What Really Differentiates Location Data Companies from One Another?
By Geoff Michener, CEO and co-founder of dataPlor
As the martech and customer data industries have grown, it has become more challenging to differentiate one business from another. Go to the website of any martech or data firm, and you’ll find similar claims of actionable insights and powerful technology. How are buyers supposed to tell solutions apart?
The same applies to the location data industry, where it is common to claim to provide global scale, granular insights, and top-notch precision. But surely, not every location data company can cover every market and every type of data asset in that market. Every company has strengths and weaknesses. So, what really differentiates one location data company’s strengths from another’s?
Three capabilities for which buyers should vet location data companies are technology that can scale, validation of data quality, and the combined use of human and artificial intelligence to supply the most accurate possible information. Here’s how buyers can think about each of those criteria when evaluating location intelligence solutions.
Technology that can scale
Many, if not most, location data companies pledge to provide international, if not global scale. But how do buyers evaluate these claims, kicking the tires to determine whether the firm actually can provide high-quality data across diverse markets?
First, buyers should question what location data companies mean when they say they have data across markets. What kind of data? Is it mobility data purporting to show where people are? If so, what does the company know about those people? Can it combine mobility with demographic information? Does it have permission to share their data? Is the data anonymized but still actionable?
If the firm offers places or point of interest data, what can it tell the buyer about those places? For example, let’s say a company claims to be able to show all the Taco Bell locations in Mexico. Is its data limited to that one company, or can it show complementary locations such as competitors and suppliers instrumental to the QSR business? How does the firm’s location intelligence open up growth opportunities?
Finally, what proof does the company have that its data is accurate in a given market? Acquiring data on places or people in international markets is feasible; verifying it is considerably harder. This brings us to the second major question those evaluating location intelligence services should be asking vendors.
Validation of data quality
As much as 70 to 90% of international location data is inaccurate. While the US has fairly extensive local business information, data in most other countries is not nearly as comprehensive. On top of that, location data vendors are incentivized to chase scale, which can lead to short-changing buyers on accuracy. To that end, buyers need to be on the lookout for low-quality data, especially when faced with guarantees of global scale.
One sign that a location data company is being honest about the accuracy of its data is that it supplies confidence scores to indicate just how sure it is of the quality of different types of data across markets. For example, location data quality is likely to vary from developed to developing markets; how long a vendor has been in a market could also be a factor. If the company can tell you how certain it is about a given data set, that’s a sign that the firm is doing the work required to help you make informed decisions and wrestle with the uncertainty that comes with data-driven decisions.
Another factor to consider is whether the company has external validation of the quality of its data. The firm should have past clients who can attest to superior quality in international markets. Here, it is important to distinguish between claims about data quality in developed economies and those abroad. Mapping the US and Canada with a high degree of accuracy is relatively achievable; doing the same in Latin America and Southeast Asia requires a different level of verification, more extensive partnerships, and local expertise. Vet the provider on each market, and don’t assume quality in one assures it in another.
Combination of human and artificial intelligence
Buyers should also evaluate the verification techniques location data providers employ. Some vendors rely too much on manual processes, surveys, and academics, introducing an unnecessary amount of human error. Others rely only on public data gathering with no confirmation from local experts. The most effective approach strikes a balance between humans and machines.
For example, dataPlor, which specializes in point of interest or POI data, takes a data-rich approach whose accuracy is amplified by last-mile human verification.
The company deploys AI call bots that call businesses in local languages to capture and check information. It uses machine learning to deduplicate and shore up the accuracy of data as well as deep learning image recognition to process data like store signs for another layer of verification. Finally, dataPlor employs local human experts in every one of its some 100 national markets to provide final verification.
Buyers of any complex tech-driven solution know that vendors sometimes make claims that are too good to be true. This practice is rampant in location intelligence, where the imperative to scale globally leads many providers to claim to offer data on markets where they cannot ensure top quality. By focusing on not only quantity but also quality and interrogating providers’ data verification processes, organizations in the market for location intelligence solutions can be sure that the insights to which they are purchasing access are as premium as data providers promise.