In this project, I developed a set of analytical tools that help auto dealers
- Accurately estimate the prices of cars they buy or sell
- Get insights about their competitors’ inventory
- Inventory volume (in quantity and Euros)
- Make, model, trim, and model year distribution
- Average inventory age (per branch, model, or trim)
- Average price (per branch, model, or trim)
- Average mileage (per branch, model, or trim)
- Calculate depreciation per make, model, and trim
- Find cars listed below market value
What did I do in this project?
- Established an ethical and conscious way to scrape publicly available listing data (No copyrighted, or personal information was scraped, throttled crawler requests) in Finnish Automotive Market
- Created a data structure that allows classifying and grouping listings per
- Make, Model, Trim, Model Year
- Auto Retailer, Branch, Sub-Branch
- Built a linear regression model to define the relationship for each model generation and mileage, model year, and package (or certain keywords in the description)
- Built a dashboard to visualize the results and allow users to search for specific information with ease
Demo
You can interact with the demo data to see the system in action. This demo is built on Google Looker Studio (formerly Googe Data Studio) and might be a bit slow. It’s retrieving around 27K individual listings, so a bit of patience might be required.
Also, the data is a snapshot from Feb 2022 prices, and market information is not current.
Also, reports are originally designed for web and are not mobile optimized