Research & Data Quality

• Research & Data Quality
• Article

The 2025 Guide on Using AI for Advertising Research

• ARF ORIGINAL RESEARCH

This update to the ARF’s first comprehensive handbook, released last year, provides an exploration into the burgeoning technology’s transformative role in marketing and advertising research. It covers the integration of AI into marketing strategies, ethical considerations, future trends and practical case studies. This handbook is an essential guide for advertising research professionals looking to leverage any of the latest AI platforms while ensuring ethical and impactful outcomes.

• Research & Data Quality
• Article

The State of Privacy-First Marketing & Redefining Performance

• ARF
• INSIGHTS STUDIOS

How we use data today looks different given evolving regulations, platform changes, consumers expectations of data transparency, and more. Our Insights Studio on January 30 explored the latest developments in data privacy and how they are impacting marketing strategies. Panelists unveiled strategies to establish consumer trust and effectively market, while aligning with privacy regulations.

• Research & Data Quality
• Article

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.

• Research & Data Quality
• Article

Overcoming the Limitations of Online A/B Testing

• MSI

This study explores how divergent delivery in A-B testing affects the accuracy of online advertising experiments. It highlights the role of algorithmic targeting and user heterogeneity in confounding test results, offering guidance for marketers to improve their experimental designs.

• Research & Data Quality
• Article

In the Age of the ‘Modeled’ Sample: Can We Still Trust Polling?

• Mark Blumenthal – Head of Election Polling, SurveyMonkey

The 2016 U.S. presidential election served a big blow to the polling community, as pre-election predictions proved to be dramatically wrong. But is there really a crisis in pre-election polling? Mark Blumenthal, Head of Election Polling at SurveyMonkey, summarizes the AAPOR’s post-election investigation that demonstrated the accuracy of national polls, but weaknesses in state-level polls. Part of the challenge going forward – correcting for sample bias in an age of lower survey response rates.

• Research & Data Quality
• Article

Why Re-Testing “Truths” Is Good Practice

• Marla B. Royne, Great Oaks Foundation professor of marketing and chair at the U. of Memphis Fogelman College of Business & Economics Dept of Market & Supply Chain Mgt.
• JOURNAL OF ADVERTISING RESEARCH

Should the marketing community support repeating a study, when so much value is placed on new research? The answer may be a resounding “Yes.” Learn why repeating a study matters, especially when marketers seek transparency and data-based support for recommendations — and how advertising research might need to change.

• Research & Data Quality
• Article

The ARF Supports ANA’s Raising 2020 Census Concerns

Troubling. Bias. Distortion. Why would the ANA use these words in a letter to the U.S. Government expressing concern about the impact of the 2020 Census on marketing quality? Learn about what led them to act, why the ARF supports them, and possibly join them in sharing your views before the feedback period ends on August 7, 2018.

• Research & Data Quality
• Article

Making Mobile Listening More Insightful

• Dave Vannette
• Qualtrics

As more consumers respond to surveys on smartphones, fewer are completing them. That trend puts the representativeness of your sample at risk. Qualtrics has developed six best practices to optimize mobile survey design and thereby minimizing the respondent burden and maximizing your data quality.