African companies must unlock the data dividend
Understanding localised consumer markets often means the difference between success and failure for businesses.
Contextual awareness is essential in emerging markets. It is also acutely difficult to acquire without spending decades pounding the pavement city by city and neighbourhood by neighbourhood. We inherently know that consumers look differently across regions. Moreover, moving just one kilometre or two within a city can dramatically shift what the population looks like, what they buy, and how they behave. In the Nairobi neighbourhood of Nyayo Estate, for example, nearly 90% of consumers are in the top tiers for purchasing power and have a bank account. Yet those figures fall to less than half of the consumers just one or two kilometres away in the informal settlement of Mukuru.
Only by understanding these spatial dynamics can businesses make truly strategic decisions about where to focus scarce resources, whether they are launching a brand campaign, opening a new store, optimising distribution networks or starting a new product or service. The businesses that do will be ones that win the future.
Dreaming of data
Thanks to recent technological advancements, it is now possible to obtain data that have long been the dream of consumer-facing companies. Satellite imagery, household surveys and cloud computing can be used to reveal population characteristics in countries, cities and neighbourhoods across the entire African continent, even down to the 1km² level. The days of being forced to make big business bets off highly aggregated market statistics are gone. The African data revolution has officially begun.
This means the ability to incorporate human-centric intelligence such as demographics, spending power, access to services, electrical grid connectivity, occupation and employment types, and media consumption patterns. These insights add an entirely new dimension to strategic planning and can answer questions like ‘Who exactly is my best customer?’, ‘Where should we focus go-to-market resources?’, ‘What’s the best way to optimise sales performance?’ and ‘How can we monitor whether our teams and distributors are performing?’.
Applications for this type of data are vast and travel across sectors. Consider a scenario in which a bank wants to establish an agent network in areas where they don’t currently have a brick-and-mortar presence. Banks across the continent are rapidly moving their operations into low-cost, highly distributed agent-based models, which are complemented by digital banking services. For these institutions, the ability to zoom in on, and surface, prospective communities that have concentrations of target unbanked households can dramatically accelerate customer acquisition. Even more so when they can consider where the competition is already located; thereby identifying true white space to exploit.
Let’s consider another example. Fast-moving consumer goods (FMCG) companies are often looking to improve their sales performance in secondary markets where they traditionally have taken a passive, distributor-led approach. For them, it’s almost impossible to analyse whether these distributors are actually performing up to potential. Candidly, most companies are unable to independently track performance the moment their products leave the factory in their distributors’ trucks. It is a massive and highly frustrating blind spot for FMCG companies. Yet by combining territory-based sales performance data with hyper-local consumer data, businesses can calculate and track their market penetration, identify latent demand and then optimise their sales and distribution networks. We’ve seen companies increase sales by up to five times by taking this approach.
Companies in need of actionable strategies
Producing high-quality data is only part of the challenge. The second hurdle is making that data accessible and actionable. Having an enterprise-wide platform simplifies the use of complex data and creates an opportunity to coordinate activities across business units and tackle problems at scale. Take financial inclusion in Nigeria as an example. Due to Nigerian government lending targets and incentive programmes, many banks are establishing business strategies for specific groups, such as smallholder farmers. An enterprise-wide geospatial data platform therefore allows banks to do the following: identify concentrations of smallholder farmers down to a 1km² level; understand crop production and seasonal income patterns; analyse pre-existing financial services usage; and overlay marketplaces where farmers may congregate on specific days. These activities clearly span numerous corporate capacities. Yet this single platform allows each to make data-driven decisions that collectively add up to a highly actionable strategy for execution. This is the power of cutting-edge geospatial data tools.
When it comes to making decisions, context is king. It increases the value of other information sources and provides a framework to operate within. By incorporating information about people’s needs and whereabouts into growth strategies and operational planning, you can pioneer the way toward more successful outcomes for African businesses and communities.