Ahead of ATS Madrid 2022, Alvaro del Castillo, Founder and CEO of Taptap, explains location intelligence, how marketers can benefit from the data it collects, and how location data can be collected and used in a privacy-centric, post-cookie world.

What exactly is location intelligence? What data categories does it include?

In the simplest terms, location intelligence means making sense of location data and understanding what is happening at a particular location. For Taptap, it also means using location not just as a data point, but as a connector of many diverse data points. We like to say that everything happens somewhere — an ad impression, a video view, a store location, the weather — and if we know about these events occurring at the same place, we can draw highly accurate conclusions about the context happening (and the people) there. Our technology, particularly our geospatial analytics platform, Sonata Location Intelligence (LI), allows us to do not only that, but also to make the data actionable, either for an analysis or for an omnichannel ad campaign. To make the data more readily usable in the platform, we typically organise it into four primary categories: audience, media, business and contextual data.

What benefits does it offer marketers?

The key benefit of location intelligence (aside from its privacy safe nature) is that it enables us to look beyond both the unique user and the data that comes entirely from the digital ecosystem – which is what most DSPs offer. Location intelligence lets us connect online and offline environments, static and dynamic data, as well as both data related to and independent from ad campaigns. This means, in practical terms, that marketers have the ability to analyse and plan using data that is a better reflection of the real world and the real context where their consumers live. Though one to one targeting has definite benefits, digital behaviour alone does not decide an outcome; rather, many factors, digital and physical, influence our buying decisions.

What are the privacy implications of collecting and actioning location data?

Alvaro del Castillo, Founder and CEO, Taptap

When we think about the collection of location data generated by users, the most important thing is that they have given consent. We apply this principle to all the data we use, and only work with partners who do the same. Using only consensual data not only helps us ensure the privacy of the individual and remain GDPR compliant, but it also provides a quality control check since consensual data is usually of much higher quality, especially when it comes to location.

The privacy implications for actioning location data, however, really depend on how you process the data and what you do with it. For one-to-one targeting, we do need to associate the data to an individual (who is anonymous) and can only do so if this individual has given consent to their data being collected and used.

Actioning and processing an individual’s location data through our location intelligence platform is entirely different. The critical difference is that we disassociate location from the individual, or more specifically, their unique cookie or ID. The event (or data point) and the location at which it occurs are counted and then aggregated in the platform, without ties to the user. In other words, the data is not used to target or retarget a particular user; rather, we target a location. For location data not generated by the user (for example, the density of OOH placements or the locations of competitors), there are very few privacy implications, as there is no unique user identifier involved. Location intelligence lets us make use of both.

How important is geospatial information in the context of the deprecation of third-party cookies?

Geospatial data provides an alternative to third party cookies – a way to get in front of the right audiences without relying on cookies. It is true that without cookies or unique IDs, we do lose some capabilities like retargeting or frequency capping, but by connecting additional data sources through location, we are actually gaining a better understanding of the context we are targeting.

Because geospatial technology is a way for us to aggregate data points that are separate and possibly unrelated, including those generated by users (like population mobility) and those completely independent of users (like weather) at a particular location, it offers a way to reduce reliance on any single data source. It is an open data ecosystem, where any geo referenced data can be onboarded and activated, including marketers’ first party data. Without an alternative to the third party cookie, it will be difficult to have confidence that we are reaching the right users in the right context.

What challenges are there at present to collecting and leveraging location data, and how can these be overcome?

Key challenges around collecting and leveraging location data are quality and scale, which represent an even greater challenge for GDPR-compliant organisations like us. As we know, the highest quality location data is not always readily available, especially as users limit access to their data, but this can be addressed through several solutions.

The first is to find a way to verify and enrich all location data so that we can use more of it. This can be done by using machine learning to analyse location signals, such as Taptap’s Location Quality Index (LQI). These systems use deterministic and high precision location to enrich location data signals, and index all data on a scale, enabling us to tailor data usage to the precision required by the strategy. They also learn from the outcome of each impression to understand how particular location signals (in conjunction with other campaign variables like supply) influence overall performance.

Other potential solutions include using extrapolation and predictive modelling of dynamic data for a future when there may be less access to this data. We are also incorporating new data sources that contain a location unit, and are independent from a user, for additional visibility and new kinds of analysis. The idea is that whilst these data points alone may not be enough to reach the right people, we can combine them with location intelligence to get the full picture.

What advancements do you foresee for geospatial data usage in the near future?

In the near future, we expect to see the volume of data sources that do not depend solely on the individual user to grow, and we are actively looking for this data to be able to onboard it to the platform. Likewise, we are preparing for a time when we use even less data that’s directly tied to users (either due to scarcity or client preference) and applying predictive modelling based on all the data to which we currently have access, both dynamic (in real time) and static sources.

On our roadmap, we intend to use this data to be able to further hone buying and bidding based on the context. In a similar way that we created different campaign scenarios based on audiences (as we still do), our idea is to be able to do the same, but instead of targeting individuals, we target the context. Much of the data provided for this technology ultimately comes from geospatial intelligence. This would include dynamically syncing ad units to real time contexts. Marketers are already familiar with a more rudimentary version of this system — serving a creative for ice cream when it is hot, for example — but through geospatial intelligence, we can build very complex contexts that again, reduce dependence on one-to-one targeting, but still ensure that we are serving relevant ads.