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Case Study,
April 2023

Holistic Customer Demand Prediction

Insight Generation

Project Infinity goal is to revolutionize revenue management by enhancing the accuracy of demand forecasting through modern machine learning (ML) techniques. Recognizing the fundamental role of demand forecasts in revenue management decisions and the importance of reacting to new booking trends quickly, our project sought to integrate deep learning empowered predictions seamlessly into the existing workflow. The solution is customizable, non-invasive and fully scalable by nature, streamlining operational efficiency and minimizing the barrier to implementing Infinity within the revenue management application.

Conventional revenue management systems often struggle to swiftly adapt to evolving market dynamics, especially in the post-COVID era and new markets. For some airlines, solely on year-over-year predictions becomes a limitation, hindering their ability to generate accurate demand forecasts in rapidly changing market conditions and therefore limiting revenue opportunities.
To address this challenge, we at zeroG developed Infinity, a cutting-edge machine learning-powered platform tailored for enhanced revenue management. Infinity employs advanced ML techniques to deliver updated, data-driven demand forecasts and inventory recommendations for all flights. By considering various dynamic factors like recent booking behaviors, competitor prices, seasonality, and broader market insights, Infinity significantly improves the accuracy of demand predictions.
Infinity's implementation yielded remarkable results, exhibiting an impressive 33% enhancement in forecast accuracy compared to previous models. This improvement translates to a substantial revenue uplift estimated in 3% added revenue.
The platform seamlessly integrates into existing revenue management systems, offering a cloud-native and non-invasive solution. Operational efficiency sees a marked improvement, reducing the workload for revenue managers and providing more efficient revenue generation.
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