marketing & media mix

When Media Effects Multiply: Evidence of Cross-Funnel Synergies

When Media Effects Multiply: Evidence of Cross-Funnel Synergies

  • ARF
  • JOURNAL OF ADVERTISING RESEARCH

Media planning frameworks often assume that channels operate independently or compete within the same funnel stage. This research challenges that assumption by demonstrating that the largest performance gains come from cross-funnel synergies, particularly between upper-funnel television, middle-funnel digital media and lower-funnel promotions. Using a large-scale CPG dataset and a novel estimation–optimization approach, the study shows that explicitly modeling these interactions can materially improve media allocation decisions while also significantly increasing incremental revenue.

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From Fragmented MMM to One-Demand Decision AI for Enterprise Growth

From Fragmented MMM to One-Demand Decision AI for Enterprise Growth

  • ARF
  • INSIGHTS STUDIOS

On January 22, we introduced a fundamentally different paradigm: One-Demand Decision AI powered by Large Causal Models (LCMs) that move enterprises from descriptive insights to prescriptive growth recommendations through counterfactual causal reasoning. Attendees gained a clear understanding of how one-demand causal AI transforms descriptive correlation into prescriptive causation, what it takes to implement unified decision platforms at scale, and why now is the moment to rethink the measurement stack from first principles.

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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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