One of the greatest benefits of our cooperation was that manual pricing work decreased.
13% more revenue and improved operations with machine learning-based dynamic pricing
Previously, Forenom's revenue management team needed clear pricing guidelines. Updating the prices required hours of manual labor and prevented the team from focusing on developing their business.
We solved the optimization problem with dynamic pricing - an online reinforcement learning model that enhances profits and makes both visitors and Forenom happier.
The project was not only about building a technical engine, but also improving the pricing operations of a growing company as a whole. Since apartments are no longer priced individually or manually, Forenom can move towards implementing a comprehensive pricing strategy.
The last A/B test showed a significant 13 % increase in revenue per room in the group of locations (23% of total capacity) included in the test. The increased revenue is based on either higher booking volume, higher pricing points, or longer booking times.