With ad effectiveness, prioritize data quality
Marketers have a tougher job than ever trying to reach on-the-go consumers with “the right message at the right time on the right device,” all under the weight of greater accountability. Data is key to their success, but marketers need the right data to understand who their customers really are, where they go, and what motivates them. This can be especially tricky when there’s so much data available – and little transparency around how good or how useful that data really is.
Location data can solve a lot of the problems marketers face today, but location data itself has a problem right now. Marketers have for years had concerns about the quality and transparency of their location data – concerns that persist to this day. As recently as 18 months ago, eMarketer shared a report that placed location data accuracy at the top of the list of marketers’ location data challenges.
With so many questions around data quality, the team at Blis thought this was a great opportunity to offer a refresher on what quality location data really is, and why it’s so important to marketers in order to drive useful real-world intelligence.
Defining Quality: Why Accuracy and Precision Matter
While there is a lot of location data available to marketers today, the key isn’t purchasing more data, it’s purchasing the highest quality data you can – while also maintaining scale. The fact is that truly precise and accurate location data isn’t always available in tremendous quantity, but it’s well worth it since the consequences of relying on low-quality data (no matter how much of it you acquire) can be dire. It could mean wasting a huge percentage of media budget by surfacing your ad to the wrong audience entirely.
Blis has incredibly high standards for both accuracy and precision when it comes to location data. Amy Fox, Head of Product, offered a clear description of these factors in a previous blog:
Why does quality matter so much?
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 US 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 particular 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.
Both accuracy and precision matter very much when it comes to data quality; you need to have a high level of both to geo-target audiences to deliver specific results. If the accuracy is correct, but the precision is off, (building on the example above) a marketer may unknowingly target the boxing gym across the street from the nail salon, thus reaching a very different, and possibly less lucrative, demographic – even if the precision is off by less than 200 meters.
However, at Blis, bad data isn’t limited to inaccurate, imprecise or even fraudulent data. We rely on the highest-possible quality sources for our location data, and then apply rigorous quality testing to ensure all data we receive meets our high standards. We consider GPS data to be the gold standard. Our filters sift through data more quickly and accurately than humans ever could.
While real-time location data is useful in many scenarios, historical location data is a powerful proxy for behavioral data. There’s a lot that can be learned about consumers by where they go. A consumer electronics company recently partnered with Blis to drive shoppers into a major retailer prior to the very competitive holiday period. Blis helped the retailer and the consumer electronics company hone in on younger, more affluent consumers, and gamers – particularly those who showed a vulnerability in their loyalty to their local gaming store. We were able to motivate buyers in target areas to visit the retailer locations – which resulted in increased foot traffic at these stores during the campaign period with a lag effect observed among some audiences for the next 21 days after the campaign.
How did we know these customers were less loyal? Blis data makes it possible to track loyalty levels to stores over time. We’re able to see whether consumers are tied to one retailer, or if they browse around as a result of a closer relationship to favorite brands through communication. In a strategy that can work again and again, Blis observed that gamers at their local or independent game stores are more likely to engage with appealing offers at national chains like Best Buy, Target, etc. Blis also measured that for this campaign specifically, more affluent consumers in prosperous areas of the country reacted quickly to our mobile advertising and were more likely than other audiences to continue shopping, even after the campaign ended.
So, for this particular campaign, we leveraged historical location data to observe young and affluent consumers, as well as category engaged audiences of gamers and visitors of local gaming stores, to gain insight for an efficient communication strategy. However, in order for that location data to be used so effectively, high quality, precision and accuracy targeting are paramount. Even if the historical location was only slightly off, it could have had a tremendous impact on campaign results.
Marketers who focus on the quality of their data rather than the quantity may need to spend more to acquire that data, but the results will likely be worth their effort and investment. At Blis, we’ve always maintained stringent standards and transparency around the quality of our data. We’re happy to do our part to keep those standards high across the industry and to educate others in the field about the importance of quality data. Meanwhile, let’s keep the conversation going and momentum moving toward quality over quantity.