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Analysis of institutional authors

Ballestar, Maria TeresaAuthorSainz, JorgeAuthor

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A Primer on Out-of-the-Box AI Marketing Mix Models

Publicated to:Ieee Transactions On Engineering Management. 72 282-294 - 2025-01-01 72(), DOI: 10.1109/TEM.2024.3519172

Authors: Estevez, Macarena; Ballestar, Maria Teresa; Sainz, Jorge

Affiliations

Univ Bath, Inst Policy Res, Bath BA2 7AY, England - Author
Univ Rey Juan Carlos, Appl Econ Dept, Madrid 28032, Spain - Author

Abstract

Marketing mix modeling (MMM) optimizes budget allocation and determines the return on advertising investment through market response analysis. MMM are vital tools to help marketers define their marketing strategies according to the firm's business and marketing objectives while reducing uncertainty in the decision-making process. As AI and automated MMM out-of-the-box packages gain popularity among marketers, it has become evident there is a theoretical and empirical gap in the understanding of the benefits and inconveniences of these new methods over traditional econometric models. To shed light on these questions, two different models using the same database from a telecommunications firm have been developed and tested using a traditional econometric model and Robyn, an AI-powered open-sourced MMM package from meta marketing science. The research compares both methods' development processes and subsequent outputs from different perspectives: technical, business, and practical. It shows the advantages and shortcomings of each, providing insightful recommendations for academics and practitioners to navigate through the process of adoption of econometric and AI models for budget allocation decision-making. Econometric models are easy to explain and replicate, while AI complexity from the combination of several methods, their parametrization, and the random initialization of iterations during training, hinders its explainability.

Keywords

AdvertisingAnalytical modelsArtificial intelligenceBiological system modelingBusinessData analyticsData modelsEconometricsInvestmentMachine learningMarketing analyticsMarketing mix modelinMarketing mix modelingResource managementRobustness

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Ieee Transactions On Engineering Management due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2025, it was in position 73/304, thus managing to position itself as a Q1 (Primer Cuartil), in the category Business.

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-06-30:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 42 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

    It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

    • The work has been submitted to a journal whose editorial policy allows open Open Access publication.

    Leadership analysis of institutional authors

    This work has been carried out with international collaboration, specifically with researchers from: United Kingdom.

    There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author () and Last Author (Sainz González, Jorge).

    the author responsible for correspondence tasks has been .