Integrating an AI-Powered Dynamic Pricing Module for a Mid-Sized Australian Car Rental Chain
Industry Car Rental / Travel
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$10B+ Client Revenues
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12+ Successful Years
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1000+ IT Ninjas
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5000+ Projects
Client's Testimonial
We knew we needed to get smarter with our pricing, but we didn't have the in-house AI/ML expertise. The AI / ML Rapid-Prototype Pod from Developers.dev was the perfect solution. They worked seamlessly with our existing tech team to build and integrate a pricing engine that has been a total game-changer for our revenue management.
Head of Revenue, [Anonymized Australian Client]
Client Overview
An established car rental company with 50+ locations across Australia was struggling with a static, manual pricing strategy. They were losing revenue during peak seasons and had low utilization during off-seasons. They needed an intelligent solution to integrate into their existing booking system that could automate pricing based on real-time market data.
Problem
The client's manual pricing strategy was inefficient and ineffective. It failed to capitalize on high-demand periods and couldn't react to competitor price changes, leading to significant lost revenue opportunities and underutilized assets.
Key Challenges
Integrating with the client's proprietary, decade-old booking system.
Integrating with the client's proprietary, decade-old booking system.
Aggregating and processing diverse data sources (competitor pricing, flight schedules, local events, weather forecasts).
Aggregating and processing diverse data sources (competitor pricing, flight schedules, local events, weather forecasts).
Developing an ML model that could accurately predict demand and recommend optimal pricing.
Developing an ML model that could accurately predict demand and recommend optimal pricing.
Ensuring the solution was easy for their non-technical revenue managers to use and override.
Ensuring the solution was easy for their non-technical revenue managers to use and override.
Our Solution
We assigned our "AI / ML Rapid-Prototype Pod" and "Python Data-Engineering Pod" to tackle the challenge.
💧 Data Ingestion Pipeline:
Built a robust data pipeline using Python to scrape competitor websites, and pull data from aviation, event, and weather APIs.
🧠 Machine Learning Model:
Developed a demand forecasting model using XGBoost that analyzed historical booking data and the new external data sources to predict vehicle demand up to 90 days in advance.
💰 Pricing Recommendation API:
Created a REST API that the client's existing system could call to get a recommended price for any vehicle class, location, and date range.
📊 Management Dashboard:
Designed a simple web interface where revenue managers could view the AI's recommendations, understand the factors influencing the price, and manually approve or adjust them.
Implementation and Execution
Our team worked in the client's time zone (AEST) for seamless collaboration.
Our team worked in the client's time zone (AEST) for seamless collaboration.
We started with a proof-of-concept on a single location to validate the model's accuracy.
We started with a proof-of-concept on a single location to validate the model's accuracy.
The data engineering team cleansed and prepared three years of the client's historical booking data.
The data engineering team cleansed and prepared three years of the client's historical booking data.
We used an iterative process to train and fine-tune the ML model, improving its accuracy from 85% to 94%.
We used an iterative process to train and fine-tune the ML model, improving its accuracy from 85% to 94%.
We provided comprehensive API documentation and worked hand-in-hand with the client's developers on the integration.
We provided comprehensive API documentation and worked hand-in-hand with the client's developers on the integration.
The solution was deployed on a scalable AWS SageMaker instance.
The solution was deployed on a scalable AWS SageMaker instance.
Positive Outcome
📈 18% Increase in Average Revenue Per Vehicle: The dynamic pricing model successfully maximized revenue during peak periods.
18% Increase in Average Revenue Per Vehicle: The dynamic pricing model successfully maximized revenue during peak periods.
🚗 12% Improvement in Fleet Utilization: Lower, incentive-based pricing during off-peak times led to more bookings.
12% Improvement in Fleet Utilization: Lower, incentive-based pricing during off-peak times led to more bookings.
⏱️ 80% Reduction in Manual Pricing Efforts: Freed up the revenue management team to focus on strategy instead of manual data entry.
80% Reduction in Manual Pricing Efforts: Freed up the revenue management team to focus on strategy instead of manual data entry.
🏆 Competitive Advantage: The client could now react to market changes in hours instead of weeks.
Competitive Advantage: The client could now react to market changes in hours instead of weeks.
Why Choose Us
🧠 Specialized AI/ML Expertise
Specialized AI/ML Expertise
⚙️ Data Engineering & ETL Capabilities
Data Engineering & ETL Capabilities
🔗 Seamless Third-Party Integration
Seamless Third-Party Integration
🤝 Collaborative Team Extension Model
Collaborative Team Extension Model
🎯 Focus on Business Outcomes
Focus on Business Outcomes
⚡ Rapid Prototyping & Iteration
Rapid Prototyping & Iteration
🐍 Python & AWS SageMaker Mastery
Python & AWS SageMaker Mastery
🗣️ Clear Communication & Documentation
Clear Communication & Documentation
💰 Proven ROI Delivery
Proven ROI Delivery
Conclusion
This project demonstrates our ability to deliver highly specialized, value-added solutions that integrate with existing systems. By providing targeted AI/ML expertise, we empowered the client to unlock new levels of profitability and operational efficiency without needing to build an expensive in-house data science team.