CASE STUDY

Revolutionising Vehicle Pricing
with Machine Learning

Artificial Intelligence
Automotive

Project Highlights

Scope

  • Developed a machine learning-based pricing optimisation solution tailored to online vehicle sales.
  • Automated dynamic pricing adjustments using daily sales data for continuous model updates.
  • Built an intuitive management panel for enhanced control over pricing models.

Challenges Overcome

Addressed inefficiencies in manual pricing adjustments.

Enhanced the platform’s ability to adapt to changing market conditions.

Provided users with accurate and competitive vehicle pricing through automation.

Business Impact

The machine learning pricing solution delivered measurable improvements.

Pricing Adjustment Speed

Accuracy in Price Optimisation

Manual Effort Reduction

Adaptability to Market Changes

These enhancements empowered the client to provide competitive and adaptive pricing, improving customer satisfaction and operational efficiency.

The Client

The client is a leading platform in the automotive industry specialising in online vehicle sales.

  • Renowned for innovation in the digital automotive marketplace.
  • Serves thousands of users daily, offering streamlined vehicle buying and selling experiences.
  • Committed to optimising pricing strategies to meet dynamic market demands.
supervisor, holding a tablet, looking at the parked trucks.

The Problem

The client faced significant challenges in managing vehicle pricing.

  • Increased demand for precise and competitive pricing in the online portal.
  • Reliance on manual, time-consuming pricing adjustments.
  • Inability to adapt to rapidly changing market conditions.
  • Lack of a robust machine learning system for dynamic and automated pricing.

The Solution

BGTS implemented a comprehensive solution to transform vehicle pricing for the client.

Machine Learning Module

Developed a Python-based module to optimise pricing dynamically.

Automated Updates

Integrated daily sales data to keep the pricing model responsive and adaptive to market trends.

Management Panel

Built using .NET Core MVC, the panel enabled intuitive control over pricing models.

Optimised Price Ranges

Delivered accurate and competitive vehicle pricing tailored to the platform's needs.

User-Friendly Interface

Simplified the pricing process for administrators, improving operational efficiency.

Team & Technology

Tech Stack Utilised

Machine Learning Module

  • Python

Management Panel

  • .NET Core MVC

Team Composition

2 backend developers

1 frontend developer

1 data scientist

1 project manager

The Outcome

The machine learning pricing system transformed the client’s approach to vehicle pricing.

Dynamic Pricing Optimisation

Automated adjustments improved pricing accuracy and responsiveness to market trends.

Streamlined Operations

Reduced reliance on manual processes, allowing the team to focus on strategic initiatives.

Enhanced User Experience

The management panel provided administrators with an intuitive interface for controlling and refining pricing models.

Competitive Edge

Accurate and adaptive pricing boosted customer trust and satisfaction, positioning the client as a leader in the digital automotive marketplace.

This innovative solution solidified the client’s market presence while aligning with its commitment to efficiency and technological advancement.