Peak Case Studies Driving Sales Growth with AI: A Case Study on Regit

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	Driving Sales Growth with AI: A Case Study on Regit Peak

Driving Sales Growth with AI: A Case Study on Regit

Peak

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
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. Read More
About 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. Read More
Customer Name	Regit Read More
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. Read More Log in to view content
Contents

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

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.

About 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.

Customer Name

Regit

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.

Impact #1	The implementation of Peak's AI platform and the integration of machine learning models into Regit's CRM system has significantly improved the company's operational efficiency. The ability to predict which users are likely to change their vehicle has allowed the company to serve their customers in a more personalized and targeted way. This has not only increased their call center revenues but also improved customer satisfaction levels. The company has been able to reduce operational costs by staffing the call-center at optimal times of day, further enhancing their efficiency. The improved services offered to customers have also contributed to growing the company's revenues.

Impact #1

The implementation of Peak's AI platform and the integration of machine learning models into Regit's CRM system has significantly improved the company's operational efficiency. The ability to predict which users are likely to change their vehicle has allowed the company to serve their customers in a more personalized and targeted way. This has not only increased their call center revenues but also improved customer satisfaction levels. The company has been able to reduce operational costs by staffing the call-center at optimal times of day, further enhancing their efficiency. The improved services offered to customers have also contributed to growing the company's revenues.

Benefit #1	27% increase in sales within just 30 days of implementing the solution
Benefit #2	Up to 35% reduction in operational costs
Benefit #3	Optimized staffing of the call-center at times of day most likely to result in a sale

Benefit #1

27% increase in sales within just 30 days of implementing the solution

Benefit #2

Up to 35% reduction in operational costs

Benefit #3

Optimized staffing of the call-center at times of day most likely to result in a sale

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Overview

Driving Sales Growth with AI: A Case Study on Regit

Operational Impact

Quantitative Benefit