Joint In-Season and Out-of-Season Promotion Demand Forecasting in a Retail Environment

Wolters, Jannik; Huchzermeier, Arnd

doi:10.1016/j.jretai.2021.01.003

Cited by 27 publications

(8 citation statements)

References 29 publications

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“…For these reasons, artificial intelligence-based methods such as ML are now being used for demand forecasting instead of classical methods to make more accurate predictions. Scientific studies on this subject are also continuing (Ali & Gürlek, 2020;Wolters & Huchzermeier, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

“…When looking at studies where promotional activities-especially advertisements-are used in forecasting demand, it is seen that in general, traditional forecasting methods have been used (Divakar et al, 2005;Ma et al, 2016;Ramanathan & Muyldermans, 2010Van Donselaar et al, 2016;Van Heerde et al, 2002). However, a few studies using ML-based demand prediction methods have been used involving promotions as a variable (Ali et al, 2009;Di Pillo et al, 2013, Ferreira et al, 2015Trapero et al, 2015;Makridakis et al, 2018;Abolghasemi et al, 2020;Aguilar-Palacios et al, 2019;Ali & Gürlek, 2020;Wolters & Huchzermeier, 2021). Other research has been conducted that aims to forecast demand based on advertising.…”

Section: Literature Reviewmentioning

confidence: 99%

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The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods

et al. 2022

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In recent years, machine learning models based on big data have been introduced into marketing in order to transform customer data into meaningful insights and to make strategic decisions by making more accurate predictions. Although there is a large amount of literature on demand forecasting, there is a lack of research about how marketing strategies such as advertising and other promotional activities affect demand. Therefore, an accurate demand-forecasting model can make significant academic and practical contributions for business sustainability. The purpose of this article is to evaluate machine learning methods to provide accuracy in forecasting demand based on advertising expenses. The study focuses on a prediction mechanism based on several Machine Learning techniques-Support Vector Regression (SVR), Random Forest Regression (RFR) and Decision Tree Regressor (DTR) and deep learning techniques-Artificial Neural Network (ANN), Long Short Term Memory B Yigit Kazancoglu

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods

et al. 2022

View full text Add to dashboard Cite

show abstract

“…time series models, have become useless in this area. Wolters and Huchzermeier (2021) investigated promotional forecasting for strongly seasonal products by first extracting seasonal patterns using harmonic regression, then fitting multiplicative promotional uplifts in a standard log–log model, including post-promotional effects and promotion

seasonality interactions. They counteracted the effects of overfitting because of the large parameter space through regularisation by ridge regression.…”

Section: Methodological Developmentsmentioning

confidence: 99%

Post-script—Retail forecasting: Research and practice

Fildes

Kolassa

2022

International Journal of Forecasting

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“…The authors' forecasting model works well for nonseasonal products, but faces issues when forecasting products with seasonal variation in demand. Wolters and Huchzermeier (2021) develop a novel forecasting model for such frequently promoted, seasonal products.…”

Section: High-low Pricingmentioning

confidence: 99%

Case Article—The RealPro Customer Benefits Program: Rekindling Shopper Loyalty Through a Subscription Service

Huchzermeier

Wolters

Uphues

2023

INFORMS Transactions on Education

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In this case study, students combine data-based insights with strategic considerations to make fundamental business decisions at the German grocery retail chain Real. In response to dwindling numbers of customers and reduced revenues, Real developed the RealPro customer benefits program to achieve a quick turnaround. For a fixed annual fee, RealPro members receive substantial and permanent discounts of 20% on nonpromoted items from a broad range of food categories. Students employ data analytics methods to extract insights from the provided data set, which contains point-of-sale information from the actual market test of RealPro. Based on these insights, decisions concerning the rollout and design of the RealPro program must be made. We provide data analysis solutions in both Excel and R to analyze 75 thousand customer transactions. In the case extension, students can apply the difference-in-differences method and two covariate balancing algorithms for in-depth statistical analyses. For this purpose, we provide an additional unbalanced data set with 83 thousand transactions, on which the students can test and analyze propensity score matching and entropy balancing models.

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Joint In-Season and Out-of-Season Promotion Demand Forecasting in a Retail Environment

Cited by 27 publications

References 29 publications

The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods

The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods

Post-script—Retail forecasting: Research and practice

Case Article—The RealPro Customer Benefits Program: Rekindling Shopper Loyalty Through a Subscription Service

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