Presentation nicole huyghe (advanced analytics) get inspired 2012

transcript

ANALYTICS & INNOVATION

NEW STATISTICAL METHODS IN

MARKET RESEARCH

solutions-2, Nicole Huyghe June 27, 2012

GET INSPIRED 2012

New methods Re-instating existing methods

Pushing boundaries

MENU BASED CONJOINT

CLUSTER ENSEMBLES

K. CHRZAN MAKE/BREAK MODEL

KANO RESEARCH

MULTI / SPARSE / EXPRESS MAXDIFF

CBC + MAXDIFF

TURF (+ SURF)

What are customers looking for when buying a product?

How are we doing and how can we

increase satisfaction/loyalty?

Are there customer segments with

different needs?

How is my brand positioned versus the competition?

NPD/Voice of customer Dashboard/Score card

Brand positioning Perceptual mapping

Segmentation

Menu based conjoint

Conjoint objective

Understanding which product

features drive purchase

decision

Business outcome

Maximising sales and profit

through optimising product

characteristics

There are different types of

conjoint methods

Choice-Based-Conjoint

Assumption: compensatory approach

Natural choice task

None option makes it even more realistic

Easy for the respondent

Fairly short exercise

All respondents do get to see all attributes

and levels

Risk of focusing on a few attributes only

Might result in poor data

Less engaging exercise

Assumptions: features are pre-bundled

Adaptive CBC (ACBC)

Menu based conjoint (MBC)

Non compensatory approach

CBC tasks are tailored

But still bundles

Adaptive CBC (ACBC)

Extension of CBC

For multi-check/configurator choice tasks

A la carte product and service configuration

ACBC and CBC: pre-bundled

Examples: Restaurant menus, Cars, Telecom

bundling, Insurance policies, Banking options

Menu Based Conjoint (MBC)

Below you will see the Land Rover L560 together with all of the additional features at different prices. For each feature, please indicate whether or not you would subscribe to that feature at that price.

If you are not interested in any of the features at any of the given prices, please tick ‘None of the above’.

Dual View £800

Bluetooth with seat belt microphones £1,100

Bluetooth phone audio connection £900

Rear seat phone with cordless handset £800

None of the above

TOTAL PRICE: £71,400

£70,500

Land Rover L560

CBC ACBC MBC

Assumption Compensatory

approach (Non) compensatory

approach

Principle

Choice between pre-bundled

concepts

Choice between pre-bundled

concepts

No bundling – respondent can chose

between levels

Mimicks real purchase process

Average length ~7 ‘ ~15 ‘ ~7’

Engaging

Programming flexibility of Sawtooth

Ease of programming design/questions

Analysis options

Simulator: What if, price sensitivty, optimum product

Simulator: Optimising bundling

Programming and analysis

cost average average expensive

Share of

preference,

revenue, profit

Price sensitivity,

value of features

Impact of or on

competition Product/Portfolio

optimisation

Cluster ensembles

Segmentation objective

Identifying groups of customers

with different needs, attitudes

and behaviour

Business outcome

Optimising products and

communication by better

understanding the (potential)

customers

Segmentation approach: cluster ensemble answers the who and the why question

Segments differ on few

dimensions

Less diffentiated on

tangeable aspects. More

focus on the why than the

Difficult to target segments

Traditional

segmentation

Cluster ensemble

segmentation

Segments differ on many

dimensions

Differentiation on both

tangeable (who aspect)

and less tangeable aspects

(why aspect)

Targeting segments is

essential and possible

Theme 1

Theme 2

Theme 3

Theme 9

Theme 10

Cluster

Ensemble

The cluster ensemble process

Traditional

segmentations are

run on each of the

different

dimensions (such

as behaviour,

needs, attitudes,

demographics, …)

All individual

segmentation

results are used as

input to an

ensemble

methode to get

segments differing

on all dimensions

Stability Integrity Accuracy Size

A succesful segmentation fulfills 4

characteristics

Stability People do not move around much from one solution to

another

Cluster Integrity – Heterogeneity The segments do differ significantly on key dimensions

Cluster Integrity – Homogeneity The people within a segment are very similar on key

dimensions

Accuracy segment membership can be easily predicted – i.e. targetable segments

Reality Prediction

Size Ideally, there are no very large, nor very small segments

K. Chrzan’s make or

break model

Model objective

Quantifying the drivers of

overall satisfaction/loyalty/NPS

Business outcome

Increasing sales through

improved performance/loyalty

/advocacy scores

***** hotel

Drivers of the customer experience

Cleanliness

Room size

Breakfast Reservations

Restaurant

Lounge area

Hotel atmosphere

Internet/Wifi

***** hotel

Drivers of the customer experience

4 Staff

9 Room

9 Cleanliness

9 Room size

8 Breakfast 7 Reservations

8 Restaurant

9 Lounge area

7 Hotel atmosphere

2 Internet/Wifi

6 Price

+ Overall experience score: 6

Regression/correlation analysis - drivers of overall customer experience - non-compensatory impact is not measured

Keith Chrzan’s Make or Break model

x Staff

Cleanliness

Room size

Breakfast Reservations

Restaurant

Lounge area

Hotel atmosphere

x Internet/Wifi

Overall experience score: 6 If < 7 ask if there were aspects so bad that they made the whole experience awful If > 8 ask if there were aspects so good that they made the whole experience wonderful

x Staff

9 Room

9 Cleanliness

9 Room size

8 Restaurant

9 Lounge area

7 Hotel atmosphere

x Internet/Wifi

6 Price

4 x Staff

9 Room

9 Cleanliness

9 Room size

8 Restaurant

9 Lounge area

7 Hotel atmosphere

x Internet/Wifi

6 Price

RESULTS – FOR EACH ASPECT

Standard weights Penalty weights for bad experiences Bonus weights for wonderful experiences

Richer and more accurate model

RESEARCH

Kano objective

Identifying the delighters and

dissatisfiers of the customer

experience

Business outcome

Increasing sales through

improved performance/loyalty

/advocacy scores

Understanding how performance

drives satisfaction/loyalty

Kano Theory

allows us to

derive how

performance

in an area

drives overall

satisation

Traditional

Methods assume that

there is

always a

linear impact

Performance

– Poor

Overall Satisfaction

Satisfied

Overall Satisfaction Dissatisfied

Performance -

Outstanding

Identify the ‘Must Haves’….

Must haves

No extra points

if you get it

perfect BUT

people will be

upset if it

doesn’t work.

Critical to fix if

performance is

DISSATISFIERS

Performance –

Satisfied

Expected / Must haves

Dissatisfiers

Performance -

Outstanding

Identify the ‘Added Bonuses’….

Added Bonus

People don’t

expect it, so

there is no

dissapointment

if it is lacking

BUT it delights

people when it

happens

Create/

Identify USPs

DELIGHTERS

Satisfied

Attractive / Added bonuses DELIGHTERS

Performance –

Performance -

Outstanding

….and the ‘Key Desired’ elements

Desired

Fall into both

categories.

These are the

key areas for a

company to

focus and

perfom on

DELIGHTERS &

DISSATISFIERS

Satisfied

Desired

Expected / Must haves

Attractive / Added bonuses

Dissatisfiers

Delighters Performance

– Poor

Performance -

Outstanding

Establish customer driven action plan

Identify Critical Fixes

Tailor Offering to Customer Needs

Create USPs

Optimise Investment

More satisfied, loyal & profitable

customers

Kano Analysis Creating a better customer experience

Multi, sparse, express

maxdiff

Maxdiff objective

Understanding which product

features are most important for

customers

Business outcome

through optimising product

characteristics

Average number of

tested items : 15 – 30

if 5 items on a screen

9 to 18 screens

WHAT IF 120 ITEMS ??

EXPRESS MAXDIFF

Each respondent only ~ 30 items

The 30 items are seen 3 times

Each respondent a different set of 30

Fully randomised sets

Full utility set for each respondent

Sample size >>

Ideal for list with > 60 items

SPARSE MAXDIFF

Each respondent sees all items

All items are only seen once

Full utiliy set for each respondent

Shorter questionnaire

Ideal for list with < 60 items

Only asks about persuasiveness

What about uniqueness, believability?

MULTI MAXDIFF

Persuasive

Unique

Believable

Average High

Maxdiff

Maxdiff + CBC

Weekday

Weekend

Headline news National news

Financial news Economic news

Sports Stock exchange

Cultural news Blogs

Local news …

International news

On ipad

On iphone

On all

1 month archive

1 year archive

1 week archive

Paper copy week

Paper copy WE

No paper copy

Weekday

On ipad

1 month archive

No paper copy

Financial news

International news

Sports

Local news

£9/month

Weekday

On ipad

1 month archive

No paper copy

National news

Cultural news

Local news

Sports

£13/month

Weekday

On ipad

1 month archive

No paper copy

Financial news

Local news

Sports

Local news

Eonomic news

Stock Exchange

£15/month

Online paper option 1 Online paper option 2 Online paper option 3

Weekday

On ipad

1 month archive

No paper copy

Financial news

International news

Sports

Local news

Weekday

On ipad

1 month archive

No paper copy

National news

Cultural news

Local news

Sports

Weekday

On ipad

1 month archive

No paper copy

Financial news

Local news

Sports

Local news

Eonomic news

Stock Exchange

Online paper option 1 Online paper option 2 Online paper option 3

Best to reduce list

before conjoint

Headline news

Financial news

Sports

Cultural news

Most likely

subscribe

Least likely

subscribe

Headline news

Financial news

Sports

Cultural news

Most likely

subscribe

Least likely

subscribe

The top 5 items for each respondent are then passed to the conjoint tailored

Turf + Surf

TURF objective

Identifying the set of products /

product characteristics which

will reach most customers

Business outcome

through optimising the product

Gelati & Sons

Which 4 our of these 8 should we chose?

There are 70 different ways to

choose 4 flavours from these 8!

1 1 1 0

0 0 0 0

0 0 0 0 0 0

1 1 0 1 1 0 0

0 0 0 0 0 1 0 0

Respondents have a 1 if they would buy the flavour.

# Flavours

Unduplicated

Reach Flavours

5 100%

Gelati & Sons

Results from all 100 respondents

= with this selection of 5 flavours, they please all respondents

< 30 items TURF

If ≥ 30 TURF + SURF

(SURF: Successive Unduplicated Reach and Frequency)

risingquestions If you have any

Nicole Huyghe

nicole@solutions2.be

www.solutions2.be