Data Driven Marketing - Aprimo Omniture Webex

Data driven marke-ng Increasing campaign response rates

through data driven targe3ng

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

October 2010 © Datalicious Pty Ltd 2

Data driven marke-ng


Media a8ribu-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 h8p://bit.ly/cAtSUN

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 plaXorms


On-‐site segments

Off-‐site segments

Combining technology


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 Con-nuously

Facebook as subscrip-on op-on


Facebook Connect gives your company the following data and more with just one click! Email address, first name, last name, middle name, picture, affilia3ons, last profile update, 3me zone, religion, poli3cal interests, interests, sex, birthday, a\racted to which sex, why they want to meet someone, home town, rela3onship status, current loca3on, ac3vi3es, music interests, tv show interests, educa3on history, work history, family and ID

(influencers only)

(all contacts)

Appending social data to customer profiles Name, age, gender, occupa-on, loca-on, social profiles and influencer ranking based on email

October 2010 14 © Datalicious Pty Ltd

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 h8p://bit.ly/cszp2o

Overes-ma-ng unique visitors


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


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

Phase Segment A/B Channels Data Points

Awareness

Considera-on

Purchase Intent

Up/Cross-‐Sell

Developing a 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

Developing a targe-ng matrix


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 liged 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 h8p://bit.ly/de70b7

Poten-al newsle8er 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 plahorm to build business rules and customise content delivery.

NPS

Customer profiling in ac-on


Using website and email responses to learn a li\le bite more about

subscribers at every touch point to keep

refining profiles and messages.

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

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 is key


ClickTale tes-ng case study


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


Email me [email protected]

Follow us

twi8er.com/datalicious

Learn more blog.datalicious.com

Data Driven Marketing - Aprimo Omniture Webex

Technology

Transcript of Data Driven Marketing - Aprimo Omniture Webex