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Driving Sales Growth with AI: A Case Study on Regit
Technology Category
- Analytics & Modeling - Machine Learning
- Robots - Autonomous Guided Vehicles (AGV)
Applicable Industries
- Automotive
- Education
Applicable Functions
- Sales & Marketing
Use Cases
- Predictive Maintenance
- Vehicle Performance Monitoring
Services
- Data Science Services
The Challenge
Regit, formerly known as Motoring.co.uk, is the UK’s leading online service for drivers. The company faced a significant challenge in predicting which of their 2.5 million users were likely to change their vehicle and when. This information was crucial for the company to serve their customers in a more personalized and targeted way. The company operates by generating leads for companies in the automotive industry by providing a platform for people to book test drives, buy and sell cars, and request brochures, among other services. However, they were unable to predict which of their users were likely to change their vehicle, or even know who had changed their vehicle until after it had happened. This lack of predictive capability was a significant barrier to increasing their call center revenues.
The Customer
Regit
About The Customer
Regit, previously known as Motoring.co.uk, is a leading online service for drivers in the UK. The company provides a platform for users to book test drives, buy and sell cars, and request brochures, among other services. They generate leads for companies in the automotive industry, serving a user base of 2.5 million. The company's primary goal is to serve their customers in a more personalized and targeted way, which they achieve by predicting which users are likely to change their vehicle and when.
The Solution
Regit partnered with Peak to leverage their AI platform to overcome this challenge. Peak's AI platform pulled together user data from Regit's website and marketing systems, as well as from the DVLA (Drivers and Vehicle Licensing Agency). They applied 'Categorical Machine Learning models' that can handle both category and variable data simultaneously. This approach provided predictions about the likelihood of users changing their vehicle, which could potentially result in a sale for Regit. The Peak platform, which utilizes AWS SageMaker to deploy machine learning models, created a simple 'lead-score'. These lead scores were then integrated into Regit's CRM system, enabling their call-center agents to prioritize their activity based on the users with the highest chance of converting to a sale.
Operational Impact
Quantitative Benefit
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