New-customer concept, repeat borrowers to retain customers
>> Read TPB Bank Case study in WSBI brochure
The following piece is taken from the new WSBI brochure showcasing institute members in Africa.
BRUSSELS, 20 December 2018 – Pride Microfinance, a Uganda-based microfinance institution and WSBI member, has successfully tackled customer / borrower retention through better understanding of the product, better client behaviour observation as well as feedback and suggestions from them. Pride Microfinance has done this through a highly acute customer-driven approach supported by a digital financial services offer and data analytics to study and predict customer financial needs and improving the customer journey.
The results show impressive gains on the retention front. The monthly microfinance retention rate has hovered above 95% for the past three years, helping boost institution profitability. This improved retention rate was reached through a highly customer-driven approach. They have applied what Pride Microfinance calls a “2by2by2” approach with new savers, which includes a call after two days after account opening to thank the customer, a follow-up call after two weeks to ensure good account functioning, and another call after two months to explore potential customer needs. Initiatives used for repeat borrowers range from adopted customer centricity model, incorporated digital financial services for client convenience and mystery shopping. They also employ financial education efforts and deep dive annually into branch operations with senior managers and strategic business unit heads assigned to work in a branch during a full week.
The retention challenge There are several reasons for a borrower to disappear from the bank’s radar. First, the credit does not sufficiently help them. Perhaps they are negatively affected by the credit or they find alternative financing sources. Another reason is the product features are too stringent or prohibitive, and eventually they also need to rest from borrowing. The bank is also addressing potential pitfalls that include issues around false creditworthiness, relaxation in loan appraisals or excess reliance on data analytics.