CSHURI - Modified HURI algorithm for Customer Segmentation and Transaction Profitability

TL;DR

This paper introduces CSHURI, a modified association rule mining algorithm that incorporates utility and profit factors to improve customer segmentation and enhance targeted marketing strategies.

Contribution

The paper presents a novel modification of the HURI algorithm, enabling more profitable customer segmentation by considering utility and profit in association rule mining.

Findings

Identifies high-profit rare item purchasers

Classifies customers based on profitability and needs

Enhances targeted marketing strategies

Abstract

Association rule mining (ARM) is the process of generating rules based on the correlation between the set of items that the customers purchase.Of late, data mining researchers have improved upon the quality of association rule mining for business development by incorporating factors like value (utility), quantity of items sold (weight) and profit. The rules mined without considering utility values (profit margin) will lead to a probable loss of profitable rules. The advantage of wealth of the customers' needs information and rules aids the retailer in designing his store layout[9]. An algorithm CSHURI, Customer Segmentation using HURI, is proposed, a modified version of HURI [6], finds customers who purchase high profitable rare items and accordingly classify the customers based on some criteria; for example, a retail business may need to identify valuable customers who are major contributors to a company's overall profit. For a potential customer arriving in the store, which customer group one should belong to according to customer needs, what are the preferred functional features or products that the customer focuses on and what kind of offers will satisfy the customer, etc., finds the key in targeting customers to improve sales [9], which forms the base for customer utility mining.