Case Study: Customer Segmentation


Utilising clustering to segment ecommerce customer data

for greater insight

Understanding your customers is a beautiful thing, and allows for a tailored experience.

A major player in the ecommerce travel arena was looking for ways to increase revenue per customer. Having tried extensive data analysis of their own, there was a desire to try an AI driven approach.


Remi AI used a combination of the following methods to propose a solution using an intelligent pricing approach:

  1. Basic Data Analysis for behavioural trends

  2. Clustering over User Types and Products

  3. Studying Demand Curves

  4. Running Price Simulations


Using the clustering output Remi AI was able to group customers into 3 key buckets and concluded that each cluster has different emotions at play during the purchase experience. Digging into the customer drivers indicated that business customers were afraid of flight delays and so were willing to pay more, whereas both holiday customer clusters cared less about the flights and more about the experience. An intelligent pricing approach would involve pricing packages based on customer’s willingness, to pay, seasonal trends, and product demand.

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