Data at Work

The world of data and its many applications. This blog will help you learn how visionary companies are utilising external data to enhance business operations.

A comparison of POI solutions on the market

Point-of-Interest (POI) data is instrumental in facilitating the operations of businesses such as food and last-mile delivery companies, those that develop mapping and navigation software, ridesharing companies, and many others. In other industries, such data is harnessed to derive insights to inform decision-making around site selection and supply chain optimisation.  

However, procuring good quality POI data is a significant challenge.  

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Dashboard: Footfall analysis for two competing fast-food brands in Charlotte City

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.


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How retail banks increase their competitive edge with location intelligence

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.

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Performing Extrapolation on Location Data to Derive Relevant Insights

Location data is collected from multiple sources of varying quality GPS signals from mobile devices, beacons, and WIFI connections, the notorious Bidstream, and more. In most cases, even genuine location data cannot represent the entire population of the region. This discrepancy can be attributed to smartphone penetration in the country, app-specific demographic variations, hardware inconsistencies, and sources of location data.

To perform meaningful analysis that accounts for mobility patterns and other trends in a larger region, data scientists use projection models to make an accurate estimation of a region’s population and normalise data counts to fit the use case. This is called data extrapolation.

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More Location Events Not A Guarantee Of High-Quality Location Data

Based on the advances in the location data space, it is surprising that a lot of companies continue to fixate on the number of events per device per day to assess the quality of location data.

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