Covariance’s next-generation external intelligence platform reveals never-before-seen detail on competitors and customers
Companies spend billions to see across the street—fueled by traditional market research.
But relying on conventional means of customer and market intelligence is extremely risky: sources conflict, lack granularity, and are easy to disprove because they do not add up to internal numbers.
Over the last decades, studies show overperformance by companies that invest heavily in internal analytics. But the world outside a company's four walls remains fuzzy. How do internal metrics compare with competitors’ performance? What do customers do when they leave the store?
Covariance believes that the winners over the coming decades will be those that invest heavily in external analytics: seeing the world outside your four walls with extreme clarity and precision.
“The problem today is very much Blind People and the Elephant,” says founder and CEO of Covariance, Mike Fleder. “Every data vendor has a part of the truth and is trying to convince you that they’re right. But you can’t just fit the puzzle pieces neatly together to get the answer—most of the puzzle has to be filled in with inference.”
Hedge funds have long used “alternative data”—large panels of data from non-traditional sources without personally identifiable information—to find insights they can’t get elsewhere. Today, over two-thirds of hedge funds use alternative data; it is now a $4.4 billion market and is projected to grow at a compound annual growth rate (CAGR) of 50 percent. By comparison, traditional market research is growing at a 4 percent CAGR.
Alternative data is notoriously difficult to work with, and the tools for doing so have been hidden away on Wall Street. Without these tools, companies are making key decisions based on an incomplete understanding of competitors and customers.
Our fully managed platform turns messy alternative data into a crystal-clear picture of competitors, markets, and customers
Covariance not only makes these tools more broadly accessible, but also takes Wall Street’s capabilities a step further with new theory developed at MIT. Estimating competitors’ financials is an extremely complicated process, but Covariance does the heavy lifting, fusing multiple data sets through complex transformations and machine learning, all while maintaining strict standards of privacy. Increased privacy regulation means that information availability will be noisier and more incomplete—exactly what Covariance was built for.
“Our fully managed platform turns messy alternative data into a crystal-clear picture of competitors, markets, and customers,” says Fleder.
Fleder’s fellow co-founder and President of the MIT-connected startup, Matt Perlein, expands on their capabilities: “We provide both a holistic and granular view of the market, from top-level financials to drilldowns by competitor, location, customer segment, channel, and more. Best of all, we can show this to you continuously and with very low lag,” he says. At the end of the day, this means Covariance customers make better decisions for every stakeholder in their ecosystem.
And it was all born out of Fleder’s PhD work with MIT Professor Devarat Shah at the MIT Laboratory for Information and Decision Systems. Beyond the theoretical contributions, their work outperformed some of the most difficult forecasting benchmarks in the world and highlighted novel zoom-and-enhance capabilities. In short shrift, companies from a broad swath of industries and sectors came knocking on Fleder’s door. “We had so much inbound that we had a customer before we even had a company,” he recalls.
In 2020, Fleder launched Covariance. Since then, he and his team have been managing inbound from companies across verticals, all of them interested in the next-generation competitive market and customer intelligence offered by Covariance. Perlein says that Covariance prioritizes partnerships with companies that are looking for an edge through best-in-class external intelligence. “We want to work with companies that will use our external insights platform day-to-day, right alongside their internal analytics,” he explains.
Meanwhile, Fleder and Shah have continued to collaborate; they recently contributed a chapter, “Nowcasting Corporate Financials and Consumer Baskets with Alternative Data,” to the forthcoming book, Machine Learning and Data Sciences for Financial Markets: A Guide To Contemporary Practices.
We want to work with companies that will use our external insights platform day-to-day, right alongside their internal analytics
Now, Perlein says he looks forward to the opportunities that come with joining MIT STEX25. “Being inducted into MIT STEX25 is unbelievably exciting. It allows us to build on the relationships we’ve already developed at MIT ILP while opening the door to even greater possibilities and exposure,” he says.
While business intelligence dashboards and customer data platforms have made significant inroads when it comes to organizing a company's internal data, those same companies lack access to information about what is happening externally. That’s where Covariance steps in. “Our vision is to make external analytics so foundational that they are the first thing the CEO looks at in the morning,” says Fleder.