Evaluating vehicles through machine learning: „Phoenix Pricing“ allows our branches to predict a used vehicle’s price precisely.
Of Used Cars
Mercedes-Benz AG
Mercedesstraße 120
70372 Stuttgart
Germany
Phone: +49 7 11 17-0
E-Mail: dialog@mercedes-benz.com
Please send queries about content on this website to any contact. You can address your concerns to us in English and your respective national language.
Represented by the Board of Management:
Ola Källenius, Chairman; Jörg Burzer, Renata Jungo Brüngger, Sabine Kohleisen, Markus Schäfer, Britta Seeger, Hubertus Troska, Harald Wilhelm
Chairman of the Supervisory Board: Martin Brudermüller
Court of Registry: Stuttgart; commercial register no. 762873
VAT ID: DE 32 12 81 763
All information about our products can be found on your country-specific Mercedes-Benz product page.
Evaluating vehicles through machine learning: „Phoenix Pricing“ allows our branches to predict a used vehicle’s price precisely.
The complexity of the used car market has been growing for years and thus also the valuation of vehicles in the used car market. Besides the increasing number of model series, other factors including the selection of vehicle features and large number of suppliers make it difficult for dealers to quickly and accurately determine the value of cars. A key success criterion in the used car business is determining a "good price offer".
To support pricing experts in Mercedes-Benz business as best as possible in determining the offer price, the "Phoenix Pricing" project was developed under the management of the Controlling and IT units of Mercedes-Benz Sales Germany. With the help of machine learning, a proposed price is calculated, which delivers the most accurate prediction of the actual sales price on the basis of diverse data records.
This data can be obvious pricing-relevant attributes such as vehicle age and mileage, as well as special features and sales prices from previous years. The more data sources are available, the more precise the predicted price, which is continuously optimized through constant adjustment by the algorithm and integration of other data sources.
