datalicious 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 §  Official Adobe & Google Analy+cs partner §  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 partners §  Driving industry best prac+ce with ADMA §  Turning data into ac+onable insights §  Execu+ng smart data driven campaigns October 2012 © Datalicious Pty Ltd 2

> Smart data driven marke,ng

October 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”

> Clients across all industries



Media a(ribu,on = Giving credit where

credit it is due

> 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


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

> 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

> 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


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

Closer

Channel 1

Channel 1

Channel 1

> Path across different segments



Channel 2

Channel 2 Channel 3

Channel 2 Channel 3 Channel 4

Channel 3

Channel 4

Channel 4

Introducer

Product A vs. B

Clients vs. prospects

Brand vs. direct resp.

> Purchase path 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 October 2012 © Datalicious Pty Ltd 18

> 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

> Tracking offline sales online §  Email click-‐through

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

§  First login aler 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, 1st 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

> 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


ChrisBartens.company.com > redirect to > company.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  […] How likely are you to apply for a loan? q Within the next few weeks q Within the next few months q  I am a customer already 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.

> Adjus,ng for offline impact


+15 +5 +10 -‐15 -‐5 -‐10

> Tradi,onal modelling to fill gaps


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).

> 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


> Media a(ribu,on example


COST PER CONVERSION

Last click a'ribu+on

Even/weighted a'ribu+on



COST PER CONVERSION

Last click a'ribu+on

Even/weighted a'ribu+on

? Email

? Direct mail

? Internal ads ?

Website content

? TV/Print

0%

> Media a(ribu,on models


$100

0% Last click a(ribu,on

Even a(ribu,on

Weighted a(ribu,on

0% 100%

25% 25% 25% 25%

Display impression

Display impression

Display response

Search response

X% X% Y% Z%

Closer

?%

?%

?%

> Media a(ribu,on models



?%

?% ?%

?% ?% ?%

?%

?%

?%

Introducer

Product A vs. B

Prospects vs. clients

Brand vs. direct resp.



ROI FULL PURCHASE PATH

TOTA

L CO

NVE

RSION VALUE

Increase spend

Increase spend

Reduce spend

Publisher 1 Publisher 2 Publisher 3 […] Publisher N

> Media a(ribu,on case studies §  Suncorp: Implementa+on of ad server data collec+on tags via

SuperTag to facilitate the collec+on of full purchase path data in the company’s DoubleClick ad server. Followed by a manual one-‐off data analysis including a'ribu+on model development in phase 1 as well as report automa+on in a dedicated Splunk environment in phase 2. –  2,078% project ROI from implementa+on of ini+al quick wins only

by reducing media waste respec+vely cost for a limited set of brands in phase 1.

§  Telstra: Implementa+on of ad server data collec+on tags via SuperTag to facilitate the collec+on of full purchase path data in the company’s Atlas ad server. Followed by a manual one-‐off data analysis including a'ribu+on model development. –  403% project ROI from implementa+on of ini+al quick wins only by

reducing media waste respec+vely cost.



Datalicious Op,maHub

> Op,maHub plaoorm architecture


SuperTag app genera,ng JS app.supert.ag

SuperTag JS hosted by client or on c.supert.ag

Client pages referencing SuperTag JS

Browser execu,ng SuperTag JS

3rd party ad server

data collec,on

SuperTag DataCollector d.supert.ag

Splunk processing Server(s)

Dedicated client Splunk server(s)

Splunk saved searches and dashboards

Addi,onal data (i.e. CRM, POS, social, etc)

JavaScript JavaScript

Requests

Tags

Data

Data

Data

> About Datalicious

101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010


> Short but sharp history §  Datalicious was founded in November 2007 §  Official Adobe & Google Analy+cs partner §  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 partners §  Driving industry best prac+ce with ADMA §  Turning data into ac+onable insights §  Execu+ng smart data driven campaigns October 2012 © Datalicious Pty Ltd 41

> 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”

> Wide range of data services


Data Plaoorms 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 Data warehouse solu,ons Single customer view

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

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

> Over 50 years of 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-‐Lol, Tourism Australia

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

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

Chaoming Li Head of Data §  Bachelor of

Technology with microelectronics focus

§  Solware and website development work experience

§  Standards Australia, DF Securi+es, Globiz, Etang

§  Developing his own CMS plazorm

LinkedIn profile

> Best of breed partners


> Clients across all industries


> Great customer feedback “[…] Datalicious quickly earned our respect and confidence […] understand our business needs, deliver value, push our thinking […]. Likeable, transparent and trustworthy. I would be happy to recommend Datalicious to anyone.” Murray Howe, Execu+ve Manager, Suncorp Group "[…] Datalicious brought with them best prac>ce analy>cs to demonstrate the true value of our marke>ng dollars […] have become a criBcal business partner […] provided great insights which have driven key business decisions.” Trang Young, Senior Marke+ng Manager, E*Trade Australia “The Datalicious guys are great to work along side […] 'no stone unturned' approach to finding solu>ons to challenges […] knowledge and passion for web analy>cs and best of breed web opBmizaBon was second to none” Steve Brown, Senior Business Analyst, Vodafone “[…] The Vodafone implementa>on of SiteCatalyst is one of the most impressive I have seen and ranks in the top 10 […]. It is an amazing founda>on for taking ac>on on the data and improving ROI.” Adam Greco, Consul+ng Lead, Omniture


> Great customer feedback "[…] Datalicious understand the value of informa>on and how to leverage it using best of breed soEware. I would recommend the team without hesita>on [...]." James Fleet, Marke+ng Director, Appliances Online "[...] Datalicious have been inBmately involved in building our analyBcs soluBon. Most importantly their knowledge of best prac>ce combined with innova>ve solu>ons has allowed our business to remain nimble and current. They are also nice guys." Tzvi Balbin, Group Digital Marke+ng Lead, Catch of the Day "[...] Datalicious are helping us to move from a last click campaign measurement model to a more accurate media aFribu>on approach. [...] potenBal to significantly change our media planning [...]. Highly recommended." Keith Mirgis, Senior Digital & Social Media Marke+ng Manager, Telstra "We engaged Datalicious to support a strategic change in our business [...] understand our customers [and their transac>ons] beFer to ensure we retained as many as possible [...]" Natalie Farrell, Direct Marke+ng Manager, Luxo}ca



Contact us [email protected]

Learn more

blog.datalicious.com

Follow us twi(er.com/datalicious

Data > Insights > Ac,on
