AI Case Study
Auto Trader improves second-hand vehicle valuation accuracy using machine learning
Auto Trader is training machine learning algorithms to extract data on car specifications and details. By analysing the data the technology is able to determine how each affects the valuation price of second hand cars resulting in greater accuracy.
Consumer Goods And Services
Automobiles And Parts
"Auto Trader makes extensive use of machine learning running against data stored in MongoDB. The car’s specifications and details, such as number of previous owners, condition, color, mileage, insurance history, upgrades, and more are stored in MongoDB. This data is extracted by machine learning algorithms written by Auto Trader’s data science team to generate accurate predictions of value, which are then written back to the database."
"Auto Trader has a data science and insights team of thirty people, and is currently building a new MongoDB cluster to store derivate data - for example, the year a specific model of car was manufactured. Having a database capable of recognising this ensures customers are being delivered accurate valuations.
'One way we are doing this is by making algorithm-based data decisions around the value of a car, and also introducing machine learning to help understand differences between features [like a satellite navigation system], and also recognise data which may not necessarily be a tangible feature,' Mohsin Patel, principal database administrator at Auto Trader told Computerworld UK. 'We have to teach our systems depending on what features a car might have, or what derivate of a car the customer is viewing as the price could differ, and that's where the [machine] learning comes in'."
"data is extracted by machine learning algorithms written by Auto Trader’s data science team to generate accurate predictions of value"
A car’s specifications and details, such as number of previous owners, condition, color, mileage, insurance history, upgrades, special features and more