Cape Town - By combining existing household data with a far more detailed layer of individual information, businesses can construct a more useful target-customer profile than has ever been possible before, according to Ben Leo, CEO of Fraym, a geospatial data analytics company focused on African.
In this way potential customers of a business can be identified at a hyper-local level by dividing the geographical landscape into far smaller sections. Using data in this way, communities can be divided into detailed areas of as little as a single square kilometre.
This is in contrast to the way surveys have often been done in the past, namely assembled at a city-wide or regional scale. In Leo's view, this traditional way of only providing data on a city-wide or regional scale, can only get a business so far.
On the other hand, more detailed data can be used to establish what the average household income is in a specific area a business is interested in for its next franchise location, for example.
With a geographically much more detailed data set, a business also establishes how many children the average family in a certain area has; how consumers in that area consume media; and how many people live and work in the area.
The far more detailed data can also be used to establish which competitors a business would be up against in a certain area and how popular they are.
"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," explains Leo.
"For a business owner hoping to tap into these growing [African] markets, the value of population and geospatial data at a granular level cannot be overstated."
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, in his view. It’s no longer enough just to ask where customers live. A business must ask how they live as well.
"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," says Leo.
"Granular consumer data that can reflect information 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."
He regards using satellite imagery and complex machine-learning algorithms as an invaluable way to process 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.
By relying only anecdotes and surveys that focus on national, aggregate results, a business could miss getting important insights into consumer characteristics beyond traditional boundaries.
"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," says Leo.
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