Common pitfalls in media attribution

> Media a(ribu,on < Media a'ribu+on or when tracking the last click is just not enough

> About Datalicious §  Datalicious was founded in November 2007 §  Strong web analy+cs background & experience §  360 data agency with team of data specialists §  Combina+on of analysts and developers §  Blue chip clients across all industry ver+cals §  Carefully selected best of breed technology §  Lobbying & defining data best prac+ce ADMA §  Execu+ng smart data driven campaigns §  Turning data into ac+onable insights May 2013 © Datalicious Pty Ltd 2

> Smart data driven marke,ng

Media A(ribu,on & Modeling

Op,mise channel mix, predict sales

Tes,ng & Op,misa,on Remove barriers, drive sales

Boos,ng ROMI

Targe,ng & Merchandising Increase relevance, reduce churn

“Using data to widen the funnel”

May 2013 © Datalicious Pty Ltd 3

> Wide range of data services

Data PlaHorms Data collec,on and processing Adobe, Google Analy,cs, etc Web and mobile analy,cs Tag-‐less online data capture Retail and call center analy,cs Big data & data warehousing Single customer view

Insights Analy,cs Data mining and modelling Tableau, Splunk, SPSS, R, etc Customised dashboards Media a(ribu,on analysis Marke,ng mix modelling Social media monitoring Customer segmenta,on

Ac,on Campaigns Data usage and applica,on SiteCore, ExactTarget, etc Targe,ng and merchandising Marke,ng automa,on CRM strategy and execu,on Data driven websites Tes,ng programs


> 50+ years of team experience


Chris+an Bartens Founder & Director §  Bachelor of Business

Management with marke+ng focus

§  Web analy+cs and digital marke+ng work experience

§  Space2go, E-‐LoV, Tourism Australia

§  SuperTag founder, ADMA Analy+cs Chair, I-‐COM EMR Board

LinkedIn profile

Elly Gillis General Manager §  Bachelor of

Communica+ons with print and digital focus

§  Digital marke+ng and project management work experience

§  M&C Saatchi, Mark, Holler, Tequila, IAG, OneDigital, Telstra

§  Australian gold medal in surf boat rowing

LinkedIn profile

Michael Savio Head of Insights §  Bachelor of Arts &

Science with applied mathema+cs focus

§  CRM and marke+ng research and analy+cs work experience

§  ANZ Bank, Australian Bureau of Sta+s+c, DBM Consultants

§  ADMA lecturer on marke+ng tes+ng

LinkedIn profile

Juan Delard Head of Data §  Engineering Diploma &

Bachelor of Science in Electrical Engineering

§  IT architecure, ERP, web analy+cs, big data, telecommunica+ons work experience

§  Quo+fy, Binaria, Codelco

§  Mathema+cs fan and avid scuba diver

LinkedIn profile

> Unique combina,on of skills


Data visualisa,on/repor,ng

Data mining/analysis

Data modelling

Fast analy,cs

Data processing/enhancing

Big data

Data collec,on

The Datalicious team §  Data scien+sts §  Business analysts §  Data engineers §  Web engineers §  Pla`orm admins §  Project managers §  Data strategists

Data strategy

> Best of breed technologies


> Datalicious product development

SCV2

Surveys Display ads Internal ads

Engage

Social media Mobile push eDMs/DMs

MIS1

1 Marke+ng informa+on system containing all data necessary to analyse and report on campaigns 2 Single customer view pla`orm containing all data across all (customer) touch points

Mass media Social media Digital media

Measure Demographics Transac+ons Campaigns


Report

Analyse

> Clients across all industries


Direct mail, email, etc

Facebook Twi(er, etc

> Channels influence each other


POS kiosks, loyalty cards, etc

CRM program

Home pages, portals, etc

YouTube, blog, etc

Paid search

Organic search

Landing pages, offers, etc

PR, WOM, events, etc

TV, print, radio, etc

= Paid media

= Viral elements

Website, call center, retail

= Sales channels

Display ads, affiliates, etc

> First and last click a(ribu,on


Chart shows percentage of channel touch points that lead to a conversion.

Neither first nor last-‐click measurement would provide true picture

Paid/Organic Search

Emails/Shopping Engines

> The ideal media dashboard

Channel Investment ROMI Return

Brand equity Baseline ($100) n/a $40

Offline TV, print, outdoor, etc $7 330% $30

Direct Direct mail, email, etc $1 400% $5

Online Search, display, social, etc

$2 1150% $25


© Datalicious Pty Ltd 13 May 2013

A(ribu,on piHall #1 Excluding brand equity

> ROMI as compe,,ve advantage


74% of marketers do not engage in any form of media a'ribu+on aside from the last click leaving 26% of marketers with a serious compe++ve advantage as their media investment is likely to generate a much higher ROMI.

> Media a(ribu,on approaches


Success $100

Success $100

Display Affiliate Search

$100 Success $100

Last channel gets all credit

First channel gets all credit

All channels get equal credit

Success $100

All channels get custom credit

Display

$100 Affiliate Search

Display

$33 Affiliate

$33 Search

$33

Display

$15 Affiliate

$35 Search

$50

> Duplica,on across channels


Display ads

Email blasts

Paid search

Organic search

$ Bid mgmt

Ad server

Email plaHorm

Google Analy,cs

$

$

$

> Duplica,on across channels


Display impression

Paid search $

Ad Server

Bid mgmt.

Web analy,cs

Display click

Ad server cookie

Organic search

Analy,cs cookie

Analy,cs cookie

Analy,cs cookie

Bid mgmt. cookie

Ad server cookie

Central analy,cs plaHorm

$

$

$

> De-‐duplica,on across channels


Display ads

Email blasts

Paid search

Organic search

$


A(ribu,on piHall #2 Mul,ple data sources

> Ad clicks inadequate measure


Only a small minority of people actually click on ads, the majority merely processes them (if at all) like any other adver+sing without an immediate response so adver+sers cannot rely on clicks as the sole success measure but should instead focus on impressions delivered

> Indirect display impact



A(ribu,on piHall #3 No ad impression data

> Full vs. par,al purchase path data


Display impression

Display impression

Display impression

$

Display impression $

Display impression


Display impression

Search response

Search response $

Display impression

Display response

Direct visit

✖ ✔ ✔ ✖

Display impression

Display impression

Email response

Search response

✖ ✔ ✔ ✔

✖ ✖ ✔ ✔

✖ ✔ ✔ ✔

> Full vs. par,al purchase path data


Display impression

Display impression

Display impression

$


Display impression


Display impression

Search response

Search response $

Display impression

Display response

Direct visit

✖ ✔ ✔ ✖

Display impression

Display impression

Email response

Search response

✖ ✔ ✔ ✔

✖ ✖ ✔ ✔

✖ ✔ ✔ ✔

5% to 65% variance in conversion a(ribu,on

for different channels due to par,al purchase path data


A(ribu,on piHall #4 Par,al purchase path data

Closer

Paid search

Display ad views

TV/print responses

> Full purchase path tracking


Influencer Influencer $

Display ad clicks

Online sales

Affiliate clicks

Social referrals

Offline sales

Organic search

Social buzz

Retail visits

Life,me profit

Organic search

Emails, direct mail

Direct site visits

Introducer

Closer

Paid search

Display ad views

TV/print responses

> Full purchase path tracking



Display ad clicks

Online leads

Affiliate clicks

Social referrals

Offline sales

Organic search

Social buzz

Retail visits

Life,me profit

Organic search

Emails, direct mail

Direct site visits

Introducer

> Purchase path data example


> Purchase path data example U123 1/1/12 12:00 RED AD YAHOO NEWS $20 U123 1/1/12 12:05 RED AD SMH FINANCE $20 U123 1/1/12 12:10 GOOGLE BRAND TERM -‐ U123 1/1/12 12:11 WEBSITE VISIT -‐ U123 1/1/12 12:12 WEBSITE EVENT -‐ U123 3/1/12 14:00 GOOGLE GENERIC TERM $20 U123 3/1/12 14:01 WEBSITE VISIT -‐ U123 7/1/12 17:00 EMAIL OPEN $20 U123 8/1/12 15:00 GOOGLE BRAND TERM $20 U123 8/1/12 15:01 WEBSITE CONVERSION $100 May 2013 © Datalicious Pty Ltd 29


A(ribu,on piHall #5 No ,me stamp data

> Taking credit for offline sales


> Tracking offline sales online §  Email click-‐through

–  Include offline sales flag in 1st email click-‐through URL aVer offline sale to track an ‘assisted offline sales’ conversion

§  First login aVer purchase –  Similar to the above method, however offline sales flag happens via JavaScript parameter defined on 1st login

§  Unique phone numbers –  Assign unique website numbers to responses from specific channels, search terms or even individual visitors to match offline call center results back to online ac+vity

§  Website entry survey for purchase intent –  Survey website visitors to at least measure purchase intent in case actual offline sales cannot be tracked


Confirma,on email, login

> Offline sales driven by online


Website research

Phone sales

Retail sales

Online sales

Cookie

Adver,sing campaign

Fulfilment, CRM, etc

Online sales confirma,on

Virtual sales confirma,on


A(ribu,on piHall #6 No offline conversion data

> Purchase path for each cookie


Mobile Home Work

Tablet Media Etc

> Purchase path for each cookie


Device path 2

Device path 1+2

> Combining purchase paths


Touch point 1

Email, login, etc

Touch point 1

Touch point 2

Touch point 3

Individual transac,on

Device path 1


Touch point 2

Touch point 1


A(ribu,on piHall #7 No cross-‐device tracking

> Filling purchase path data gaps


> Filling purchase path data gaps


+15 +5 +10 -‐15 -‐5 -‐10

> Tracking offline responses online

§  Search calls to ac+on for TV, radio, print –  Unique search term only adver+sed in print so all responses from that term must have come from print

§  PURLs (personalised URLs) for direct mail –  Brand.com/customer-‐name redirects to new URL that includes tracking parameter iden+fying response as DM

§  Website entry survey for direct/branded visits –  Survey website visitors that have come to site directly or via branded search about their media habits, etc

§  Combine data sets into media a'ribu+on model –  Combine raw data from online purchase path, website entry survey and offline sales with offline media placement data in tradi+onal (econometric) media a'ribu+on model


> Search call to ac,on for offline


> Search call to ac,on for TV


Consumers are now experts at mul+-‐tasking, especially while watching TV. They are constantly online and ready to search so a unique search call to ac+on is ideal to track responses from TV ads. In addi+on, consumers also remember search terms be'er than phone numbers or vanity URLs which increases overall response rates and it is easier to control the user experience from a search response (i.e. what landing page to send people to).

domain.com/chris,anbartens > redirect to > domain.com?

CampaignID=DM:123& Demographics=M|35& CustomerSegment=A1& CustomerValue=High& CustomerSince=2001& ProductHistory=A6& NextBestOffer=A7& ChurnRisk=Low [...]

> Personalised URLs for direct mail




What promoted your visit today? q  Recent branch visit q  Saw an ad on television q  Saw an ad in the newspaper q  Recommenda+on from family/friends q  […]

> Website entry survey


Channel % of Conversions

Straight to Site 27%

SEO Branded 15%

SEM Branded 9%

SEO Generic 7%

SEM Generic 14%

Display Adver+sing 7%

Affiliate Marke+ng 9%

Referrals 5%

Email Marke+ng 7%

De-‐duped Campaign Report

} Channel % of Influence

Word of Mouth 32%

Blogging & Social Media 24%

Newspaper Adver+sing 9%

Display Adver+sing 14%

Email Marke+ng 7%

Retail Promo+ons 14%

Greatest Influencer on Branded Search / STS

Conversions a'ributed to search terms that contain brand keywords and direct website visits are most likely not the origina+ng channel that generated the awareness and as such conversion credits should be re-‐allocated.

> Website entry survey example


In this retail example, the exposure to retail display ads was the biggest website traffic driver for direct visits as well as visits origina+ng from search terms that included branded keywords – before TV, word of mouth and print ads.


A(ribu,on piHall #8 No offline media data

> Econometric media mix modelling


Use of tradi+onal econometric modelling to measure the impact of communica+ons on sales for offline channels where it cannot be measured directly through smart calls to ac+on online (and thus cookie level purchase path data).

> Econometric media mix modelling


Total revenue

Total revenue

Total revenue

Total revenue

Spend channel 1

Spend channel 1

Spend channel 1

Totals week N

Spend channel N

Spend channel N

Total revenue

Totals week 1

Totals week 2

Totals week 3

Totals week 4

Spend channel 2

Spend channel 2

Spend channel 2

Individual path 1

Individual path 1

Individual path N

> Individual purchase path tracking


Touch point 1

Touch point 2


Touch point 1


Touch point 2

Touch point 1

Touch point 2

Touch point N


Touch point N

Touch point N

Individual path 1

Touch point N

> Pathing & modelling combined


Touch point 1

Touch point 2


Spend channel 2

Spend channel N

Spend channel 1

Individual path 1

Touch point N

Influencing factors §  Offline media spend §  Compe++ve ac+vity §  Geo-‐demographics §  Transac+on history §  Client sa+sfac+on §  Social sen+ment §  Interest rates § Weather §  Pricing

Influence factor N

Influence factor N

Influence factor N


A(ribu,on piHall #9 Excluding custom data

> Purchase path vs. a(ribu,on

§  Important to make a dis+nc+on between media a'ribu+on and purchase path tracking –  Not the same, one is necessary to enable the other

§  Tracking the complete purchase path, i.e. every paid and organic campaign touch point leading up to a conversion is a necessary requirement to be able to actually do media a'ribu+on or the alloca+on or conversion credits back to campaign touch points –  Purchase path tracking is the data collec+on and media a'ribu+on is the actual analysis or modelling


> Standard a(ribu,on models §  The First/Last Interac,on model plus … §  The Linear model might be used if your

campaigns are designed to maintain awareness with the customer throughout the en+re sales cycle.

§  The Posi,on Based model can be used to adjust credit for different parts of the customer journey, such as early interac+ons that create awareness and late interac+ons that close sales.

§  The Time Decay model assigns the most credit to touch points that occurred nearest to the +me of conversion. It can be useful for campaigns with short sales cycles, such as promo+ons.


> Media a(ribu,on models


$100

Even/linear a(ribu,on

Time decay a(ribu,on

Custom a(ribu,on

10% 15% 25% 50%

Display impression

Display impression

Display click

Search click

10% 10% 50% 30%

25% 25% 25% 25%

10% 30% 10% 50%

10% 50% 30% 10%

> Custom (weighted) a(ribu,on


$100

Weighted a(ribu,on

$100

Weighted a(ribu,on

Display impression

Display impression

Display click

Search click

Display impression

Search click

Display impression

Display click

> Custom models most effec,ve


56% of marketers consider a unique or custom (weighted) media a'ribu+on approach that does not use a standard out-‐of-‐the-‐box methodology as most effec+ve.

Touch point 1

> Analy,cs to pick the best model


Touch point 2

Touch point 3

Touch point N

Closer Influencer Influencer $ Introducer

Touch point 1

Touch point 2

Touch point 3

Touch point N

Touch point 1

Touch point 2

Touch point 3

Touch point N

✖

✔

✖

Closer

Touch point 1

Touch point 1

Touch point 1

> Path across different segments



Touch point 2

Touch point 2

Touch point 3

Touch point 2

Touch point 3

Touch point N

Touch point 3

Touch point N

Touch point N

Introducer

Product A vs. B

Clients vs. prospects

Segment A vs. B


A(ribu,on piHall #10 Selec,ng the right model

> A(ribu,on models compared


COST PER CONVERSION

Last click a'ribu+on

Custom (weighted) a'ribu+on

> Insights to maximise media ROI


COST PER CONVERSION

Last click a'ribu+on

Even/weighted a'ribu+on

? Email

? Direct mail

? Internal ads ?

Website content

? TV/Print

> Generic paid search overvalued


Last click a(ribu,on Generic search terms should deliver more ROI in a weighted a(ribu,on

model assuming branded search terms usually make up the

majority of last clicks, correct?

> Generic paid search overvalued


Full path a(ribu,on Incorrect! ROI is based on revenue and cost and generic search terms

have historically received too much credit, hence high CPCs were ok but in reality they are too high thus leading

to an overall nega,ve ROI!

> Redistribu,ng media spend


ROI FULL PURCHASE PATH

TOTA

L CO

NVE

RSION VALUE

Maintain spend

Increase spend

Reduce spend

Publisher 1 Publisher 2 Publisher 3 […] Publisher N

> ROI & revenue target simulator


> Media a(ribu,on


Aussie purchase path tracking and media a'ribu+on modelling in close coopera+on with Amnesia designed to op+mise the overall Aussie budget mix across paid and earned media resul+ng in an overall project ROI of 910%.

> Media a(ribu,on


Suncorp purchase path tracking and media a'ribu+on modelling in order to op+mise the overall Suncorp insurance budget mix across paid and earned media resul+ng in an overall project ROI of 2,078%.

> Poten,al next steps §  Phase 1: Purchase path tracking –  Requires (SuperTag) container tag

§  Phase 2: Data collec+on §  Phase 3: One-‐off a'ribu+on –  Ini+al a'ribu+on model (one product or segment only) –  Ac+onable recommenda+ons (i.e. shiV spend, etc)

§  Phase 4: Ongoing a'ribu+on (op+onal) –  A'ribu+on model maintenance –  Addi+onal a'ribu+on models (products, segments) – Model enhancements (i.e. add interest rate, offline, etc) –  Report automa+on (daily reports)



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Data > Insights > Ac,on


Date post:	19-May-2015
Category:	Technology
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Common pitfalls in media attribution

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