Date post: | 17-Jul-2015 |
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Creating Clever ClustersSegmentation for Profit
Andrew HoodManaging Director
@lynchpin
www.Lynchpin.com
What is Marketing?
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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
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)
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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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
GA Value Segmentation
• Apply segments to reporting
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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 questionThen try again…And again…
Direct Mail Segmentation Party
Limited behavioural dataNice clustering
algorithms
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Key ChallengeGetting the data to model
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).
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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Creating Clever Clusters
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BEHAVIOURAL
DEMOGRAPHIC
VALUE
PSYCHOGRAPHIC
GEOGRAPHIC
OFFER BASED
BENEFIT BASED
Example: Car Rental
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High ValueHigh Potential
High ValueLow Potential
Low ValueLow Potential
Low ValueHigh Potential
Value
Potential
Customer Rental
Records
Current ValueRental revenue vs cost over past 12 months
Expected ValueBasic model based on recency / frequency
Example: Car Rental
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Clustered By
Membership schemeAgeGenderCar group rentedPickup locationsRental durationTime of yearBusiness / 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 ValueHigh Potential
High ValueLow Potential
Low ValueLow Potential
Low ValueHigh Potential
Value
Potential
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
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)
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
Outcome30% increase in retention.
Tool Time
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Rstudio http://www.rstudio.com/
Rattle http://rattle.togaware.com/
Tool Time
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KNIME http://www.knime.org
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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Stats Tutorials
– http://www.autonlab.org/tutorials/
– http://rattle.togaware.com/rattle-examples.html
Handy Tutorial links