ExactTarget Media Attribution

> Media A)ribu-on < Cross-‐channel media a0ribu3on best prac3ce and technology op3ons

> Short but sharp history §  Datalicious was founded in late 2007 §  Strong Omniture web analy3cs history §  1 of 4 preferred Omniture partners globally §  Now 360 data agency with specialist team §  Combina3on of analysts and developers §  Carefully selected best of breed partners §  Driving industry best prac3ce (ADMA) §  Turning data into ac3onable insights §  Execu3ng smart data driven campaigns July 2012 © Datalicious Pty Ltd 2

> Smart data driven marke-ng

July 2012 © Datalicious Pty Ltd 3

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”

> Media a)ribu-on

101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010


> 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

> Duplica-on across channels


Banner Ads

Email Blast

Paid Search

Organic Search

$ Bid Mgmt

Ad Server

Email PlaNorm

Google Analy-cs

$

$

$

Central Analy-cs PlaNorm

$

$

$

> De-‐duplica-on across channels


Banner Ads

Email Blast

Paid Search

Organic Search

$

Direct mail, email, etc

Facebook Twi)er, etc

> Campaign flows are complex


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

Call center, retail stores, etc

= Sales channels

Display ads, affiliates, etc

> Success a)ribu-on models


Banner Ad $100

Email Blast

Paid Search $100

Banner Ad $100

Affiliate Referral $100

Success $100

Success $100

Banner Ad

Paid Search

Organic Search $100

Success $100

Last channel gets all credit

First channel gets all credit

All channels get equal credit

Print Ad $33

Social Media $33

Paid Search $33

Success $100

All channels get par-al credit

Paid Search

> 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

Closer

Paid search

Display ad views

TV/print responses

> Full purchase path tracking


Influencer Influencer $

Display ad clicks

Online sales

Affiliate clicks

Social buzz

Offline sales

Organic search

Website events

Direct mail, emails

Life-me profit

Social referrals

Retail store visits

Direct site visits

Introducer

Closer

Channel 1

Channel 1

Channel 1

> Path across different segments


Influencer Influencer $

Channel 2

Channel 2 Channel 3

Channel 2 Channel 3 Product 4

Channel 3

Channel 4

Channel 4

Introducer

Product A vs. B

New prospects

Exis-ng customers

> Tracking offline responses online

§  Search calls to ac3on for TV, radio, print –  Unique search term only adver3sed in print so all responses from that term must have come from print

§  PURLs (personalised URLs) for direct mail –  Brand.com/clientname redirects to new URL that includes tracking parameter iden3fying response as direct mail

§  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 a0ribu3on model –  Combine raw data from online purchase path, website entry survey and offline sales with offline media placement informa3on in tradi3onal media a0ribu3on model


> Search call to ac-on for offline


VickyCarroll.myspaday.com > redirect to > myspaday.com?

CampaignID=DM:123& Demographics=F|35& CustomerSegment=A1& CustomerValue=High& CustomerSince=2001& ProductHistory=P1|P2& NextBestOffer=P3& ChurnRisk=Low [...]

> Personalised URLs for direct mail


> Tracking offline sales online

§  Email click-‐through –  Include offline sales flag in URL parameter in welcome email click-‐through URLs (or 1st email newsle0er aner offline sale) to trigger a custom ‘assisted offline sales’ conversion event

§  First login aner purchase – Similar to the above method, however offline sales flag happens via JavaScript parameter defined on login rather than URL parameter


Confirma-on email, 1st login

> Offline sales driven by online


Website research

Phone order

Retail order

Online order

Cookie

Adver-sing campaign

Credit check, fulfilment

Online order confirma-on

Virtual order confirma-on

> Understanding channel mix


> Website entry survey


Channel % of Conversions

Straight to Site 27%

SEO Branded 15%

SEM Branded 9%

SEO Generic 7%

SEM Generic 14%

Display Adver3sing 7%

Affiliate Marke3ng 9%

Referrals 5%

Email Marke3ng 7%

De-‐duped Campaign Report

} Channel % of Influence

Word of Mouth 32%

Blogging & Social Media 24%

Newspaper Adver3sing 9%

Display Adver3sing 14%

Email Marke3ng 7%

Retail Promo3ons 14%

Greatest Influencer on Branded Search / STS

Conversions a0ributed to search terms that contain brand keywords and direct website visits are most likely not the origina3ng channel that generated the awareness and as such conversion credits should be re-‐allocated.

> Adjus-ng for offline impact


+15 +5 +10 -‐15 -‐5 -‐10

> Purchase path example


Closer

25%

> Media a)ribu-on models


Influencer Influencer $100

25% Even A)rib.

Exclusion A)rib.

Custom A)rib.

25% 25%

Introducer

33% 33% 33% 0%

? ? ? ?

> Media a)ribu-on example


COST PER CONVERSION

Last click a0ribu3on

Even/weighted a0ribu3on

> Purchase path vs. a)ribu-on

§  Important to make a dis3nc3on between media a0ribu3on 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 a0ribu3on or the alloca3on or conversion credits back to campaign touch points –  Purchase path tracking is the data collec3on and media a0ribu3on is the actual analysis or modelling


> Single source of truth repor-ng


Insights Repor-ng

> Where to track purchase path


Referral visits Social media visits Organic search visits Paid search visits Email visits, etc

Web Analy-cs Banner impressions

Banner clicks +

Paid search clicks

Ad Server

Lacking ad impressions Less granular & complex

Lacking organic visits More granular & complex

> Purchase path data samples Web Analy-cs data sample (AD IMPRESSION >) AFFILIATE > SEARCH > $$$ SEARCH > SOCIAL > DIRECT > $$$

Ad Server data sample 01/01/2012 11:45 AD IMPRESSION 01/01/2012 12:00 AD IMPRESSION 01/01/2012 12:05 SEARCH 07/01/2012 17:00 DIRECT 08/01/2012 15:00 $$$


> PlaNorm op-ons

101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010


> Purchase path op-ons §  Google Analy3cs – Mul3-‐channel funnels

§  Adobe SiteCatalyst –  Cross-‐visit par3cipa3on JavaScript plugin – DoubleClick/Mediamind Genesis integra3on –  Light Server Calls

§  Atlas, Mediamind, DoubleClick, etc –  Campaign touch point report (Mediamind) –  Raw cookie level data & manual analysis

§  ClearSaleing


> Google Analy-cs §  Mul3-‐channel funnels

–  Concatenates and groups campaign codes across visits §  Pros

–  Free of charge as part of main tool –  Out of the box reports that require no addi3onal tags –  Does not require data warehousing and custom analy3cs

§  Cons –  Raw data cannot be exported for advanced modelling –  Cannot combine purchase paths across devices –  Doesn’t include ad impressions (maybe DoubleClick in the future) –  Channels can be grouped but not re-‐classified in the interface

§  Offline responses via search call to ac3on show up as search, etc –  Platorm cannot accommodate offline data (surveys, CRM, etc) –  Purchase path data only, no a0ribu3on modeling or ROMI


> Adobe SiteCatalyst §  Cross-‐visit par3cipa3on & light server calls

–  Concatenates campaign codes across visits into single string –  Light Server Call adds missing ad impressions into campaign code string

§  DoubleClick/Mediamind Genesis –  Imports last ad impression, click and ad costs into SiteCatalyst as events

§  Pros –  Easy add-‐on for SiteCatalyst clients, requires JavaScript change only –  Doesn’t require addi3onal data warehousing as stored in SiteCatalyst –  Campaign responses can be re-‐classified aner the fact (SAINT)

§  Cons –  Conflic3ng un-‐integrated solu3ons (Light Server Call vs. Genesis) –  Addi3onal costs due to a light server call for each ad impression –  Light Server Call tracks ad impression but lacks more granular ad data –  Cannot concatenate campaign codes across different top level domains –  Data can only be exported as concatenated strings without 3me stamps –  Platorm cannot integrate offline data into modelling (surveys, CRM, etc) –  Basic a0ribu3on modelling only that cannot be changed in retrospect –  Purchase path data only, no flexible a0ribu3on modeling or ROMI


> Purchase path data samples

Web Analy-cs data sample LAST AD IMPRESSION > SEARCH > $$$| PV $$$ AD IMPRESSION > AD IMPRESSION > SEARCH > $$$

Ad Server data sample 01/01/2012 11:45 AD IMP YAHOO HOME $33 01/01/2012 12:00 AD IMP SMH FINANCE $33 01/01/2012 12:05 SEARCH KEYWORD -‐ 07/01/2012 17:00 DIRECT $33 08/01/2012 15:00 $$$ $100


> Atlas, Mediamind, DoubleClick §  Campaign touch points (Mediamind only)

–  Last 10 touch points before conversion aggregated across users §  Raw cookie level data (all ad servers)

–  Full list of all ad touch points for each cookie ID §  Pros

–  Low to very low cost for raw data –  Complete raw data available (cookie level op3on only) –  Increased flexibility due to complete and very granular data –  Campaign responses can be re-‐classified aner the fact –  Solu3on can accommodate offline data (surveys, CRM, etc) –  Ad server can track across different domains due to 3rd party cookie –  Enables advanced ROMI and a0ribu3on modelling with offline data –  A0ribu3on model can be changed in retrospect and recalculated

§  Cons –  Requires custom JavaScript tags to capture organic touch points –  Requires addi3onal data warehousing and custom analy3cs


> Purchase path data samples

Ad Server summary data sample AD IMPRESSION > DIRECT > SEARCH > $$$ 10x AD IMPRESSION > AFFILIATE > SEARCH > $$$ 5x

Ad Server data sample UID123 01/01/2012 11:45 AD IMP YAHOO $33 UID123 01/01/2012 12:00 AD IMP SMH $33 UID123 01/01/2012 12:05 SEARCH -‐ UID123 07/01/2012 17:00 DIRECT $33 UID123 08/01/2012 15:00 $$$ $100


> ClearSaleing

§  Pros –  Fully managed media a0ribu3on platorm –  Extensive out of the box repor3ng func3onality –  Can customise a0ribu3on model (not black box) –  Supports ROMI calcula3on and a0ribu3on modeling

§  Cons –  Priced on percentage of media spend –  Requires website tagging and backend integra3ons – Only pre-‐processed data export, not raw data


> Forrester: ClearSaleing ROI/TEI

§  Improved decision making and media buying §  Labor savings and improved produc3vity §  Accurate media repor3ng and improved visibility §  Improved flexibility and faster market response §  Transparency and consistency of metrics §  Increase in online adver3sing budget July 2012 © Datalicious Pty Ltd 42

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