Production
https://prod.org.br/article/doi/10.14488/1980-5411.20250127
Production
Research Article

Promotional strategies and channel cannibalization in multichannel retail

Luiz Gustavo Cardoso Rosa; Bruna Rigon de Oliveira; Enzo Morosini Frazzon

http://dx.doi.org/10.14488/1980-5411.20250127 Production, vol.36, e20250127, 2026
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Abstract

Paper aims: This study introduces a data-driven approach to evaluate how promotional strategies redistribute demand across channels and are associated with cannibalization in multichannel retail.

Originality: Transitioning from descriptive analysis to a predictive machine learning approach, this research addresses a literature gap by empirically evaluating mitigation strategies, such as coordinated promotions and loyalty incentives, to provide a basis for strategic promotional planning.

Research method: Demand prediction models, including gradient boosting, linear, and neural network models, were benchmarked using historical sales data from a regional Brazilian supermarket. Model performance was evaluated using Root Mean Squared Error (RMSE) and R-squared (R2). The validated model was used to run counterfactual simulations of mitigation scenarios.

Main findings: eXtreme Gradient Boosting (XGBoost) demonstrated robust physical-channel forecasting, identifying cross-channel displacement associations of 11.23% (physical-to-online) and 19.34% (online-to-physical). Simulations suggest that while isolated promotions exacerbate channel conflict, coordinated promotions attenuate cannibalization by 30.04%. Furthermore, integrating loyalty incentives yielded the largest demand uplift (+3.5%), transforming conflict into synergy.

Implications for theory and practice: Theoretically, the study validated XGBoost paired with SHAP-based interpretation as a simulation framework for testing channel-synergy propositions. Practically, it offers a replicable, data-driven approach to mitigate demand displacement and foster channel synergy through integrated promotional planning.

Keywords

Demand forecasting, Promotional cannibalization, Machine learning, XGBoost, Consumer behavior

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Submitted date:
12/19/2025

Accepted date:
08/19/2026

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