What if there’s a solution that tells you which user would book with or without a discount!? This would increase revenue effectively while reducing the cost of discounts.

With BD4’s solutions, we help travel companies to:

  • Increase revenue per user
  • Minimise churn
  • Reduce retail friction
  • Avoid unnecessary discounting
  • Ensure effective allocation of incentive funds, optimizing budgets

Read our airline case study and discover how we have helped an airline increase revenue with personally targeted incentives.

Download the full case study
Airlines Case Study - Increasing revenue while reducing the cost of discounts

Situation before the case study

A leading airline wanted to improve the performance of its digital sales to maximize revenue from existing visitors. An obvious way to do this is using incentives. However, these risk diluting revenue where some passengers would buy tickets anyway. In most cases this means that an unquantifiable number of vouchers are used when customers would complete transactions regardless. So BD4 was enlisted to drive higher revenue for the lowest possible cost of vouchers.

Case study challenge

Airlines have traditionally segmented digital audiences with business rules. If clients fulfill certain criteria or act in a given way, they would be targeted as part of a segment, offered vouchers or an incentive to book. When this occurs at a segment level there is inevitable wastage. The benefits of any uplift are therefore diminished if the number of vouchers results in unnecessary dilution. But measuring this is hard!

Segments are made up of a wide variety of individuals with different motivations. There is an understandable fear among Revenue Management Executives that
if discounts are made available to adhere to a set of rules, these can be learned and “gamed” by customers to trigger unnecessary incentives.

So the challenge was set: is there a way to use individual profiling and machine learning to target incentives more efficiently, achieving the goals of e-commerce (higher conversion) while avoiding revenue dilution.

Case study approach

The airline worked with BD4 to implement its personalization platform for a customer-centric approach, utilizing real-time customer-level signals in the user journey to respond automatically to shopping behaviors. In this use case, the airline sought ways of proving that individual-level modeling and real-time intervention could contribute to the following:

  • Increase revenue per user
  • Minimize churn
  • Reduce retail friction
  • Avoid unnecessary discounting
  • Ensure effective allocation of incentive funds and optimize budgets

It is self-evident that rule-based segmentation is a blunt tool. Yet certain criteria need to be followed in order to support the overall commercial strategy of the business. Traditional exit layer methodologies rely on triggers and segmentation to deliver an action such as a voucher.

BD4's Airline Case Study - Increasing revenue

Deploying automated AI to increase revenue

Typically airlines experiment with a set of segment-driven approaches modeled against a control group. BD4 worked with the airline to develop a set of real-time models that made behavior much more transparent including:

Airline Case Study - Deloying automated AI

Case study results after evaluating the effects of those models

Airlines Case Study - Results

During the period of the test it was established that a significant uplift in revenue per user was achieved (between 3 and 6% depending on seasonality and time).

In order to get that level of result using a traditional distribution of vouchers would have cost more than $1M USD over 10 months – a figure that translates directly to the bottom line. Thus the airline had both a higher revenue per customer and a lower cost to achieve it. Further, the airline had a leading edge approach for ongoing experimentation and delivery at the level of the individual.

Access the full case study with all results