Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/318781 
Year of Publication: 
2022
Citation: 
[Journal:] Business Systems Research (BSR) [ISSN:] 1847-9375 [Volume:] 13 [Issue:] 1 [Year:] 2022 [Pages:] 35-45
Publisher: 
Sciendo, Warsaw
Abstract: 
Background: Decision makers use the process of determining the best course of action by processing, analysing & interpreting the data to gain insights, known as Business Intelligence. Some decision support systems use sales figures to predict future expansion, but few consider the effect of customer data. Objectives: The main objective of this study is to build a model that will give a forecast based on fine-tuned sales numbers using some customer-centric features. Methods/Approach: We first use the RFM model to segment the customers into distinct segments based on customer buying characteristics and then discard the segments that are irrelevant to the business. Then we use the ARIMA model to do the sales forecasting for the remainder of the data. Results: Using this model, we were able to achieve a better fitment of the data for the prediction model and achieved a better accuracy when used after RFM analysis. Conclusions: We tried to merge two different concepts to do a cross-functional analysis for better decision-making. We were able to present the RFM-ARIMA model as a better metric or approach to fine-tune the sales analysis.
Subjects: 
Business Intelligence
Customer Analysis
Sales Forecasting
Exploratory Analysis
Segmentation
Decision Support System
Recency
Frequency & Monetary Value (RFM)
Auto-Regressive Integrated Moving Averages (ARIMA)
Long short-term memory (LSTM)
JEL: 
C53
E30
E37
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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