Suggested Marketing Strategy Using Apriori and FP-Growth Algorithms in retail sales in Egypt

Abstract

Due to the increase of retail sales in Egypt and all over the world, came the importance for the managers of supermarkets to develop marketing strategy to maximize their profits, by getting rid of inactive products. Product bundling is one of the most important marketing strategies used to get rid of stock by making integrated bundles of inactive products and demanded products with discount prices. We can do that through our recommendation system and also increase customers' faith by keeping up with their purchase habits changes in low prices. In this study, first association rules are applied to find the best-integrated bundles with optimal suggested bundle size according to customer habits. Second testing these resulting product bundles to eliminate bundles didn't contain Products aim to get rid of them. Finally the given suggested bundles’ elements replaced with stagnant products that are the same kind of product in the bundle but with another trade. During that study algorithms (Apriori and FP-growth) were studied. Although the two algorithms give strong association rules, and the results were so near. But FP-Growth algorithm was more efficiency as Apriori algorithm caused problems with minimum support parameter as, in a small system transaction, big minimum support did not work using it. Also, it makes the memory PC faces memory hangs when minimum support was very low. On the other hand, all of this didn't appear with FP-Growth algorithm, and it was faster in dealing data.