Customer-Centric Sales Forecasting Model: RFM-ARIMA Approach

Abstract Background: Decision makers use the process of determining best course action by processing, analysing & interpreting data to gain insights, known as Business Intelligence. Some decision support systems sales figures predict future expansion, but few consider effect customer data. Objectives: The main objective this study is build a model that will give forecast based on fine-tuned numbers using some customer-centric features. Methods/Approach: We first RFM segment customers into distinct segments buying characteristics and then discard are irrelevant business. Then we ARIMA do forecasting for remainder Results: Using model, were able achieve better fitment prediction achieved accuracy when used after analysis. Conclusions: tried merge two different concepts cross-functional analysis decision-making. present RFM-ARIMA metric or approach fine-tune