- Cars.com has built a machine learning model to help buyers determine when and how to act on a purchase
- The technology is called “Hot Car” and has been built on over 20 years of data using over 50 factors
- The initial testing resulted in a double digit increase in sales
One of the most essential and common tasks of any business is forecasting sales. Before, it used to be done manually by pouring over sheets of numbers. Now, machine learning has taken over and performs the calculations in a matter of minutes (if not seconds).
So Cars.com, one of the most popular websites for cars, has announced that it is using machine learning to predict the sales of cars, and to help buyers determine when and how to act on a purchase. This technology is being called “Hot Car” by the company and is already live on their pricing page.
“Hot Car” is being used to predict which vehicles are most likely to sell quickly, based on several variables including car make and model, geographic demand, time on lot, pricing and consumer shopping behaviors. According to Tony Zolla, chief product officer, said that the developers had taken data from the past 20 years and built the model on over 50 factors.
Used vehicles that have a 70% chance of being sold within seven days from their addition to Cars.com will feature a “Hot Car” badge. Brand new vehicles will earn this badge when they had a 70% chance of being sold within 20 days of their addition to the site.
The initial testing phase of using this model resulted is double digit increase in the conversion! It performed particularly strong on mobile.
Our take on this
This should be one of the obvious uses of machine learning. It benefits both the user as well as the organization. It crunches the numbers give the buyer insights using which they can make data driven decision on purchasing a car.
This machine learning model will drive up sales, and benefit the dealers by increasing views on car pages, lead conversion and inventory turnover rate. Have you ever used something similar in your projects? Use the comments section below to let us know your views!
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