[ Data driven marke.ng ] Reducing waste and increasing relevance through targe3ng
Jan 22, 2015
[ 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 ]
September 2010 © Datalicious Pty Ltd 3
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 ]
September 2010 © Datalicious Pty Ltd 4
[ Using data to reduce waste ]
September 2010 © Datalicious Pty Ltd 5
Media aMribu.on
Op.mising channel mix
Tes.ng Improving usability
$$$
Targe.ng Increasing relevance
[ Increase revenue by 10-‐20% ]
September 2010 © Datalicious Pty Ltd 6
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 ]
September 2010 © Datalicious Pty Ltd 7
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 ]
September 2010 © Datalicious Pty Ltd 8
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 ]
September 2010 © Datalicious Pty Ltd 9
September 2010 © Datalicious Pty Ltd 10
September 2010 © Datalicious Pty Ltd 11
On-‐site segments
Off-‐site segments
[ Combining technology ]
September 2010 © Datalicious Pty Ltd 12
[ Datalicious SuperTag ]
September 2010 © Datalicious Pty Ltd 13
§ 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 ]
September 2010 © Datalicious Pty Ltd 14
Customer profile data
+ The whole is greater than the sum of its parts
Website behavioural data
[ Behaviours plus transac.ons ]
September 2010 © Datalicious Pty Ltd 15
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 ]
September 2010 © Datalicious Pty Ltd 16
§ 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
September 2010 © Datalicious Pty Ltd 18
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 ]
September 2010 © Datalicious Pty Ltd 20
[ Sample site visitor composi.on ]
September 2010 © Datalicious Pty Ltd 21
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 ]
September 2010 © Datalicious Pty Ltd 22
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 ]
September 2010 © Datalicious Pty Ltd 23
[ Affinity targe.ng in ac.on ]
September 2010 © Datalicious Pty Ltd 24
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 ]
September 2010 © Datalicious Pty Ltd 25
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 ]
September 2010 © Datalicious Pty Ltd 26
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 ]
September 2010 © Datalicious Pty Ltd 27
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
September 2010 28 © Datalicious Pty Ltd
Phase Segment A/B Channels Data Points
Awareness
Considera.on
Purchase Intent
Up/Cross-‐Sell
[ Exercise: Targe.ng matrix ]
September 2010 29 © Datalicious Pty Ltd
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 ]
September 2010 30 © Datalicious Pty Ltd
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 ]
September 2010 31 © Datalicious Pty Ltd
[ Small changes with big impact ]
September 2010 © Datalicious Pty Ltd 32
[ Bad campaign worse than none ]
September 2010 © Datalicious Pty Ltd 33
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 ]
September 2010 © Datalicious Pty Ltd 34
September 2010 © Datalicious Pty Ltd 35
ADMA short course “Analyse to op.mise”
In Melbourne & Sydney October/November
By Datalicious
September 2010 © Datalicious Pty Ltd 36
Email me [email protected]
Follow us
twiMer.com/datalicious
Learn more blog.datalicious.com