Welcome to the Location Data Blog!

 

Learn how companies all over the globe are utilising location data to enhance business operations and improve profitability. Keep up with industry updates, best practices, and key learnings from location intelligence projects we have executed.

Preview: The big book of Point-of-Interest data use cases

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.

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Monetizing mobile location data: publisher questions and answers

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.

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How transport economists use mobile location data to improve public transit

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.

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Using mobility data to measure retail and supply chain performance

Businesses that want to thrive must improve their performance across different functions to retain and increase market share. Nowadays, companies are tapping into a powerful form of business intelligence to achieve this goal: mobile location data. As a leading provider of geospatial information, Quadrant supplies data to customers that allows them to execute various analyses like measuring marketing ROI, site selection for expansion, benchmarking against competitors, and predicting performance. These analyses facilitate the optimisation of different operations which increases revenue and decreases costs.

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Using AI to clean Personally Identifiable Information from user-generated data sets

Having access to large repositories of data enables businesses to optimise operations in several ways. This includes personalised advertising, greater supply chain efficiency, and more satisfying customer experiences.

However, people have grown increasingly wary of trusting businesses and governments with their data. Several high-profile data privacy breaches at LinkedIn, Alibaba, and Yahoo (to name a few) collectively impacted billions of users.

Ethically managing data and making sure no Personally Identifiable Information (PII) makes it to big data sets is a challenge we take very seriously at Quadrant.

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