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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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