Is Your Location Data Any Good? Here’s How You Can Tell


As many marketers know, data can be overwhelming. In today’s advertising and marketing ecosystem, we have more data to work with than ever before, and it can all get very daunting.

However, data – and good quality data – are imperative to ensure accuracy and effectiveness in digital advertising. So, while it may seem intimidating, it’s important to understand what the data you’re using represents, and how to know you’re getting the exact the data you’ve paid for.

Accuracy vs. Precision

“Accuracy” and “precision” are words you’ll hear often to describe location data, and while they are equally important, they’re definitely not the same.

Accuracy refers to the correctness of the data in relation to its description. If that data says it represents US citizens, and in fact it does reflect actual lat/long information for U.S. mobile users, then it’s accurate. That’s very helpful if you plan to run a campaign targeting Americans. It’s less helpful if you’re targeting women who go to a chain of nail salons every week in the St. Louis metro area.

Precision refers to how specifically targeted the data is. Data that can narrowly target women who go to the Ten Spot Nail Salon on Main Street in Springfield, Missouri every week is precision data. That’s data that can be used to build a behavioral profile.

However, if data says it represents women who visit the nail salon in Springfield, but it actually includes men and women who visit businesses within 1,000 meters of the nail salon it’s not really accurate, is it? Apart from not being limited to women, it also may include people who patronize the coffee shop next store, the dry cleaner across the street, and the supermarket around the corner.

If your campaign is intended for women in that neighborhood who get weekly manicures, that data – even though it’s only about 750 meters off – will not perform very well in your campaign. This is why both precision and accuracy are so important.

Bad data is…

Bad data doesn’t necessarily mean inaccurate or fraudulent data. It refers to data that is simply too imprecise. As in the example above, imprecise data can wreak havoc on a programmatic campaign.

To help marketers execute the most successful campaigns possible, it is important to identify misrepresented and imprecise data. There are a number of filters that can be used to catch this inaccurate data. Some of the “red flags” to look out for include:

  • Centroids
  • Lack of precision in lat/long data
  • No country code/Wrong country code
  • Repetition of uniques
  • Not enough data
  • The Equator Test/ Greenwich Test
  • Bad publisher name

It would be impossible for humans to manage all these filters and capture every unreliable data point. However, technology has helped many location companies (like Blis) detect and flag patterns of unnatural human behavior within data.

If you look at visualizations of bad data, it’s easily discernable from reliable data. Human behavior, plotted out on a map, looks irregular and unpredictable, generally. Bad data on the other hand often generates straight lines or repetitive patterns, clashing with how people actually behave in the real world.

When we find bad data….

With so much data from so many sources, marketers need to understand the context in order to know for certain if it is bad data or the occasional anomaly. The time frame from which the data comes can help identify patterns over time. To that end, looking at weeks of data vs. days can help discern patterns.

If reviewing one day of data from one publisher, it is difficult to say if that publisher provides good or bad data. However, looking at weeks at a time, marketers can start to see if the data is consistent, and if a given publisher can be trusted to deliver quality data. It is important to catch bad data, remove it from the data set and monitor future data from each source in order to see if the data improves in quality.

To help raise the bar for data accuracy and precision, the IAB and the Mobile Marketing Association are making earnest efforts to educate marketers about the important questions they need to ask about their data. The industry as a whole are working to educate and protect marketers who rely on clean, quality data.

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