Many organizations worry
Do we have enough data to gain insights and take meaningful commercial action? The short answer is ‘yes’. The long answer is ‘almost certainly yes’. In this case study, a Nordic giant in the automotive import and servicing business was facing a real crisis in their service centres where low utilisation was driving them to be unprofitable. They were initially sceptical that they would have enough quality data to gain practical insights, but at the end of this project they were astounded by the depths of insights and commercial actions delivered from the analysis, not just to reduce churn but across the business, their customers, markets, and competitors.
With customer contact typically only happening once annually at the scheduled service time and little opportunity to follow up with lapsed customers, there was some reason to be worried that we would not have sufficient data. However, there was data, albeit not consistent or complete for all customers or service stations; we used:
- Information about the car, including model, extras, sale price, recalls, and more.
- Information about the usage, primarily mileage at the time of service.
- Information about the customer, including purchase history and other transactions.
- Geographic information about the customer, including home and work address.
- Feedback from the customer, especially from the Net Promoter System.
- Information about the service stations, including geography (eg distance to customers), staff changes, shifts, and other operational data.
Insights into churn were developed from predictive models (when or how likely something will happen) and descriptive models (why something will happen). This project used both to ensure clear, relevant, and timely messages to the customers.
A key driver turned out to be the Net Promoter Score and related customer feedback. The project clearly made the economic case for the Net Promoter System™ (NPS). Their score was already world-class, but they found that there was still clear commercial value to be had from expanding the programme.
It turns out that churn is very predictable from service data, and this gives deep insights into why customers are not coming back. Based on these insights a clear list of immediate action was developed in three broad areas: Commercial changes, data opportunities, and improvements in execution capabilities.