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The Limitations of Big Data Collection in the Big City

In a recent Advertising Age article, reporter Kate Kaye allowed five consumer data gathering companies, including Catalina, 4Info, and Speedeon Data, to track her every move for three weeks in and around New York City.

Regarding the accuracy and effectiveness of consumer data tracking in the big city, the results of this experiment were not only fascinating, they were eye opening. “This admittedly unscientific study,” writes Kaye, “showed that, even though I agreed to let the trackers watch me all they wanted, gaps in what they could have learned about me abound.”

If anything else, Kaye’s 3-week journey into the deepest depths of consumer data tracking reveals that location-based data tracking methods in particular can get pretty murky — sometimes downright imprecise — in densely populated urban areas. “I live in a large city,” says Kaye, “and the places I frequent are not easily distinguishable from others near them.”

This could pose a big problem for marketers and advertisers for two reasons. Firstly, millennials — the coveted sweet spot demographic targeted by most of today’s major brands — are flocking to big cities, or their immediate suburbs. Brand marketers and advertisers who rely heavily on imprecise data tracking techniques to tell their consumers’ stories will never be able to make accurate, authentic, relevant connections to city-dwelling millennials.

The second reason is about money. The inferences made by complex location-based data tracking algorithms can be straight up wrong. For example, location trackers may think that you like going to a certain store when, in reality, all you’ve ever done is routinely walk past it every day on your way to and from work. For advertisers working with (likely programmatic) mobile ad technologies, this misinformation can be misread to form (incorrect) assumptions about a person’s preferences with respect to location. Pushing out irrelevant, mis-targeted ads is waste of ad spend dollars, not to mention a missed opportunity to make a brand-consumer connection based on contextually relevant location-based data.

Another limitation of data tracking is its inherent inability to collect “off the grid” information. This happens most frequently when, for consumers, using mobile apps or loyalty cards isn’t necessary, or isn’t an option. For example, when Kaye payed cash at a farmers market, none of the location-based data trackers ever knew about it. And in big cities like New York, millions of urban shoppers make most, or sometimes all, of their CPG-type purchases at corner stores, bodegas, and mom-and-pop markets — all businesses that are too small to offer any kind of a trackable customer loyalty program. It’s just another way data tracking literally misses out on an opportunity to collect data on frequent brand purchases, which could be used for personalized marketing and targeted advertising efforts.

To quote the 1980s singer, Rockwell: “I always feel like somebody’s watching me.” But sometimes, whoever is passively watching via big data isn’t necessarily getting the full picture. As Kaye sums up her article, ultimately, her project “was a reminder that information that can be quite telling about individuals is not always evident in data.”

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