Increasing campaign response rates through data driven targeting

1. Data driven marketing
Increasing campaign response rates
through data driven targeting

2. Datalicious company history
• Datalicious was founded in late 2007
• Strong Omniture web analytics history
• 1 of 4 Omniture Service Partners globally
• Now 360 data agency with specialist team
• Combination of analysts and developers
• Making data accessible and actionable
• Evangelizing smart data driven marketing
• Driving industry best practice (ADMA)
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3. Data driven marketing
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Media attribution
Optimising channel mix
Testing
Improving usability
$$$
Targeting
Increasing relevance

4. Increase revenue by 10-20%
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By coordinating the consumer’s end-to-end experience,
companies could enjoy revenue increases of 10-20%.
Google: “get more value from digital marketing”
or http://bit.ly/cAtSUN

5. The consumer data journey
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To retention messagesTo transactional data
From suspect to To customer
From behavioural data From awareness messages
TimeTime
prospect

6. Coordination across channels
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Off-site
targeting
On-site
targeting
Profile
targeting
Generating
awareness
Creating
engagement
Maximising
revenue
TV, radio, print,
outdoor, search
marketing, 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

7. Off-site
targeting
On-site
targeting
Profile
targeting
Combining targeting platforms
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8. October 2010 © Datalicious Pty Ltd 8

9. October 2010 © Datalicious Pty Ltd 9

10. On-site
segments
Off-site
segments
Combining technology
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11. Campaign response data
Combining data sets
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Customer profile data
+ The whole is greater
than the sum of its parts
Website behavioural data

12. Behaviours plus transactions
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one-off collection of demographical data
age, gender, address, etc
customer lifecycle metrics and key dates
profitability, expiration, etc
predictive models based on data mining
propensity to buy, churn, etc
historical data from previous transactions
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 promotion responses
emails, internal search, etc
Site Behaviour
Updated Continuously

13. Facebook as subscription option
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Facebook Connect gives your
company the following data
and more with just one click!
Email address, first name, last name,
middle name, picture, affiliations, last
profile update, time zone, religion,
political interests, interests, sex, birthday,
attracted to which sex, why they want to
meet someone, home town, relationship
status, current location, activities, music
interests, tv show interests, education
history, work history, family and ID

14. (influencers only)
(all contacts)
Appending social data to customer profiles
Name, age, gender, occupation, location, social
profiles and influencer ranking based on email
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15. 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
overestimated visitors by up to 7.6 times whilst a cookie-based
approach overestimated visitors by up to 2.3 times.
Google: ”red eye cookie report pdf” or http://bit.ly/cszp2o
Overestimating unique visitors
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16. Maximise identification 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 identification through Cookies
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17. Sample site visitor composition
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30% existing customers with extensive
profile including transactional history of
which maybe 50% can actually be
identified 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

18. Phase Segment A/B Channels Data Points
Awareness
Consideration
Purchase Intent
Up/Cross-Sell
Developing a targeting matrix
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19. Phase Segment A/B Channels Data Points
Awareness Seen this?
Social, display,
search, etc
Default
Consideration 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 targeting matrix
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20. Potential home page layout
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Branded header
Rule based offer
Customise content
delivery on the fly
based on referrer
data, past content
consumption or
profile data for
existing customers.
Targeted
offer Popular
links,
FAQs
Targeted
offer
Login

21. Prospect targeting parameters
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22. Affinity targeting in action
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Different type of
visitors respond to
different ads. By
using category
affinity targeting,
response rates are
lifted 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 http://bit.ly/de70b7

23. Potential newsletter layout
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Closest
stores,
offers
etc
Rule based branded header
Data verification
Rule based offer
Profile based offer
Using profile data
enhanced with
website behaviour
data imported into
the email delivery
platform to build
business rules and
customise content
delivery.
NPS

24. Customer profiling in action
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Using website and email responses
to learn a little bite more about
subscribers at every
touch point to keep
refining profiles
and messages.

25. Potential landing page layout
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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 action

26. Avinash Kaushik:
“The principle of garbage in, garbage out
applies here. […] what makes a behaviour
targeting platform tick, 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
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27. ClickTale testing case study
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28. 1. Define success metrics
2. Define and validate segments
3. Develop targeting and message matrix
4. Transform matrix into business rules
5. Develop and test content
6. Start targeting and automate
7. Keep testing and refining
8. Communicate results
Keys to effective targeting
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29. October 2010 © Datalicious Pty Ltd 29
ADMA short course
“Analyse to optimise”
In Melbourne & Sydney
October/November
By Datalicious

30. October 2010 © Datalicious Pty Ltd 30
Email me
cbartens@datalicious.com
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twitter.com/datalicious
Learn more
blog.datalicious.com