Bihar DISCOMs Rural Revenue Franchisee (RRF) scheme 2013 involves hiring locally contracted ‘off-
rolls staff’ to read electricity meters, distribute bills, and collect revenues from consumers. However,
the RRF program has not been able to improve service coverage.
SPI initiated the “enhanced Rural Revenue Franchise Program” (e-RRF) in partnership with the
DISCOMs to improve the customer service experience, add micro-enterprises and improve the overall
billing and collections.
The key focus areas include customer engagement informing them about the benefits of grid
connection, enhancing e-RRF performance by creating a performance monitoring framework, using IT
based interventions to eliminate human errors in billing, instituting customer indexing with DT
metering to understand village’s billing efficiency, training and capacity building of DISCOM staff and
During the pilot phase of six months, a total of 16,000 connections totalling to nearly 80,000 people
SPI also developed an AI-based “Optical Character Recognition” (OCR) technology that would
eliminate human errors in billing.
A need for creating a customer relationship management (CRM) tool was also realized. Going
forward, an inclusive app will be designed which will have an integrated ORC and CRM.
The performance of e-RRFs in a short period of implementation has paved the path for developing
customer-centric models leveraging customer satisfaction. e-RRF provide win-win for both DISCOMs
and customers. It can improve financial viability of rural customers for DISCOMs through improved
revenues, on the other hand, for customers it ensures better services in rural areas.
Owing to the efforts, the number of paying customers increased from 30% to 47%, DISCOMs
financials improved due to an additional collection, and there is an increase in the number of
customers with 341 Kw load added to the grid. A significant improvement in billing was also witnessed
with 16% growth in the number of customers receiving timely bills.
SPI is in process of developing a customer centric, scalable and enhanced model integrating e-RRF
program’s learnings that could be executed state-wide.