A Novel Approach for Customer Segmentation and Product Recommendation to Boost Sales using Machine Learning

Personalized marketing is one of the most important marketing strategies which drives sales for a business by recommending the right products to the right customers. With the growing volume of consumers, it becomes necessary to divide them into suitable groups to take appropriate action for the correct set of customers. The prime objective of this study is to segment the customers into meaningful groups, suggest different marketing strategies for various segments, and recommend suitable products for each customer within the groups. This paper analyses historical sales and customer interaction data of the business to derive the right attributes. This study has used
$\mathbf{K}$
-Means clustering, an unsupervised machine learning algorithm, to obtain five separable clusters with a silhouette score of 0.57. For product recommendation, a voting ensemble-based recommendation system has been used to suggest up to the top 10 products for each customer. The ensemble method consists of four techniques: user-based collaborative filtering, LightFM algorithm, KNNWithMeans algorithm, and neural network. The paper aims to amalgamate customer segmentation and product recommendation problem statements for developing a single solution to help businesses design personalized marketing strategies.