E-commerce Customer Segmentation_Problem Statement - K means
Abstract:
A key challenge for e-commerce businesses is to analyze the trend in the market to increase their sales. The trend can be easily observed if the companies can group the customers; based on their activity on the ecommerce site. This grouping can be done by applying different criteria like previous orders, mostly searched brands and so on.
Problem Statement:
Given the e-commerce data, use k-means clustering algorithm to cluster customers with similar interest.
Dataset Information:
The data was collected from a well known e-commerce website over a period of time based on the customer’s search profile.
Variable Description:
Remaining 35 features (brands) contains the number of times customers have searched them
Scope:
● Analyzing the existing customer data and getting valuable insights about the purchase pattern ● Data pre-processing including missing value treatment ● Segmenting customer based on the optimum number of clusters (‘k’) with the help of silhouette score
Learning Outcome:
The students will get a better understanding of how the variables are linked to each other and will be able to apply cluster analysis to business problem such as customer segmentation.