Welcome to the Location Data Blog!

Point-of-Interest (POI) data is the digital representation of physical spaces that are of interest to individuals, businesses, and governments – such as malls, restaurants, train stations, residential complexes, hospitals, and schools. In other words, a POI can be anything that someone wants to find on a map. POI data acts as the lynchpin of operations in several industries, including ridesharing, last-mile delivery, logistics, real-estate, retail, marketing and the public sector. Even companies whose business model is not primarily built on POI data can still harness it to glean insights that offer a tangible, competitive advantage. Data scientists generate meaningful insights on the distinct characteristics of neighbourhoods, people’s movement patterns, and an area’s vulnerability to natural disasters by analysing POI data alongside mobile location data, demographics, purchase data, and environmental records.

Footfall analysis is a powerful form of business intelligence that allows brands to understand the visitation patterns around various POIs (Points-of-Interest). For retail stores, restaurants, and other POSs, footfall analysis can highlight important patterns such as the busiest time of day, competitor traffic analysis, and more. Whereas for government and public agencies, footfall analysis can highlight the consumption and demand of public services for the betterment of citizens’ lives.

Similar to other industries, financial services have been leveraging mobile location data to improve its operations and services.

Investment firms analyse mobility data in conjunction with POI data to forecast revenues for various retail outlets. This enables these companies to determine which businesses will deliver a good ROI.

Retail banks utilise location data in a number of ways. An important application includes limiting or preventing fraudalent transactions. Retail banks compare the IP address of a Point-of-Sale (POS) with the GPS coordinates provided by a customer's phone to prevent credit card fraud.

Banks rely on location intelligence to drastically improve the experiences they provide their customers. Here are three examples of how retail banks can use mobility data to enhance customer service and increase competitiveness. 

In each example, anonymized mobility data around Downtown Los Angeles between 1 October 2016 and 31 October 2016 was used.

Most publishers monetize their apps through one-off purchases, subscriptions, and in-app advertisements. However, subscription-based apps only account for 10.5% of the total revenue, and in-app advertising is disruptive to a degree that it directly causes churn. There is a third, better way of generating revenue from apps: data monetization. It is a new method and publishers often come us with a long list of questions. Here, we answer the most common ones.

Businesses catering to a community need to keep up with changes in the region's population, demographics, and cultural traits to remain relevant. In addition, urban spaces need to be constantly redeveloped to accommodate changing movement patterns. For example, real estate companies need movement data to discern the residential needs in growing neighborhoods. Public transport authorities need to monitor travel patterns to assess demand and expand transit services to underserved areas. The analysis of mobile location data can provide a clear image of human mobility, and changes in public interaction with businesses and government facilities.