In today’s competitive world, it is crucial to understand customer behavior and categorize customers based on their demography and buying behavior. This is a critical aspect of customer segmentation that allows marketers to better tailor their marketing efforts to various audience subsets in terms of promotional, marketing and product development strategies.
This project demonstrates the concept of segmentation of a customer data set from an e-commerce site using k-means clustering in python. The data set contains the shopping-sales data of customers and their spend on an e-commerce site. We will use the k-means clustering algorithm to derive the optimum number of clusters and understand the underlying customer segments based on the data provided.
Clustering the data based on the purchase behaviour of customers, revealed 4 separable clusters to analyze. Cluster analysis helped to identify the customers in each cluster based on their customer IDs. This is useful to understand the different customers that build the customer base in each cluster. This further helped to divide the cluster and attach meaning to it. Thus, the question is answered by identifying four customer segments based on their purchase behaviour and demography.
In this section, we ran through a basic application of K-means clustering based on the purchasing behaviors of historical customers. This type of analysis can be run for virtually any company with the requisite data. Ecommerce companies, SaaS companies, service-based companies, you name it.
Detection of clusters can help the business to develop a specific strategy for each cluster base. Clustering can also be used to understand the purchase behaviour of customers by keeping track of customers over months and detecting the number of customers moving from one cluster to the other. This helps the business to organize strategies better to increase revenue at different shops. Customer Segmentation insights obtained can be further utilized by the business to better focus their marketing efforts on the right customers, Eg. Discounts and offers related to a particular shop can be sent to only those customers who usually purchase at that particular shop without bothering the customers of other shops. Thus, targeting the right customers for the right deals can help to cut down the marketing costs, generate more revenue and increase customer satisfaction.