Pricing predictions through machine learning and predictive analytics for leading automotive repair organisation


In order to get and maintain a competitive edge, dynamic pricing is vital for businesses in the automotive industry. Our client, which offers vehicle maintenance and servicing through its autocentre business, was a business that really needed to implement a dynamic pricing model. 

Vehicle service providers aren’t able to present a customer with a fixed service price upfront and can only give estimates, so a customer may end up paying more than was originally estimated, which can result in a negative customer experience. Providing customers with estimates impacts the transparency of the service for the customer and makes it difficult for autocentre managers to forecast revenue accurately.


The analytics practice at Valcon designed and delivered a multi-algorithm solution that predicts the most suitable fixed price that a customer will pay upfront via the application of statistical, predictive and machine learning (ML) models. 

The solution calculates a ‘price corridor’ through statistical methods, the safe range in which to quote a price based on actual historical prices paid for services for a specific vehicle make and model. The solution predicts the likely workload for a given autocentre on a given day, leveraging machine learning. 

How busy an autocentre is will determine where a final price falls within the ‘price corridor’. The pricing metrics for a particular vehicle and service are presented to the customer via an API to the company’s website, supporting 3m bookings per year on average, or 6m predictions. 

The solution is delivered on the Microsoft Azure cloud using the best-of-breed services available and utilising modern approaches to operationalise and productise analytical solutions from DevOps to proactive ML model management using Valcon’s proven architectural patterns and methodologies.


Dynamic pricing has proved important for our client in terms of maintaining their competitive edge. Key benefits include: 

  • Uplift in sales: our client found that they experienced a greater conversion in sales, which was due to transparent and predictable pricing
  • Increased customer retention: customers are happier that they pay the price they are estimated and not caught out by surprise hikes in the final cost
  • Better financial forecasting: the dymanic procing solution that the client was able to conduct accurate revenue forecasting at an autocentre on a regional and national level
  • Improved margins: ultimately, a more effective way to provide estimates, improved sales conversion and better client retention and satisfaction has resulted in an uplift in profit. 

Case Studies