Jake Moskowitz, head of the Emodo Institute, debunks some myths about location data. Here’s the first shocking one: Location data can’t find you 60 million devices that visited a Hyundai dealership within the last month or two… or three, because that’s impossible. Throughout all of 2017, across the entire US, there were only about 17 million cars sold in total. That includes Hyundai, Honda, Ford—indeed, all brands. In data stores, users run across super-sized segments all the time. It’s not uncommon for vendors to claim that their single-brand auto dealership visitor segments include tens of millions of consumers. Location data is powerful, but it can’t make up shoppers.
Jake Moskowitz: In media, transparency demands accountability. In other words, it means asking media suppliers to “prove it.” It means expecting suppliers to “show me the viewability and fraud percentages, and allow me to suppress ads from running next to unsafe content.” Today, when it pertains to data, transparency just means “tell me where the data came from”—that’s it. That is not enough.
You need proven industry benchmarks if you want to set realistic goals and expectations for location data-driven marketing. Going forward, these norms can help you form and answer key questions about location data-based tactics, so you can make more informed data decisions.
Digital advertising is an incredibly sophisticated industry, yet when it comes to selling in the data store, vendors lack incentive and opportunity to focus on quality. Instead, they’re restricted to sales tactics—none of which are good for buyers, brands, or the industry.
In order to be effective, marketers need to know how various segment options stack up and measure up in terms of accuracy. Just like with increasing viewability, the first step toward a fix in data quality is realizing and acknowledging the problem.
When selecting data for mobile campaigns, don’t base your decision solely on claims about precision or how many decimal places appear in the coordinates, and definitely don’t mistake precision for accuracy. Precision is important, but the value of precision hinges on data accuracy.