A large car dealership sought to improve their customer experience by resolving inefficiencies in the workshop and improving client-service agent communications when the client drops their car off for repairs. The amount of time taken by the diagnostic technicians to review the backlog of vehicles and their symptoms was effecting the way the garage was communicating with clients, leading to low satisfaction and inefficient work scheduling.
They sought an improved repair diagnosis approach that would:
Data Up: Heron used Machine Learning to build a model of relationships between symptoms and faults on data from multiple sources, which can be mapped to the repairs required and a cost estimate.
Human Down: Heron spent time with both Service Agents and mechanics to model their diagnostic approach in a knowledge map that would allow better questions to be asked of the client in order to provide more accurate diagnosis of the problems experienced.
A solution with an integrated feedback loop that provides both Service Agents and customers with the confidence that the diagnosis and recommended repair is correct and aligned to the pricing options provided.
Future integration with additional customer and car data will create a predictive model to inform what the client can expect in the next 6 months of the car’s use.