Welcome to the Location Data Blog!

Mobile location data is a valuable resource for retailers, as it provides insights into the movement patterns of consumers in the physical world. By analyzing this data, retailers can gain a deeper understanding of human mobility and use it to improve their marketing campaigns, gain a competitive edge, and identify and address operational inefficiencies. In this article, we explore several ways in which retailers can use location data to enhance strategic decision-making, reduce costs, and boost profitability. 

The ride-hailing industry is characterized by stiff competition and new businesses are entering the fray every year. By 2026, the market size for ridesharing services is expected to more than double compared to 2021. For companies to thrive in this fiercely competitive landscape, they must optimize their routing systems to expedite pick-ups, match demand, and deliver more rides per driver for maximum profitability.  

Ridesharing services need accurate and up-to-date POI data to function. However, the underlying maps that enable their operations are outdated, and changes in the physical world are not reflected nearly as quickly as needed. A lack of current and contextual information can cost rideshare companies in terms of lost driver productivity, rider churn, and overall operational inefficiency. 

Urban planners and transport economists need location data to build better cities and facilitate equitable and convenient access to public transit. The movement of people within an area and between various Points-of-interest (POI) can provide valuable insights for city administrations, governments and businesses. Inaccessible facilities and public transit networks impact the socio-economic conditions of people living in an area, and therefore, affect the quality of their lives.  

Business Intelligence (BI) and data science companies harness location data to help their clients understand target audiences better. Insights derived from analyzing human mobility patterns can help retailers, FMCG, online services, and many other companies conduct effective marketing campaigns, expand to new areas, measure ROI, find underserved consumers, and more. 

A screenshot of Mapoteca’s analytics platform showing O-D analysis for specific POIs based on mobility data. 

Countless platforms are powered by Point-of-Interest (POI) datasets that are outdated.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.