marketing & media mix

An Introduction to Robyn’s Open-Source Approach to Media Mix Modeling

An Introduction to Robyn’s Open-Source Approach to Media Mix Modeling

  • MSI

As privacy-centric changes reshape the digital advertising landscape, deterministic attribution and measurement of advertising-related user behavior are increasingly constrained. In response, there has been a resurgence in the use of traditional probabilistic measurement techniques, such as media and marketing mix modeling (m/MMM), particularly among digital-first advertisers. To address the gap for small and midsize businesses, marketing data scientists at Meta have developed the open-source computational package Robyn, designed to facilitate the adoption of m/MMM for digital advertising measurement.

Robyn is a widely adopted and actively maintained open-source tool that continually evolves. This article explores the computational components and design choices that underpin Robyn, emphasizing how it “packages up” m/MMM to promote organizational acceptance and mitigate common biases. The solutions described are not definitive but outline the pathways that the Robyn community has embarked on.

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Improve Marketing Mix Model (MMM) Accuracy by Identifying these Effects

Improve Marketing Mix Model (MMM) Accuracy by Identifying these Effects

  • MSI

This study explores the identification of nonlinear and time-varying effects in marketing mix models (MMM). It highlights the challenges of conflation in model selection and proposes a framework for simulating and estimating these effects using Gaussian processes. The study emphasizes the importance of accurately identifying the underlying response to optimize marketing spending.

The research provides insights into the complexities of marketing effectiveness and offers practical solutions for improving model accuracy. By addressing the issue of conflation, the study aims to enhance the decision-making process in marketing strategies.

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Here Are Some Unintended Consequences of Privacy Regulations

Here Are Some Unintended Consequences of Privacy Regulations

  • MSI

The intention behind recent privacy regulations is to protect consumers from unauthorized use of their data. However, this Marketing Research Institute (MSI) working paper finds unintended consequences that are not good for the consumer or the marketplace. Researchers found such regulations reduce satisfaction with search results and increase search costs. The personalization in products and services is thus degraded, as many smaller and midsize firms are no longer able to provide the level of efficiency and personalization they once could. Larger firms, however, benefit from increased search activity which leads to increased purchase activity. As a result, such regulation leads to unintended market concentration.

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