Martínez, AndresSalafranca, AlfonsoSipols, Ana E.Simón de Blas, ClaraVan Hengel, Daniel2024-01-112024-01-112022Martínez, A., Salafranca, A., Sipols, A. E., de Blas, C. S., & van Hengel, D. (2022). Distributed lags using elastic-net regularization for market response models: focus on predictive and explanatory capacity. Journal of Marketing Analytics, 1-19.2050-3318https://hdl.handle.net/10115/28339Over the course of several decades, extensive research has been dedicated to Market Response models, often lacking validation in purely predictive tasks and frequently overlooking the adherence of methods to underlying assumptions and conditions, such as the capacity to delineate widely accepted effects of advertising actions. This study introduces an improved method for market response models that aligns with these underlying assumptions. The proposed method is grounded in Distributed Lag Models and distinguishes itself by incorporating regularization in its estimation, employing a cross-validation framework, and implementing hold-out testing. Additionally, it presents an empirical approach to extracting the effects of the model. This methodology facilitates the construction of models in an exploratory and straightforward manner, thereby unlocking the potential to uncover underlying effects and proving suitable for extensive samples and numerous variables. To illustrate its practical application, a real-world data example is provided, accompanied by an unprecedented set of empirical explainability assessments and a high level of predictive capability under circumstances similar to those encountered in corporate decision-making processes.For many decades, considerable research has been conducted on Market Response models. Mostly without any attempts to validate the results in strictly predictive tasks and often ignoring if the methods comply with the underlying assumptions and conditions, like the method’s ability to outline the broadly accepted effects of advertising actions. This work presents an enhanced method for market response models consistent with the underlying assumptions of such. Our method is based on Distributed Lag Models with the novelty of introducing regularization in its estimation, a cross-validation framework, and hold-out testing, next to present an empirical manner of extracting its effects. This approach allows the construction of models in an exploratory and simple manner, unlocking the possibility of extracting the underlying effects and being suitable for large samples and many variables. Last, we conduct a practical example using real-world data, accompanied by an unprecedented set of empirical explainability assessments next to a high level of predictive capability in similar circumstances to how it would be used for decision-making in a corporate setup.engDistributed Lag ModelAdvertisingLagged effectsMachine learningPredictioninfo:eu-repo/semantics/article10.1057/s41270-022-00204-4info:eu-repo/semantics/openAccess