Customer Classification by Past Purchase Data Analysis

Abstract

Customer categorization is an essential strategy for companies seeking to maximize their advertising spend. Businesses can boost client involvement and sales rates of conversion substantially by identifying specific customer segments, tailoring products or services to their preferences, minimizing the hassle of irrelevant advertisements, and increasing customer satisfaction, resulting in improved long-term interactions with clients. This paper presents a classification model that uses Keras and support vector machine stacked classification passed on to a meta-learner to predict the customer segment, and RFM analysis is performed to identify the customer segment. This focused strategy lowers marketing costs and boosts income, increasing the business's efficiency. Temporal mining helps us predict the next purchase of a customer using a time series model.