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.
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.
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.
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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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.
via Broadcasting & Cable
(source: Kelly Abcarian – SVP, Nielsen Product Leadership & Molly Poppie – SVP, Nielsen Data Science)
The race is on to understand identity across both TV and digital, and when it comes to using big data to understand audiences, there is no such thing as perfect information. The biggest misconception today is that set-top box data represents the universe of actual TV viewing behavior. In reality, it’s far from it, and we must first ask ourselves if this data even represents true-person’s behavior.
Professors Michael Smith and Rahul Telang answered the following questions on this article:
“The making of House of Cards illustrates how a bunch of different changes coming together at the same time can be really disruptive to the traditional industry. The thing that Netflix had that nobody else in the industry had was they knew exactly who those Kevin Spacey fans were and they could use the platform to target them directly. So, Netflix went out and created nine separate trailers for House of Cards and targeted them directly to those users. So, I think part of the story is the power of detailed customer data to help you do a better job of marketing the content.”
Companies have invested millions of dollars in big data and analytics, but recent reports suggest most have yet to see a payoff on these investments. In an age where data is the new oil, how are smart companies extracting insights from these vast data reservoirs in order to fuel profitable decisions?
Companies that have been successful in harnessing the power of data start with a specific business problem and then seek data to help in their decision-making. Contrary to what Anderson preached, the process starts with a business problem and a specific hypothesis, not data.