Revolutionizing Used Car Price Prediction with Neural Graph Analytics

Challenge

The US research division of a leading Japanese OEM, specializing in mobility solutions, faced significant challenges in accurately understanding and representing the complex and dynamic nature of the American used car market. Traditional analytical models and data structures were insufficient in capturing the complex relationships and patterns that define this market. The OEM required a more advanced solution to better analyze market trends and improve their ability to predict used car prices.

Solution

The OEM implemented the GiQ platform to leverage its advanced neural graph-based analytics capabilities. GiQ's innovative approach mapped out the intricate relationships between vehicle specifications and broader market trends, providing a comprehensive analysis that was previously unattainable with conventional methods.

Data integration and analysis

The solution utilized over one million vehicle listings from cars.com and other used cars websites, as well as additional datasets with EV charger locations and information on CO2 emissions. This extensive data integration enabled GiQ neural graphs to generate a holistic view of the used car market and highlighted the complex interconnections within the market, allowing the OEM to understand how various factors influenced used car prices.

Predictive modeling

Leveraging GiQ's graph-based analytics, the OEM could then accurately predict used car prices based on defined criteria. The neural graphs delineated therelationships between vehicle specifications, market trends, and pricing, providing a clear and precise model for forecasting. This predictive capability was essential for the OEM in developing strategic insights and making informed decisions.

Configurable recommendation system

Using the insights gained from the neural graphs analysis, GiQ developed a configurable recommendation system for the OEM. This system utilized the detailed market analysis to provide personalized vehicle suggestions to potential buyers. By understanding the preferences and needs of individual customers, the recommendation system significantly improved the user experience, making it easier for buyers to find suitable vehicles.

Outcomes

Enhanced market understanding

The implementation of GiQ's platform provided the OEM with a much deeper understanding of the used car market. The advanced analysis capabilities allowed the company to capture and interpret the complex relationships within the market, leading to more accurate and insightful market assessments.

Accurate price predictions

GiQ's graph-based predictive modeling enabled the OEM to forecast used car prices with high accuracy. This capability was crucial for both strategic planning and operational efficiency. Accurate price predictions helped the OEM to better position their used vehicles in the market and respond promptly to market changes.

Improved user experience

The configurable recommendation system developed using GiQ's insights greatly enhanced the user experience. Potential buyers received personalized vehicle suggestions tailored to their preferences, making the car buying process more intuitive and satisfying.