Welcome to the Location Data Blog!

As users, we are not surprised to discover that a restaurant or a store on the map does not exist in the real world. It might have closed, moved, or never existed in the first place. Low quality POI datasets might be a mere inconvenience in our personal lives; however, this is a multi-million dollar issue for businesses.

Until recently, we couldn't even tell how big the problem is. After collecting and verifying 5,000 electric vehicle charging stations in Amsterdam in person, we have an answer: it is enormous.

We discovered that more than 14% of the locations in the city’s database were incorrect, and more than 11% of the data from a popular mapping platform was outdated. And this is just the tip of the iceberg. 

Mobile app development and maintenance is a resource intensive and time-consuming process – which is why the primary objective of publishers is maximising the revenue they generate from their apps. One of these is data monetization.

Data monetization, however, is not what usually comes to mind first. Since several successful platforms like Spotify, Tinder, and Netflix use subscriptions, one might be inclined to believe this is the most common monetization strategy. However, out of the $111 billion generated in mobile app revenue in 2020, subscription-based apps on both Apple’s App Store and Google Play collectively earned only $11.7 billion. This statistic offers a key insight: subscription models capture the imagination but only account for approximately 10% of all revenue generated from apps. Therefore, it is important to explore alternative models for generating revenue. 

Businesses that want to leverage location data must procure high-quality datasets because erroneous data will result in false insights, and therefore, poor decision making. However, not all market participants are transparent about their data practices. In this article, we share background information about how the team at Quadrant analyses the quality of location data we provide our buyers – some of the steps we take to ensure it is of the highest quality possible for their particular use cases. 

Cities worldwide are dealing with rapid urbanization, changing travel patterns, and most recently the aftermath of a global pandemic. As cities sprawl outwards to form low-density localities, it is difficult and expensive to serve these suburbs. Transit systems were also one of the worst-hit sectors by Covid-19, and post-pandemic recovery has been complicated due to remote and hybrid working. 

For years, Quadrant has been offering high-quality, authentic, and reliable mobile location data. In our interactions with customers and prospects, we discovered that many of them seek Point-of-Interest (POI) data to go with their mobility analysis. Good quality POI data is critical for operational efficiency, especially in sectors like transportation, delivery of consumer services, supply chain logistics, freight, and many others.

However, existing off-the-shelf POI databases are plagued with issues. They are not updated frequently enough, and the way they are assembled -- most often through web scraping -- leads to inevitable inaccuracies.