Taking OB strategy
to the next level.
OVBO predicts no-show passenger by passenger, then sends an optimal lid straight to your PSS every day. Your RM team sets the rules and gets back to the core business.
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29,355Optimized flights
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5,471,892Optimized passengers
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+15 yearsRevenue management experience
the challenge
Overbooking is a margin decision made with incomplete information.
Average compensation paid to passengers involuntarily bumped from flights in the US, 2018.
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Denied boarding is expensive in more ways than one
Negative PR, unhappy passengers, frustrated airport staff — and compensation payments on top.
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No-show behaviour is hard to read
Without passenger-level prediction, balancing revenue against customer satisfaction stays guesswork.
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Playing it safe costs real revenue
Conservative overbooking leaves seats unsold on flights that could have gone out full.
what OVBO does
Automatically optimize your overbooking strategy and stay focused on core RM work.
Efficient
OVBO optimizes every flight considering:
Automatic
Full integration with your PSS, sending daily lid updates.
Flexible
A clear interface where RM teams set the parameters that matter.
the data
PNR data explains no-show more than we believe.
Long AP passengers tend to show up less at the airport
Passengers whose PNR was modified also show up less
+20 features available in PSS data to understand no-show behaviour
Graphs and statistics shown here are based on trends observed among our customers and are provided for illustration only. Actual results vary by airline and by operation.
how it works
From PSS data to a lid recommendation, every day.
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01
Processing
OVBO processes and stores airline data from the PSS, plus custom integrations where needed.
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02
Training
Flown data and OVBO-calculated features train an AI model to predict future no-show.
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03
Prediction
Each day the model scores every passenger’s no-show probability across all upcoming flights.
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04
Lid recommendation
Using the RM analyst’s settings, the optimizer produces an optimal lid for every flight.
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05
PSS
OVBO writes the lid recommendation straight back to the PSS.
accuracy
Numbers speak for themselves.
OVBO’s error distribution sits clearly tighter than a traditional approach. Less spread means you can push the lid with more confidence.
Absolute error mean
Error (%) distribution — prediction vs. real boarded
Graphs and statistics shown here are based on trends observed among our customers and are provided for illustration only. Actual results vary by airline and by operation.
expected impact
Increase RASK by more than 3% with OVBO.
OVBO performs best where there is most to win: high load factors and higher no-show rates.
↑ load factor above 88%
OVBO performance improves with higher %LF and no-show rates
main features
What you get that legacy tooling doesn’t.
Online platform
A UI where RM analysts customize their own strategies.
Predictions with machine learning
+30 features feed the model that scores each passenger’s no-show probability.
Denied boarding cost optimization
Optimizes margin, not just revenue. Set denied boarding cost per market.
Automatic alerts
Configurable alerts confirm the process ran and flag flights worth a second look.
OB rules management
Set lid rules to keep unwanted limits out — peak dates, last-day flights and more.
Snowball effect prevention
Every recommendation reads nearby flights’ load factor, so bumped passengers don’t cascade.
track record
Built by industry natives, already live in production.
Integrated with
navitaire an Amadeus company
Our partners