Pandemic-fueled lockdowns and disruptions in transportation systems hit the supply chain and logistics industry hard. From empty shelves to skyrocketing prices, disrupted supply chains have brought on inflation all over the globe. According to the Labor Department’s consumer-price index, inflation in the U.S. alone rose above 8.5 % after averaging about 1.7% for the past decade.
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.
To increase operational efficiency and maintain a profitable business model, businesses must gauge the performance of various processes and identify focal points that require change and strategic innovation. Business leaders must also develop a firm understanding of consumers and prospects, at scale, so that they can create effective marketing campaigns with broad appeal. Mobile location data is a powerful tool that helps companies achieve these objectives across business verticals by providing meaningful, actionable insights.
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