Data Driven Marketing - Advanced Analytics and Targeting

[ Data driven marke.ng ] Reducing waste and increasing relevance through targe3ng

[ Quick company history ]

§  Datalicious was founded in 2007 §  Strong Omniture web analy3cs history §  1 of 4 global Omniture Preferred Partners §  Now 360 data agency with specialist team §  Combina3on of analysts and developers §  Evangelizing smart data driven marke3ng § Making data accessible and ac3onable §  Driving industry best prac3ce (ADMA)

September 2010 © Datalicious Pty Ltd 2

[ Wide range of data services ]


Data Pla=orms Data collec.on and processing Web analy.cs solu.ons Omniture, Google Analy.cs, etc Tag-‐less online data capture End-‐to-‐end data pla=orms IVR and call center repor.ng Single customer view

Insights Repor.ng Data mining and modelling Customised dashboards Media aMribu.on models Market and compe.tor trends Social media monitoring Online surveys and polls Customer profiling

Ac.on Applica.ons Data usage and applica.on Marke.ng automa.on Aprimo, Trac.on, Inxmail, etc Targe.ng and merchandising Internal search op.misa.on CRM strategy and execu.on Tes.ng programs

[ Clients across all industries ]


[ Using data to reduce waste ]


Media aMribu.on

Op.mising channel mix

Tes.ng Improving usability

$$$

Targe.ng Increasing relevance

[ Increase revenue by 10-‐20% ]


By coordina.ng the consumer’s end-‐to-‐end experience, companies could enjoy revenue increases of 10-‐20%.

Google: “get more value from digital marke.ng” or hMp://bit.ly/cAtSUN

Source: McKinsey Quarterly, 2010

[ The consumer data journey ]


To reten.on messages To transac.onal data

From suspect to To customer

From behavioural data From awareness messages

Time Time prospect

[ Coordina.on across channels ]


Off-‐site targe.ng

On-‐site targe.ng

Profile targe.ng

Genera.ng awareness

Crea.ng engagement

Maximising revenue

TV, radio, print, outdoor, search marke3ng, display ads, performance networks, affiliates, social media, etc

Retail stores, call centers, brochures, websites, landing pages, mobile apps, online chat, etc

Outbound calls, direct mail, emails, SMS, etc

Off-‐site targe3ng

On-‐site targe3ng

Profile targe3ng

[ Combining targe.ng pla=orms ]




On-‐site segments

Off-‐site segments

[ Combining technology ]


[ Datalicious SuperTag ]


§  Central JavaScript based container tag § One tag for all pla^orms incl. Omniture §  Either hosted internally or externally §  Faster tag implementa3on and updates §  Consistent network wide re-‐targe3ng §  Transfer or profiling data between sites §  Iden3fica3on of exis3ng customers §  Re-‐targe3ng by brand preferences

Campaign response data

[ Combining data sets ]


Customer profile data

+ The whole is greater than the sum of its parts

Website behavioural data

[ Behaviours plus transac.ons ]


one-‐off collec3on of demographical data age, gender, address, etc customer lifecycle metrics and key dates profitability, expira.on, etc predic3ve models based on data mining

propensity to buy, churn, etc historical data from previous transac3ons

average order value, points, etc

CRM Profile

UPDATED OCCASIONALLY

+ tracking of purchase funnel stage

browsing, checkout, etc tracking of content preferences

products, brands, features, etc tracking of external campaign responses

search terms, referrers, etc tracking of internal promo3on responses

emails, internal search, etc

Site Behaviour

UPDATED CONTINUOUSLY

[ Using Pion to enrich CRM data ]


§  Single point of data capture and processing

§  Real-‐3me queries to enrich website data

§ Mul3ple data export op3ons for web analy3cs

§  Enriching single-‐customer view website behaviour

The study examined data from two of the UK’s busiest ecommerce websites, ASDA and William Hill. Given that more than half of all page impressions on these sites are from logged-‐in users, they provided a robust sample to compare IP-‐based and cookie-‐based analysis against. The results were staggering, for example an IP-‐based approach overes3mated visitors by up to 7.6 3mes whilst a cookie-‐based approach overes.mated visitors by up to 2.3 .mes. Google: ”red eye cookie report pdf” or hMp://bit.ly/cszp2o

[ Overes.ma.ng unique visitors ]

Source: White Paper, RedEye, 2007


Datalicious SuperCookie Persistent Flash cookie that cannot be deleted

[ Maximise iden.fica.on points ]

20%

40%

60%

80%

100%

120%

140%

160%

0 4 8 12 16 20 24 28 32 36 40 44 48

Weeks

−−− Probability of iden3fica3on through Cookies

September 2010 19 © Datalicious Pty Ltd

[ Sample customer level data ]


[ Sample site visitor composi.on ]


30% exis.ng customers with extensive profile including transac3onal history of which maybe 50% can actually be iden3fied as individuals

30% new visitors with no previous website history aside from campaign or referrer data of which maybe 50% is useful

10% serious prospects with limited profile data

30% repeat visitors with referral data and some website history allowing 50% to be segmented by content affinity

[ Poten.al home page layout ]


Branded header

Rule based offer

Customise content delivery on the fly based on referrer data, past content consump3on or profile data for exis3ng customers.

Targeted offer Popular

links, FAQs

Targeted offer

Login

[ Prospect targe.ng parameters ]


[ Affinity targe.ng in ac.on ]


Different type of visitors respond to different ads. By using category affinity targe3ng, response rates are liked significantly across products.

Message CTR By Category Affinity

Postpay Prepay Broadb. Business

Blackberry Bold - - - + 5GB Mobile Broadband - - + - Blackberry Storm + - + + 12 Month Caps - + - +

Google: “vodafone omniture case study” or hMp://bit.ly/de70b7

[ Poten.al newsleMer layout ]


Closest stores, offers etc

Rule based branded header

Data verifica.on

Rule based offer

Profile based offer

Using profile data enhanced with website behaviour data imported into the email delivery pla^orm to build business rules and customise content delivery.

NPS

[ Customer profiling in ac.on ]


Using website and email responses to learn a lille bite more about customers at every touch point in order to keep refining customer profiles and customising future communica3ons.

[ Poten.al landing page layout ]


Rule based branded header

Campaign message match

Targeted offer

Passing data on user preferences through to the website via parameters in email click-‐through URLs to customise content delivery.

Call to ac.on

Exercise: Targe.ng matrix


Phase Segment A/B Channels Data Points

Awareness

Considera.on

Purchase Intent

Up/Cross-‐Sell

[ Exercise: Targe.ng matrix ]


Phase Segment A/B Channels Data Points

Awareness Seen this? Social, display, search, etc Default

Considera.on Great feature! Social, search, website, etc

Download, product view

Purchase Intent Great value! Search, site, emails, etc

Cart add, checkout, etc

Up/Cross-‐Sell Add this! Direct mail, emails, etc

Email response, login, etc

[ Exercise: Targe.ng matrix ]


Avinash Kaushik: “The principle of garbage in, garbage out applies here. […] what makes a behaviour

targe<ng pla=orm <ck, and produce results, is not its intelligence, it is your ability to actually feed it the right content which it can then target […]. You feed your BT system crap and it will quickly and efficiently target crap to your

customers. Faster then you could ever have yourself.”

[ Quality content key to success ]


[ Small changes with big impact ]


[ Bad campaign worse than none ]


1.  Define success metrics 2.  Define and validate segments 3.  Develop targe3ng and message matrix 4.  Transform matrix into business rules 5.  Develop and test content 6.  Start targe3ng and automate 7.  Keep tes3ng and refining 8.  Communicate results

[ Keys to effec.ve targe.ng ]



ADMA short course “Analyse to op.mise”

In Melbourne & Sydney October/November

By Datalicious


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Technology

Data Driven Marketing - Advanced Analytics and Targeting