Research & Data Quality

MSI Blue Ribbon Report Preview Charts the Future of MMM

  • MSI

The practice of marketing mixed modeling (MMM) is increasing, research finds, partially because of the implementation of privacy restrictions. Today, finding appropriate providers to optimize marketing investments remains challenging. This was the impetus behind the Marketing Science Institute (MSI)’s Blue Ribbon Report on MMM. This free preview of the full white paper outlines what best practices to employ in a rapidly changing landscape. It identifies challenges for MMM veterans and ways to meet them. It also introduces executives to the opportunities that exist when introducing MMM into their firm. The report preview provides a brief overview of the top-level findings found in the full report, which is only available to MSI members.

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A Guide to Validating Marketing Performance Models

Over the last dozen years, marketers have seen numerous analytical models surface to help guide data driven decision-making. Yet, many remain unsure of how trustworthy such models are. That’s why the Attribution Working Group of the ARF Cross-Platform Measurement Council put together this guidebook to help arm all stakeholders with a basic understanding of marketing performance model validation issues. This paper includes a set of considerations and questions that encourage and facilitate dialog between end clients and model providers about the performance of marketing performance models.

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Are Marketers Using the Right Metrics?

  • MSI

Which is more efficient when it comes to advantageous decision outcomes, marketing-mix metrics or financial metrics? Researchers in this award-winning MSI working paper investigated this question, basing their research on a behavioral framework and statistical model. On average, according to their sample, marketing-mix metrics are more effective than financial. Even so, managers seem more eager to use financial metrics, possibly, because they are easier to understand across the organization.

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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.

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Deriving Social Insights by Mining Meta Data

  • Preriit Souda, Consultant, Data Science & Strategic Insights, Independent

How sad to get only a fraction of the available insights from social media! You can change that by using a new approach to link a range of data related to social posts, thereby generating more information about your followers’ attitudes, behaviors, and experiences.

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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.

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.

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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.

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.

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