The location data ecosystem is maturing. We’ve played in this space from its earliest days and have always embraced the promise that is increasingly being delivered upon.
As we progress as a community, the way we see it, companies in our ecosystem naturally form into a kind of pyramid—NOT a hierarchical one but one that reflects how the different location companies fit into the ecosystem, who does what, their limitations, and their areas of focus. This image to the left is what we see.
It goes like this: data aggregators feed raw location data to human mobility companies that then feed contextualized location data to location data application companies. It’s super helpful to understand not only HOW these companies fit/play together and feed one another in our ecosystem but what they all are going to become.
Because we’ve spent so much time thinking about and helping improve upon the connective tissue of these relationships, we want to share our view and analysis. Each set of companies has different characteristics that separate it from the others and will influence these layers’ future paths.
This is where the location data industry once started. Data aggregators license and aggregate location data and sell it to buyers, in more or less the same format as the data originated. If you do a quick LinkedIn and Crunchbase search, you´ll see that these companies are often 3-10 people, staffing few engineers/data scientists and operating with limited funding (friends, family, and angels). Some also sell data segments and simpler products that other entities go on to leverage within their own data solutions.
This area of the industry is one where we can expect a great deal of consolidation, because there are many players, with little differentiation. Further, I believe it will be challenging for these players to retain the bandwidth to comply with privacy standards and regulations, including GDPR. As a result, they will struggle to move into the European market and may even find it difficult to operate in the U.S when new privacy laws are enforced, which we should expect to happen, as we are already seeing with California.
Human Mobility Companies
There is a limited number of companies that can leverage raw location data from the data aggregators mentioned above, because expertise in geospatial data and contextualization is needed. As a result, human mobility companies have transformed over the recent years. These companies take in raw location data and, by using algorithms, machine learning, and exercising a true understanding of the capabilities of location data signals, turn it into actionable data sets about how humans move around on the planet. The result is datasets ready to be used by other companies in multiple verticals to build better products and make better decisions. These datasets are typically used by data teams, analyst teams, data scientist and product teams.
Resolving complex and fragmented geospatial signals is a time-consuming and hard task and requires a strong data engineering and data scientists team to do it right. Keep in mind, location data is the convergence of two amazing technologies: GPS satellites, 31 in total, orbiting the planet at mind blowing 12,000 miles above us, and a tiny GPS chip located on our smartphone. With such distances, the margin of error in the data is real, and it is hard to be 100% accurate. Therefore, human mobility companies have, over the last few years, raised large rounds of funding to invest in people and processing to interpret all the signals and provide the best possible understanding of how people move around on planet Earth.
Human mobility companies started selling their contextualized data sets to the marketing industry, mainly because companies in this industry are very data savvy. They are always looking for new innovation in a fiercely competitive space, and they have the infrastructure, teams, and understanding to use large amounts of data. In the last 18 months, human mobility companies have increasingly focused on serving and building datasets tailored for companies within transportation, city planning, retail analytics, research, and real estate.
To succeed, human mobility companies need to be extremely agile, ensuring sufficient domain expertise within each industry that allows them to understand what location dataset they need to build to solve their clients’ business challenges, without going too deep and getting stuck, blocking them from approaching other verticals.
Location Data Applications Companies
As location data contains insight into what people do 70% of our waking time, several other industries have set their sights on this dataset and how it can enable them to build better products and make better decisions. This final part of the location data ecosystem includes companies using contextualized location data as part of their understanding to make their core products better, stronger, and more exciting for their end user and clients.
Industries such as transportation (determine fastest route from A to B), city planning (where to build bus stops and parks), retail analytics (understand where customer go before and after a visit), research (demographic behavior based on movement), and real estate (choose where to open or close a store) were mentioned above. In addition, we believe we will see further adoption among companies in other verticals such as e-commerce (tailor online sites based on visits in physical stores), autonomous cars (make driving safer), hedge funds (use foot traffic data to bet/short stocks), and insurance companies (better calculate premiums). While these developments are not fully realized, this is where each of these verticals is going—and soon.
The maturity level for the companies in all verticals above vary, but what they all have in common are data teams that know how to leverage quality data. Adoption is happening rapidly as use cases get proven out and the value of contextualized location data becomes evident. And the clear trend we see is that more companies across multiple industries invest and hire data teams to onboard location data and other data sources to make their core products and services better. They simply have to, to keep up with the competition.
As the location data industry further evolves and unleashes its massive potential, we will see new branches and new directions taken by highly innovative companies. What do you think location data will be used for in the future? Where will it lead us?