The market for location data is heating up. Many brands are investing resources into location-based marketing, with one ad exchange alone reporting a 170% increase in the number of ad requests containing location data. But the relative newness of location technologies and approaches means that many are still unsure what exactly location data can do for them, especially with regards to potential use cases.
Furthermore, the sheer variety and complexities of the data has made it difficult for companies to interpret and apply the data on their own, forcing them to rely instead on third-party providers to turn that information into something actionable. The appetite for location data may be huge, but there’s still a gap between what people want and what they can do.
At the recent ProxSummit hosted by Unacast, the top global location and proximity platform, the CMO of Groundtruth (formerly xAd) Monica Ho made a convincing argument for the creation of a third-party verification system that would work to grade the quality of location data. She is not alone in this; as of late, there have been more and more people calling for standardization in the location and proximity industry.
This is because of the vast number of players already present in the space, each of which is free to make claims about the services on offer without providing third-party verification of such claims. As a result, in order to make it a fair playing field for all, companies must be held to a standard accepted by the entire location industry.
If you’re a company like Pandora with 50 million monthly active users, what’s the best way to map out and model their everyday behaviors? After all, a computer program can only go so far. That’s why Pandora and many others in the location industry are turning to machine learning to perform a myriad of functions, from modeling how people are likely to behave to verifying data accuracy to finding insights that would otherwise have been hidden to the human eye.
At ProxSummit, Jeff White of Gravy Analytics pointed to machine learning and AI as the tools that “will help predict human behavior” and create effective strategies for attribution. Eventually, machine learning will help data providers and brands use the information they have to go beyond marketing in real-time, and instead anticipate people’s needs and locations.
Generally speaking, nobody wants a complicated solution to a problem when a simpler one would do. The same principle goes for location and proximity: No client really wants to get into the nitty-gritty of location data quality, its origin, etc — the only thing that matters is what it can be used for, and how easy it is to use. As David Spitz, CMO of mParticle, mentioned at ProxSummit, “Clients are interested in ease-of-use and whether data can be used in different channels… they don’t want to be confined to one use case.”
Location is — and should be — an important component of any marketer’s toolbox. But data providers and data aggregators should be mindful of clients’ needs, and work to educate them about the uses (and hazards) of using location and proximity data.
By this, I don’t mean that the appetite for location and proximity data is slowing down. Instead, I’m talking about the fact that clients, having had more experience and exposure to the location industry, are now asking harder questions and demanding more accountability from data aggregators and suppliers. As an industry, we have to figure out how to move forward and continue to innovate and provide value to our clients.
Finally, I believe that one of the biggest issues faced by the proximity and location space is the issue of transparency. As mentioned earlier, there should be a way to grade the quality of data so that buyers can be sure that the information they receive is of the highest caliber. However, we also need to be educating the public about where the data is coming from, as well as how best to use it.
By providing more transparency to clients, we will be able to interrogate the data from multiple angles: from the buyer’s point of view, the aggregator’s point of view, etc. If we show ourselves to be mindful of people’s needs, we can better fulfill our role as the industry that equips buyers with the information required to make better decisions.
All location and proximity data are not created equal, but all location and proximity data have a use case.
Published on Mobile Marketing Watch by Michael Essany