Retail Promotion Forecasting: A Comparison of Modern Approaches

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Abstract

Promotions at retailers are an effective marketing instrument, driving customers to stores, but their demand is particularly challenging to forecast due to limited historical data. Previous studies have proposed and evaluated different promotion forecasting methods at product level, such as linear regression methods and random trees. However, there is a lack of unified overview of the performance of the different methods due to differences in modeling choices and evaluation conditions across the literature. This paper adds to the methods the class of emerging techniques, based on ensembles of decision trees, and provides a comprehensive comparison of different methods on data from a Danish discount grocery chain for forecasting chain-level daily product demand during promotions with a four-week horizon. The evaluation shows that ensembles of decision trees are more accurate than methods such as penalized linear regression and regression trees, and that the ensembles of decision trees benefit from pooling and feature engineering.

Original languageEnglish
Title of host publicationAdvances in Production Management Systems. Towards Smart Production Management Systems : IFIP WG 5.7 International Conference, APMS 2019, Austin, TX, USA, September 1–5, 2019, Proceedings, Part II
EditorsFarhad Ameri, Kathryn E. Stecke, Gregor von Cieminski, Dimitris Kiritsis
Number of pages8
Volume2
PublisherSpringer
Publication date2019
Pages575-582
ISBN (Print)978-3-030-29995-8
ISBN (Electronic)978-3-030-29996-5
DOIs
Publication statusPublished - 2019
EventIFIP WG 5.7 International Conference, APMS 2019 - Austin, United States
Duration: 1 Sep 20195 Sep 2019

Conference

ConferenceIFIP WG 5.7 International Conference, APMS 2019
Country/TerritoryUnited States
CityAustin
Period01/09/201905/09/2019
SeriesIFIP AICT - Advances in Information and Communication technology
Volume567
ISSN1868-4238

Keywords

  • Grocery retail
  • Machine learning
  • Promotion forecasting

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