This document discusses using machine learning for customer segmentation. It first provides context on machine learning applications in retail, including eliminating overstocks/out-of-stocks, adjusting prices, customer sentiment analysis, and chatbots for customer support. It then defines customer segmentation as dividing customers into groups based on common attributes like demographics, behaviors, or other attributes. The benefits of customer segmentation are described as enabling personalized communication, upselling/cross-selling opportunities, and higher returns on marketing investments. A case study example is presented analyzing customer data to identify segments most responsive to different types of offers like BOGO, discounts, or informational promotions.

1. Identifying Customer
Segments using
Machine Learning
Presented By: Shubham Goyal
Data Scientist
Knoldus Inc. & MachineX Intelligence

2. 2
Our Agenda
01 Machine learning in the retail industry
02 What is Customer Segmentation?
03 Benefits of Customer Segmentation
04 Case Study

3. 3
About Knoldus MachineX
MachineX is a group of data wizards.
We are a team of Data Scientist and engineers with a product
mindset who deliver competitive business advantage.

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9. Machine Learning in Retail

12. Some AI use-cases in Retail

13. 13
Eliminating
overstocks and
out-of-stocks
FishEye
AI helps retailers replenish supplies by identifying demand
for a particular product based on
● sales history
● location
● weather
● promotions
● trends
● … and so on.

15. 15
Adjusting
prices
FishEye

16. Customer Sentiment analysis

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Chatbots for
customer
support
FishEye

18. Anticipate Customer Behaviour

20. Why is Anticipation Important to BRAND
GROWTH?

21. ● Anticipation precedes all action.
● Anticipation impacts consumer decisions.
● Anticipation is a source of customer value.
● Anticipation can be a business’s competitive advantage.
Why is Anticipation Important to BRAND
GROWTH?

22. “Just because your strategy has worked
up until now doesn’t mean that it will work
in the future.”

23. What is customer segmentation ?
Customer Segmentation Strategy

25. What are customer segmentation ?

26. Customer segmentation Strategy

27. ● Demographic Customer Segmentation Strategy
● Psychographic Customer Segmentation Strategy
● User Engagement Customer Segmentation Strategy
● Goal Customer Segmentation Strategy
Customer segmentation Strategy

28. Customer segmentation Analysis

29. Benefits of Customer segmentation

30. ● Ability to Personalize Communication
● Upselling / Cross-selling Opportunities
● Higher ROI and CRO
Benefits of Customer segmentation

31. Advantages of Customer segmentation

32. Case study

34. Target
● Gain understanding what types of customer
characteristics and demographics are there.
● What offer should be sent to each customer
?

35. Datasets
● portfolio.json — containing offer ids and meta
data about each offer (duration, type, etc.)
● profile.json — demographic data for each
customer
● transcript.json — records for transactions,
offers received, offers viewed, and offers
completed

36. ● BOGO Cluster who response to bogo offering
○ Cluster 0 : above average income, average spending amount, offer completed rate
○ Cluster 2 : slightly below average income & spending, and lower offer completed rate
○ Cluster 4 : the highest income and average spending amount, older group
○ Cluster 5 : similar to cluster 0, but very response to informational offer
● DISCOUNT Cluster who response to discount offering
○ Cluster 1 : below average income, spending and offer completed rate
○ Cluster 3 : similar to cluster 1 with the lowest income and spending
○ Cluster 6 : above average income, spending and offer completed rate who very responsice to informational
and discount offering
● BOGO & DISCOUNT Cluster who respons both bogo and discount offering
○ Cluster 8 : very responsive to informational and discount, and also repsonsive to bogo offering, above
average income, spending with most offer completed rate with the highest difficulty
● INFORMATIONAL Cluster who response informational offering
○ Cluster 4, 5, 6, and 8, and some cluster 7
● Probably not a targetted cluster:
○ Cluster 7 : newer member who has the lowest income & offer completed rate, and probably never have a
spending, may have responsive to informational offer.

38. 38
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