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big data

CMO Briefs: Understanding Biases in Panel and Census Research Using AI/ML

  • Posted: July 7, 2021

Numerous bias types can impact panel and census research. Artificial intelligence (AI) and machine learning (ML) algorithms can perpetuate them. This is true of surveys, panels and the big data sets that are often used to calibrate each other. The following report touches on some of the biases that can occur, where they may affect panel and census research that use AI/ML and mitigation efforts to account for them. Read the article.

KaH: Understanding Biases in Panel and Census Research Using AI/ML

  • Posted: July 7, 2021

Numerous biases can impact panel and census research. While some believe that artificial intelligence (AI) and machine learning (ML) offer easy ways to circumvent them, the truth is, algorithms can perpetuate such biases. This is true of surveys, panels and the big data sets that are often used to calibrate each other. The following report covers the numerous biases that can occur, where they may infiltrate panel and census research that use AI/ML and mitigation efforts to account for them. Read the article.

NYCU: Much Ado About TikTok

  • Posted: July 17, 2020

The popular TikTok app is owned by ByteDance, a Chinese multinational internet technology company. Three articles address the recent controversies about a possible ban.

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CMO Brief: 2019 Organizational Benchmark Survey, The Advertiser Report

  • Posted: March 31, 2020

Advertising and market research have seen significant changes in the last couple of years. ARF members have been inquiring about different aspects of these changes. To answer their questions, the ARF Analytics Council developed the very first Organizational Benchmark Survey of the industry. The aim was to see how companies collect research data, what departments conduct research, how they organize around research and data, and, if small advertisers differ from large ones. Read more.

KaH: The 2019 Organizational Benchmark Survey, The Advertiser Report

  • Posted: March 31, 2020

Advertising and market research have seen tremendous changes in the last couple of years. As a result, ARF members have been inquiring about different aspects of these changes, from what to call their departments to what tools and techniques are considered best practices. For instance, should it be called a “research department,” “data science” or “customer experience” department? Is it better to have a centralized or decentralized structure? Do such departments provide positive ROI, according to stakeholders? And should they use R, Python, SPSS or SAS?  Read more.

Get the Intel on Artificial Intelligence

  • Posted: September 12, 2018

Some say Artificial Intelligence is a broad field that includes everything from simple if-then rules for playing Checkers to complex ensembles of deep neural networks for piloting autonomous vehicles. In marketing, AI is a term that is applied to several very different techniques and functions. However, the bigger questions are: how do you best implement AI and what are the commercial solutions?  Read More.

The Dark Side of Big Data’s Effect on Firm Performance

  • Posted: May 3, 2018

Editor’s note: A Marketing Science Institute (MSI) Best Paper Award Winner (short summary)

As marketers’ use of big data is increasing, their data management efforts may increase customer data vulnerability or at least perceptions of susceptibility to harm. Yet most firms have little insight into the potential negative ramifications of their big data efforts or how to prevent them.
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Rethinking the Profession Formerly Known as Advertising: How Data Science Is Disrupting the Work of Agencies

  • Posted: January 17, 2018

Editor’s Note: We are repeating this Journal of Advertising Research (JAR) piece so that you can now read it via email. This is a “Speaker’s Box” article in the Journal of Advertising Research. The JAR invites academics and practitioners to identify potential areas of research affecting marketing and advertising. Here are a few excerpts from this article:

There is nothing new about the claim that advertising is not what it used to be. In 2012, the annual report of WPP noted, “We are applying more and more technology to our business, along with big data. We are now Math Men as well as Mad Men (and Women). Thus, we go head-to-head not only with advertising and market research groups such as Omnicom, IPG, Publicis, Dentsu, Havas, Nielsen, Ipsos, and GfK, but also new technology companies—such as Google, Facebook, Twitter, Apple and Amazon—and then with technology consulting companies such as Infosys, Wipro, Accenture and Deloitte.”

At a minimum, it must be clear that a profession that changed hardly at all in the 70-odd years since the commercialization of television is not recognizably that profession any longer. By all that defines a profession—skills, assets, clients, and heritage—it is time to declare a new regime.

When a new technology is born, nothing is more certain than that it will be deployed, whether for good or for evil, and data science will not be an exception. We will receive its benefits, and we will learn to live in and around its costs. But what role will the institutions and people of the advertising profession play in the emerging practice of data-driven marketing communications and customer management?

Deighton, J. (2017, December 1). Rethinking the Profession Formerly Known as Advertising. Journal of Advertising Research.