How to use dynamic pricing AI for revenue optimization - Carborn and AMPE case

How to use dynamic pricing AI for revenue optimization - Carborn and AMPE case

As the automotive industry becomes more competitive, manufacturers, dealerships and rental companies are looking for creative ways to scale their business. Dynamic Pricing, also known as surge or demand pricing, is an innovative way to increase revenue.

How does Dynamic Pricing work? For successful launch AI model needs relevant supply and demand retro data, data about user behavior – does he or she start the rent or not, for example and market conditions. Data granularity is also important - the best results will be on daily and hourly granular datasets. Ideally it needed up to 2 years of retro data to harmonize and get stable seasonality. Retro can be gathered for lower or higher period, but 2-3 months of data cannot provide full information about seasonality and demand curve, 2 years and more can contain more outdated and non-relevant information to current state of business.

Dynamic pricing solutions AI already available for different markets, including adjusted solutions for automotive industry. AI dynamic pricing engine allows rental, mobility and infrastructure operators to react instantly to market conditions, improve and scale up their revenue.

For better results, it makes sense to look at solutions that allow you to integrate a company's own retro data into an existing model which already built on relevant data. This approach will reduce the time required to install and refine the model and will also ensure a quick transition from training and finetuning to providing price forecasts that can be immediately used.

Successful dynamic pricing services was trained on enterprise size-level vehicle park and contains up to 1 petabytes of data about supply and demand in different cities, including seasonality and demand curves. Now it successfully works for rentals and carsharing businesses in the USA and United Arab Emirates.

Considering the requirement for speed of installation and implementation, it is also recommended to use solutions that can be easily integrated into the current technological contour and comply with all global and local legal requirements, including the EU AI Act, GDPR.

Best dynamic pricing AI engines start to work from day 1 – by the end of the day it can demonstrate about 70-75% of accuracy, by the end of the second week – about 90% and one month to get 100% accuracy and efficiency.

For example, implementation of AMPE dynamic pricing solution helped to improve revenue per car by up to 46% Carborn – one of our car rental clients in Dubai, with results starting in the first weeks. The previous price optimization solution was based on manual analytics, while AMPE utilizes pre-established data for forecasting on a proven high-quality dataset. This not only saves time on deployment and fine-tuning but also provides almost instant improvements.

The solution should work both for large enterprises and small operations (i.e. with a fleet of 50 vehicles), what varies is the number of data points that is taken into account.

With dynamic pricing AI businesses can quickly respond to market fluctuations, capitalize on peak demand periods, and drive business growth while delivering exceptional customer experiences.”

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