CASE STUDY

Vehicle Prediction and Pricing Optimization for Leading Automotive Company

Industry | Automotive
Technology | AI/ML
Location | USA
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Our client is at the forefront of the American automotive industry, blending over a century of engineering excellence with cutting-edge technology. Founded in 1967, its heritage is rooted in innovation and a commitment to the principles of sustainability and performance. Based in Detroit, Michigan, the client has evolved into a flagship American car manufacturer, known for its robust lineup of vehicles including fuel-efficient sedans, SUVs, and electric vehicles.

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Challenges

Traditional pricing strategies don't consider real-time market dynamics.

Fixed pricing models lacked flexibility, which led to missing opportunities to maximize revenue

The client lacked dynamic pricing capabilities.

The client struggled to respond to competitor actions.

Key Outcomes

4%
Increase in Total Revenue
3-7%
Enhanced CLV (Customer Lifetime Value)
2.5%
Improvement in Pricing Strategies

Solutions

Collected historical sales data, vehicle features, market trends, competitor pricing, and customer demographics

The dynamic pricing engine developed was based on two-stage machine learning.

Deployed the ML-based pricing optimization model into production environments

Built intuitive dashboards and visualization tools for stakeholders.

What Customers Say About Royal Cyber

Congratulations, and a big thank you to everyone who worked on the project and successfully implemented it. The team did a great job working through coming up with vehicle price optimization solution and hats off to everyone who worked on this project.

Sr. Director AI/ML

80%

Increase in Customer Activity

Audience

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