There’s an explosion of mobile application companies (mostly startups) in East Africa, and especially in Kenya, and from what I’ve been able to gather most of them are very focused on the customer experience and the front-end technology. This is not unreasonable, as it’s the customer experience as provided by the front-end (on the phone) that is likely to make or break adoption of the app, especially in the early stages of a company.
However, a big part of the value that I see in mobile apps – both in general and specifically here in Kenya – lies in the ability to aggregate apps into “meta-apps” and in the potential value of the data that is flowing through the apps. Any app developer who moves data from the phone to somewhere else will have to do something on the back end to store and process that data, and structuring the data from the beginning with analytics in mind will prove beneficial in the long run.
Consider a health application that delivers pre-natal tips to pregnant women (such as Grameen Foundation’s MoTeCH initiative). The primary purpose of an application like this would be to provide information to women that helps them to ensure the best possible health outcomes for themselves and their children. Beyond that, though, the application could answer questions that the mothers ask via an SMS or USSD interface. There’s some complexity in building the parsing engine needed to interpret the questions and find the right answers to send back, certainly, but it would also be super valuable to build the back-end in a way that the organization could analyze the questions being asked, cross-reference that against the information being sent out, and make better decisions about how to further improve the outcomes of the app.
Apps that involve mobile commerce, or politics, or just about any other domain are likely to have very interesting analytics implications. Further, many apps may have interesting cross-referencing possibilities. For example, with a common back-end that serves both health and financial purposes (and gathers health and financial data), the aggregators of that data will have the ability to mine the data to find correlations (maybe between savings and health?) that can be used to improve the apps and to identify new potential apps for specific target markets.
The key thing in getting interesting use from the data – including usage patterns, data collected, and the like – is getting enough scale to make the analytics possible. Building for the back-end from the beginning rarely makes sense, and instead scale will come from killer apps that get hundreds of thousands or millions of users over time. A couple of apps at scale, integrated on the back-end, would provide an incredible dataset for the application developers and other stakeholders.
For my current work looking at Mifos possibilities in Ghana and Kenya, this becomes very interesting. Mifos could become the back-end platform for a wide variety of microfinance-oriented apps (and eventually for other poor-focused apps). These might include access to account information for microfinance clients, loan officer apps to improve productivity and security, and management tools that tell MFI executives what’s happening in their organization and where their attention is needed. Mifos could be the back-end for those apps even if the MFI wasn’t using Mifos as its core technology platform.
The business model, like most technology ventures, may be the hardest part. If we were to pursue this idea, getting the apps and other services to scale is the necessary pre-requisite to building a business on the back-end data aggregation and analysis for both individual MFIs and (with the right privacy protections) for meso- and macro-level stakeholders. A methodology like the Customer Development Process that starts with a strong product vision but then uses a rigorous approach to testing hypotheses and building a viable business model before trying to get to scale would be key to making this work.