Site icon Sagar Mandal

How to segment based on customer behavior data and AI/ML ?

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As not all people are the same, segmentation helps divide your customers into various groups and then be able to drive specific marketing messages. Every business defines marketing segments as a set of business rules that are usually static and do not accommodate for changing customer behaviour. But what if we could?

Meet Happy Credit Cards

For our business scenario today, lets assume a credit card enterprise named Happy Credit Cards. Competing with the likes of American Express, HCC aims to bring exceptional experiences for its customers. The business is trying to understand its various customer segments that would be core to the business and be able to drive better decision making.

To find these core customer segments we have credit card usage data from the last couple of months sourced from various systems.

How does AI/ML help with segmentation?

In the AI/ML world ‘clustering’ is the process of identifying relationships between data attributes and be able to group them together. For marketing use cases, it identifies relationship between customer attributes coming from your CRM or CDP and then groups them together as ‘segments’ or ‘clusters’.

How many segments is too many segments?

The challenge with customer segments is to find those unique set of attributes that are common amongst a group of customers. Often these attributes might be exceptions or outliers that marketers cannot identify and end up placing them in the generic bucket. For a marketer, higher number of segments always seems attractive to create more opportunities to message these customers. But over time these many segments start overlapping and the same customer gets spammed & bombarded with multiple marketing messages.

To find the optimal number of segments we explored machine learning algorithms for answers based on the credit card usage data.

Elbow Method

This is the most optimal and popular method for finding the optimal number of clusters when classifying data into groups. The idea is to group our data into a range of clusters ( say between 1 to 15) and calculate the difference between the data and the cluster center. As the variation changes rapidly and then slows down leading to an elbow formation in the curve. That elbow point is considered as the number of clusters we can used in our algorithm.

For our credit card data we find that the number of clusters is identified as 7 using Elbow Method of clustering

I did try other means of clustering as well but research had shown that the Elbow Method has been the most optimal of them all.

How are the segments build ?

Before we deep dive into the outcomes, lets look at some of the algorithms available and commonly used for similar use cases. The objective is to explain the grouping/clustering methods to enable the right decision making.

K Means Clustering

The main objective is to find k centroids and assign each point to the set based on the nearest centroid such that the intra-cluster distance is minimized. The optimal centroids are found by taking the sum of squares of distances of points from its centroid.

Hence data attributes that are closely related would be centred around a common centroid !!

DBSCAN

DBSCAN or Density Based Spatial Clustering of Applications including Noise is a clustering algorithm with the main objective to find clusters around high density of data. Isolated data points are hence considered as noise.

DBSCAN doesn’t require number of clusters to be defined (like kmeans) , but requires to be provided size of data neighbourhoods and the minimum number of observations that constitutes a cluster.

Animation Source – https://dashee87.github.io/data%20science/general/Clustering-with-Scikit-with-GIFs/

Gaussian Mixture Model

Gaussian Mixture Models (GMMs) assume that there are a certain number of Gaussian distributions, and each of these distributions represent a cluster. Hence, a Gaussian Mixture Model tends to group the data points belonging to a single distribution together.

A Gaussian distribution is defined as  a bell-shaped curve, and it is assumed that during any measurement values will follow a normal distribution with an equal number of measurements above and below the mean value.

What were the segments created?

As a starting point, I decided to continue with K Means Clustering method to find the 8 segments that we found using Elbow method earlier. There is use case in the future to create segments using multiple cluster algorithms and measure their performance towards business goals.

Applying the clustering algorithm on the data resulted in the below graph where you can see the variance of each data points or columns in each cluster. The variations are factored into defining the business rules for each cluster. Feel free to take a guess at what the segment rules would be like based on the graph below

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data:image/png;base64,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

Cluster 1 Customers use credit card as a loan : highest balance ($5000) and cash advance (~$5000), low purchase frequency, high cash advance frequency (0.5), high cash advance transactions (16) and low percentage of full payment (3%)

Cluster 2 Customers have high purchase frequency (0.9) who use payment installment facility the most (highest installment frequency 0.83), pay in full whenever possible (second highest Percentage of full payment = 25%) and do not use costly cash advance service

Cluster 3 Customers are active buyers who pay in full. Cluster with highest purchase frequency (0.93), second highest purchase transactions and one-off purchases, highest % of payment in full (29%)

Cluster 4 Customers have high credit limit $12K and highest percentage of full payment, target for increase credit limit and increase spending habits

Cluster 5 Customers have low tenure (7 years) and low balance

Cluster 6 Customers pay least amount of interest charges and careful with their money, Cluster with lowest balance ($104) and second lowest cash advance ($303), Percentage of full payment = 24%

Cluster 7 Customers who represent an exception or edge case for the business have record-high minimum payments level of nearly $ 28K

Cluster 8 Customers use the less their card (lowest purchase frequency) and with the lowest purchase amount

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