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Oct 25, 2012

Renee Gosline Explains Social Media's Reach and Value When the Right People are Touting the Brand

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Given the plethora of mobile apps on the market today and the estimated $60 billion revenue stream they are expected to generate by 2017, why would businesses spend resources on non-product-specific apps?  To build trust and brand awareness, according to new research released this month by MIT’s Center for Digital Business.


Two studies aimed at assessing the impact of so-called “benevolent apps” on consumer brand preferences, found “strong value” for the business in developing customer-centric apps where product messaging may be absent or very subtle. The research, conducted by CDB chair and MIT Sloan Professor Glen Urban and Fareena Sultan, Professor of Marketing at Northeastern University, found that the apps “provide consumers with helpful information that can increase trust in and preference for a company.”


In fact, “companies should devote resources to developing, testing and promoting” these apps along with others, the researchers conclude in an article in the Winter 2015 issue of the MIT Sloan Management Review.


Advancing Consumer Interests

While many companies’ smartphone apps and advertising today successfully “focus on pushing product sales,” the authors write that one of the most effective uses of mobile media “will be apps that are designed to build trust.” Urban and Sultan call these benevolent apps because “their value is not directly tied to selling products but rather to advancing consumers’ interests and advocating for their needs ahead of a company’s own corporate profits.”


Apps --such as Kraft Food offering recipes, or The North Face offering snow reports-- build trust by providing consumers with information that help them solve problems or make decisions. Indirectly, the authors say, advice, services and conveniences can improve users’ image of the brand and increase their willingness to purchase products. A wide spectrum of benevolent apps exists. (See chart).Benevolent Apps.Urban Exhibit 102314.jpg


At the left end of the spectrum on the chart are apps whose goal is to sell product. On the right end are apps that provide benevolent service. In between are apps that display differing degrees of benevolence.


Case Studies

Two cases were studied in depth to evaluate the effectiveness of various approaches.  For Liberty Mutual Insurance in Boston, researchers set out to determine if and how a benevolent app could build brand, customer preference and sales potential. In the second study, for Suruga Bank in Japan, they attempted to determine the impact of benevolent apps in a global setting with a different cultural context. To do this, they developed a mobile app to help Suruga customers select new homes.


Specific analysis and results can be found in articles and blogs here and here, but in both cases, customers surveyed after the apps deployed were much more likely to purchase services from the company than they were previously. The authors conclude that: “Using mobile apps that exude benevolence can significantly impact sales at a low cost — and thus improve profitability,” even in diverse cultural contexts.


“Benevolent apps lead to increases in perceptions of trust, thus promoting consideration, preference and intention to use the brand. These are powerful forces that can lead to increased revenue from trusting consumers who perceive that the brand has their interests in mind.”



Glen L. Urban is the David Austin Professor of Marketing at the MIT Sloan School of Management and chairman of the MIT Center for Digital Business. Fareena Sultan is a professor of marketing at Northeastern University’s D’Amore-McKim School

Will Cukierski and his fellow data scientists at Kaggle take the concept of crowdsourcing very seriously. In fact, and they’ll hold a contest for your company–and offer cash prizes-- to prove its effectiveness.


And many global organizations are signing on. In two years since Kaggle has offered its service, at least 40 companies, governments and researchers have ‘crowdsourced’ their toughest problems to Kaggle’s network of experts. The solution-seekers get answers and the problem-solvers get prize money for the winning model. Both sides seem to like the arrangement.


Cukierski, himself a data scientist with a degree in physics from Cornell University and a Ph.D. in biomedical engineering from Rutgers, told a group of MIT CDB faculty and guests recently about Kaggle and “how organizations are using predictive modeling competitions to surpass their best efforts.” His presentation was titled, Crowdsourcing Predictive Analytics: Using 50,000 Heads Without Losing Yours.


To be sure, Kaggle is not American Idol, nor is it aimed at just anyone looking for a part-time job. It does, however, adhere to tech executive and venture capitalist Bill Joy's adage that the best minds may not be working at your company. Kaggle's community is comprised of thousands of PhDs from quantitative fields such as computer science, statistics, econometrics, math and physics. “They come from over 100 countries and 200 universities. In addition to the prize money and data, they use Kaggle to meet, network and collaborate with experts from related fields.”


According to its Web site, “most organizations don't have access to the advanced machine learning and statistical techniques that would allow them to extract maximum value from their data. Meanwhile, data scientists crave real-world data to develop and refine their techniques.” Kaggle hopes to bridge the gap with its "flavor" or crowdsourcing as noted below:



Cukierski said that the contests are not intended to replace domain knowledge expertise, but to tap into resources not otherwise available. To me, it’s interesting to compare this discussion to another recent seminar presented by Jonathan Zittrain at MIT CDB. In his presentation of Minds For Sale, Zittrain noted that while it may be quite acceptable—and even “hyper-efficient marketing” -- to offer payment for people to solve everyday product problems, intellectual property (IP) and security concerns have to be addressed as well.


At Kaggle, IP ownership and contest rules vary with the sponsor, Cukierski said. Some, like banks, want to keep the results proprietary, while others are more open and transparent about the solutions they get. Kaggle also hosts private competitions where contributors must be invited to participate and are placed under nondisclosure agreements. I can see those options becoming even more popular given regulatory constraints and other restrictions that many businesses and public-sector agencies must address.


Here are some results that they have found with predictive models so far:



What are your thoughts about this crowdsourcing model? Is it one you would consider as a research organization? As a data scientist? Share your ideas with this community.

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