Forecasting a FastMoving Consumer Goods (FMCG) Company's Customer Repurchase Behavior via Classification Machine Learning Models

With numerous businesses offering clients equivalent products, the FMCG (Fast Moving Consumer Goods) industry is very competitive. Retaining client loyalty and encouraging them to return to make product purchases is a big concern for businesses in this sector. One of the main issues this bleak business needs to overcome is customer retention. Failure to repurchase by customers is a sign that they do not trust the brand, which will increase attrition rates and have an adverse effect on the company's revenue. These issues were addressed by attempting to predict the customer repurchase rate and approaching the target segments in accordance with that prediction, but this was done entirely from the perspective of the consumer and not from the retailer, and it ignores other factors like location, the salespeople they work with, the wholesaler they are affiliated with, and the customer programme they have chosen. The retailer's repurchase pattern must be predicted using a more accurate and effective model that considers all the variables. Retailers play a significant role in the supply chain for FMCG businesses. Different models like KNN, Naïve Bayes and Logistic Regression was explored to find the best fit. By keeping them, the business can forge enduring connections that are crucial for preserving stabilityand dependability in the distribution network and having the resources necessary to serve its clients.