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.
