De-risking a geolocation-based feature for an Indian fintech’s new smartphone app

Eko is a fintech with over 13 million users in India, mainly migrant workers from poorer states in the country. These users send money home using a vast network of “cash-in / cash-out” retail agents that are part of the Eko network. The start-up wanted to assess a feature inspired by ride-sharing companies. The Eko app would match individuals who needed cash with other nearby users who could provide it within a geo-referenced area. The executives felt this would help users address cash crunches for unplanned scenarios or when agents were closed or otherwise inaccessible. CGAP, a financial inclusion think-tank housed at the World Bank, commissioned Digital Disruptions to help Eko with the assessment, particularly to find ways to reduce perceived risk and overall pricing structure.

Approach & Outcome

With an aggressive timeline from Eko to have concrete recommendations for a new and improved app, Digital Disruptions designed a 6-day product sprint. The sprint composed of focus groups with existing and prospective customers, usability testing, internal ideation meetings with the Eko and CGAP teams, and low-fidelity prototype building and testing sessions.

A key recommendation was for Eko to take a more prominent and visible role in “matchmaking” between the two sets of users. Through the interviews, several participants expressed reluctance to meet with strangers simply based on their nearby location. However, during the prototype testing phase, they reacted positively to the concept of Eko “suggesting” a matching counterpart while also allowing the user requesting the service to select other prospects. This recommendation, among others, helped Eko launch its first smartphone app with the feature shortly thereafter.