Blog
Posted on March 26, 2018

Beyond big data: how can we learn more about our clients?

Capturing how clients rationalise their take-up, usage or rejection of financial services and solutions.

The financial services for the poor, or more specifically financial inclusion, industry has realised in the recent past that more and better information about its target client-base is essential to delivering services effectively. We have seen an increased interest in concepts such as ‘client centricity’ and marketing approaches and tools, such as ‘human-centred design’ that in-fact are symptoms of a broader acknowledgement by the industry that we need to understand our clients better.

We are seeing recurring examples of different providers that are using these concepts or tools to inform the design and launches of their product lines for hard-to-reach markets; such as CARD Pioneer Microinsurance’s customer-centric business strategy model or Zoona’s use of HCD.

We are also seeing more intelligent support services being offered to these providers to develop products and services, and gather the data needed to do so; such as hands-on support from Dalberg’s Design Impact Group, ideas42 or 17 Triggers.

More importantly, we are seeing that this adaptive way of thinking about clients is appreciated by the clients themselves, with several examples of increases in demand for tailored and more carefully designed services by traditional providers and fintechs across the globe.

Complementary to these approaches, we have also seen a rise in the use of big data or alternative data to better understand clients. These types of data have allowed providers to be more creative in hedging risk and thinking about low-level liquidity. We have seen a migration to platform-based lending or virtual relationship brokering, and have started using non-financial and payment data to establish client-behaviours, usage patterns and therefore credit-worthiness. Industry is increasingly willing to be more open-minded, innovative and creative to understand clients, not only to serve them better, but also to encourage healthy financial practices and responsible behaviours. This puts the market in a better position to serve the ‘credit invisibles’ or support access to SME finance.” influence client behaviour and outcomes across the customer journey from uptake to use. Applying realist evaluation to financial inclusion, we look at the outcomes of financial service use as the result of a three-way interaction involving the intension/business model of the provider, the decision-making process of consumer and the context in which the interaction takes place. In this way, we aim to deconstruct the mechanisms that produce the customer experience and outcome, i.e. why and how a service is used (or not) in a particular way. As we apply this approach we are realising that the process by which the data is gathered under a realist framework can potentially be simplified (into a toolkit for example) to allow for a rapid assessment of client usage and preferences.

What differentiates this technique from more traditional market analyses is that it fundamentally draws on qualitative data to reflect the experiences of users. Unlike big data sets that depend on quantitative or statistical trends and patterns within the data, or HCD which is drawn from marketing techniques, the realist framework aims to capture how clients rationalise for themselves the take-up, usage or rejection of a service or product at different stages in the customer journey. This allows us to glean insights into why clients make the decisions they do, and to tap into these when designing and delivering solutions. FSD Kenya and Oxford Policy Management are continuing to explore how this approach can be practically applied within the industry to improve the win-win value proposition for financial services for providers and their clients.

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