Welcome to the Location Data Blog!

In the insurance business, many decision-making processes hinge on location intelligence and real-world geospatial information. Point-of-Interest (POI) data in particular is used for building contextual profiles of neighborhoods and determining precise risk scores for commercial and residential properties - which enables the creation of better policies. 

Many insurance businesses use satellite and aerial imagery to guide risk assessment and underwriting. However, neither of these tools can offer the comprehensive insights that can be unlocked through ground level geospatial data.  

Releasing a new consumer-facing physical product is difficult. In today’s hyper-competitive landscape, only companies that execute a data-driven marketing strategy during a product’s pre-launch, launch, and post-launch phases can expect to hit or even exceed – their goals. In this article, we elaborate on how businesses can leverage mobile location and Point-of-Interest (POI) data to make marketing initiatives across these three critical timeframes more effective.  

This week, we are celebrating collecting a quarter million Points-of-Interest with Geolancer. We have reached the 250,000 POI milestone in just 12 months, starting in two countries in Southeast Asia, and gradually expanding to five. Scaling a new product from zero is something we are proud of; however, our platform’s real strength lies in its quality. There is not a single dataset in the industry that even comes close to Geolancer’s accurate, complete, and rich metadata.

Businesses in need of accurate or customized Point-of-Interest (POI) data don't have good choices: crowdsourced or web-scraped POIs are often questionable in quality, and managed services are impossible to scale at a reasonable cost. Our proprietary POI Data-as-a-Service platform, Geolancer, is unique because it delivers the quality of managed services, the coverage of crowdsourcing, and the scalability of web scraping.

In this article, we highlight how we scaled Geolancer from zero to thousands of POIs in just a matter of weeks on markets as different as The Netherlands and Indonesia.

Countless platforms are powered by Point-of-Interest (POI) datasets that are outdated. More importantly, these datasets are not even tailored to the unique requirements of different businesses. For instance, even though a restaurant represents one POI, a specialised search engine will need information on the dietary restrictions that are catered to by the establishment while a food delivery platform needs to know if it has a drive through – data that represents distinct, custom attributes.