From Microsoft to Self-Driving Cars, Invention Springs From Data

True breakthroughs come from product insights

In June, Microsoft bought LinkedIn, one of the largest acquisitions in the history of tech, and it boils down to one thing: data.

Microsoft CEO Satya Nadella said as much in a letter to employees. Nadella wants to marry LinkedIn with its flagship software, Office 365, and crucially its CRM product Dynamics. Since the acquisition, we've started seeing how Microsoft will bolster Dynamics against rivals like Oracle. Microsoft—and we learned, a handful of other companies—recognized the potential of LinkedIn's customer data to improve its own products and sales, and LinkedIn understood that its real value as a company was the trove of data it collects.

Peter Reinhardt Alex Fine

Microsoft's acquisition reflects a new reality for software: a beautiful app, user interface or even becoming the leading professional social network no longer guarantees long-term success.

Market dominance comes down to how customers use the product, and then, especially, how companies interpret that usage and feed it back into the experience to innovate and invent. You can't build truly lasting businesses—or put another way, create an impenetrable competitive advantage—without capturing, analyzing and activating vast amounts of first-party data.

Product data has become the new software. Leading companies have analysts, product managers, designers and developers poring over data to make decisions that will improve the customer experience. Today's products are living things that get better over time as data teams filter customers' likes, dislikes and behavior back into products. What separates (and protects) modern tech giants—Netflix, Amazon, Facebook, Google and others—is the way they leverage their massive scale to invent product experiences directly on top of their customer data.

Netflix already recommends content based on your favorite actors or directors, and now growth teams there are starting to analyze the show covers you select, suggesting shows with similar designs. Amazon's user data is the backbone of its customer support program, which uses your purchase history, for example, to contextualize support requests. The company has even patented the ability to ship your items before you order them based on what its data set infers you'll order.

Facebook, for its part, is arguably the best of the bunch at building experiences on top of its customer data. Analysts like Ben Thompson attribute Facebook's rapid rise in large part to how much better the user (and advertiser) experience became when it switched to an algorithmic News Feed.

The product data arms race is also at play in the nascent but exploding space of self-driving cars. While design will certainly be important, self-driving cars will succeed or fail depending on how well product teams interpret the complex data revealed as a car is on the road. The company that ultimately wins may very well be the one that is able to capture, then learn from the most comprehensive data set.

With its long history of leveraging customer data, Google was among the first companies to collect and implement traffic data—it's how Waze optimizes your commute by avoiding traffic. However, Uber currently has millions of cars on the road to which it could attach sensors, and Tesla logs more sensor miles daily than Google has in its history. All these companies have access to vast amounts of first-party data, but the real competitive advantage will come down to crafting the best experiences around that massive data set.

These are scary times for anyone who's not running Google, Netflix, Amazon and the like—that is, most of us. But it doesn't have to be. The reality is that your customers are generating data whether you collect it or not, and it's never too late to start putting it to work. There are a few steps required to do it right. For starters, every company needs a team dedicated to using that data. These teams—often a mix of analysts, marketers, product people and engineers—can assess the technical, structural and cultural systems needed to properly collect and activate customer data. The specific tools used—there are a lot to choose from—matter less in the long run than creating a data warehouse and establishing organizational processes for revealing and acting on what's in that data.

Don't chase the mythical 360-degree view of the customer. Rather, collect data in a central repository so each business unit can access the data it needs. After all, there's only one set of customer data, but the customer service team, sales team and user experience team all need different views on that data. A good growth team will be able to take all those learnings and build new product experiences on top of them.

Companies like Netflix and Amazon don't invent new products based on their extensive data just because it's a challenging technical puzzle (though often it is). They do it because that's the only way to stay ahead.

Companies that successfully combine deep customer data sets with new product experiences will be the ones that matter in the future. If someone else understands your customers better than you do, you should be worried—your customers might not stay your customers for long.

Peter Reinhardt (@reinpk) is an aerospace engineer turned CEO and co-founder of Segment, a customer data platform.

This story first appeared in the August 22, 2016 issue of Adweek magazine.

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