Leveraging Panel Data in Retailing Research with Machine Learning Analytics: An Empirical Application

Michele Azzone (), Mattia Quartuccio () and Alessandro Iuffmann Ghezzi ()
Additional contact information
Michele Azzone: Politecnico Di Milano
Mattia Quartuccio: KPMG
Alessandro Iuffmann Ghezzi: Catholic University of the Sacred Heart

A chapter in Advances in National Brand and Private Label Marketing, 2025, pp 93-102 from Springer

Abstract: Abstract This study explores the application of machine learning (ML) techniques, specifically Long Short-Term Memory (LSTM) neural networks, to forecast retail sales using scanner data. We demonstrate that LSTM models significantly outperform other benchmark models in terms of forecasting accuracy. Our empirical analysis, based on Circana ILD weekly data from 9,268 hypermarkets in Italy, shows that LSTM models achieve average prediction errors as low as 10%, outperforming the meta-learning framework by reducing errors by 50%. The proposed LSTM framework simplifies the forecasting process by eliminating the need for complex base forecasters, making it more interpretable and actionable for retail practitioners. Key features influencing the model’s predictions include historical sales volumes, promotional prices, and sales volumes without promotions. The results highlight the potential of ML to bridge the gap between retail research and practice, offering a robust tool for accurate sales forecasting in the retail sector.

Keywords: Sales forecasting; Machine learning; Scanner data; Empirics-first research (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-031-97133-4_11

Ordering information: This item can be ordered from
http://www.springer.com/9783031971334

DOI: 10.1007/978-3-031-97133-4_11

Access Statistics for this chapter

More chapters in Springer Proceedings in Business and Economics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().