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How Analytics Can Transform Business Models

April 16, 2013 | Renee Boucher Ferguson | Data, AI, & Machine Learning

There are 52 million Latinos in the United States, with $1.5 trillion of purchasing power.

Entravision Communications Corporation a Spanish-language media company, reaches about 96% of that U.S. Latino audience through its numerous television and radio stations and digital platforms. It uses that extraordinary reach to provide media solutions to marketers interested in tapping into the Latino consumer market.

Entravision's sweet spot is its ability to offer hyper-local and regionalized channel marketing — what Latinos in Los Angeles are interested in versus what Latinos in Yuma or Tampa are interested in. But to provide more efficient and deeper insights to marketers, Entravision executive Franklin Rios decided to utilize empirical and transactional data obtained through licensing agreements. The increased data coming into Entravision's traditional data environments caused bottle necks — in processing power, in latency, in response times. As a result, the company embarked on a "big data" infrastructure implementation that included new analytics tools, the cloud and Hadoop.

The addition of external data, coupled with the new platform and new algorithms, resulted in fine-grained behavioral insights. But the story doesn't end there. Entravision clients became interested in the new analytics results, outside of their implications for media buys. And with increased client demand for those insights, an analytics division was created. In effect, analytics transformed Entravision's business model.

In March 2012, Rios became the president of Luminar Insights an entirely new unit of Entravision that specializes in big data Latino insights. Not just for media buyers, but also for those in other industries, such as CPG and retail. Rios spoke with MIT Sloan Management Review contributing editor Renee Boucher Ferguson about the process of analytics innovation that led to the development of Luminar.

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