Customer Segmentation Analysis

This tutorial will guide you through building a clustering model using Catalyst QuickML. Clustering models help segment records into clusters where each one is characteristically different from another.

Using customer data such as demographics, transaction behavior, and transaction data as a training dataset, clusters can be formed out of similar customers so the business can advertise to specific customer segments. Targeting the right audience with the right strategy provides value to the customer,improving customer experience. We ’ ll provide you with a sample dataset that contains data about credit card customers . With this dataset , we’ll build a clustering model that can segment these customers into clusters with different behaviors.

In the credit card service industry, customers can be offered a range of credit card options with rewards, benefits, and introductory and purchase offers , but targeting the right customers with relevant offers can be difficult . It involves identifying the spending behavior of customers and segmenting them into meaningful groups so that marketing teams can focus on building the right campaigns for the right segments .

To learn more about clustering , check out our documentation below:

• Clustering
• Algorithms
• Evaluation metrics

Now, let’s have a quick overview of what you’ll learn.

Overview

1. Preprocess the dataset

The customers’ credit card spending dataset has customer IDs, current balances, balance update frequencies, total purchases, one-off purchases, installment purchases, cash advance amounts, purchase frequencies, one-off purchase frequencies, installment purchase frequencies, cash advance frequencies, cash advance transaction counts, purchase transaction counts, credit limits, total payments, minimum payments, percentages of full payments made, and customer tenures. Together, these features describe how each customer spends, repays, and uses credit over time.

All the data is numeric except customer ID, so we’ll need a few preprocessing steps like dropping customer ID and filling in missing values.

2. Build the clustering pipeline

In the clustering pipeline, we’ll need to select a suitable algorithm for our dataset. For our customer segment analysis, we’ll use the K-means algorithm. K-means clustering uses proximity to determine the similarity between customers. This will give us our clustering model, along with downloadable information about the clusters created for the training dataset.

3. Deploy the model via an endpoint

After training, the model can be deployed directly from the Catalyst console by generating an endpoint URL. This endpoint allows external applications to send customer information and get the cluster ID that the customer belongs to.