How many people live in Kibera? For a long time, Kibera was considered the biggest slum not only in Nairobi, but in all of Africa, with allegedly over 1 million inhabitants. The 2009 Kenya Population and Housing Census counted 170.070 inhabitants. In 2010, UN Habitat estimated Kibera’s population at 500.000 to 700.000. Such is the quality of data for many, if not most low-income markets in developing countries. If we don’t even know how many people live in a certain area, imagine how little we understand about their incomes, spending patterns, or consumer preferences. Low-income markets remain dark data-wise.
Without data, investing becomes a gamble. Companies and entrepreneurs building new business in low-income markets usually collect their own data via surveys. This is expensive, and usually leads to poor quality of data because respondents often answer strategically, or do not understand the question or do not know the answer. The best way to find out whether a product will be bought and for what price is to sell it, leaving entrepreneurs reliant on a costly and lengthy trial-and-error approach. Business plan calculations for revenues are pure guesswork at this stage. Not only the entrepreneurs, also the investors know this. Besides “patient investors”, who are sometimes not even expecting to get their money back, capital for early stage entrepreneurs is extremely limited. In the absence of reliable numbers, investors understandably want to see a solid financial track record of multiple years before they provide equity or even debt capital.
Accurate, deep and up-to-data data could unleash investment in low-income markets. And the good news is: this data is already being collected. Off-grid energy providers measure when energy is used and on which devices. They also receive payments from their customer’s mobile phones and hence know who can pay reliably, and who can’t. Mobile money operators register billions of transactions by low-income customers, receiving salaries, paying for water, buying soap, or sending money to relatives. Mobile money service MPESA processes 6 billion transactions in 2016. If we can pool and analyze these and other data sets, we can get a robust understanding of the low-income market.
Artificial intelligence can digest the available “big data” into useful market information. By combining different sources of data and analyzing it using smart algorithms, we can now create much more reliable data on an ongoing basis. This requires collaboration between the various organizations providing data as well as artificial intelligence experts. Ideally, investors come in at an early stage to specify their demand for data.
At ii2030, we will explore how to capture this opportunity. Off grid energy providers, network operators, AI experts, intermediaries, and investors will work together to understand which data exists, what is needed, and how to use artificial intelligence to get from A to B. Join the online event, and become part of the solution!