Research Article
Influence of Promotion and Pricing on Purchase Incidence, Demand, and Sales Using Machine Learning
@ARTICLE{10.4108/eetismla.5082, author={Rahul D Shanbhogue and Anwesh Reddy Paduri and Narayana Darapaneni}, title={Influence of Promotion and Pricing on Purchase Incidence, Demand, and Sales Using Machine Learning}, journal={EAI Endorsed Transactions on Intelligent Systems and Machine Learning}, volume={1}, number={1}, publisher={EAI}, journal_a={ISMLA}, year={2024}, month={4}, keywords={Promotion, Pricing, FMCG, Machine Learning, Demand}, doi={10.4108/eetismla.5082} }
- Rahul D Shanbhogue
Anwesh Reddy Paduri
Narayana Darapaneni
Year: 2024
Influence of Promotion and Pricing on Purchase Incidence, Demand, and Sales Using Machine Learning
ISMLA
EAI
DOI: 10.4108/eetismla.5082
Abstract
The consumer goods industry is a dynamic and fast-paced sector that faces significant challenges in meeting the consumer’s ever-evolving demands and preferences. Today’s retail businesses focus on optimizing their supply and retail execution to maintain a competitive edge in the market and remain profitable. The most impactful method is to offer promotional events that stimulate large-scale purchases and attract new customers. The patterns of normal sales days, promotion days, and non-promotion days are different and it is vital to capture the influence of promotions on demand and sales. Thus, it is vital to understand the effects of promotion and plan them. This paper aims to understand the influence of promotion and pricing strategies for FMCG retail businesses to maximize demand for each brand. Explore the use of Machine Learning (ML) and Deep Learning models such as Clustering and Neural Networks to identify and understand the various demand patterns to analyse the influence of promotion and pricing on demand, and enable businesses to respond more quickly to changes in the market by enabling them to make better-informed decisions that can mitigate risks associated with the impact of disruptions and to ensure the continuity of the business.
Copyright © 2024 R. D. Shanbhogue et al., licensed to EAI. This is an open-access article distributed under the terms of the CC BYNC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.