Data modelling presentation by Andrew Hood, MD of Lynchpin . Creating clusters in data to target and predict behaviour for profit. There are some handy links for tools including R and rattle.

1. Creating Clever Clusters
Segmentation for Profit
Andrew Hood
Managing Director
@lynchpin
www.Lynchpin.com

2. What is Marketing?
13 April 2015 © Lynchpin Analytics Limited, All Rights Reserved 2
Satisfying needs and wants
through an exchange process
Philip Kotler
“
”

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The management process responsible for
identifying, anticipating and satisfying
customer requirements profitably
Chartered Institute of Marketing
What is Marketing?
“
”

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Marketing is now as much science as art due to the confluence of two
simultaneous revolutions.
The first is technological advances in platforms like Adobe Marketing
Cloud, that allow marketers to gather, analyze and elegantly visualize
large and complex data sets to gain customer insights.
The second is the explosion of digital devices and channels - from smart
phones to social media - which are the common currency of digital
natives whose lives are transacted in pixels.
Michael Schinelli, Chief Marketing Officer at the University of North Carolina’s
Kenan-Flagler Business School
“
”
2014?...

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It’s more important to
REACH the people who COUNT
than to
COUNT the people you REACH
The Segmentation Principle

6. Segmentation Sophistication
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Just eat it: we know
what’s good for you.
Fun for all the family.
(If you all fit these 4 buckets)
Completely personalised.
(If you play guitar for Blur)

7. GA Value Segmentation
Average Transaction Value
only defines the immediate
value of a single session
Value Segmentation looks
at the longer term value
of a user (customer)
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8. GA Value Segmentation
• Choose some buckets (max 4)
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Segment No Value Low Value Medium Value High Value
Definition No Purchases Exactly 1
Purchase
Exactly 2
Purchases
3 or More
Purchases

9. GA Value Segmentation
• Apply segments to reporting
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10. Digital Segmentation Party
One of richest
sets of
behavioural data
Some of the least
sophisticated segmentation
on the planet
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Key Challenge:
Think of an interesting question
Then try again…
And again…

11. Direct Mail Segmentation Party
Limited behavioural data
Nice clustering
algorithms
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Key Challenge
Getting the data to model

12. Clustering (Segmentation Modelling)
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Cluster analysis or clustering is
the task of grouping a set of
objects in such a way that objects
in the same group (called
a cluster) are more similar to
each other than to those in other
groups (clusters).

13. Real Advanced Segmentation
• Unsupervised learning
– The algorithm helps find
relationships/outcomes you
didn’t already know about
• Anchored on profit
– Not a value hunch
• Statistically based
– Extracts the signal from the
noise
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14. Creating Clever Clusters
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BEHAVIOURAL
DEMOGRAPHIC
VALUE
PSYCHOGRAPHIC
GEOGRAPHIC
OFFER BASED
BENEFIT BASED

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Let’s talk money

16. Example: Car Rental
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High Value
High Potential
High Value
Low Potential
Low Value
Low Potential
Low Value
High Potential
Value
Potential
Customer
Rental
Records
Current Value
Rental revenue vs cost
over past 12 months
Expected Value
Basic model based on
recency / frequency

17. Example: Car Rental
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Clustered By
Membership scheme
Age
Gender
Car group rented
Pickup locations
Rental duration
Time of year
Business / leisure profile
…
“High value customers tend to rent car groups K, F, L, Z,
older demographic, less corporate, different pick-up/drop-
off locations”
“Frequent renters tended to be short haul for business
and standard domestic, male, aged 36-56 ”
High Value
High Potential
High Value
Low Potential
Low Value
Low Potential
Low Value
High Potential
Value
Potential

18. Example: Car Rental
Target relevant offers to
encourage low value / high
potential group
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Focus upgrade offers and
also loyalty / retention
offers to high value / high
potential group
Marketing options

19. Outcome
£6m incremental revenue over 18 months

20. Example: Home Insurance
Have a
Policy
Cancel
Renew

21. Example: Home Insurance
Have a
Policy
Cancel
Renew
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• Length of relationship (summarised
transactional data)
• Satisfaction level (scores from research)
• Number of quotes
• Age of policy
• Level of cover
• Additional covers taken
• Price
• Payment method
• Number of claims
• Demographics
• Number of products held
• Ratings district
• Home details (e.g. year built)
• Financial position (e.g. overlay of Experian
data)

22. Example: Home Insurance
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• Length of relationship (summarised
transactional data)
• Satisfaction level (scores from research)
• Number of quotes
• Age of policy
• Level of cover
• Additional covers taken
• Price
• Payment method
• Number of claims
• Demographics
• Number of products held
• Ratings district
• Home details (e.g. year built)
• Financial position (e.g. overlay of
Experian data)
Have a
Policy
Cancel
Renew

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Test and target retention
campaigns on segments
with the highest
likelihood of success
E.g. targeted renewal
discounts
Identify high value
segments with high
probability of lapsing and
focus efforts on them
E.g. getting onto direct
debit
Marketing Actions
Outcome
30% increase
in retention.

24. Analytics Ecosystem
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25. Analytics Ecosystem
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26. Analytics Ecosystem
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27. Analytics Ecosystem
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28. Digital Clustering Opportunities
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Useful Tools. Not this!

30. Tool Time
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Rstudio http://www.rstudio.com/
Rattle
http://rattle.togaware.com/

31. Tool Time
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KNIME http://www.knime.org

32. Handy Tool links
• R Integrations
– http://skardhamar.github.io/rga/
– https://code.google.com/p/r-google-analytics/
– http://randyzwitch.com/rsitecatalyst/
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33. Stats Tutorials
– http://www.autonlab.org/tutorials/
– http://rattle.togaware.com/rattle-examples.html
Handy Tutorial links

34. Andrew Hood
Managing Director
@lynchpin
www.Lynchpin.com
Cheers Folks
www.linkedin.com/in/andrewhood