As part of our expansion strategy, we are preparing to open a new used car sales branch in Mississauga, Ontario. To ensure data-driven decision-making, we analyzed historical sales data from our Toronto branch using SPSS Modeler and Cognos. This analysis helps us understand key market trends and determine pricing strategies.
- Understand general market trends.
- Explore key patterns in sales data.
- Identify key factors influencing sales volume using a Decision Tree.
- Predict sales volume through a Linear Regression Model.
- Develop a resale price calculation model using Linear Regression.
- Removal of null values.
- Calculation of price differences.
- Additional feature engineering.
- Total units sold
- Resale value
- New car price
- Price difference
- Manufacturer
- Model
- Vehicle Type (Car/Passenger)
- Engine Size
- Horsepower
- Dimensions (Length, Width, Wheelbase)
- Curb Weight
- Fuel Capacity & Fuel Efficiency
Key factors influencing sales volume include:
- Price
- Resale Value
- Engine Size
- Horsepower
- Curb Weight
- Fuel Capacity
- Fuel Efficiency
- The Linear Regression model using all available variables performed slightly better than the model using only high-correlation variables.
Our analysis provides a data-backed strategy for pricing and inventory management in the new Mississauga branch. By leveraging insights on key sales influencers and resale pricing, we can enhance profitability and customer satisfaction.
