<abstract xmlns="http://www.w3.org/1999/xhtml">

<bold>Background:</bold> Decision makers use the process of determining the best course of action by processing, analysing &amp; 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.
<bold>Objectives:</bold> 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.
<bold>Methods/Approach:</bold> 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.
<bold>Results:</bold> 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.
<bold>Conclusions:</bold> 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.
</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.

Customer-Centric Sales Forecasting Model: RFM-ARIMA Approach

Business Systems Research Journal

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Customer-Centric Sales Forecasting Model: RFM-ARIMA Approach"}

Background: Decision makers use the process of determining the best course of action by processing, analysing & interpreting the data to gain insights, known...

Customer-Centric Sales Forecasting Model: RFM-ARIMA Approach

Data publikacji: 15 paź 2022

Zakres stron: 35 - 45

Otrzymano: 29 gru 2021

Przyjęty: 08 maj 2022

DOI: https://doi.org/10.2478/bsrj-2022-0003

Słowa kluczowe
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)

© 2022 Sanket Tanaji Londhe et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Customer-Centric Sales Forecasting Model: RFM-ARIMA Approach

Data publikacji: 15 paź 2022

Zakres stron: 35 - 45

Otrzymano: 29 gru 2021

Przyjęty: 08 maj 2022

DOI: https://doi.org/10.2478/bsrj-2022-0003

Słowa kluczoweBusiness 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)

© 2022 Sanket Tanaji Londhe et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
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)