Influence of Promotion and Pricing on Purchase Incidence, Demand, and Sales Using Machine Learning
DOI:
https://doi.org/10.4108/eetismla.5082Keywords:
Promotion, Pricing, FMCG, Machine Learning, DemandAbstract
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.
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References
Joanna Henzel, Marek Sikora. "Gradient Boosting Application in Forecasting of Performance Indicators Values for Measuring the Efficiency of Promotions in FMCG Retail." IEEE Conference, 2020.
Hongyan Dai, Haoyang Yu, Qing Xiao, Weihua Zhou. " A Clustering-based Sales Forecast Method for Big Promotion Days in O2O." IEEE International Conference, 2019.
Gao Jun-jun, Kang Ting. " A Joint Decision Model of Inventory Control and Promotion Optimization based on Demand Forecasting." IEEE International Conference, 2009.
Qianwen Zhuang, Xiaodong Zhang, Pei Wang, Bin Deng, Hua Pan. " A Neural Network Model for China B2C E-commerce Sales Forecast Based on Promotional Factors and Historical Data." IEEE International Conference, 2019.
K.H. van Donselaar, J. Peters, A. de Jong, R.A.C.M. Broekmeulen. " Analysis and forecasting of demand during promotions for perishable items." ScienceDirect, 2015.
Harsha Chamara Hewage; H. Niles Perera. " Comparing Statistical and Machine Learning Methods for Sales Forecasting During the Post-promotional Period." IEEE International Conference 2021.
Karan Wanchoo. Retail demand forecasting: a comparison between cnn and gradient boosting method of univariate time series. IEEE, 2019.
Samuel Rabi Dimitris Folinas. 1. estimating benefits of demand sensing for consumer goods organizations. Springer, pages 245–261, 2012.
Animikh Aich Chetana Hegde Akshay Krishna, Akhilesh V. Sales-forecasting of retail stores using machine learning techniques. 18 3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions (CSITSS), 2018.
Chui-M. Brown B. Bughin J. Dobbs R. Roxburgh C. Manyika, J. and A. Hung Byers. Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute, pages pp.64–75, 2011.
Kakalejcík-L. Bucko, J. and M. Ferencová. Online shopping: Factors that affect consumer purchasing behavior. ˇ Cogent Business Management, pages 5(1), p.1535751, 2018.
Yi Xiang Xingyu Lu Chunling Li Shasha Mo, Luqiao Zhang. Research on commodity sales forecasting based on combination model. 22 IEEE 2nd International Conference on Data Science and Computer Application (ICDSCA), 2022.
Kao L.-J Lu C.-J. A clustering-based sales forecasting scheme by using an extreme learning machine and ensembling linkage methods with applications to computer server. ScienceDirect, pages 231–238, 2016
Langrock R. Pesch R. Senge R Ulrich M., Jahnke H. Classification-based model selection in retail demand forecasting. ScienceDirect, pages 209–223, 2022.
Fatima-Zohra Benhamida, Ouahiba Kaddouri, Tahar Ouhrouche, M. Benaichouche, D. Casado-Mansilla, D. López-de-Ipiña. " Stockbuy: A new demand forecasting tool for inventory control."5th International Conference on Smart and Sustainable Technologies (SpliTech), 2020.
Sahay K.B Singh A. Short-term demand forecasting by using ann algorithms. IEEE, pages 1–4, 2018.
Uysal M. Akyokus S. Uysal M.O. Atak Bulbul B. Ekmis M.A Kilimci Z.H., Akyuz A.O. An improved demand forecasting model using deep learning approach and proposed decision integration strategy for supply chain. Complexity, pages Article ID–9067367, 2019
Janisha A Kamil R. Demand sensing using deep learning. IRJET, pages 1–5, 2018
Zhang N Li Z. Short-term demand forecast of e-commerce platform based on convlstm network. Hindawi, pages Article ID 5227829–10, 2022
Houria Laaroussi, Fatima Guerouate, and Mohamed sbihi. Deep learning framework for forecasting tourism demand. ICTMOD, 12:666875, 2020.
Haotian Zhu. A deep learning-based hybrid model for sales prediction of e-commerce with sentiment analysis. 21 2nd International Conference on Computing and Data Science (CDS), 2021.
M. Kasahara H. Goto, M. Tanaka. Short term replenishment method for short lifecycle goods. 08 4th IEEE International Conference on Management of Innovation and Technology, 2008.
Rony Mitra Dyutimoy Das Sushmita Narayana Manoj K. Tiwari Priyam Saha, Nitesh Gudheniya. Demand forecasting of a multinational retail company using deep learning frameworks. ScienceDirect, pages 395–399, 2022.
Hongjiao Fu Mingming Wang Xun Liang Ming Gao, Wei Xu. A novel forecasting method for large-scale sales prediction using extreme learning machine. 14 Seventh International Joint Conference on Computational Sciences and Optimization, 2014
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Copyright (c) 2024 Rahul D Shanbhogue, Anwesh Reddy Paduri, Narayana Darapaneni
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