analysis of catchment population profiles and analysis

of an organisation^s attender base.

See also:

AREA PROFILE Gpage 70K

CATCHMENT AREA Gpage 97K

SOCIAL GRADE GpageI6RK





ANCILLARY INCOME vs CORE INCOME or CORE

TURNOVER

Issues and rationale here:

There are times when it is desirable for arts organisations to document

and report on the total income they generate from their activities. But

on other occasions people want to categorise income according to

what sorts of activity led to the related income generation. The first of

these considerations entails having comprehensive coverage Gand not

omitting any sources of incomeK. The second is about understanding

the contribution made by each of the components that W when added

together W make up total income.

This makes it prudent to break down the types of income stream

involved. :ne way of doing this is according to how close each income

generating activity is to an organisation^s core mission and purpose.

Zence core income Gor core turnover K is any money that has beenearned by or generated to support an organisation^s core purpose Gie

its whole reason for existingK. =o the core purpose of a gallery is likely

to relate to something like ]providing exhibitions of the visual arts^. The

core purpose of a dance company could be something along the lines

of ]making and presenting dance performances^. "nd the core purpose

of an artists^ workshop collective could be ]providing working spaces for

artists^.

Mn contrast to this ancillary income is any money that is generated or

received through activities that do not essentially relate to an

organisation^s core purpose. Yor instance it would be wise to treat a

allery^s caf receipts confectionary sales in a theatre and stationery

sales in a concert hall as being income that is ancillary to the core

purpose.





ANNOTATIONS, ACCREDITATION and REFERENCING

Issues and rationale here:The whole point of this resource is to enable arts managers and

workers to put in place an enhanced approach to analysing and

understanding audience data. This relies on working at an appropriate

level of accuracy precision and consistency. But even once this has

been achieved it could be pointless W unless the results of the

research and analysis are communicated in a way that is robust and

convincing to the reader.

:ne way of thinking about a report on research or analysis findings is

to consider it as if it was a case being made in a court of law. ach

assertion or observation made not only needs to be put in terms that

seem reasonable and logical but also need to be backed up by a

certain rigor in documenting and substantiating them.

=o good practice in reporting on research findings makes it essential

that statements and assertions made are reinforced through an

appropriate use of caveats acknowledgement of sources and

referencing.





Related procedures and protocols here:

Caveats and annotations

Mf there is any aspect relating to the way in which a finding was arrived

at that is ùestionable $ or could potentially undermine the resulting

conclusions $ this needs to be pointed out. The way to do this is to use

of annotations.

Zence where appropriate accompany any report of a finding or

conclusion with a detailed note on

U Zow it was arrived at Gie the methodology and assumptions usedK

U hen and where the relevant research was carried out

U The sie of the sample being used

and

U "ny other issues that may compromise the conclusions. GYor

instance such as the days of the week involved the number of

times a similar exercise has been carried out or the nature of the

research instrument Geg self$completion ùestionnaire analysis of

partial sales data etcKK.

Not being too adamant

Mt is dangerously easy to over state a claim or observation so that

readers potentially remain unconvinced by it. Zence iles and Zedge

I99P advocate hedging your bets





The expression ‘to hedge your bets’ here means to be careful that you

say nothing that could be refuted. For example, it would be foolish to

write ‘No manager has ever agreed completely with Managwiz.’ How

do you know? Can you be sure? If this is really your opinion you need

to do two things. Firstly, you need to hedge your bets – try something

like ‘Few managers agree with Managwiz.’ Secondly, you have to back

up, to provide evidence for your assertion. Never assume that the

reader… is going to agree with you. Always ask yourself “Can I prove it

beyond doubt?’ If you can, do so decisively, confidently and without

hedging; in not, hedge your bets. I0

"nd being too adamant is a particular danger when reporting on

numeric findings. =o here it is advisable to be clear whether you are

dealing with something that ]proves^ ]demonstrates^ or ]shows^

something. :r rather is it a case of something that ]suggests^ or

]possibly indicates^ your claimed assertion

Acknowledging sources

Mt is unethical and unprofessional to use someone else^s work without

paying due acknowledgement to that person. Zence ]paying credit

where credit is due^ avoids the danger of plagiarism. " ùick and

simple way of doing this is to ensure that where other people^s work is

being drawn upon this is recorded with a footnote or an endnote Gsee

]"ppropriate referencing^ belowK.

!" Source* !"#$%&'"($)$*&+,&$-"./"($012234 The Manager’s Good Study Guide+ Milton

1eynes4 The Open 9niversity+ p;<=





Appropriate Referencing

Zussey and Zussey I997 make a strong case for referencing any

sources used including the vital idea that if appropriate referencing is

used others reading your finished work will be able to trace the original

sources of information easily II

.

The importance of making it possible for any reader to reconstruct your

thought processes also underpins a number of the procedures and

protocols mentioned in this section and epitomises good practice in

reporting on data and findings.

Formats to use here

There are two different formats that can be used when referencing and

annotating sources.

The first is the Harvard system. This acknowledges a source or

reference by putting the author^s name and the date of publication in

the body text GZussey and Zussey I997K then giving full details of the

publication involved either as a footnote or in a bibliography. Zarvard

is the preferred referencing system in management studies and the

social sciences.

The second way of recording references is the Vancouver System.

This works by numbering each source and then ust putting the number

in the text whilst a numbered list of sources is provided as the

bibliography. The Hancouver system tends to be preferred in the arts

and humanities.

Because it is typically used in a management studies setting and it

makes it easier to make changes without wholesale rearrangement of

text the use of the Zarvard system is strongly recommended here.

!! Source* 5&''$-6(("7$)$89/":$-6(("7$0122;4 Business Research – a practical guide for

undergraduate and postgraduate students+ Basingstoke4 MacMillan Business+ p;!"





Instances of when to use:

This whole group of procedures should be used whenever it is

especially important for a report or set of conclusions to be considered

as being robust and compiled according to good practice.

Further resources and references:

Ken Giles & Nicki Hedges [1994] The Manager’s Good Study Guide

ilton Seynes; The :pen ,niversity

Jill Hussey & Roger Hussey [1997] Business Research – a practical

guide for undergraduate and postgraduate students Basingstoke;

acillan Business

www.library.rdg.ac.ukhelpcitingsystems



http://www.library.rdg.ac.uk/help/citing/#systems

http://www.library.rdg.ac.uk/help/citing/#systems



AREA PROFILES or AREA PROFILE REPORTS


ffective planning for organisations at a local level is highly dependenton local data. Thus a potent and relatively new source of such data is

the ]area profiles^.

The area profiles Gor area profile reportsK are model data $ statistical

reports that provide indicative information for any defined area based

on any given location in reat Britain. GThese are not currently

available for Aorthern MrelandK. Based on data commissioned from

("(M Gusing its Mn=ite systemK these reports have been made

available thanks to funding provided by "rts (ouncil ngland.

(opies of an area profile report Gfor any given areaK can be obtained

from the three Aational "udience Development "gencies that have

been commissioned to undertake this roleI. Zowever they can only be

provided once an initial order for them has been authorised by the

relevant "rts (ouncils. The primary contacts here are

U ngland W 3eter Herwey "rts (ouncil ngland

U =cotland W Yiona =turgeon =cottish "rts (ouncil

U ales W "nn Sellaway The "rts (ouncil of ales

hilst an authorised order placed through the relevant "rts (ouncil is

essential Gin that the area profile data cannot be supplied unless an

order has been authorisedK there can be particular and additional

value in asking your local "udience Development "gency to work with

you on the "rea eports once it has been obtained. Gspecially since

your local "gency will be able to discuss your particular needs and

I

These agencies are Arts About Manchester Gtelephone 0I6I R P500K; AMH formerly Arts Marketing Hampshire Gtel 0I96 RP6 96K; and Audiences Yorkshire Gtel 0R70 I60PP00K.





help you with the interpretation of the reports once they have been

receivedK.

ach and every area profile report contains the following elements.

! " map showing the central point from which the relevant area is

defined.

! " two page ‘overview’ table giving details of a range of

information on the people who live within the entire drivetime

area covered. These data relate to

U the area^s total population Gfrom the 00I (ensusK

U total number of adults Gwhere ]adult^ denotes people aged

I5K

U Total adult females and total adult males in the area

U " breakdown of the area^s I6 to 6P year old population

according to its social grade

U " breakdown of the area^s population by ethnic group

U Yigures for the area population^s economic activity Gbut here

for individuals aged I6 to 7P years of ageK

U The numbers of students shown for the area of their term$

time address

U The occupations of adults aged I6 to 7P years of age

U The numbers of adults Gaged I6 to 7P years of ageK in the

area who are unable to work due to long$term illness or

disability

U The numbers of people aged IR years and above who hold

Zigher ducational or Hocational ùalifications

U The levels of car ownership in the area

U elsh speakers Gales area reports onlyK

U The patterns of arts attendance Gbased on the Target roup

MndexK





U The relevant "(:A profile of people in the area aged I5

years of age or higher

U Details of the extent of newspaper readership

and

U Yigures for internet usage among the area^s population.

"ll the above data are presented as numbers as percentages of

the area^s population and as indices comparing the area

percentages with the relevant percentage for the whole of reat

Britain.

! " postal sectors numbers report showing the numbers of

people residing in each postal sectors falling within the relevant

area.

! "nd a postal sectors percentage report that shows the

percentages for each postal sector and data item as it relates to

the area^s total population together with an index which

compares a sector^s percentage with that for the area being

reported on.


"rea profile reports are available for use by not$for$profit organisations

in the arts sector Gincluding those that are part of a ocal "uthorityK and

to venues receiving work from clients of one of the ,S "rts (ouncils.

To obtain reports and current charges you should contact your local

"rts (ouncil using the primary contacts as follows

U ngland W 3eter Herwey "rts (ouncil ngland

U =cotland W Yiona =turgeon =cottish "rts (ouncil

U ales W "nn Sellaway The "rts (ouncil of ales





"n order form will shortly be available from each of the (ouncils web$

sites.

The data contained in the area profiles is extremely valuable but

commercially sensitive. Zence there are a number of stipulations and

protocols that apply to their use.

ost importantly they are provided for the sole use of the organisation

applying for them and should not be disseminated circulated or

published to other organisations. "lso the reports are not directly

available to consultants.

Mn all cases the source of the data should be made clear whenever and

wherever it is used.

Formulae and calculations to use and worked examples

Zere see the relevant pages in section B5 Gie IR$I9IK.


The area profile reports can be used to assess organisational

performance relative to the local market potential. They can also be

used to inform targeting activity by identifying ]hot spots^ Gie sectors

where your organisation seems to be doing better than might be

expected from the relevant potential and where presumably some

other factor $ such as an advocate or a bus route W is having an effectK

together with ]cold spots^ Gplaces where penetration needs to be

improvedK.





Resources and references

Peter Verwey [2004] Key to area profiles ondon; "udience & market

Development Department "rts (ouncil ngland.

Peter Verwey [2004] Area Profile Reports ondon; "udience &

market Development Department "rts (ouncil ngland.

John Ozimek [ 1993] Targeting for success – a guide to new

techniques for measurement and analysis in database and direct

response marketing ; ondon; craw$Zill Book (ompany.

See also:


INDEX Gpage I5K

POTENTIAL AUDIENCE Gpage P5K





ASSOCIATE (ATTENDER)


Mn the circumstances where a number of people make an attendanceas a result of one booking it can often be useful to distinguish between

the person who made the actual booking and the other people who

come with them.

The person responsible for making the booking should be termed ]a

booker’ whilst the people coming with them should be thought of as

]associates^.

This thus enables a further distinction to be made between total

bookers Gie the total number of people making bookings on the one

hand and total number of attendances made or places used Gie the

total number of people coming or tickets soldK on the other. Zowever it

should be noted that the unidentified nature of an associate attender

means that there will be an aspect of the data that is essentially partial

W that is whilst the number of attendances made will be known the

number of attenders making these attendances will probably not be

available.

See also:

ATTENDER





ATTENDER


"n ]attender^ is defined as someone who actually comes to an event

or a performance. " number of key distinctions need to be noted here.

U Yirstly the difference between an ]attender^ and ]attendances^.

]"ttender^ refers to discrete individuals who attend and thus ]total

attenders^ is the count of people who come to a facility. Zowever

]attendances^ relates to the total number of times that visits to anevent have been made. The total for this G]total attendances^K

therefore refers to seats or places occupied and not to the people

doing this.

U =econdly a distinction needs to be made between ]attenders^ and

]bookers^. "ttenders are people who actually come to an event

whereas bookers are the people who organise the tickets for an

attendance but who might not actually come themselves Gsince

they could be booking the tickets for someone else or having

booked a ticket might not use itK.

Related procedures and protocols to use

"nother big issue here relates to how the status of an attender should

be defined. This is particularly challenging since research carried out

as part of the study which led to the creation of this document

suggests that different individuals and organisations define the various

types of attender in a variety of ways.

"ndrew cMntyre suggest that there is evidence that the typical

longevity of an attender Gie the period they remain an active user of an





organisationK tends to work according to four year cycles. Zis company

W orris Zargreaves cMntyre W are also developing an approach to

classifying attenders using a process based on ]half$lifes^ Gie Zow long

does it take before half a group of audience have stopped coming then

how long does it take before half the remaining audience stop coming

and so onK.

jet at the same time oger Tomlinson and Tim Baker make a logical

case for the time periods that define attender status to be treated

dynamically. Zence they argue that because of the diversity of event

types and organisations involved it would be imprudent and misleading

to set an absolute national standard here.

Then a number of organisations Gincluding some of those represented

at the initial definitions and protocols symposiumK do use absolute time

periods to define attender status.

(onseùently to sùare this particular circle and in an attempt to

resolve this conundrum the recommended definitions and protocols

use a combination of some absolute figures and some dynamic ones

that will need to be decided on an organisation by organisation basis.

These are as follows Gwith the relevant reasoning shown in bracketsK.

!" " current attender is someone who has been to an event at an

organisation within the last I months G12 months is the

recommended time frame here since it mirrors one of the

conventional timeframes used for business reporting purposesK.

! " regular attender is someone who has been to an event a

minimum of two times in the last I months Gsince this appears

to be the freùency most commonly used by organisations for

this type of attenderK.





! " frequent attender is someone who comes to an event

organisation or facility at a recurring rate that is higher than the

average for that facility^s audience. GThis definition is therefore a

dynamic one and will depend on what the mean rate of

attendance is for a typical member of an organisation^s

audienceK.

! " lapsed attender is someone who has a history of attending

but who has either asked to be removed from the organisation^s

records or who has appeared to have stopped coming after a

period eùivalent to the average GmeanK period that a typical

customer remains active plus one year. GThe first of these

aspects has been included at the suggestion of oger

Tomlinson whose research with the Data (ommissioner is

understood to have identified the Data 3rotection "ct need to

respond immediately to a reùest to be removed from an

organisation^s records. The second is a hybrid of the need for a

dynamic definition that is set by each organisation combined

with the pragmatic addition of an extra year to allow for that fact

that lapsed attenders might reactivate their attending behaviourK.

! Then a revived attender is a lapsed attender who has started

attending again. G"nother case of a dynamic definition how long

to leave before an attender is udged to be truly and fully inactive

and never likely to return can only be decided by each

organisation on the basis of its own patterns of user behaviourK.

Instance of when to use this:

Mdentifying and counting attenders $ especially according to the

categories listed above W is of particular use when you either want to

evaluate how effective your organisation is being in retaining users Gor





how bad it is at losing usersK. Mt can also be vital intelligence if your

organisation is considering running a campaign to try and increase the

rate of use of current attenders or to reenergise lapsed attenders.

G=ince in all these cases it will be necessary to identify which category

a particular user falls intoK.


=ee

"ndrew cMntrye^s online paper for the "rts arketing "ssociation on

Z^s new approach to arts marketing management called ]"udience

Builder^ at

httpwww.a$m$a.co.ukimagesdownloadsaudiencebuilder.pdf

and

=tuart Aicolle^s online paper for ]YuelP"rts^ G"ustraliaK on collecting

handling and using box office data Gespecially ]Repeat Attendance^ on

page PK at

httpwww.fuelParts.comfilesattachBox:fficeDataAicolle0005.pd

f

GAB embership registration free reùired to access thisK.

See also

ASSOCIATE ATTENDER Gpage 75K

BOOKER Gpage 9IK



http://www.a-m-a.co.uk/images/downloads/audiencebuilder.pdf

http://www.fuel4arts.com/files/attach/BoxOfficeData_Nicolle_030305.pdf




http://www.a-m-a.co.uk/images/downloads/audiencebuilder.pdf



AVERAGE


"n ]average^ is defined as a measure of a data set^s central tendencywhich is the single value that best typifies the data set.

Mn this sense then averages are single summary values that epitomise

the essential flavour of a collection of data. But not all averages are

eùal. "nd not all averages are found in the same way. This is

because there are a number of different ways of finding an average W

basically because they each serve different purposes. =o selecting the

appropriate average to use is all about choosing the most appropriate

tool for the ob in hand.

The various averages are explained in the following ]related protocols

and procedures section^ whilst the relevant formulae and worked

examples are provided in section B5 starting from page I9.

Related procedures and protocols here

The three most common approaches to finding an average are

U The mean Galso known as the ]arithmetic^ meanK

U The median

and

U The mode.





The mean

The mean uses a mathematical calculation to produce a value that

represents the data^s ]centre of gravity^. Aeil =alkind 000 says that

The mean is like the fulcrum on a seesaw. It’s the centremost point

where all the values on one side of the mean are equal in weight to all

the values on the other side of the meanI.

That is it summarises the essential flavour of the entire collection of

data by providing a value that is based on all the data and thus to

which each element of the data can be related.

The mean is easily worked out. This is done by first finding the total of

all the values in the data set and then dividing the result by the number

of values being used.

The mean has a number of advantages. Mt is a commonly understood

concept that is easily depicted graphically and so can be useful in

communicating findings. Zowever it does have a few pitfalls. Yor

instance it can be influenced by outlying or extreme values in the data.

ùally the fact that sometimes the actual mean may not occur as a

value in the dataset may seem contrary to commonsense.

Aevertheless the mean is the appropriate average to use when all the

data available needs to be taken into consideration. The formula for

calculating the mean together with a worked example will be found in

section B5.

I Neil Salkind [2000] Statistics for people who (think they) hate statistics ondon; =age

3ublications page





The mode

This is the most freùently occurring and thus the most popular value

in the data set. That is it is the value that crops up the most times.

hen it comes to choosing between the different approaches to finding

averages the mode is especially appropriate when the data being used

refer to categories rather than to numbers. =ee section D5 for further

details on finding the mode.

The edian

This sort of average is the value that sits exactly in the middle of a data

set. "s such it is particularly useful when there are extreme values that

otherwise could distort any calculated version of the average Gsuch as

the meanK.

:nce more the technicalities involved in finding the median together

with a worked example are provided in section B5.

This is an instance of choosing the most appropriate procedure for the

ob in hand W a case of ]horses for courses^.

Zowever do not be perplexed if the mean median or mode for the

same data set occasionally produce different values. Yor instance

consider the following data on the total amount spend by customers on

tickets in one year.





Example data set for total customer spend

on tickets in one year

"nnual spend Ao. of customers,nder 50 I0

50 but under 75 I56

75 but under I00 I9R

I00 but under I5 I76

I5 but under I50 IIR

I50 but under 00 76

00 but under 50 7

50 but under 00 P9

00 but under 500 I9

500 and more 6

G"fter (urwin & =later I99IK

Zere the three different averages for this data set are

U ean q IP.I0

U ode q 9.00

and

U edian q I0.7.

Thus visual depiction gives the following chart.





Annual customer spend on tickets

0

50

100

150

200

250

,nder 50 50but

under

75

75but

under

I00

I00but

under

I5

I5but

under

I50

I50but

under

00

00but

under

50

50but

under

00

00but

under

500

500and

more

Spend (£s)

eanedian

ode

This sample data set illustrates how depending on the distribution or

]skew^ of the data values the averages involved can be in differentplaces. Zere the data set has more observations relating to the lower

spend values. "s a result the mode is located towards the lower spend

value part of the data the median is very clearly in the middle of the

data and the mean is in the data^s exact arithmetical centre.


"verages should be used when a single figure summarising and

epitomising a collection of data Gand which therefore typifies the dataK

is reùired. Zowever depending on the task in hand it is always useful

to select the most appropriate approach to finding the data^s average.






Jon Curwin and Roger Slater [1991] Quantitative methods for

business decisions (third edition) ondon; (hapman Zall pages 5 to

P7

Rob Dransfield [2003] Statistics made easy (heltenham; Aelson

Thorne td pages $7

Neil Salkind [2000] Statistics for people who (think they) hate

statistics ondon; =age 3ublictions pages I to

See also

AVERAGE DAILY ATTENDANCE Gpage R6K

AVERAGE RATE OF ATTENDANCEGpage RRK

AVERAGE SPEND PER HEAD Gpage 90K





AVERAGE DAILY ATTENDANCE

Issues and rationale here:ne measure of an arts facility^s comparative performance is the

mean number of people it is attracting to one day^s worth of activity.

This mean number of people attending per day is calculated as

Total number of attendances in a given period

Aumber of days covered by that period


Zere to keep things consistent it is recommended that the number of

days covered should be every day from the start to finish of that period

and not ust the days when there are events or performances on. Gie

days when a facility is dark should be included in the count hereK.





AVERAGE PRICE PAID


"verage price paid can be a useful measure of an organisation^s

marketing and financial performance. Mt can also provide an indication

of the level to which discounts are being used.

Related procedures and protocols here:The average price paid can be found through the calculation

"chieved financial capacity

"chieved physical capacity

Instances of when to use this:

"s part of management reports on the overall performance and

effectiveness of an organisation.

See also:AVERAGE Gpage R0K

CAPACITY Gpage 9PK







This is a commonly used measure of the typical freùency with which

members of an organisation^s audience tend to attend.

Mt is found by working out


Total number of people making these attendances


"lthough it may not be possible to distinguish between bookers and

associates here by dealing with totals the above formula allows the

average rate of attendance to be worked out on a ]top down^

]aggregated^ and ]overall^ basis.

Mf a ticketing system is available the relevant data and its analysis

should be available as a matter of course. Zowever facilities without

such resources could get the reùired data from their manual records.

Mn both cases the rate found through calculation can be checked by

using a survey that asked people ]how often on average they came^ to

the facility in ùestion.





Instances of when to use this:

,se when a summary measure of the freùency of attendance W and

thus of attender activity and loyalty W is reùired.

Worked example

=ee section B5 on page I99.





AVERAGE SPEND PER HEAD


" basic measure of the economic activity of a facility^s users this is

should be found by working out

The total gross income generated in a given period

Total footfall during that given period.

Mt is recommended that gross income be used here since this is likely

to give the widest possible measure of the money flowing into the

organisation and thus ignores any deductions made for commissions

booking fees or for H"T. "nd through the same logic the use of total

footfall is proposed since this would reflect the total number of people

passing through a facility rather than ust the number of people

engaging in a specific and defined activity Gsuch as attending an event

buying a ticket or purchasing a drinkK.


The extension of the logic advanced above is that figures for gross

income used in the calculation should relate to all income generated in

an arts facility as a result of someone passing through it. Zence for the

period under consideration total gross income should include

"ll core income 3,= all ancillary income.


"s part of an assessment of an organisation^s economic impact.





BOOKER


The high level definition of a ]booker^ is a person making an advance

reservation Gor reservationsK for a seat at a ticketed event or

performance. Zence a booker can be different from an attender W for

instance it is possible for someone to make a booking for someone

else or not turn up to use their booking. ùally there is the world of

difference between a booking that has been paid for and one that is

still to be paid for. GThis last one is an ]unpaid reservation^K.


iven this logic it could well be desirable for the overall term ]booker^

to be subdivided according to a number of categories.

Breaking these down logically show that here are four potential

categories for bookers that could be usefully reported on or used as a

basis for a follow$up campaign. These are

U "n attending booker W ie someone who makes and pays for a

booking and then attends themselves

U "n attending associate W ie someone who has a booking made and

paid for them by someone else

U " no show W someone who either has made and paid for a

reservation for themselves but does not use it or someone who has

a booking made and paid for them by someone else but then does

not use it; and







See also:

ASSOCIATE ATTENDER Gpage 75K

ATTENDER Gpage 76K

No show Gpage PIK

Reservation Gpage PRK

Unpaid reservation Gpage 56K





CAPACITY

Issues and rationale hereThe expression ]capacity^ is used to denote a measure of the

accommodation seats places or spaces an arts facility has available

to fill or sell.

Zowever there are a range of different forms of ]capacity^ in regular use

across the arts sector with each using a different basic unit to calculate

the total capacity Gfor instance potential seating available seating

financial capacityK. Zence the appropriate form of capacity will depend

on the purpose for which it is being used.

There is a particular need for arts organisations to be clear which form

of capacity is being used Gand reportedK on when compiling figures.

Yurthermore it seems preferable for ust one form of capacity to be

recommended for use. This is because unless there is a uniform and

consistent approach to calculating and reporting on capacity it will not

be possible to make comparisons between organisations. GThis

becomes a particular issue when technical or presentational reasons

lead to seats or spaces being excluded from the calculation of

capacityK.


(onsultations carried out with leading industry consultants and through

the "udience Data ,S symposium suggest that uniformity and

consistency is most likely to be secured if capacity is defined as

]physical capacity^





]total units available for use’ Geg seats available for sale spaces

available to be occupiedK

"t the same time it can be desirable to also use a measure of

]financial capacity^ ie the total amount of income that can be

generated from a discrete event or performance if all the admissions

were charged at full face value.

Formulae and potential calculations to use here

Therefore for a seated venue it is recommended that physical capacity

be calculated as

Seats, wheel chair and standing places available for use

ie = total seats, wheel chair and standing places available LESS

any seats, wheel chair places or standing places removed from

use for operational reasons Gsuch as the placing of sound desk or

to safeguard sight$lines because of the nature of scenery).

ùally ]financial capacity^ should be calculated as

The total full-price face value of all the places available for sale.

Yor visual arts facilities capacity should be defined as

The maximum number of persons the Local and Fire Authorities

have licensed the relevant space (or spaces) to accommodate.

"nd for non$seated or facilities capacity should be calculated as





The total number of persons the Local or Fire Authorities have

licensed a space or place to accommodate.

Zere if the space is unlicensed Gsuch as a street performanceK ocal

"uthority or 3olice estimates of the number of people who could watch

an event whilst staying within safety limits should be used as the basis

for the reported capacity figure.

See also:

FINANCIAL CAPACITY Gpage IIK

Occupancy GpagePIK

PERCENTAGE OCCUPANCY Gpage IP5K

Yield Gpage 57K





<=><-?@*>$=8@=$9:$<A8@$<=><-?@*>$

$

Issues and Rationale here

This is the geographic area around an arts facility which is the source

of the largest and most important proportion of actual users and

attenders for an event series of events organisation or facility.

Related protocols and procedures to use here

There are a number of potential ways of visualising and depicting anarts facility^s catchment. "mong these are

U drawing a regular distance radius from the facility Gfor instance 0

milesK

U drawing the 0 minute drive time

or

U identifying the postal sectors which are the source of an important

proportion of attenders.

Mn developing and using the Area Profile Reports a combination of

the last two of these approaches are used. Thus first the postal sectors

falling within the 0 minute drive time are identified. Then any

additional postal sectors that are not only adacent and contiguous to

them but also are a source of an important proportion of attenders are

added. This process is continued until the area represents the source

of 80% of a facility^s attenders. G=ee diagramsK.





Depicting the core catchment area

GSey hite oval q 0 minute drivetime

grey ovals are additional sectors from which high proportions of

attenders are drawnK

XThe facility





30 minute drivetime catchment for Canterbury, Kent

uickTimez and aTMYY G%K decompressor

are needed to see this picture.

eo =harrock of "Z Gformerly "rts arketing ZampshireK points out

that in adding extra sectors a balance needs to be struck between the

catchment^s essential homogeneity and the degree of fragmentation

created by adding other sectors.

$

The proportion of R0 of attenders is used in creating such a

catchment partly because it has been a tried and tested convention to

do so but also because this would seem to accord to the Pareto

Principle. Gie roughly 0 of the area from which a facility is attracting

users are generating roughly R0 of those users W see page PK.






hen a greater understanding of a facility^s sphere of influence and its

impact in attracting attenders is reùired. (an also be used to

substantiate the level of service delivered to the local community.

See also

AREA PROFILE REPORTGpage 70K

Geoanalyis and Geodemography Gpage PK





CAVEATS and QUALIFICATIONS

=ee ANNOTATIONS, ACCREDIATATION and REFERENCING Gpage

65K

CHURN


The measurement of the turnover Gor ]churn^K of a customer or

audience base is a techniùe that was first developed and used in the

mobile phone cable television and publishing sectors. "s such it

enables both the relative loyalty of a group of customers to be

ùantified and the rate at which that customer base is being refreshed

or denuded.

The techniùe is founded on the notion that any audience or group of customers can be thought of as being like a ]leaky bucket^. =o that the

prevailing level of active customers depends on assessing how many

customers are being kept are many new customers are being gained;

and how many existing customers are being lost.

etainedcustomers

Aew or revived

customers apsed or lost

customers






" ]churn analysis^ works rather like the audience eùivalent of the

cashflows used in financial management.

To build one three basic statistics are reùired namely

U The number of customers and users there were at the start of

the period being analysed

U The number of new people acùired as customers and users by

the organisation during that period

and

U The number of customers and users lost during the period in

ùestion.

ith this data to hand the churn calculation is performed as follows.

U Yirst record the number of customers there were at the start of

the period being analysed Gthis will be called ]"^K

U Then find the number of new customers gained during that

period G]B^K

U "lso identify the number of customers lost during the same

period G](^K

U The number of customers being carried forward to the next

period G]D^K can now be calculated as " 3,= B MA,= (

U "s can the number of customers retained during the period G]^K

Gand thus included in the number carried forwardK W this eùals

" MA,= (





U "lso the net change G]Y^K in the sie of the audience can be

found through the calculation B MA,= ( Gan alternative way of

finding this is to work out D minus "K..

Two other calculations using these figures can also usefully be carried

out to give an indication of the relative rate of this audience^s turnover.

The percentage churn eùals

Aet change I00 q Y I00

=tarting Aumber "

The percentage retention rate eùals

Aumber retained I00 q I00

=tarting number "

hen carried out on a regular basis Gsay every season or every yearK

these calculations can be a useful way of monitoring what has been

happening to an audience. But please remember that W as yet W there

are no national standards for what represents a ]good^ or a ]bad^ rate of

churn.

ùally interpretation of the resulting statistics will depend on your

organisation^s aspirations and intentions. Zence if one of your main

aims is to attract new audience members a high or big rate of churn

might be good news. But if your core aim is to retain audience

members and low rate of churn and a high rate of retention would

constitute good news.





Worked example

=ay an organisation decides to start its churn analysis by examining

the year I99R$I999. =o this will be the initial period to be considered.

Mt finds that

U "t the start of this period there were I0IIP customers

U During that same period P6 new customers were attracted

and

U "t the same time 7R customers were lost.

Thus applying the calculation described above gives the following

table.

Aspect / period 1998/99

(ustomers at start G"K I0IIP

(ustomers added GBK P6

(ustomers lost G(K $7R

(ustomers at end cf

GDK

99R

etained included in

cf GK

976

Aet (hange $I

ge churn $I.I

ge retained 96.6

The same organisation decides to repeat the calculation for the next

year Gie I999 W 000K.

Zere





U The number of people brought forward from the previous period

eùals 99R

U The number of customers aùired during the year eùals 57

and

U The number of people lost during the year eùals I.

This gives the following expansion of the previously created table.

Aspect / period 1998/99 1999/00

(ustomers at start G"K I0IIP 99R

(ustomers added GBK P6 57

(ustomers lost G(K $7R $I

(ustomers at end cf

GDK

99R I0I6

etained included in

cf GK

976 9R59

Aet (hange $I P

ge churn $I.I .P

ge retained 96.6 9R.77





Yrom this new version of the table it can be seen that

U :verall the audience had grown

U "s has the number of people retained

U There is now a faster rate of churn than in the first year and this

is now positive Ghere note that the larger the number for

percentage churn the faster that rate is and that a positive

number indicates that the audience is growing whilst a negative

figure indicates that it is shrinkingK

and

U There is now a stronger retained percentage Gie it has increased

form one year to the nextK.


This techniùe can usefully be employed when a ùantitative measure

of the organisation^s performance in terms of audience growth

retention and turnover is reùired.

See also:

ATTENDER Gpage 76K





COMMAS (numeric)


Big numbers Gie those greater than 999K can often be difficult and

confusing to read.

Related protocols and procedures here

Zence to conform to numeric best practice commas should always beused to separate thousands and millions.

Thus not P65 but P65

"nd not I567R9I but I567R9I





COMPLIMENTARY or COMP.

(complimentary admission, complimentary ticket etc)


Mt can be a common practice for arts venues galleries and other

facilities to offer free admission to selected individuals. Zowever the

issuing of such ]complimentaries^ Gor ]comps.^K can be done for a range

of reasons. =ome of these reasons are entirely legitimate others W

such as those that could be construed as essentially being individual

favours $ teeter on the boundary with the less legitimate.

(onseùently because it seems only fair to assume that all colleagues

working in the arts want their practice to be ethical and above board

there is a pressing need to introduce an enhanced degree of rigour in

recording and documenting such transactions. specially if they could

be open to misinterpretation.

hat^s more not classifying the issue of such complimentrary

admissions according to their purpose runs the risk of obscuring the

true patterns of resource deployment income and admissions for an

organisation. This in turn potentially undermines the picture of that

organisation^s operations provided to its funders and stakeholders.


reater clarity will be obtained if Gwhere possibleK the ]complimentaries^

issued are classified according to their purposes according to the tree

or ]schemata^ shown below.





Complimentary tree or schemata according to purpose

Compli-mentar

Businesspurposes

Non-business

3 purposes

Businesspromotion &development

=ales promotionor as part of marketingcampaign

3ure gratuities

(ustomer relations

apology

3ress or HM3admissions

=ponsor or

donor comps;

" part of aT:Y or

B::YY offer ;

=taff or cast

3ersonal

Zowever it is recognised that few W if any W arts organisations might

consider it practical to introduce a swathe of complimentary ticket

types.

Therefore in recognition of the need for a practical solution combined

with the varied purposes to which such complimentary admissions can

be put it is recommended that the terminology being used be clarified

and changed slightly.

Mt is suggested that ]a complimentary^ be considered recorded and

reported upon as

A free admission that has been allowed to a charging space for no

charge, that has been issued from its own supply of tickets as a

gratuity by the contractually benefiting organisation, and where

the implied loss of income applies to that organisation.





=uch ]complimentaries^ therefore will include admissions made at no

charge for business reasons Gfor instance for business development

press and 3 issued as a favour Gor gratuityK as a customer service

apologyK as part of an audience development scheme or as a reward

for a group booker. Thus free admissions made for these or similar

purposes should be recorded as ]complimentaries^.

Zowever where relevant if a T:Y GTwo tickets for the price of

oneK or a B::YY GBuy one get one freeK offer is made both tickets

should be recorded and reported as tickets issued at 50 of their full

face value. But at the same time the price printed on the notionally

]free^ ticket should be 0.00.

Formulae and calculations to use here

" conseùence of the above logic is that any ticket for which income is

received should be included in a calculation of average yield. But any

complimentaries should not be included in such calculations . Zence

here the preferred formula for average yield would be

Total income generated through admissions

Aumber of paid admissions for which money has been received

See also:

Yield Gpage 57K





CONCESSION

Issues and rationale here:rganisations freùently have a variety of reasons for reducing the

cost of admission charged to a paying attender. These reasons range

from those arising from a customer^s social background and a facility^s

social inclusion exclusion policies through to more commercial

marketing initiatives and promotions.


"s a result and for purposes of clarity if a price is reduced in

recognition of customer^s social background or in keeping with an

organisation^s social inclusion exclusion policies these should be

recorded as a ]concession^. But if a price reduction is offered as part of

a marketing initiative special offer or sales promotion campaign this

should be recorded and reported as a ]discount^.

See also:

DISCOUNTGpage IIRK





CORE INCOME or CORE TURNOVER


:ne approach used in business analysis is to separate anorganisation^s ]core activities^ from its other or ]ancillary^ activities. Thus

a ]core activity^ is something that a business does as a direct

conseùence of its mission essential purpose and reason for being.

By extension such analyses then go on to consider a business^s core

income and core costs Gie income and expenditure relating to its core

purposeK as things that are distinct from its ancillary income and costs

Gie income and expenditure items that relate to things that are outside

its core purposeK.

This distinction can usefully be applied to the analysis of income

generated from customers and users.

Related procedure and protocols here

(onseùently core income is any money that is generated as a direct

conseùence of things that are done in fulfilment of an arts facility^s

essential reason for existing. Typically these will include things such

as ticket sales engagement fees and public funding subsidy to

maintain an organisation^s basic operation or proect.

This should be distinguished from ancillary income Gie monies that are

generated from activities that are not a direct conseùence of an

organisation^s core purposeK. Zence items to be treated as ancillary

income include money earned from catering and merchandising

money from confectionary sales and money earned from gift shops.

See also:

ECONOMIC IMPACT Gpage IK





DATA CAPTURE RATE


The extent to which arts organisations are managing to trap and record

a full set of name address and other data about a customer is an

important indicator of that organisation^s potential to analyse and

understand its customers.

:ne measure of this is the percentage of customers for which name

and address details are obtained as compared with the total number of customers.

But unfortunately this data capture rate tends to vary between

organisations. GYor instance in a report on dance attenders in =cotland

consultants Zeather aitland and Tim Baker report data capture rates

ranging from IP to I00IP

K.

This has two implications. Yirstly it is desirable for arts organisations to

monitor their achievements in capturing data by constantly assessing

their data capture rates "nd secondly W because of the variety of

organisations and settings involved W it is not possible to set an

industry standard for a reùired data capture rate.

IP Heather Maitland and Tim Baker [2002] Profile of Dance Attenders in Scotland –section

two box office data analysis, final report =cottish "rts (ouncil; dinburgh p.6.





Procedures and protocols to use here

The data capture rate is calculated as

GAo. of customers for which name and address data is recordedK I00

Total number of customer transactions

3ossibly the easiest way of finding the ]number of customers for which

name and address data is recorded^ is to find the total number of

customers and =,BT"(T from this the number of customers for

whom there is not a full and usable record of name and address.

See also:

RESPONSE RATE Gpage IP9K





DATE or CUSTOMER REFERENCE DATE


To allow detailed analysis to be done the date of a given customer

transaction is freùently useful. Yor instance this can enable the

plotting of booking patterns in relation to the time of event. :r it can

assist an analysis of an attender ̂s status Gie are they a current or a

lapsed attenderK.

Zowever if an organisation has the benefit of a computerised ticketing

system many transaction times and dates will be recorded as a matter of course so a range of such dates will be available. Zere =tuart

Aicolle advocates the value of not only recording the transaction date

but also of categorising transactions according to their timeI5


(learly if an organisation is dealing with a lot of different date

information it may not be possible for it all to be used. Zence some

decisions may have to be made as to which parts of it are most useful.

This sense of usefulness will flow from what exactly an organisation

wants to do with the data. Yor instance the date of first contact might

be helpful in determining how long a customer has been using the

organisation Gand is thus a measure of their loyaltyK. "nd the time of

booking and date of the event attended could be used as part of an

analysis of typical booking leadtimes.

I5 Stuart Nicolle [2005] Turning Box Office Data into Knowledge =ydney; YuelP

"rts page . Downloadable from

httpwww.fuelParts.comfilesattachBox:fficeDataAicolle0005.pdf GAB Yreeregistration reùired to access thisK.







But if a decision cannot be made between the various uses the key

date to record would seem to be the date of a customer^s last activity.

This recommendation is made because ]date of first activity^ is needed

for a range of uses such as

U Zow currently active a customer is Gwhich will thus give their

attender or booker statusK

U Zow their behaviour relates to any corresponding marketing activity

U Their most recent pattern of activity

and

U Zow long it has been since the customer in ùestion last did

something.


Mf this data is recorded it can be used to categorise people according

to their attender or booker status; to group and code people as a basis

for mailing campaigns; and to develop an understanding of typical

patterns of customer loyalty and purchasing behaviour.






=ee Stuart Nicolle [2005] Turning Box Office Data into Knowledge

=ydney; YuelP "rts page . Downloadable from

httpwww.fuelParts.comfilesattachBox:fficeDataAicolleII005.I

.pdf GAB Yree registration reùired to access thisK.

See also:

ATTENDER Gpage 76K

BOOKER Gpage 9IK

Loyalty scheme Gpage RK



http://www.fuel4arts.com/files/attach/BoxOfficeData_Nicolle_110205.1_.pdf






DISCOUNT

Issues and rationale here" ]discount^ is a generic term for a reduction in the price charged to a

customer for an admission ticket. Mt is therefore includes discounts

made as a concession and as part of a sales promotion campaign.


But a ]discount^ is not exactly the same sort of price reduction as a

]concession^. (oncessions are price reductions offered as part of an

organisation^s social inclusion exclusion policies. hereas discounts

should be seen as a price reduction offered as part of a more

commercial marketing and sales promotion campaign.

(onseùently techniùes such as special ]save money^ offers ]two for

the price of one^ and ]buy one get one free^ should all be recorded

analysed and reported on as ]discounts^.


hen such data is available it can be used to monitor the take up

effectiveness and relative success of any special offers made.

See also:

CONCESSION Gpage IIIK





DOOR SALES


Because door sales Gor ]walk ups^K tend to happen immediately beforean event or performance identifying them will provide an indication of

any late surges or flurries in purchase transactions.

But because they will also need to be handled at speed this also

means that full name and address details may not be recorded.

(onseùently this will reduce an organisation^s data capture rate Gsee

page IIK.


,nrecorded or untrapped customer information is potentially a missed

opportunity.

Zence a number of techniùes have been developed and tried to

overcome this. These include

U Mnviting a ticket purchaser to enter a competition where they have to

return proof of purchase Gie their ticketsK with the entry form

U "sking ticket purchasers to fill in a mailing list membership form

or

U (ollecting names addresses and ticket details as part of an

audience survey.





DRIVETIME or ISOCHRONE


Zere this term does not relate to the broadcasting expression meaning]the time of day when people are travelling to and from work^. ather

when used in relation to audience data a drivetime Gor isochroneK is

a line drawn around all the places in an organisation^s catchment area

that reùire roughly the same number of minutes car travel to get to a

specific arts facility. GYor instance a 0 minute drivetime surrounds all

those places that lie 0 minutes car travel away from the facilityK.

"lthough it assumes that attenders and visitors always come to a

facility using a car it is still an extremely useful indication of a key part

of a facility^s catchment area. "s ohn :imek notes

Very simple models may be couched in terms of crow-fly distance, but

for short distances it is very dangerous to ignore the effect of the actual

road and communications networks. A [facility] may be a very short

measurable distance away from a potential customer, but be on the

other side of a river or railway line, so drive time is usually a more

accurate way of determining [the facility’s] catchment area.I6

Related protocols and procedures here

Mn reat Britain the "rea 3rofile eports take as their cornerstone the

0 minute drivetime area surrounding a given facility.

Thus it should be noted that these areas are drawn using data showing

the average and standard times for driving from one place to another

on the types of roads available in that area Gfor instance motorways

I6

John Ozimek [1993]Targeting for Success – a guide to new techniques for measurement and analysis in database and direct response marketing ondon; craw$Zill Book(ompany page I79.





" and B roadsK. Zowever although this builds in an intrinsic accuracy

that reflects the nature of the local road network this is an indicative

model. "nd should be used as such.

=o each organisation might wish to consider using the area mapped for

the area profile and W where necessary W making allowances for the

different times of day that events actually take place. GYor instance

evening rush hour weekends etcK.


,se to identify key parts of an organisation^s catchment as the basis for

customer analysis and of targeted marketing campaigns. "lso can be

used as one way of substantiating the local usefulness of an arts

facility to ocal "uthorities and egional funding agencies.

References and resources:

John Ozimek [1993] Targeting for Success – a guide to new

techniques for measurement and analysis in database and direct

response marketing ondon; craw$Zill Book (ompany

Peter Sleight [1997] Targeting Customers (second edition) – how to

use Geodemographic and lifestyle data in your business Zenley on

Thames; AT( 3ublications imited.





ECONOMIC IMPACT

Rationale and issues here

This resource defines ]economic impact^ as

The gross financial contribution made by an organisation or facility to

the overall economy of its base area (for example its town, region or

nation).

Zowever in a 00P online paper Thomas Zainski Gthe managing

director of the (hicago based consultancy ZH= (onvention =ports &

ntertainment Yacilities (onsultingK suggests that

Economic impact can be broadly defined as a change in wealth or

utility of producers and consumers that results from investment in a

project. In the language of micro-economics, this is called a change in

consumer and producer surplus. I7

Zence in practical terms economic impact studies have become one of

the tools available to arts organisations wishing to demonstrate and

ustify their relevance to the communities in which they are based by

substantiating the economic contribution they make. G" comprehensive

review of such approaches by ichelle eeves 00 is available to

download at the "rts (ouncil ngland websiteIR

K

I7 =ee www.hvsinternational.comstaticcontentlibrary00P$050I$000.aspx

IR

This is Michelle Reeves [2002] Measuring the Economic and Social Impact of the Arts – areview ondon; "rts (ouncil ngland. Downloadable fromhttpwww.artscouncil.org.ukdocumentspublicationsP0.pdf



http://www.hvsinternational.com/staticcontent/library/2004-0501-000.aspx

http://www.artscouncil.org.uk/documents/publications/340.pdf


http://www.hvsinternational.com/staticcontent/library/2004-0501-000.aspx




ssentially measuring economic impact of an arts facility is a process

concerned with carrying out a comprehensive and ùantified audit of

the economic activity that has been stimulated in an area as a

conseùence of that organisation^s existence. Yor instance in 00P a

study by Dominic =hellardI9

of the ,niversity of =heffield reports that

three theatres undertook a detailed exercise looking at their local

economic impact:

! Everyman Theatre, Gloucestershire – a medium-scale subsidised

theatre – £4.1 million

! The Royal Centre, Nottingham – a large-scale commercial theatre –

£9.4 million

! Derby Playhouse – a small-scale subsidised theatre – £3.9

million0

enerally speaking such economic activity can take the form of either

spending or earning activity. Mt can also be direct or indirect.

Zence an initial list of items to include in a research instrument

intended to audit economic impact would include the following.

! Income Gdirect to organisationK

U Ticket sales

U "ncillary sales Geg merchandise and cateringK

U vent hires and fees

U oom and eùipment hires and fees

U 3ublic =ubsidy to the organisation

I9 Dominic Shellard [2004] Economic Impact of UK Theatre – a report commissioned by

Arts Council England ondon; "rts (ouncil ngland downloadable fromhttpwww.artscouncil.org.uk documentspublicationsphpu=g5.doc0

Mbid. 3age 5





U Donated and sponsorship income

! Income Gfor other organisations indirectly engendered through

the arts facility^s existenceK

U oney spent elsewhere as part of a visit to an arts facility Geg

as part of a shopping trip on catering on accommodation

and on public transport parkingK.

! Expenditure Gdirectly by the organisationK

U 3urchases from suppliers

U =taff wages salaries and fees paid

U ent and rates

U Mncome tax H"T and Aational Mnsurance paid.

Zowever the =hellard report 00P used two specific formulae to

calculate economic impact. These Gand the related commentaryK are

reproduced belowI

.

_______________________________________________________________

Shellard Formula 1:

Calculating the economic impact of … venue excluding turnover

G"dditional visitor spend salaries subsistence allowances goods

and services expenditureK x a multiplier of I.5

GThe multiplier takes into account the knock$on effect in the local

economy.K

Yormula can be used to define economic impact as the total

economic activity generated by a theatre Gin other words what

economic activity an area would lose in total if the theatre was

IMbid. page 9.





not thereK. This second more comprehensive formula also

includes turnover GincomeK.

Shellard Formula 2: Calculating the economic impact of … venuesincluding turnover

GTurnover overseas earnings additional visitor spend salaries

subsistence allowances goods and services expenditureK x a

multiplier of I.5

GThe multiplier takes into account the knock$on effect in the local

economy.K

Mncluding turnover in this formula establishes the scale of the

economic activity related to the theatre and economic impact is

viewed as inputs and outputs rather than profit and loss. =o for

example turnover is made up of money from customers

funders and businesses and produces a specific economic

effect while a theatre^s expenditure on wages and supplies

produces a completely separate economic effect. Mt is not a

strictly linear model.

This defines economic impact as what a theatre contributes to

the local and national economy.

:nce such an auditing scheme is in place a key aim of an economic

impact study will be to identify the ]"dditional Hisitor =pend "H=. This

is the amount of monetary economic activity stimulated in the local

economy as a whole. Thus the =hellard report found that

The average AVS per audience member outside the West End is

£7.77. In the West End it is £53.77 .

:nce such figures are available they can be turned into a ratio showing

an organisation^s ]multiplier effect^ $ ie the number of pounds spend as

"H= for every pound spent directly on the organisation being studied.

Mbid. 3age 6







EVENT


Mn its definition section this resource defines an ]event^ as " discreteone$off happening such as an exhibition performance or participative

workshop. The plural of event is a series or run.

Zowever this is potentially in contradiction to a convention used by a

number of box office system suppliers. Zere ]event^ is used to signify

an offering or production which when allocated to a specific space

place and time becomes known as ]a performance^.

This document^s recommendation $ that a discrete ]one off^ happening

should be referred to as ]an event^ $ is made because this seems in

keeping with common parlance.

Protocols and procedures to use here

(onseùently it is proposed that when talking or writing about a ]real

world^ event Gas meant by common parlanceK this should be called ]an

event^. Zowever if something that is a system supplier convention is

being discussed this should be termed a ]system event^. "lternatively

the suppliers might be encouraged to change their terminology.





FAMILY

Issues and rationale hereThe shape and structure of household arrangements in the ,nited

Singdom is changing.

=hifts in work and life patterns now mean that it is unsafe to assume

that ]a family^ typically conforms to the conventional ]Janet and John^

model or the one used in the title of the BB( television comedy ]2.4

children^.

" BB( news item published on its website in ay 00 noted that

Mr and Mrs Average no longer have 2.4 children - latest statistics

show the figure is now 1.64. That is the lowest average level of births

per woman since records began in 1924. The data from the Office for

National Statistics also showed there was the lowest number of live

births in 2001 since 1977 .

ore recently Gin 00PK the :ffice for Aational =tatistics released

figures showing that

Family size increased from 2.07 children for women born in 1920 to a

peak of 2.46 children for women born in 1934. This peak corresponds

with the 1960s ‘baby boom’. Family size declined for subsequent

generations and is projected to decline to around 1.74 children for

women born in the mid-1980s. Women born in 1955, and now at the

end of their childbearing years, had an average of 2.03 children.P

=ee httpnews.bbc.co.ukIhihealthI990679.stm P =ee

httpwww.statistics.gov.uk((Mnugget.aspMDq76&3osqI&(olankq&ankqI000



http://news.bbc.co.uk/1/hi/health/1990679.stm

http://www.statistics.gov.uk/CCI/nugget.asp?ID=762&Pos=1&ColRank=2&Rank=1000

http://www.statistics.gov.uk/CCI/nugget.asp?ID=762&Pos=1&ColRank=2&Rank=1000

http://news.bbc.co.uk/1/hi/health/1990679.stm




The recent "rts (ouncil ngland W Aew "udiences Yamily Yriendly

initiative has provided a renewed focus on family attenders and the

arts.

Zence W for purposes of consistency W it is recommended that the

definition of ]a family^ be based on the one used by consultant 3amela

3frommer in her report ]Yamily Yriendliness^ which states

The word ‘family’ has been used throughout this report to mean any

adult attending or participating in a cultural event with a child under the

age of 16 .5

(onseùently the suggested definition to use for data relating to a

family is a party which enjoys an association based on kinship,

that is made up of any adult attending or participating in a cultural

event with a child under the age of 16.


"ny occasion when an arts organisation wishes to identify measure

and report on the extent to which its facilities are used by families.

5 Pamela Pfrommer [2002] Family friendliness – an audit of recent research and

recommendations for the development of family audiences in the arts ondon; "rts (ouncilngland 3age I0. Downloadable from httpwww.newaudiences.org.ukresource.phpidq5



http://www.newaudiences.org.uk/resource.php?id=5





Arts Council England [2004] Family Friendly in England’s North West

– introductory pack anchester; "rts (ouncil ngland. Downloadable

from httpwww.artscouncil.org.ukdocumentsnewsphpHh`w.pdf

Pamela Pfrommer [2002] Family friendliness – an audit of recent

research and recommendations for the development of family

audiences in the arts ondon; "rts (ouncil ngland Downloadable

from httpwww.newaudiences.org.ukresource.phpidq5

See also:

AGE GROUP Gpage 6IK

YOUNG PERSON GpageI7RK



http://www.artscouncil.org.uk/documents/news/phpVhqXWw.pdf



http://www.artscouncil.org.uk/documents/news/phpVhqXWw.pdf



FINANCIAL CAPACITY

Rationale and issues here:

:ne way of ùantifying the capacity available to be used by an arts

facility is to calculate the relevant financial capacity W ie how much

money could be generate from an event performance workshop or

series of events.

Procedures and protocols to use here:

Mf the event in ùestion is one for which admission is charged at a

single price level then the financial capacity will eùal

The number of available places the full price being charged

Zowever if the facility involved has a more complex pricing structure

and uses ]price breaks^ between different price areas the financial

capacity should be calculated as

GAo. of seats in area " full price of an area " seatK

GAo. of seats in area B full price of an area B seatK

GAo. of seats in area ( full price of an area ( seatK

and so on.

Instance of when to use:

Besides providing a means of setting financial targets and a source of

information for management performance reports this calculation is

also crucial in working out the average price paid.





See also:

AVERAGE PRICE PAID Gpage R7K

CAPACITY Gpage 9PK





GROSS INCOME


]ross^ is a standard accounting term that means the value of something before any deductions or reductions have been made. GYor

instance a ticket price paid including H"T and before deductions for

agency charges and credit card commissionK.


=ince the gross value of a ticket will be perceived by a customer as its

face value it seems logical to report on financial details from this

perspective. (onseùently it is recommended that wherever possible

all financial analyses relating to audience transactions should use

gross values.

:ne way of envisaging gross Gand netK income is to consider all the

income relating to a particular payment for an admission ticket. =ome

of it is included in the published face value of the ticket whilst other

aspects of it sit outside Gand thus are additional toK the published faced

value. G=ee diagram overleafK.

Thus gross income from a ticket should all the income relating to each

ticket purchased Gie both the income that is part of the full face value

3,= any other ticketing charges made in relation to that ticket such

as handling fees credit card charges and postageK.





The components of gross income

Yullfacevalue

"dditionalcharges

Base price for ticket

Halue "dded Tax

(redit card charges

Zandling fees

3ostal charges

gross income

See also:

NET INCOME Gpage IPPK

Yield Gpage57K





INDEX or COMPARATIVE INDEX

Rationale and issues here"n ]index^ is a single figure that shows the result of comparing a

specific instance of interest with the overall norm for the data from

which the specific observation has been taken. This overall norm is

also known as the ]base^ or the ]basis for comparison^. GYor instance

the proportion of people matching a particular classification in one

postal sector compared with the proportion of people matching the

same classification in an entire catchmentK.

Because of the way an index is calculated Gsee belowK a value of I00

for the index shows that the specific instance being examined is

happening at the same rate Gor ]on a par^K with its appearance in the

wider basis for comparison. Zence a figure greater than I00 indicates

something occurring at a greater rate than within the base. "nd an

index of less than I00 indicates a rate of occurrence at a lower level

than is happening in the base.

"lso here care needs to be taken that readers and users are not misled

by what The conomist Aumbers uide I99I calls ]index

convergence^6

. This is an effect where different index values for

different observations seem to converge on the norm. hilst this does

not necessarily mean any findings are incorrect it does highlight the

sensitivity of indexes to what is chosen as the basis for comparison.

Yor instance the two charts overleaf use the same data for the postal

sectors shown but compare it with different bases. Zence the first

compares the Birmingham postal sectors shown with the averages for

the catchment and the second one compares the same data with the

,S data. =o the results look different. Please note here that the data

used is not real and is for illustrative purposes.

6=ee The Economist [1991] The Economist Numbers Guide ondon; The conomist

3ublications page





Indexes for propotion of ABs in postal sectors compared with proportion of ABs inentire catchment

0

20

40

60

80

100

120

B1 1 B1 2 B1 3 B1 4 B1 5

Postal sectors

Indexes for proportion of ABs in postal sectors compared with proportion of ABs inthe UK population (UK =100)

0

20

40

60

80

100

120

140

160

180

B1 1 B1 2 B1 3 B1 4 B1 5

Postal sectors






The formula for finding a comparative index is

GThe specific instance of interestK I00

The overall basis for comparison

Yor example say that your arts facility was attracting 50 of its

audience from a particular social grade compared to the population of

your catchment area which had only 0 of its population in that

social grade. Then in this case the comparative index for the use of the

facility by people having the specific social grade compared with the

presence of people from that social grade in the catchment area as a

whole would be

50 0 I00 q 5 I00 q .5 I00 q 50.

References and Resources

Curwin and Slater [1991] Quantitative Methods for Business

Decisions ondon; (hapman Zall 3ages 6I to 70

The Economist [1999] The Economist Numbers Book ondon; The

conomist 3ublications pages I to P

See also:

AREA PROFILE REPORT Gpage70K






MARGIN FOR SAMPLING ERROR (aka ‘MARGIN FOR

ERROR’)


Mn an ideal world the statistics used to make and shape decisions $ or

to provide an understanding of audiences $ would be completely

accurate and reliable. " sense of accuracy and robustness inevitably

inspires confidence in the reader and so enhances the credibility of

any observations or claims being made.

But sadly life^s not always like that. Mndeed findings resulting from

market research $ or from the analysis of data $ are a striking instance

of this if they are based on an examination of a sample. Yor instance

an audience survey based on a sample of 500 people might suggest

that 47% of attenders at a particular event came because they saw a

poster . jet it^s always wise to ask how big a ]pinch of salt^ should this

finding be taken with

=tatistical theory recognises this potential and inherent inaccuracy of

such findings by providing a way of dealing with it. This enables us to

use less than reliable data but to still draw conclusions about the real

overall situation from it in a way that is convincingly robust. This is

thanks to the set of techniùes known as ]statistical inference^.





"chieving the absolute perfection of completely robust findings means

examining every instance of the issue under consideration. Gie in the

example used here everyone who came to the specific event in

ùestionK. =uch a research exercise that deals with the entire relevant

population7

is known as a ‘census’.

Zowever practical issues Gsuch as cost resource and time factorsK

mean that it^s not always possible to conduct a census. Mnstead we

might resort to what the terminology calls ]a sample based survey ’.

hen a sample based survey is carried out this tends to be based on

a sample of a wider population. Mt^s already been noted that complete

accuracy $ in terms of any findings Gsuch as the proportion of people

who do a particular thing or who fit a particular profileK $ would only be

available if we were to carry out a survey of the entire given population

Gie a censusK. =o a survey based on a sample drawn from a wider

population can only ever be a snapshot of the relevant population. G=ee

diagramK.

" 3:3,"TM:A

" ="3

C= here ]population^ means all the cases that have a particular characteristic W in this

instance everyone who came to a particular event. Zence elsewhere ]population^ can meananything from everyone who falls into a certain age group through to everyone who lives in a

certain area town or country;





"nd because we are working with a snapshot here this means that any

findings drawn from that sample based survey will potentially suffer

from a degree of inaccuracy W because the findings may be slightlyunrepresentative of the population as a whole. Zence any sample

based finding is only an approximation of the true state of the

population.

:ne branch of statistical knowledge Gknown as ]sampling theory^K

enables us to calculate the degree to which a sample based finding

Gsuch as ]P7 of people who came came because they saw a poster^K

could be inaccurate. The extent of this degree of inaccuracy is stated

as a plus or minus Ga $ or a ]give or take^K figure and is known as ]the

margin for sampling error on a proportion finding^. Yurthermore it^s

worth noting that the sie of this margin gets bigger as the sie of the

sample used gets smaller.


Typically calculations of this kind are carried out at the 95

confidence level Ggenerally because using a 95 confidence level is a

sort of ]industry standard^ for statisticiansK. hen survey findings are

analysed at the 95 confidence level this means that we can be

confident that if the same survey was done I00 times we would be

likely to get the same findings on 95 of these I00 occasions.

To find the margin for sampling error Gat the 95 confidence levelK

relating to a given survey finding that is a percentage you could

MTZ





U look up the margin for the finding you get and the sie of the

sample involved by using the ready$reckoner table shown

below

OD

U for greater precision $ you could work it out using the formula

shown in the ]formulae and worked examples section GB5K.

$

$ $ Ready Reckoner - Margins for sampling error

SIZE OF SAMPLE 100 250 500 1,000 1,500 2,000

5% P. .7 I.9 I.P I.I I.0

10% 5.9 .7 .6 I.9 I.5 I.

15% 7.0 P.P .I . I.R I.6

20% 7.R 5.0 .5 .5 .0 I.R

25% R.5 5.P .R .7 . I.9

30% 9.0 5.7 P.0 .R . .0

35% 9. 5.9 P. .0 .P .I

40% 9.6 6.I P. .0 .5 .I

45% 9.R 6. P.P .I .5 .

FINDING 50% 9.R 6. P.P .I .5 .

55% 9.R 6. P.P .I .5 .

60% 9.6 6.I P. .0 .5 .I

65% 9. 5.9 P. .0 .P .I

70% 9.0 5.7 P.0 .R . .0

75% R.5 5.P .R .7 . I.9

80% 7.R 5.0 .5 .5 .0 I.R

85% 7.0 P.P .I . I.R I.6

90% 5.9 .7 .6 I.9 I.5 I.

95% P. .7 I.9 I.P I.I I.0

GTable shows the percentage to add or subtract from a finding to infer the

likely case for the overall population in ̀ uestion at the sample sies

shown. Mdea for ready reckoner drawn from Hill, O’Sullivan and O’

Sullivan [1995] Creative Arts Marketing , Butterworth Heinemann with

calculation of above figures performed by current authorK.

Yor instance say a survey finding suggests that P0 of an audience

travels to an arts facility by car and this is based on a sample of 500

people then the likely proportion of all attenders at that facility who





come by car is P0 $ P.. =o between P0$P. and P0P.

come by car ie 5.7 and PP. Gestimated at the 95 level of

confidenceK. G=ee diagramK.

$

$

$

$

$

$$

$

$

$

$

40%35.7% 44.3%

=urvey findingowest estimate of population characteristic

Zighest estimate of population characteristic

Margin for sampling error = + or – 4.3%

$


This set of techniùes procedures and protocols is particularly useful

when evidence of the robustness of the finding is reùired. Yor

instance when any decision based upon it is one of high importance or

when you want the reader to be convinced of the finding arrived at.

Further resources and references

Deborah Rumsey [ 2003] Statistics for Dummies Zoboken A; =age

3ublications pp.I6I to I76

Neil Salkind [2000] Statistics for people who (think they) hate

statistics ondon; =age 3ublications p.I

www.amstat.orgsectionssrmsbrochuresmargin.pdf www.robertniles.comstatsmargin.shtml



http://www.amstat.org/sections/srms/brochures/margin.pdf

http://www.robertniles.com/stats/margin.shtml

http://www.robertniles.com/stats/margin.shtml

http://www.amstat.org/sections/srms/brochures/margin.pdf



See alsoANNOTATIONS Gpage 65K

SAMPLE SIZE Gpage I5K

Percentage GpagePK

PERCENTAGE CHANGE GpagePPK





NET INCOME


]Aet^ is a standard accounting term which denotes an amount of money

after any necessary deductions have been made. Zowever common

parlance often use ]net^ to mean ]net of H"T^ Gie a sum of money after

any relevant Halue "dded Tax has been taken awayK.

Procedures and protocols to use hereYor purposes of consistency it is suggested that any ]net income^

figures should reflect the total monies being generated for an

organisation. Zence it is recommended that ]net^ should always be

taken to mean

The amount paid by a customer MINUS any deductions for VAT

and also MINUS any deductions payable to another party (such as

Agency fees and Credit Card Charges).

iven the potential for confusion here unless there is a valid

accounting reason it is also strongly recommended that the use of ]net^

figures be avoided.

See also:

GROSS INCOME Gpage IK

Yield Gpage 57K





PERCENTAGE CAPACITY and PERECENTAGE

OCCUPANCY


The definitions section of this text makes an important distinction

between potential ]capacity^ and potential ]occupancy^. These are both

measures of the extent to which a facility^s accommodation has Gor isK

being used expressed as a percentage of the total accommodation

available.

But percentage capacity should be thought of as the potential level of accommodation use that could be achieved. 3ercentage occupancy

relates to the actual use of the accommodation that has actually been

achieved. Mt might also prove desirable for ticketed facilities to

subdivide the analysis of occupancy into ]paid for occupancy^ and

]unpaid occupancy^.


The formulae to use here are as follows.

(alculating percentage capacity

Aumber of places that is hoped will be used I00

Total number of places available for use

(alculating percentage occupancy

Aumber of places used I00






(alculating percentage paid occupancy

Aumber of places used for which money was received I00


"nd calculating unpaid occupancy

Aumber of places used for which no money was received I00


See also:

Yield Gpage57K





RESEARCH OBJECTIVE

Rationale and issues here" research obective Galso called a ]research problem^ by Zussey and

Zussey I997R

K is a statement of the exact things a research exercise

is intended to find out.

"s such this is the crucial starting point from which flows the type of

research to be done the sample to be used and the research

instrument survey or ùestionnaire to be put into action.

"udience Data consultant =tuart Aicolle observes Gand warnsK that

Any researcher will tell you that the key to a successful piece of

research is to clearly set out what it is that you want to find out. Too

many people want to “know more about my audience” and whilst this

is an admirable thing to do, the possibilities are too endless to use this

as a starting point!” 29

Protocols and procedures here

Thus examples of research obectives include things such as

U ]Mdentifying the profile of people attending a particular exhibition^

U ]Yinding the average age of a regular attender^

U ]ocating key hotspots within a venue^s catchment area^

R=ee Hussey and Hussey [1997] Business Research - a practical guide for undergraduate

and postgraduate students ondon; acillian Business pages II6 to I9

=ee Stuart Nicolle [2005] Turning Box Office Data into Knowledge =ydney; Yuel P "rtspage P downloadable after free registration fromhttpwww.fuelParts.comfilesattachBox:fficeDataAicolleII005.I.pdf







or

U ]"ssessing the effectiveness of a new marketing tool^.

Being specific about the research obective that applies is vital because

from it flows the type of research survey to be used the approaches to

be taken in analysing the results and how the findings are reported.

Zence this is very much an issue of doing things in the right order. "nd

it is inadvisable to first devise a survey or ùestionnaire before then

deciding what the whole point of a research exercise is intended to be.

Resources and References

Hussey and Hussey [1997] Business Research - a practical guide for

undergraduate and postgraduate students ondon; acillian

Business pages II6 to I

Stuart Nicolle [2005] Turning Box Office Data into Knowledge

=ydney; Yuel P "rts page P downloadable after free registration from

httpwww.fuelParts.comfilesattachBox:fficeDataAicolleII005.I

.pdf

See also:

SAMPLE Gpage I5K









RESPONSE RATE


This is a ùantified measure of the success of a marketing or research

exercise. Mt works by making a comparison between the actual replies

achieved and the initial number of items Gor opportunities to replyK

issued.

"s such it can be used in relation to direct mail and other marketing

campaigns and research activities such as surveys and

ùestionnaires.

"lthough response rate may be important in evaluating the success of

a marketing campaign it is vital in assessing the validity of surveys and

ùestionnaires since the extent to which different types of people

respond will have a bearing on how representative the sample used is.

esponse rates are usually stated as percentages. But here it should

be noted that this is another instance where there is no universal

standard for a good or bad response rate.

Zowever the Aew jork based research and consultancy organisation

]uidestar^ gives a useful digest of the sorts of response rates that can

be expected from different sort of survey exercises0

. These are

reproduced in the table overleaf.

0

Zere see Guidestar [2005] Research Basics -response rates Aew jork; uidestar (ommunications page I and . "ccessible online athttpwww.guidestarco.comresearchbasicsresponserates.html



http://www.guidestarco.com/research_basics/response_rates.html




Survey types and expected response rates

G"fter information made available online by uidestar (ommunicationsK

=urvey type esponse rates to expectmployee =urveys 75 to R5

holesalers suppliers & distributors P5 to 55

Zelp Desk =urveys 50

Zigh spending customers 0 to 0

ower spending customers I0 to I5


=o that response rates can be analysed and evaluated both the initial

number of items sent out or dispatched Geg surveys or mailing itemsK

and the usable number of items returned will need to be monitored.

This will then allow the percentage response rate to be worked out

using the formula

3ercentage esponse ate q I00

D

here D q the number of items sent out or dispatched

and

q the number of items returned.

Zence if W as part of a postal survey 000 ùestionnaires are sent out

and 700 are returned then the percentage rate q

700 000 I00 q 0. I00 q 23.3%






This techniùe is a useful one when organisations want to make

comparisons Gboth internally and externallyK of the effectiveness of their

marketing and research activities. Mt is also essential for assessing the

validity of sample findings.

References and resources

Guidestar [2005] Research Basics -response rates Aew jork;

uidestar (ommunications page I and . "ccessible online at

httpwww.guidestarco.comresearchbasicsresponserates.html

See also:

Percentage Gpage PK

SAMPLES Gpage I5K







SAMPLES, SAMPLING, and SAMPLE SIZE


" sample is an extract from an overall population. =uch an extract is

like taking a ]snapshot^. Mt is used to find things out about the

population and to make valid generalisations about it. GYor instance

things such as the population^s average age; its social background; or

attendance patternsK.

Zence ]sampling^ is the process of taking such a sample. Typically this

is done using a survey a ùestionnaire or through other analysis of data extracted from a wider data set.

hilst not ùite as definitive as examining the entire population being

considered Gwhen the relevant exercise would be a ]census^K using

samples has three particular virtues.

!Yirst it tends to reùire lower costs time and effort thansurveying the entire population.

! =econd it tends to be more easily done.

! "nd third $ thanks to a techniùe known as ]statistical inference^

$ it is still possible to draw conclusions about the population on

the basis of findings generated from the sample.

But there are also a number of potential drawbacks complications and

vital considerations involved in using samples. These are explained in

the following procedures and protocols section.





Related procedures and protocols

" sample then is a snapshot of a larger population. "s a result

although it is still possible to make generalisations about that

population based on sample findings these findings are inevitably less

firm Gie less accurateK than ones built on an examination of the entire

relevant population. =ome important procedures for reducing the

inherent inaccuracies of samples are outlined below.

"voiding bias

Yor a start the robustness of sample findings can be undermined if the

way in which the sample is devised and surveyed has not taken care to

reduce bias.

Deborah umsey warns that

Bias is systematic favouritism that is present in the data collection

process, resulting in lopsided, misleading resultsI.

Mn fact such bias is so insidious that it can creep into a survey process

in three ways.

!" ]Sample bias^ Galso sometimes called ]design bias’ K happens

when a researcher has not taken care to use a sample that is

representative of the entire population. This danger can be

reduced either by using a random sample Gwhere every case

has an eùal chance of being included $ eg every fifth personK or

by using a ]stratified sample^ Gie one which has been specially

devised to reflect the population from which it has been taken W

for instance in terms of the mix of age groups gender mix or

social backgroundK.

I Deborah Rumsey [2003] Statistics for Dummies

® , Zoboken Aew ersey; iley

3ublications Mnc. p. PP.





!" ]Measurement bias^ results from not asking the appropriate

ùestions that would actually help in meeting the research

obective. Zere in a useful online tutorial

The oper (enter

sic. at the ,niversity of (onnecticut observes

“Measurement Error is error or bias that occurs when surveys do

not survey what they are intended to measure. This type of error

results from flaws in the instrument, question wording, question

order, interviewer error, timing, question response options, etc.

This is perhaps the most common and most problematic

collection of errors facing the polling industry” 33 . This sort of

bias can be avoided by taking pains to ensure that the ùestions

asked of a sample during a research exercise are a logical

conseùence of what you are trying to find out.

!" Then ]non-response bias^ can also have an undermining and

damaging affect on survey findings. This is all to do with low

response rates leading to problems with the survey^s coverage

of the population. Zence although there is no definitive industry

standard as to what is a ]good^ response rate if the proportion of

people responding from one part of the population exceeds the

proportion of respondents drawn from another part of the

population Gfor instance if more men respond than do womenK

then there will inevitably be further built in bias to the sample

and its findings. Thus it will be unrepresentative of the

population. "n important consideration here then is to ensure

that the sample is representative of the population. This means

staying alert to the patterns of response and non$response both

to a survey as a whole and to the individual ùestions within it.

"ccessible at httpwww.ropercenter.uconn.edupompollingI0I.html The Roper Centre [2004] Polling 101 – the basics of public opinion research, Connecticut ;

,niversity of (onnecticut page 6.





Deciding how confident you want to be

:nce everything that can be done to avoid bias has been done the

next stage in making the most of a sampling exercise is to decide

which level of confidence you want to work at.

(onfidence levels Galso known as ]confidence intervals^K are an

indication of how much trust and certainty can be placed in a set of

sample findings. Mn other words this is a measure of how likely it is that

the findings will represent the true state of the population.

These levels are usually written as a percentage and as a result make

a definitive statement about a sample finding^s accuracy. Zowever take

care how such intervals are understood and interpreted. hen a

sample is working at the 99 level of confidence commonsensical

usage might suggest that you were more or less certain of the findings.

Zowever a confidence level of 99 means much more than ]you can

be more or less certain of something^. hat it is actually saying is that

if the sampling exercise was repeated I00 times you would be likely to

get the same findings on 99 out of those I00 occasions. Gie 99I00 q

99K.

=tatisticians typically prefer to use a 95% level of confidence as an

industry standard. "nd although no one seems to know why 95 is

preferred Grather than 9P or 96K it is here recommended that W

wherever possible W 95 is used.

:ther Aumerical issues

ven if painstaking steps have been taken to avoid the effects of bias

and to set a desired level of confidence the findings resulting from a

survey based on a sample can still be compromised by a number of

other numerical considerations.





This is because the robustness of the sample results are influenced by

a trio of factors. These are

U The give or take Gie plus or minus { K tolerance of error you are

willing to accept for your results W this is the tolerable ]limit of error’

you would be happy to work with

U The expected findings you think may result from a particular

ùestion

and

U The actual size of the sampleP

.

P Zere see Curwin & Slater [1991] Quantitative Methods for Business Decisions (third

edition) ondon; (hapman & Zall page IR6.





Factors impinging on the reliability of survey findingsG"fter Curwin & Slater [1991]K

Tolerable limit of error

obustness

of samplefindings

Expected finding Sample size

Zere ]limit of error^ is actually about how wide a margin for sampling

error you would be happy to live with before your findings were

undermined.

Then the matter of ]expected findings^ is essentially about what finding

you would expect to get from a ùestion. et^s say that in previous

surveys your organisation has asked people to rate its services. "s a

result you found that the average percentage of people who said that

they feel the services are ]good^ is P0. (onseùently you should use

P0 as the predicted response here.

"lternatively you could run a small pilot survey to establish the

expected response or use your experience skill and udgement to

estimate the likely response. Mf all else fails the safest default to use for

this value if 50. G=imply because a finding of 50 will be the one that





will have the most extreme and pronounced impact on the reliability of

the findings hereK.

astly there is the issue of how large a sample should be used.

But unfortunately again there is no hard or fast view as to the

optimum sample sie to use.

=ome writers do however suggest certain ]rules of thumb^. Thus

(urwin and =later I99I5 recommend using a sample of more than

0 instances. GThis is because the maths involved behave differently

for samples that are less than 0 in sieK. Yurthermore the Yaculty of

(omputing Mnformation and nglish at the ,niversity of (entral

ngland Business =chool point out that

Where the number of results is under 50 it will not be feasible to

analyse any sub-categories of the data”36.

Then Deborah umsey 00 points out that for a finding to be

reliable the sample sie multiplied by the expected finding Gexpressed

as a proportionK needs to eùal at least 5. The sample sie multiplied

by one MA,= the expected finding Gstated as a proportionK also needs

to be at least 5.

lsewhere other authorities commend different minimum sample sies.

Yor instance the data analysts group from the Aational "udience

Development "gencies commend using a sample of at least I50. The

allup :rganiation W famed for its opinion polls in the ,nited =tates $

uses an average sample sie of I000 for its Aational surveys.7. "nd

5Mbid. 3age I90.

6

UCE, Faculty of Computing, Information and English [2005] Research MethodsWebsite at httpwww.cie.uce.ac.ukdialrmmethodssamples.htm7

Zere see httpwww.gallup.comhelpY"spollI.asp





in an extremely useful guidance note on collecting data using surveysR

Gwhich forms part of its ]=trategy =urvival uide^K the 3rime inister^s

=trategy ,nit says that

The sample size needs to be an adequate size, in order to generalise

from the survey's findings. Provided that the sample size is

representative of the population, the larger the sample size, the more

confident you can be that the results are an accurate reflection of the

population as a whole. The key factor is the absolute size of the

sample, rather than the proportion of the population that gets included

in the sample. Adequate samples can be estimated from the expected

variation in the major variables of interest, and will therefore depend on

the specific question or hypothesis to be tested. As a general rule of

thumb, adequate samples will generally involve more than 30 events or

people. Most market research companies use samples of around

1000-2000 .9

G(urrent author^s emphasisK.

=o what $ in the face of such varied advice $ is an under$resourced arts

organisation to do

The whole point is that starting with the sie of the sample you wish to

use is rather like trying to push a piece of string along. Mt is starting the

process from an unhelpful place.

Mt is far more helpful to begin by deciding on four basic but essential

factors. Aamely

! hat you want to find out Gie your research objective K

! The level of confidence you want the sample to work at

R

"vailable online at httpwww.startegy.gov.uksusurvivalguideskillsebsurveys.htm 9 Prime Minister’s Strategy Unit [2005] Strategy Survival guide – strategy skills; building

the evidence base ondon; 3=, page I of 7



http://www.startegy.gov.uk/su/survivalguide/skills/eb_surveys.htm




! The { degree of tolerance you are willing to have for sampling

error Gthe ]limit of error^K

and

! The finding you expect to get Gif in doubt use 50 hereK.

:nce you have established all of this it is possible to work out the

optimum sample sie to use. The actual formula involved is provided in

the next ]formulae and worked examples section^. But in case you

prefer to avoid the algebra the ready reckoner overleaf shows the

optimum sample sies for a range of expected findings and tolerable

limits of error. These are all worked out assuming that the sample

exercise will work at the 95 confidence level.





Ready reckoner for Sample Sizes working at the 95% confidence level40

Tolerable limit of error ±1% ± 2% ± 3% ± 4% ± 5%

Expected finding 95% IR5 P56 0 IIP 7

90% P57 R6P RP I6 IR

80% 6IP7 I57 6R RP P6

70% R067 0I7 R96 50P

60% 90 05 I0P 576 69

50% 960P P0I I067 600 RP

40% 90 05 I0P 576 69

30% R067 0I7 R96 50P

20% 6IP7 I57 6R RP P610% P57 R6P RP I6 IR

5% IR5 P56 0 IIP 7

Yor instance if you want to work at the 95 confidence level can

tolerate a margin for sampling error of { and are using 50 as the

value for the expected finding you should use a minimum sample of

1,067 or more.

There^s one further thing to bear in mind. enerally speaking the

larger the sample sie used the more reliable the findings that can be

generated. G"s is borne out by the ready reckoner since it can be seen

that the smaller the tolerable limit of error $ and thus the greater the

desired reliability $ gets the larger the sample sie needed becomesK.

Zowever here its worth noting that whilst in the case of samples sie is

important the relationship between sample sie and the reliability of

the sample findings is not a ]straight line^ one. Mn other words doubling

the sample sie does not result in a doubling of the findings^ reliability.

P0 Table inspired by Hill, O’Sullivan & O’Sullivan [1995] Creative Arts Marketing ,

Butterworth Heinemann with figures calculated by current author.





The diagram below shows how there is a ]diminishing returns^ effect

here. Zence the chart shows the extent to which reliability improves Gin

terms of the incremental reduction in the margin for sampling errorK as

a conseùence of an increase in sample sie. GThe situation depicted

here assumes that the confidence level being used is 95 and the

expected answer used is 50K. Zere the scale down the left hand side

shows the reduction of the margin for sampling error when compared

to the previous sample sie charted whilst the horiontal scale along

the bottom shows the relevant sample sies. (onseùently it can be

seen that increasing the sample sie from 50 to 500 leads to a

narrowing of the margin for sampling error by I.R percentage points.

But increasing the sample sie from 500 to 750 only leads to a

reduction of the margin for sampling error of 0.R percentage points.

"nd so on. =o it is always as well to identify at which point the

improvements in accuracy are outweighed by the costs and effort

reùired to increase the sample sie to a particular level.

Improvements (ie reductions) in MfSE as Sample Size Increases

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

2.00

$ 50 500 750 I000 I50 I500 I750 000 50 500 750 000 50 500 750 P000

Sample Size






=amples and sampling theory can be applied to a range of research

activities. Zence this is particularly useful when used in relation to

surveys and ùestionnaires as well as analysis of data extracted from

a wider data set.


Zere see

Curwin & Slater [1991] Quantitative Methods for Business Decisions

(third edition) ondon; (hapman & Zall page IR6.

Deborah Rumsey [2003] Statistics for Dummies® , Zoboken Aew

ersey; iley 3ublications Mnc. p. PP.

httpwww.ifad.orghfstoolshfsanthropometryant.htm

httpwww.gallup.comhelpY"spollI.asp Prime Minister’s Strategy Unit [2005] Strategy Survival guide –

strategy skills; building the evidence base ondon; 3=, page I of 7

at httpwww.startegy.gov.uksusurvivalguideskillsebsurveys.htm

The Roper Centre [2004] Polling 101 – the basics of public opinion

research, Connecticut ; ,niversity of (onnecticut page 6 at

httpwww.ropercenter.uconn.edupompollingI0I.html

httpwww.ukmarketingmanagement.comTesting=amplingtesting

sampling.html



http://www.ifad.org/hfs/tools/hfs/anthropometry/ant_3.htm

http://www.gallup.com/help/FAQs/poll1.asp


http://www.ropercenter.uconn.edu/pom/polling101.html

http://www.ukmarketingmanagement.com/Testing___Sampling/testing___sampling.html




http://www.ropercenter.uconn.edu/pom/polling101.html


http://www.gallup.com/help/FAQs/poll1.asp




University of Central England, Faculty of Computing, Information

and English [2005] Research Methods Website at

httpwww.cie.uce.ac.ukdialrmmethodssamples.htm

See also

MARGIN FOR SAMPLING ERROR Gpage IRK

RESEARCH OBJECTIVE Gpage IP7K

RESPONSE RATE Gpage IP9K



http://www.cie.uce.ac.uk/dialrm/methods/samples.htm#3

http://www.cie.uce.ac.uk/dialrm/methods/samples.htm#3



SEGMENTATION


=egmentation is an approach that can be adopted to breakdown a

wider market into smaller sections Gor sub$setsK as a way of providing

greater focus to marketing campaigns activities and research

exercises. Mts underlying intention is to build in the potential to

understand each discrete segment better and to treat it differently from

other segments.


There are many ways in which a market can be segmented and which

one is used will depend upon the purpose of the specific activity being

undertaken. Zowever ilson and illigan I997 point out that the

majority of segmentation techniùes can be grouped into four

categories

U geographic Gie where members of a segment could be found

and reachedK

U demographic G ie the by the segment^s age economic

circumstances and life$stageK

U behavioural Gie how members of the segment act in relation to

your organisationK

and





U psychographic Gor ]attitudinal^ ie according to the segment

members^ attitude to the arts your organisation and other

factors such as riskK.PI

"s might be expected not all segments are of eùal use or eùal

value. Zence the same authors provide a list of ]factors affecting the

feasibility of segmentation^P

. These are reproduced below with

additional commentary by the current author.

U Measurable Gcan the sie of a segment be ùantifiedK

U Accessible Gis it possible to reach a given segmentK

U Substantial Gis a segment large enough to ustify the effort and

investment reùired to reach itK

U Unique Gcan a segment be distinguished from other segments in

the marketK

U Appropriate Gdoes a segment fit with the purposes of the

exerciseK

and

U Stable Gcan a segment^s future behaviour be predicted with a

sufficient degree of confidenceK.

PI Richard M S Wilson & Colin Gilligan [1997] Strategic Market Management Gsecond

editionK. :xford; Butterworth$Zeinemann page 79. xplanations in plain text by currentauthor.P

Mbid. 3age 75.






" segmentation approach can be usefully employed in a number of

settings. These include

U "nalysing an audience as the basis for gaining enhanced

understanding of it

U To inform plans for future marketing campaigns and activities

U "s a basis for coding the records kept relating to an individual

and

U "s a means of structuring and devising research instruments

and exercises.

See also:

AGE GROUP Gpage 6IK

Frequency Gpage K

Geodemographic profile Gpage PK

SOCIAL GRADE Gpage I6RK





SOCIAL GRADE


=uch approaches provide a means of classifying individuals according

to their socio$economic status. =o they are a crucial tool for profiling

Gand understanding aspects ofK an organisation^s catchment markets

and customer base.

But whereas the M(A" Aational eadership =urvey classifications

Gie " B (I ( DK and the egistrar eneral^s =ocial (lass system

Gie M MM MMMA MMM MH HK have been used in the past the introduction of

a new system for use in the 00I Aational (ensus Gthe so called

Aational =tatistics =ocial and conomic (lassifications A=$=( K

changed things considerably.

This maor shift resulted from dissatisfaction with the previous systems

which were felt to be increasingly unrepresentative of ,S society and

the new patterns of work and employment within it. hat^s more theadvent of the A=$=( system has led to a vigorous debate between

marketers and sociologistsP.

The key issues in this debate are that on the one hand the M(A"

categories are thought to be more commonly understood that those

from the other systems. But on the other hand the new A=$=(

system was used by the 00I (ensus and has been built to reflect the

current shape of employment and occupations. Zence these two

classification systems work on different conceptual bases. "nd as a

result classifications using one cannot be easily converted into

classifications using the other. This has maor implications for anyone

wanting to combine data based on the M(A" social grades Gsuch as

P

Yor instance see Bob Cervi [1999] Classifying of consumers: Row over the new Social Grades in The Source public management journal, accessible online athttpwww.sourceuk.netarticlesf0057I.html



http://www.sourceuk.net/articles/f00571.html

http://www.sourceuk.net/articles/f00571.html






=ocial rade classification systems can be used to support the profiling

of catchment areas potential markets and audiences. "s such they

can be used as part of wider research and analysis activity and as an

aspect of report writing.


Donkin, Lee and Toson [2002] Implications for changes in the UK

social and occupational classifications in 2001 for vital statistics

downloadable from

httpwww.statistics.gov.ukarticlespopulationtrendssococclassificati

onsptI07.pdf

MRS CGG [2004] MRS Social Grade Approximation for the 2001

Census Gsee httpwww.mrs.org.uknetworkingcggcggsocialgrade.htm

ith full paper Downloadable from

httpwww.mrs.org.uknetworkingcggdownloadssocial0gradeap

proximation.pdf K

See also:

AREA PROFILE REPORT Gpage 70K



http://www.statistics.gov.uk/articles/population_trends/sococclassifications_pt107.pdf


http://www.mrs.org.uk/networking/cgg/cggsocialgrade.htm

http://www.mrs.org.uk/networking/cgg/downloads/social_%20grade_approximation.pdf




http://www.mrs.org.uk/networking/cgg/cggsocialgrade.htm





SUBSCRIPTION and SUBSCRIPTION DISOUNT


=ubscription is a sales promotion device writ large. Yirst brought to the

,S by "merican Danny Aewman it was set out in his seminal I977

book ]=ubscribe Aow|^ whilst its ]dynamic subscription promotion^

process was championed by Seith Diggle.

The whole process works by offering a price reduction on an overall

package of tickets so that each ticket can be purchased at a reduced

price.

Typically the promotional communications relating to such

subscriptions have tended to focus on the percentage saving being

made or the number of tickets that can be acùired notionally for free.

Related protocols and procedures

The calculation of the overall subscription discount being offered Gas a

percentageK can be found using the procedure.

Yirst find the overall subscription saving == being offered. This will be

== q GY3IY3

Y3

Y3

P Y3

5 Y3

6 Y3

7 Y3

R Y3

9K W

G=3I=3

=3 P

=3

P =3

5 =3

6 =3

7 Y3

R =3

9K

here Y3I Y3 Y3 and so on are the full price of the individual

tickets included in the package

and





=3I =3

=3

and so on are the subscription prices at which the

individual tickets included in the package are offered.

The resulting subscription saving == can then be interpreted as a

certain number of full price tickets.

Then the percentage subscription discount is found through the

calculation

== I00

GY3IY3 Y3 Y3P Y35 Y36 Y37 Y3R Y39 K

ie The total subscription saving divided by the total full price value of

the tickets in the package then multiplied by I00.


hen constructing and promoting a subscription ticketing scheme.


Danny Newman [1977] Subscribe Now! – building arts audiences

through Dynamic Subscription Promotion Aew jork; Theatre

(ommunications roup.

See also:

DISCOUNT Gpage IIRK

Sales promotion Gpage P9K





TOURIST or VISITOR


"cting in response to uropean ,nion directives and to identifiedpolicy needs in 00P the Department for (ulture edia and =port

D(= placed a renewed emphasis on the economic importance of

tourismP9.

The relevant , Directive calls for the creation and collection of

uniform statistics on tourists and tourism. "nd in an extension to this in

=eptember 00P the D(= initiated a process of setting up ],S

Tourism =atellite "ccounts^50

. (entral to this initiative is a clear

reùirement that organisations and ocal "uthorities working with

tourist related activities

U a. ,se a consistent definition of ]tourist^

and

U b. "dopt a uniform approach to the data collected.

Related procedure and protocols here

Zence to enable arts facilities to align their data collection with the

reùirements of egional Development "gencies D"s the ,S

Department for (ulture edia and =port D(= and the uropean

,nion it is recommended that tourists be defined as

P9Zere see DCMS [1998] EU Tourism Directive – A briefing note for the Department of

Culture, Media and Sport ondon; D(= Gdownloadable fromhttpwww.culture.gov.ukArdonlyresI(5BB79$B69$PY65$R95B$R0B77"9"D6I0,Tourismstatsdirective9597D(==ummary.pdf K and DCMS [2004] Tomorrow’s Tourism Today ondon; D(= Gdownloadable fromhttpwww.culture.gov.ukArdonlyres7YD(D$B("6$P$"6R$

6(6Y(Y5Y79B0TomorrowsTourismToday.pdf 50Zere see the December 00P (ulture =outh est news release available at

httpwww.culturesouthwest.org.uknewsarticle.asp"rticleMDqI5



http://www.culture.gov.uk/NR/rdonlyres/1C5B2B79-B693-4F65-895B-80B727A9AD61/0/EUTourismstatsdirective9597DCMSSummary.pdf


http://www.culture.gov.uk/NR/rdonlyres/73FDC3ED-BCA6-4323-A683-63C6FCF5F79B/0/TomorrowsTourismToday.pdf


http://www.culturesouthwest.org.uk/news/article.asp?ArticleID=153








persons travelling to and staying in places outside their usual

environment for not more than one consecutive year for leisure,

business and other purposes…There are three elementary forms of

tourism in relation to a given area:

- Domestic tourism (the activities of residents of a given area

travelling only within that area, but outside their usual environment),

- Inbound tourism (the activities of non-residents travelling in a given

area that is outside their usual environment),

- Outbound tourism ( the activities of residents of a given area

travelling to and staying in places outside that area and outside their

usual environment5I.

"t the same time urostat defines a visitor as any person travelling to

a place other than his / her usual environment for less than twelve

consecutive months and whose main purpose of travel is other than an

the exercise of an activity remunerated from within the place visited ….

The term visitors (domestic and international) comprises tourists and

same-day visitors.

The key distinction between a ]tourist^ and a ]visitor^ is that ]tourists^

does not include people making day visits whilst ]visitors^ does. Zence

it is also recommended that this basic difference be adopted and used

by arts organisations.


These definitions should be used when carrying out research and

analysis intended to produce data to be incorporated in reports on

economic impact for ocal "uthorities and D"s and for the D(=

Gvia the ,S "rts Yunding =ystemK.

5I

=ee Coded – The Eurostat concepts and Definitions Database accessible online athttpforum.europa.eu.intircdsiscodedinfodatacodedengl0069R.htm







References and Resources

Coded – The Eurostat concepts and Definitions Database

accessible online at

httpforum.europa.eu.intircdsiscodedinfodatacodedengl0069R.ht

m

Culture South West news release [2004] UK Tourism Satellite

Accounts available at

httpwww.culturesouthwest.org.uknewsarticle.asp"rticleMDqI5

DCMS [1998] EU Tourism Directive – A briefing note for the

Department of Culture, Media and Sport ondon; D(=

Gdownloadable from

httpwww.culture.gov.ukArdonlyresI(5BB79$B69$PY65$R95B$

R0B77"9"D6I0,Tourismstatsdirective9597D(==ummary.pdf K

DCMS [2004] Tomorrow’s Tourism Today ondon; D(=

Gdownloadable from

httpwww.culture.gov.ukArdonlyres7YD(D$B("6$P$"6R$

6(6Y(Y5Y79B0TomorrowsTourismToday.pdf



















TRANSACTION, TOTAL TRANSACTION and TOTAL

TRANSACTION VALUE


" ]transaction^ is a discrete one off interaction between a user and an

arts facility. " chain of such transactions W working in combination to a

particular end W thus make up a total transaction Gie from the start to

finish of a seùence of interactionsK.

Protocols and procedures to use here

Yollowing the above logic the total transaction value will thus be the

overall combined financial value of all interactions involved in a

particular seùence of transactions Gie the value of the overall

transactionK. Zence working from start to finish total transaction value

should be calculated as

Halue of all purchase transactions within the interaction seùence

3,=

"ny additional payments made in relation to the transactions Geg

=ervice and credit card chargesK

==

"ny refunds made or discounts taken.






:n any occasion when an organisation wants to evaluate the overall

value of each series of interactions with a customer.





YOUNG PERSON


]joung person^ is a nebulous term.

Mt tends to be used as a catchall phrase for anyone who is not an adult.

But there is arts related research that treating all ]non$adults^ as a

homogenous whole can be inappropriate.

Yor instance the 00I ]Yunky on your flier^ report on the ](rossing the

ine^ seminars and their conclusions notes that young people do not

necessarily conform to strict age breaks and definitely do not perceive

themselves W first and foremost as ]young people^.

Zence in this report ichard Mngs writes

What is fascinating is that the speaker [a 15 year old boy] describeshimself as a new person, rather than a young person. He is, in effect,

defining himself as a new entity and, as such, in contrast, and possibly

in conflict, with an old way of life. Such a perception would no doubt

affect how likely he is to attend events that are viewed as old or

apparently aimed at older people. 5 G:riginal author^s emphasesK.

jet whilst such a relativistic approach seems logical and warranted

there may still be a need to set a clear boundary between what

constitutes a young person and what constitutes an ]adult^.

The lectoral (ommission has recently reviewed the ,S age of

lectoral maority and recommends the retention of IR years of age as

5

Richard Ing [2001] Funky on your flier – a report on the Crossing the Line seminarsondon; "rts (ouncil ngland page 5. Downloadable fromhttpwww.artscouncil.org.ukdocumentspublicationsI9.pdf







the minimum voting age5

. There is therefore slight tension between

this and the age used for the "rts (ouncil ngland ]Yamily Yriendly^

initiative to define a dependent child Gie I6 years of ageK5P

.

Zowever The lectoral (ommission report has an appendix55

that lists

the various minimum legal ages. Zence the considerations that apply

to I6 year olds include those shown overleaf. Yrom this it can be seen

that I6 seems to mark W for all intents and purposes the start of

practical and civic maority.

Yurthermore there also seems to be virtue in allowing arts

organisations to define a ]young person^ in relation to that

organisation^s programme and work.


(onseùently taking all the above issues into consideration it is

recommended that whilst arts organisations should use their own

definition of a ]young person^ the standard age used for defining a

young person should fit with the definition used by "rts (ouncil

ngland for its ]Yamily Yriendly^ initiative and with the onset of

practical civic maority. That is a young person is someone aged 16

or younger .

5=ee The Electoral Commission [2004] Age of Electoral Majority – report and

recommendations ondon; The lectoral (ommission. Downloadable fromhttpwww.electoralcommission.org.uktemplatessearchdocument.cfm9PI5 5P

Zere see Pamela Pfrommer [2002] Family friendliness – an audit of recent research and

recommendations for the development of family audiences in the arts ondon; "rts (ouncilngland 3age I0. Downloadable from httpwww.newaudiences.org.ukresource.phpidq555

:p. (it "ppendix D page R5.



http://www.electoralcommission.org.uk/templates/search/document.cfm/9415






=ource The lectoral (ommission 00P

U Be liable to pay income tax and Aational Mnsurance.

U (hoose your own doctor and consent to medical or dental treatment

U eave home with the consent of your parents

U (onsent to sexual intercourse with another person over the age of I6

U oin a trade union

U et married as long as your parents consent

U eave school after the last Yriday in une and start full time work on

the following onday

U oin the "rmy oyal Aavy or oyal "ir Yorce as long as your parents

give their permission

U Buy and drink beer or cider to have with a meal in a pub restaurant or

hotel

List of things that 16 year olds can do legally (extract)

"t I6 legally you can






This protocol should be used whenever an arts organisation wishes to

segment research and report on discrete groups within its audiences

attenders and visitors.


Richard Ing [2001] Funky on your flier – a report on the Crossing the

Line seminars ondon; "rts (ouncil ngland page 5. Downloadable

from httpwww.artscouncil.org.ukdocumentspublicationsI9.pdf

The Electoral Commission [2004] Age of Electoral Majority – report

and recommendations ondon; The lectoral (ommission.

Downloadable from

httpwww.electoralcommission.org.uktemplatessearchdocument.cfm

9PI5

See also:

AGE GROUP Gpage 6IK

FAMILY Gpage IRK











B5 Digest of formulae with worked examples

Area Profile Reports

The "rea 3rofile eports are a source of ]model data^. That is a

summary of statistical data that give a representation Gor indicationK of

the nature of an area and the people who live in it. inking data from

an "rea 3rofile eport to that for a venue^s Gor facility^sK actual

attenders can be done as follows. "nd even if you don^t intend to do

this yourself understanding how the figures are arrived at will help your

interpretation and use of an "rea 3rofiles eport. G3lease note here

that the data shown is for example purposes only and is not the real

data for the area shownK.

Yirst analyse the venue^s bookers and users according to the number

of them in each postal sector. These would be BI B and so on. "lso

find and record the total for this.

.





Example of attender analysis based on an area profile – step one(formulae)

3ostal=ector

Aumber of ticketbuyers insector

AI I BI

AI B

AI B

TOTALS Btotal

Yor instance Gusing an example based on dummy numbers K

Example of attender analysis based on an area profile – step one(using dummy numbers)

3ostal=ector


AI I 00

AI 00

AI P00

TOTALS 900





Aext for each postal sector included in the "rea 3rofile record the

population figures shown on the "rea 3rofile eport. GZence these

would be 3I 3

and so onK. "gain find the total here W this time for the

population.

Example of attender analysis based on an area profile – step two(formulae)

3ostal=ector

Aumber of ticket

buyers insector

3opulationfor sector

AI I BI 3P

I

AI B 3P

AI B 3P

TOTALS Btotal

PP

total

Zence the worked example Gagain using hypothetical and thus not real

dataK would look like this

Example of attender analysis based on an area profile – step two(using dummy numbers)

3ostal=ector



AI I 00 IP60P

AI 00 I005P

AI P00 7P

TOTALS 900 31,992





Aow again from the "rea 3rofile eport write in the estimated

numbers of potential attenders in each postal sector. GZere it is

important to note that these numbers are not an absolute statement of

the number of people in each sector who attend. ather they are an

indicative estimate based on the numbers of people in each sector who

have a particular geodemographic profile which has been

manipulated to reflect the known arts attendance of people who match

that geodemographic profileK. Zence this gives "I " and so on. G:nce

more you will have to add these figures up to get a totalK.

Example of attender analysis based on an area profile – step three(formulae)

3ostal=ector



stimatednumber of potentialattendersin sector

AI I BI

3P

I

"I

AI B 3P

"

AI B

3P

"

TOTALS Btotal

PP

totalA

total

Example of attender analysis based on an area profile – step three(using dummy numbers)

3ostal

=ector

Aumber of

ticketbuyers insector

3opulation

for sector

stimated

number of potentialattendersin sector

AI I 00 IP60P I9I9

AI 00 I005P III5

AI P00 7P I06I

TOTALS 900 31,992 4,095





Then work out the number of actual buyers for each sector as a

proportion of the estimated attenders. Gie BI

divided by "I B

divided

by " and so onK and multiply the results by I00. This is the sales

penetration percentage and is shown below as =I =

and so on.

Example of attender analysis based on an area profile – step four (formulae)

3ostal=ector

Aumber of ticket

buyers insector


stimatednumber of

potentialattendersin sector

=alespenetration

percentage

AI I BI

3P

I"

IB

I "

I

I00 q =I

AI B 3P

" B " I00 q =

AI B

3P

"

B

"

I00 q =

TOTALS Btotal

PP

totalA

totalB

total/

A

total

X 100= Soverall

Example of attender analysis based on an area profile – step four (using dummy numbers)

3ostal=ector



stimatednumber of potentialattenders

in sector

=alespenetrationpercentage

AI I 00 IP60P I9I9 I0.P

AI 00 I005P III5 6.9I

AI P00 7P I06I 7.70

TOTALS 900 31,992 4,095 21.98





jou now need to work out the market potential percentages for each

postal sector. This is done by dividing the estimated number of

potential attenders G "K by the actual population G3K for each sector

and multiplying by I00. Gie "I

3I

I00 " 3

I00 and so onK.

These are show as I

etc.

Example of attender analysis based on an area profile – step five(formulae)

3ostal=ector

Aumber of ticket

buyers insector


stimatednumber of

potentialattendersin sector

=alespenetration

percentage

arketpotential

percentage

AI I BI

3P

I"

IB

I "

I

I00 q =I

"I 3

I

I00 q I

AI B 3P

" B " I00 q =

" 3 I00 q

AI B

3P

"

B

"

I00 q =

" 3

I00 q

TOTALS Btotal

PP

totalA

totalB

total/

A

total

X 100= Soverall

Atotal

/

PP

total

X 100= Moverall

Example of attender analysis based on an area profile – step five(using dummy numbers)

3ostal=ector

Aumber of ticketbuyers in

sector


stimatednumber of potential

attendersin sector


arketpotentialpercentage

AI I 00 IP60P I9I9 I0.P I.IP

AI 00 I005P III5 6.9I II.09

AI P00 7P I06I 7.70 IP.P7

TOTALS 900 31,992 4,095 21.98 12.80





Aow in the three last stages of this process comparative indices can

be used. These will compare both the sales penetration percentages

and market potential percentages for each postal sector with the

overall average percentages for the area in ùestion. G"nd here it is

worth remembering that an index of I00 means that something is

happening at the same rate for the area as a wholeK.

=o first calculate the comparative index for sales penetration. This is

found by dividing each of the sales penetration percentages G=I = =

and so onK by the overall sales penetration figure G=overallK and then

multiplying each result by I00. This produces the sales penetration

indices G=MK. Zence

Example of attender analysis based on an area profile – step six(formulae)

3ostal=ector



stimatednumber of potentialattenders

in sector



=alespenetrationindex

AI I BI 3P

I "I BI "I I00 q =

I"I 3I I00 q

I=I =overall

I00q =M

I

AI B 3P

" B " I00 q =

" 3 I00 q

= =overall

I00q =M

AI B

3P

"

B

"

I00 q ="

3

I00 q =

=

overall

I00q =M

TOTALS Btotal

PP

total

Atotal

Btotal

/A

totalX 100

= Soverall

Atotal

/PP

totalX 100

= Moverall

Soverall

/S

overall

X 100= SI

overall





Example of attender analysis based on an area profile – step six(using dummy numbers)

3ostal=ector







AI I 00 IP60P I9I9 I0.P I.IP P7.P

AI 00 I005P III5 6.9I II.09 I.P

AI P00 7P I06I 7.70 IP.P7 I7I.5P

TOTALS 900 31,992 4,095 21.98 12.80 100.00

Then a similar comparison is done in terms of the market potential of

each sector. This time this provides the market potential index and is

worked out by dividing each of the market potential percentages GI

and so onK by the overall market potential percentage G

overallK

and then multiplying each result by I00.

Example of attender analysis based on an area profile – step seven(formulae)

3ostal=ector







arketpotentialindex

AI I BI 3P

I "I BI "I I00 q =

I"I 3I I00 q

I=I =overall

I00

q =M

I

I overall I00

q M

I

AI B 3P

" B " I00 q =

" 3 I00 q

= =overall

I00q =M

overall I00q M

AI B

3P

"

B

"

I00 q =

" 3

I00 q

= =

overall

I00q =M

overall

I00q M

TOTALS Btotal

PP

totalA

totalB

total/

Atotal X 100= S

overall

Atotal

/PP

total X 100= M

overall

Soverall

/Soverall

X 100= SI

overall

Moverall

/Moverall X

100 =MI

overall





Example of attender analysis based on an area profile – step seven(using dummy numbers)

3ostal=ector







arketpotentialindex

AI I 00 IP60P I9I9 I0.P I.IP P7.P I0.66

AI 00 I005P III5 6.9I II.09 I.P R6.6P

AI P00 7P I06I 7.70 IP.P7 I7I.5P II.0

TOTALS 900 31,992 4,095 21.98 12.80 100.00 100.00

Yinally to interpret this table it needs to be remembered that

U an index score of I00 indicates that the relevant factor Gie

penetration or potentialK is happening at a rate on a par with the

overall average for that factor across the area under

consideration

U an index score that is less than I00 indicates that the factor is

happening at a rate below the average for the area being

considered

and

U an index score higher than I00 shows that the factor isoccurring at a rate above the average for the area under

consideration.

=o the complete table can now be used to locate rapidly

U =ectors of relatively high sales penetration and moderately Gie

above averageK high potential Gfor instance AI K W therefore this





is a sector where the organisation is doing relatively well in

converting the estimated potential in actual attendances

U =ectors with strong sales penetration but low potential Gfor instance

AI in the worked dummy dataK W hence this is a sector where

the organisation is doing better than might be expected

U =ectors with below average sales penetration but average potential

GAI IK W a case of somewhere where the organisation ought to

be doing better than it is

and

U =ectors with high levels of relative penetration and high relative

potential Gno example of this in dataK W this is an area where the

organisation would seem to be doing well and is exceeding the

levels of attendance that might be expected but where there is the

potential to do even better.

G=ee diagramK.

ZMZ

Relativesales

penetration

Relative potential:

ZMZ

g AI g A I

g AI I

Ao examplein data





AVERAGES

The three different but commonly used forms of average Gthe mean

mode and medianK each reùire different approaches to working them

out. These are set out below with worked examples.

The ean

"s the arithmetic centre of a set of data the mean is found by finding

the total of all the values in the data and then dividing this by the

number of values. Zence the formula for this is

q !

A

here

is the mean

! indicates finding the sum or total

is each of the values in the collection of data

and

A is the number of values in the data.

To work this out entails the following steps.

a. ist the entire set of values

b. ork out the total of all the values Gie !K





c. Then divide this total by the number of values Gie AK

Worked example

=ay that the data for attendances at three different exhibitions shows

the following

Exhibition Total attendances

enaissance masterpieces 56

onet the impressionists and light P06I

ocal watercolours R99TOTAL 7,523

"s can be seen this is based on values. =o the average GmeanK

attendance per exhibition is

q !

A

q 56 P06I R99

q 75

q 2,507.7





" variation on the mean to take account of freùency

eans can also be used when issues of freùency are involved Gsuch

as when an average figure for attendance is reùiredK.

Zere the formula to use is

Y q !Y

!Y

here

Y is the freùency mean

! indicates the sum or total

Y is the freùency or number of times a value appears

and

is the various values.

=o to use this as a calculation reùires the following steps.

a. Take the data to be analysed and identify the categories it applies to

b. ork out the number of times each value appears Gthis is the

freùency YK

c. Yind the total of all the freùencies Gie !Y K

d. Aow multiply the value for each category by the freùency with

which it occurs Gthis is YK





e. Yind the total for all the Y numbers Gie !YK

and

f. astly divide the total for the Y numbers Gie !YK by the total

freùencies Gie !YK.

Worked example

=ay that the attendance pattern of people coming from a particular

postal sector is examined. There are P5 such individuals and the data

for the number of times they have been to the venue look like this

I P

I I P

5 I I

5 I P I I

I P I

I P 5 I

I I

I 5 I

I I

Mdentifying the categories for ]numbers of time attended a year^ and

finding the number of times these freùencies occur gives the

following





Number of attendancesmade in year

Frequencyof occurrence

(X) (F)I IR

II

7

P 5

5 P

Totals

Aow the total for the freùencies can be worked out G!YK as can the

freùencies times the number of attendances made GYK and the total

for this G!YK

Number of attendancesmade in year

Frequencyof occurrence

AttendancesX frequency

(X) (F) (fx)

I IR IR

II

7 I

P 5 0

5 P 0

Totals 45 101

Yinally the average GmeanK freùency of attendance for these people is

found by dividing the total for Y by the total for Y. Thus this eùals

I0I P5 q ..

=o the average GmeanK freùency of attendance for people from this

postal sector is 2.2 times a year .





The ode

The mode is the most freùently occurring value Gie the most popular

oneK. This can be found by

a. isting all the values in the data but list each one only once

b. Tally or count the number of times each value occurs

c. Yind the value that appears most often W this is the mode.

=o in the attendance data used above the mode is once a year Gsince

I time a year appears the most times ie on IR occasionsK.

The edian

The median is the mid$point of a set of data. =o to find this entails

working as follows

a. =ort or arrange the data in order W either from lowest to highest} or

from highest to lowest

b. Aow find the value that^s in the middle W this is the median.

Zence in the attendance data example used above the sorted list of

values Gtogether with the count order in which they appearK is shown on

the next page. =ince there are P5 observations or values here the

median will be the value that is midway between the nd

and the rd

observation. =o the median here is 2 times a year .








(alculated using the formula


Total number of people making these attendances

Yor example say

U The period in ùestion is a calendar year Gie I monthsK

U the total number of attendances made in that year is 600

and

U that during this year the total of number of attenders is I000.

Then the average GmeanK rate of attendance of these people during the

given period is

600 I000 q .6 times per year.

See also:

AVERAGES Gpage I9K





DISCOUNT and PERCENTAGE DISCOUNT

The formula for calculating a percentage discount is

D I00

Y3

here

D is the discount made

and

Y3 is the full price.

Yor instance if your organisation was intending to offer a reduction

on tickets that normally had a full price of I0 then the percentage

discount would be

I0 I00 q 0. I00 q 0.

=o the percentage discount being offered here is 20%.





$

MARGIN FOR SAMPLING ERROR

The formula for finding the margin for sampling error Greferred to hereas ]Y=^ for shortK relating to a finding that is a percentage done at

the 95 confidence level is

Y= q I.96 G3 GI00$3KK

n!$

here

MFSE is the margin for sampling error

1.96 is the constant used for doing this calculation at the 95

confidence level Gie " figure of I.96 is always used when working at the

95 confidence levelK

!$means the sùare root of the part of the calculation shown to the

right of the sùare root sign

P is the finding being test and

n is the sample sie.

Zere please note that the sùare root found is that of everything that

comes after the sùare root sign Gie G3 GI00$3KKn K .

"lso please note that this formula only works for samples that are

larger than 0.





Worked example

"n art gallery carries out a visitor survey to which I000 people

respond. This finds that of responding visitors are in the age

group aged I5 to P years old.

The allery wants to know what proportion of all visitors are likely to be

aged I5 to P years old. "nd it wants to know this at the 95 level of

confidence.

=o the calculation here will be

Y= Gthe margin for sampling errorK will q I.96 GG GI00$KK

I000

$

$

q I.96 G 77K

I000

q I.96 I77I

I000

q I.96 I.77I

!

!

" q I.96 I.

q .6

$

Zence the proportion of visitors aged 5 to P years old among the

entire population of gallery visitors is likely to be $ .6. That is

it will fall between 0.P and 5.6.





:ne last thing to bear in mind. Mf your aim in performing such a

calculation is to inspire enhanced confidence in the reader it^s always

advisable to annotate the finding with the aspects of the situation that

has led to it. That is don^t ust say Between 20.4 and 25.6% of

attenders at the Gallery are likely to be aged 15 to 24 years old but

add a note Gin brackets as a footnote or endnoteK that also says

Sample size = 1,000, margin for sampling error = 2.6% at the 95%

confidence level)” .

$

$





SAMPLE SIZE

ules of thumb and ]ready reckoners^ can be used to give an

approximate indication of the sample sie to use. jet even doing thisreùires that the desired confidence level the tolerable limits of error

and the expected research finding are known.

"nd if these issues have been thought about then researchers are well

placed to use them in a calculation that will give an exact figure for the

sample sie needed.

This formula can be found in a number of online resources56

. Mt is

=ample sie q %

G3 GI$3K K

here

% q the standard multiplier for the desired confidence level

3 q the expected finding Gexpressed as a proportionK

and

q the limit for error Gagain expressed as a proportionK.

Zence if you were wanting to work at the 95 confidence level Gwhere

the standard multiplier to use is always 1.96K the expected finding is

56:ne such resource is provided by the Mnternational Yund for "gricultural Development

MY"D at httpwww.ifad.orghfstoolshfsanthropometryant.htm

whilst another Gprovided by the independent direct marketing consultancy ,S arketinganagement imited ,S K is athttpwww.ukmarketingmanagement.comTesting=amplingtestingsampling.html .









0 Gor 0. when expressed as a proportionK and the tolerable limit of

error is { 5 Gor { 0.05 when expressed as a proportionK the

calculation would be as follows

="3 =M% q I.96

G0. GI$0.KK

0.05

q .RPI6 G0. GI$0.KK

0.05

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A - Z Definitions and Protocols 1 (5)

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