Design of Machine Learning Framework for Products Placement Strategy in Grocery Store

The well-known and most used support-confidence framework for Association rule mining has some drawbacks when employ to generate strong rules, this weakness has led to its poor predictive performances. This framework predict customers buying behavior based on the assumption of the confidence value, which limits its competent at making good business decision. This work presents a better Association Rule Mining conceptualized framework for mining previous customers transactions dataset of grocery store for the optimal prediction of products placement on the shelves, physical shelf arrangement and identification of products that needs promotion. Sampled transaction records were used to demonstrate the proposed framework. The proposed framework leverage on the ability of lift metric at improving the predictive performance of Association Rule Mining. The Lift discloses how much better an association rule is at predicting products to be placed together on the shelve rather than assuming. The proposed conceptualized framework will assist retailers and grocery stores owners to easily unlock the latent knowledge or patterns in their large day to day stored transaction dataset to make important business decision that will make them competitive and maximized their profit margin

Building an association rules framework for target marketing

The discovery of association rules is a popular approach to detect cross-category purchase correlations hidden in large amounts of transaction data and extensive retail assortments. Traditionally, such item or category associations are studied on an ‘average’ view of the market and do not reflect heterogeneity across customers. With the advent of loyalty programs, however, tracking each program member’s transactions has become facilitated, enabling retailers to customize their direct marketing efforts more effectively by utilizing cross-category purchase dependencies at a more disaggregate level. In this paper, we present the building blocks of an analytical framework that allows retailers to derive customer segment-specific associations among categories for subsequent target marketing. The proposed procedure starts with a segmentation of customers based on their transaction histories using a constrained version of K-centroids clustering. In a second step, associations are generated separately for each segment. Finally, methods for grouping and sorting the identified associations are provided. The approach is demonstrated with data from a grocery retailing loyalty program