A food delivery app wanted to expand into other geographies but lacked data intelligence to evaluate the attractiveness of said markets and prioritize its expansion plan.
The company faced a particularly challenging situation given that the regions of interest were considered fragile state countries, where data is often scarce, outdated, or unreliable.
CrossBoundary Data Analytics gathered several open access demographic datasets that use satellite imagery, UN population estimates, surveys, and other primary sources to create fine-grained population estimates. Applying hot spot analyses to compare these sources of information allowed for an increased level of certainty of the quality of the data. The team then combined the curated data with internal client data to train a model that could predict the size of any market at a 1×1 km level of granularity. The results where mapped using geographic information systems (GIS) software to visualize and crosscheck the insights with field data.
We provided our client with a selection of cities and their most attractive areas at the desired locations by mixing in ground expertise, with data analytics. Additionally, we helped our client build internal expertise to fine tune and further train the model, allowing them to replicate the analysis to assess and execute future expansion strategies.