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.
The intersection of the Internet of Things (IoT) and big data has great business impact potential. This article by Chuck Martin, writing for Media Post, presents findings from the study “IoT Meets Big Data and Analytics.” This study, conducted by Dimensional Research for ParStream, sought to understand the stakeholder experiences and challenges related to IoT. The study presents the results of an online survey of 200 business people with professional responsibility for IoT projects.
Among the findings:
-While 83% of these stakeholder collect data, only 8% are making the most of it by fully capturing and analyzing it in a timely fashion.
-77% view data collection and analysis as important in their IoT projects.
-44% believe there is too much data to analyze effectively.
-36% feel that it is difficult to capture useful data.
-34% of the respondents either don’t store or don’t analyze any IoT data.
If the challenges involved in IoT projects could be resolved:
-76% of respondents would collect and store more data.
-70% believe they would make improved business decisions if they were able to capture and store data faster and more cost effectively.
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Alexandra Samuel, writing for the Harvard Business Review analyzes data journalism, which the author views as an important trend. Data journalism draws on the growing availability of data sets and data analysis tools to uncover and tell stories. Big data offers marketers the opportunity to develop data-driven stories involving new insights, to tell stories in a compelling manner, and to have the stories disseminated via social media.
However, Samuel points out that data visualizations driven by original data are rare. While infographics are frequently used by corporations and marketers, these infographics do not focus on original data-driven content.
Benefits of offering data as a content marketing resource include:
-Increased traffic: data visualizations and reports are likely to be shared on social media.
-Value: by offering unique information and actionable insights, the company’s content will provide value.
-Authority: by offering a report or infographic with new information or key trends, the company’s expertise is highlighted.
-Learning: by releasing some of the corporation’s data in a form that readers can use to create their own charts or analyses, new insights may be revealed.
-Transparency: by offering information on the patterns revealed by consumer data, brands can help consumers feel more comfortable with the use of their personal data.
Samuel feels that some companies have successfully used their own data to drive original stories, such as OKCupid, General Electric, Kickstarter, and Jawbone. However, the potential of data-driven marketing is not being fully utilized by most companies.
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For more on this topic, check out the Marketing Tab in Morning Coffee.
In this Forbes article, Bernard Marr reminds readers that data on its own is meaningless. To avoid drowning in data, and for data to be useful, marketers must first know what data is needed. Otherwise, too much data will obscure the needed answers.
Marr stresses the importance of starting with a strategy and identifying the right questions. Know what you are trying to achieve, then develop the questions to which answers are needed.
By knowing the critical questions, the data needed to answer those questions can be accurately identified. By following this process, you will be able to improve performance and harness the primary power of data. Decision-making will be supported by the data developed by asking the right questions
Marr concludes that data is a strategic asset, but it must be used constructively and appropriately, as he has outlined above, in order to deliver optimal results.
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