Throughout my career in the food retail sector, a recurrent challenge has been estimating market potential effectively. Whether it involves brick-and-mortar stores or e-commerce platforms, determining where the consumer market lies has always been crucial. The fundamental question lies here: where are the consumers who truly resonate with your value proposition?
Historically, traditional food retailers relied on market research firms or industry sales data to answer this question. These methods were practical before the advent of today’s advanced data processing capabilities. Now, almost every significant country offers some form of census data on consumer behavior at a highly granular level. While these datasets can be challenging to locate and often hide in the obscure corners of government websites, they are typically free and, in liberal democracies, reliably accurate. From personal experience, having conducted these studies in over 20 countries, I can attest to their value, albeit their accessibility may require some digging.
These census datasets are invaluable for assessing market sizes at very localized levels— often down to neighborhood blocks. They usually provide essential metrics like population, income and food consumption patterns (both in-home and out-of-home), offering all the necessary building blocks to gauge market potential.
Whether you are part of a well-established company or a startup venturing into the food retail market, I highly recommend dedicating resources within your analytics team to mine sales and market data and put it to strategic
Using this data requires geospatial processing, which is now supported by nearly all database tools, making it feasible to create detailed market potential overlays. One particularly useful tool is the H3 geospatial indexing system, which can transform any consumption potential map into a standardized set of hexagons. These hexagons can be stored as strings rather than geographic objects, simplifying the application of various analytical processes significantly.
By employing these tools, we can gain insights into how cities function as social spaces, with different demographics occupying distinct areas. Such understanding can inform decisions about where to locate stores or distribution centers (like dark stores) to capture the optimal audience. The typical notion of placing a store in the most obvious, high-traffic location might not always be the best strategy if the target demographic does not align with the area’s characteristics.
Analyzing the socio-economic and demographic data from these censuses can reveal whether the expected target audience for specific products might be insufficient to sustain profitable operations. This might indicate a need to expand market vision or reconsider product offerings.
Whether you are part of a well-established company or a startup venturing into the food retail market, I highly recommend dedicating resources within your analytics team to mine this data and put it to strategic use. From product assortment to store positioning, all critical decisions should be guided by a thorough understanding of the geographic market potential. With adequate sales data, you can even begin to forecast specific sales points, enhancing your strategic planning and operational efficiency.
