This document discusses customer segmentation using an unsupervised machine learning model. It analyzes transaction data from a brewery's 99,708 customers over 2 years to group them into meaningful marketing segments without prior customer information. A self-organizing map (SOM) model was used to iteratively cluster customers based on similarities. The optimal number of clusters was determined to be 3 based on cluster distances and averages. The 3 clusters identified were "Promising", "Explorers", and "High Value", with the latter found to be the best target segment for marketing due to its high sales percentage and visit frequency.

1. Customer
Segmentation
By
Swagat Ranjan Behera

2. 2
AGENDA
1. Introduction
2. About Data
3. Approach
4. About Model
5. Results
6. Visualizations
7. Conclusions
Swagat Ranjan Behera

3. Customer Segmentation – Introduction &
Business Objective
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 Businesses always face a lot of dilemma whom to target for marketing
purposes. If they don’t do appropriate targeting, both from prospective
revenues and marketing costs perspective businesses suffer huge losses.
 Customer segmentation is all about understanding customers by leveraging
data and machine learning techniques to identify certain customers as a
segment which help business to differentiate from rest for marketing
purposes.
 Thus, business objective is to provide actionable insights to the businesses
about their customers in different segments for targeting purposes.
Swagat Ranjan Behera

4. About Data
 Client belongs to brewery industry and it has many million customers, that resulted hard
in determine appropriate customers for marketing purposes.
 As, client has many million, knowing much about individual customers demographics will
be too tedious to target and has not provided such data.
 Client has provided only customer id, his transactions details such as transaction date,
amount, type, and product details.
 Thus, using above information (sufficient), we need to group the customers into
meaningful marking segments.
 Customer sample was for 99,708 near to million customers performing multiple
transactions for given data period (two years)
 Customer has spent an average amount of Euro 38,643 for different products related
brewery and an average visits at 40 trips.
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Swagat Ranjan Behera

5. Approach
 As, business objective is to understand customers, however, we don’t have
any prior information about them (i.e. no target variable in our case).
 Hence, we went ahead to find out possible clusters (un-supervised) self-
organized maps machine learning algorithm that can provide distinguished
customer instances as segment.
 Approach is of an iterative & distance based clustering, which processes and
provides (or in other words runs iterations), till an optimal cluster instances
are obtained.
 Also, verified the optimal clusters with averages of columns that went into
modelling before concluding the best.
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Swagat Ranjan Behera

6. SOM (About Model)
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Swagat Ranjan Behera
 SOMs (self-organizing maps) are invented by Prof. Kohonen** in early 1980’s and soon
become as one of the most popular artificial neural networks algorithms for grouping similar
data points together though the use of self-organizing neural networks.
 Similar to artificial neural networks, they operate only with numeric data.
 This un-supervised machine learning technique is good at providing clusters (segments)
for obtained data.
 So, by exploiting SOM clustering methods, current study tries to find out similar quality
customers as a group for marketing purposes.
 Further, these SOMs work better by providing fully machine driven customer’s segments
that get rid of manually constructed segments through traditional RFM (Recency, Frequency,
Monetary) analysis that take both time and man-power.
** Kohonen, T., Self-Organization and Associative Memory, New York : Springer-Verlag, 1988.

7. Visualization of the Results & Choosing ‘k’ and
Evaluation
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With simple strategy of going from k equals to 3, 4, 5, 6, and 7, empirical evidence helped to choose k=3.
Swagat Ranjan Behera
SOM neighbour distances 3 Clusters
6
8
10
12
14
16
SOM neighbour distances 4 Clusters
15
20
25
30
SOM neighbour distances 5 Clusters
5
10
15
20
25
30
SOM neighbour distances 6 Clusters
10
20
30
40
SOM neighbour distances 7 Clusters
10
20
30
40
50
60

8. Results – What each cluster is saying
about Clusters?
 First Cluster (“Promising”) –
 First Cluster has 69.6% of total customers with sales percent of 46% and 18% of
frequent visits.
 Second Cluster (“Explorers”)–
 Second Cluster has 16.9 of total customers with sales percent of 9.5% and 14% of
frequent visits.
 Third Cluster (“High Value”)–
 Third Cluster has 13.4 of total customers with sales percent of 44.5% and 68% of
frequent visits.
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Swagat Ranjan Behera

9. Conclusions – Insights to Businesses
 Cluster three (“High Value”) is best customer segment for immediate target.
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Swagat Ranjan Behera