Doubled realisation rates for data subject access requests
Utilised machine learning and the firm’s legal delivery centre within a £5m employment practice.
Realisation rates increased from 40-50% to more than 90% for this fixed fee matter type.
Context
The employment practice was receiving Data Subject Access Requests (DSARs) to process on behalf of key clients, but were unable to offer these at a competitive fixed fee.
This was putting the wider client relationships at risk, and the law firm was at risk of losing the higher margin employment litigation work as a consequence.
Approach
On review of the delivery model, I identified that the team was running the matter in a similar manner to a traditional document review exercise; a very manual process using associates within London.
We reworked the delivery model to:
Enable better delegation to more junior, and cost effective fee earners
Enable use of lower cost regional fee earners outside of London
Use an Artificial Intelligence product (Relativity) to reduce the hours required to perform the work
Impact
Against the fixed fee expectation stipulated by the client, these changes increased realisation against the firm's standard rates from 40-50% to in excess of 90%.
This made the fixed fee matters more profitable for the law firm than the discounted hourly rate litigation work the DSARs were meant to be a loss leader for.