Affinity Analysis part 1: Introduction

Affinity analysis, also known as market basket analysis, is a powerful data mining technique that helps retailers understand which products are frequently purchased together by customers. By analyzing customer transaction data, retailers can gain insights into the relationships between products and use that information to make strategic decisions, such as optimizing product placement, pricing, and promotions.

Let's consider a hypothetical example of a supermarket chain that has recently implemented affinity analysis to improve their sales performance. After analyzing their transaction data, the supermarket chain found that customers who bought bread were also likely to purchase butter and eggs. Armed with this information, the supermarket chain decided to implement a cross-promotion strategy in which they placed bread, butter, and eggs in close proximity to each other, and offered a discount to customers who bought all three products together.

To evaluate the effectiveness of this strategy, the supermarket chain compared sales data from before and after the implementation of the cross-promotion. The results were impressive - the sales of bread, butter, and eggs increased by 15%, 10%, and 8%, respectively, compared to the same period the previous year. Additionally, the supermarket chain observed that customers who bought all three products together had a higher average basket size and spent more per transaction than customers who only bought one or two of the products.

This example illustrates the benefits of affinity analysis in the FMCG retail industry. By identifying product relationships, retailers can optimize their merchandising and promotional strategies to increase sales and customer satisfaction.