What we learned from the “Matching Products & Preferences” Webinar

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Last Friday’s Bankers without Borders panel discussion on product development explored the central role operational tools and data analysis play in allowing practitioners to measure their impact and better determine client needs. Leading the session were Jesse Marsden of the Microcredit Summit Campaign, JD Bergeron of Truelift, and Guy Stuart of Microfinance Opportunities. A video recording of the webinar will be released shortly.

A framework for change

Jesse Marsden

Jesse Marsden explained the central role that data plays in helping institutions understand their clients. He began by describing the rapid evolution of the industry. In 1997, microfinance service providers had reached only 13 million clients around the world. Nine years later at the 2006 Microcredit Summit, nearly 100 million worldwide had received financial services from MFIs. Marsden explained that although this statistic is impressive, this kind of outreach goal must be accompanied by equal emphasis on an outcomes goal. Gaining clients is not enough. Institutions must use cost-effective poverty measurement tools to help strengthen their performance. Marsden concluded by outlining changes that practitioners can make to support clients moving out of poverty:

  • Building partnerships among institutions that leverage existing work to create outcomes that are more far-reaching than possible individually
  • Informing practitioners about the importance of data use in guiding institutions in making the right changes in products and services
  • Using data to ensure that new product development and innovations in delivery channels are truly addressing client needs
  • Demonstrating the power of collaborative and creative effort to encourage other organizations to implement similar changes

Rewarding innovation and adapting failure

JD BergeronSpeaking second, JD Bergeron began by describing the Truelift “Seal of Excellence.” He compared the Seal to a fair trade label:  i.e., a trust mark that signifies commitment to real and sustainable growth out of poverty.

Progress within Truelift diagram

The Truelift Progression

Since changing its name from the “Seal of Excellence for Poverty Outreach and Transformation in Microfinance” to Truelift, the organization has renewed its focus on the pro-poor objective of microfinance and is striving to create a learning environment known as the Poverty-focused Microfinance Community of Practice (PovCoP), which recognizes practitioners doing the most for their poor clients.

Bergeron explained that the concepts that Truelift is struggling with are not new–rather they are the basic tenets of customer service.  He noted details such as the language-abilities and gender of loan officers as central to client retention rates. He also named the disaggregation of data by poverty level as a rigorous way of tracking progress over time and suspects that the next few years will show new tools in this area.

Bergeron later described one of the main challenges facing progress in microfinance – the fact that there is no reward mechanism for failure. “Things are often cast aside rather than reconsidered,” he explained. Because trial and error innovation cycles can be costly and challenging for MFIs, Bergeron wants to create a social reward for practitioners and institutions pioneering new tools and methods. Through PovCoP, Truelift is striving to find a way to highlight those emerging organizations facing challenges and grant them the technical support and investments they need to succeed. (Join PovCoP today!)

Using “big data”

Guy StuartGuy Stuart described the two frameworks in which data can be considered. First, breakdowns, such as an increase in the default rate, can result from an over-reliance on performance management indices. Setting too much stock in such specific data sets can create a conflict between the incentives provided to front-line employees and an organization’s business model.

digdataHe went on to discuss the possibilities of “big data”–the myriad data sets that both banks and MFIs collect but are not using or considering to use for decision-making. Stuart gave examples of the many variables, including mobile voice, text, and money transfer data that could help predict creditworthiness for a client. He concluded by explaining challenges inherent to data use; for instance, there is no consistent quality on mobile number data for customer follow-up.

Marsden, Bergeron, and Stuart thus explored both the advantages and challenges of designing financial products to fit the needs and preferences of clients. Although well-developed and flexible bench-marking technologies adeptly allow practitioners to measure the outcomes of their work, many organizations face major difficulties developing these tools or initiating their use.

Microfinance transaction

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