Optimizing Reconditioning Costs in Used Automobiles: A Data Science Approach

Company Information

The Challenge

The Results

Procogia’s Approach

Understood the data for training the recon model – from source to feature engineering to the final state.

Studied the incumbent Recon model to get a better understanding of what improvements could be made.

Recommended the client use mechanic notes as an additional layer for training a more accurate model. Our approach was to introduce more data sources to the recon model. One data set the client had not previously considered was a car history/mechanical information history.

Built an enhanced recon model that used pre-existing training data, which trains the algorithm to predict outcomes in the design/model. At the same time, integrating a vector matrix to process qualitative data into quantitative data to build and enhance the model.

The Detail

This architectural redevelopment has had positive effects across the organization, from optimizing where the products are sold, to how sizes are displayed on the client’s e-commerce site. Improved capabilities include:

The enhanced model performed 50% better than the base model. D2 Tweedy score of 0.4 as compared to 0.26 for the base model.

Delivered a set of strategic questions – that used the important words and features from the improved model – to ask the seller more pertinent questions that would help better estimate the price of the vehicle being sold.

We recommended a new set of strategic questions for sellers to answer on their website to capture their customers’ needs and preferences more accurately, as well as adapt to seasonal trends.

We demonstrated through computer vision how you can assess the condition of used vehicles, which could be integrated into different components of the buying/selling journey.


The collaboration between Canada Drives and ProCogia underscores the transformative potential of data science in the automotive industry. By harnessing the power of advanced algorithms and diverse data sets, companies can not only solve complex challenges but also drive innovation and efficiency. As we witness the evolution of data science, one wonders: what other industries could benefit from such a profound technological integration?

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