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The social media chain reaction

It’s one thing for our culture to develop an awareness of the power of microtargeting on social media—influencing people to buy a certain product or vote in favor of or against a certain candidate or issue. What has received less attention is the chain reaction that ensues as a result of microtargeting. Those who have been influenced go on to influence and not just on social media.

Once a microtargeting campaign changes the “influencer’s” ideas or behavior, that person may well go on to influence the behavior of friends and family outside the realm of social media says MIT Sloan’s Sinan Aral and Paramveer Dhillon in a new study published in Nature Human Behavior. “If you can change behaviors through microtargeting,” the authors say, “what role do influencers play in spreading those behaviors beyond the reach of the targeting campaign itself? That’s a crucial question for those wishing to influence everything from elections to health campaigns to product adoption.”

In the same way that Facebook data can be used to improve the targeting of persuasive messages, this pioneering study proves that empirical data can actually improve the identification of influencers. Aral and Dhillon demonstrate that the use of empirical data can maximize influence by up to 87%, simply by reaching—and changing the behavior—of more people. The influencers identified through data have more cohesive, embedded ties with their contacts.

Data can be used for good or ill

Aral adds a cautionary note that such data can be used for good or ill. For example, influencers can be tapped to interfere with legitimate democratic elections through the spread of propaganda. At the same time, they can encourage people to quit smoking or help them recognize the signs of heart disease.

Sinan is a leading expert on social networks, social media, and digital strategy. He has worked closely with Facebook, Yahoo, Microsoft, the New York Times, IBM, Cisco, Intel, Oracle, SAP and many other leading Fortune 500 firms to realize business value through social media and information technology investments.

Sinan’s research focuses on social contagion, product virality, and measuring and managing how information diffusion in massive social networks such as Twitter and Facebook affects information worker productivity, consumer demand, and viral marketing. He has received a number of awards for his research, including the Microsoft Faculty Fellowship, the PopTech Science Fellowship, an NSF CAREER Award, and multiple “best paper” awards. Sinan was also recently named among “The World’s Top 40 Business School Professors Under 40” by Poets & Quants. Coauthor Paramveer Dhillon, who is working closely with Sinan in this area, is a postdoctoral associate in management science at MIT Sloan.

Read more about Sinan Aral.

Intentional analytics

How fresh is your data? Do you know why you gathered it in the first place? Is there a rhyme to your reason when it comes to analytics? Abhi Yadav, SF ’13, launched the MIT spinout ZyloTech because he realized that even the best data-educated personnel at major companies were unable to deal with the continual stream, variety, and mind-bending complexity of omnichannel customer data.

ZyloTech was actually born in the New Enterprise class taught by MIT Sloan Professor Bill Aulet. Yadav then recruited Michael Cusumano, who taught his Business of Software class, to the company’s board. Along with a team of data scientists, engineers, and digital marketers from the Cambridge ecosystem, Yadav wanted to make it possible for companies to leverage all their customer data in near real time so as to continuously access advanced customer analytics that deliver vastly more accurate and actionable insights.

“It’s futile to try and get good results from a marketing campaign when you’re working off old, incomplete data and ad-hoc analytics,” Yadav says. “What we’re doing is bottom-up analytics. We are unifying and curating a customer’s identity, which includes past behavior, intent-based data points, and basic contact info. We continuously track each existing customer action as it’s happening to determine what that customer likes an doesn’t like and what their signature behaviors are.”

Leveraging MIT research

Yadav and his team tapped MIT research in consumer science and automated machine learning to create a proprietary technology that performs entity resolution while integrating a probabilistic and a deterministic data unification approach. “When you combine these two approaches with deep-learning (AI) to discover patterns,” he explains, “you attain an unprecedented level of knowledge about your customer from raw data.”

Setting aside the technical terms, what Yadav and his team are doing is distilling all that information to get the real juice out of it, to take timely action, and to discover what a company really needs to know about the individualized motivations, habits, and predilections of its customers. As a result, they will be able to offer individualized promotions at the right time and through the right channel.

Given the volume and variety of data getting generated every second, Yadav says, it’s essential to make the most of it through timely insights. “We see businesses getting frustrated with the classic modern challenge of big data versus big insights,” he notes. “They don’t see where it’s getting them, because running after IT or hiring lots of engineers has not furthered their objectives. My goal is to help a business go beyond the lip service on customer centricity with real customer-centric marketing that unlocks the riches that lie in customer data. The result: a better, smarter experience for consumers—and for the companies that hope to win them.”

 

What companies can learn from United Airlines—and from trust-based marketing

How does a company build and keep consumer trust—and more important, win it back when that trust has been compromised? Thanks to viral video, the world has seen United Airlines “involuntarily deboard” a passenger, to use the airline’s term, and drag that passenger kicking and screaming from a legitimately purchased airline seat. The result? In the days following the incident, customers cut up United credit cards, shares in United Airlines stock slipped by 4%, and the company’s market value plummeted by $1 billion.

How can a company like United that has lost consumer trust gain it back? MIT Sloan Professor John Hauser says that it’s not enough to tell consumers that they can and should trust a company. “It’s critical to actually prove, again and again, that a company and its products can indeed be trusted – and customers must be provided with tangible, observable proof that a company has changed its ways.”

Four years ago, Hauser, MIT Sloan professor and former dean Glen Urban, and Gui Liberali of the Erasmus School of Economics in Rotterdam published a study on trust-based marketing called “Competitive information, trust, brand consideration and sales: Two field experiments.” The team tracked four marketing strategies by an American automaker with an ailing brand. The company had suffered from decades of negative publicity over the quality of its products and was working on several fronts to correct public perceptions.

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