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Mining geospatial data to design targeted marketing and sales strategies

Mining geospatial data to design targeted marketing and sales strategies

By Ben Leo, CEO of Fraym, a geospatial data analytics company using more than 2000 unique indicators for 200 African cities

Africa is a place of extreme contrasts, with a booming middle class and small, rapidly growing clusters of wealth existing alongside swathes of widespread poverty. For a business owner hoping to tap into these growing markets, the value of population and geospatial data at a granular level cannot be overstated.

How, for example, is a restaurant or coffee chain that is already experiencing promising growth in East Africa to accurately understand neighbouring markets, into which they hope to aggressively expand in the coming months and years?

Buying survey data from traditional providers, and assembled at the city-wide or regional scale, can only get a business so far. After all, knowing how many potential consumers are present in a city with millions of people does little to help you capture the right clientele, when much of a business’s turnover is dependent on local foot traffic and patronage of customers mostly based in the immediate area.

Location, Location, Location
What is the average household income in the area you’re eyeing-out for your next franchise location? How many children does the average family have? How do they consume media? How many people live and work in the area? Which competitors will you be going up against, and how popular are they? Knowing the answers to these questions can mean the difference between success and failure in any market, especially those as rapidly evolving as the ones in many African urban centres.

By combining existing household data with a far more granular layer of individual information, it becomes possible to construct a more useful target-customer profile than has ever been possible before. At Fraym, for example, we help our clients to identify potential customers at the hyper-local level through division of the geographical landscape into far smaller sections, often grids that divide communities into detailed areas of as little as a single square kilometre.

A competitive landscape analysis that takes as many high-quality data points into account as possible is essential to reducing chances of failure in growing markets. Fortunately, the digital data disruption is adding unique advantages to the practice. When customers are clearly understood, a business can make the most informed decision possible when searching for a location that will expand their operations successfully. Satellite imagery and complex machine-learning algorithms are an invaluable way to process such large amounts of data, looking for the common thread between household survey data sets and the geo-coordinates of the respondents, to identify new opportunities that would previously have gone unnoticed.

Getting Big Impact from Small Areas
Very often, businesses will consider locations based on anecdotal evidence or expensive survey data – inefficient, often dated, and usually not aligned to the data points that a business would find relevant to their own product or service offering. These anecdotes and surveys also often focus on national, aggregate results, missing important insights into consumer characteristics beyond traditional boundaries. But our experience has proven time and time again that success rests on understanding small clusters of consumers at the neighbourhood level, with pockets of untapped consumers often popping up at the intersections of traditional geographic divisions.

The Bottom Line: Quality Over Quantity
Ask any multi-national company for a quick opinion about the African business landscape, and you’ll likely hear that it’s intimidatingly low on consumer data, but also rich with potential that almost no other region can match. Granular consumer data that is informed at the individual and community level, rather than the aggregate level, is essential for businesses hoping to get a foothold in these fast-growing pockets of potential. It’s no longer enough to ask where customers live. We must ask how they live as well, and answering this question will depend on new data sources and creative ways of looking at them, if companies are looking to grow across Africa in the coming years.


 

 

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