One example I find particularly exciting is an initiative we are piloting. This involves providing customers with dongles that monitor key vehicle health metrics, including battery condition. Its a significant shift from the traditional reactive model, where customer interactions primarily occur during breakdown situations, which isn't ideal for relationship building or service expansion. When someone's stranded, they're understandably focused on getting back on the road - it's not the right moment for broader conversations. With this, the approach is more proactive, focussing on moving toward proactive customer engagement and value delivery. Looking ahead, I see enormous potential as vehicles become more connected and as regulations around smart vehicle data evolve. we'll likely have access to detailed health metrics - like engine performance, battery status, tyre health - well in advance of potential failures. This opens the door to truly predictive servicing and enabling us to prevent breakdowns before they occur and fundamentally transform how we serve our customers.

From an operational perspective, accurate location intelligence plays a critical role in optimising the service delivery. For example, guiding vehicles along the most efficient routes based on real-time conditions can significantly improve response times, which is especially crucial in time-sensitive situations. In areas where customer positioning is more challenging, such as rural locations, we’re also exploring ways to enhance location accuracy beyond the capabilities of public APIs. These efforts are designed to improve precision, ensuring better outcomes and a more seamless experience for customers.