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Picking the optimal pricing strategy

Customer Segmentation | Price Sensitivity | Demand Forecasting | Simulation

Τhis leading airline group was working towards a restructure of its pricing and wanted to explore the impact of alternative pricing scenarios on demand and resulting profitability.

CHALLENGE

Analyse the future impact of a range of pricing scenarios.

In house solutions could not provide a reliable answer within the required short time-frame. We were asked to build a model that could, within a few weeks, analyse the future impact of a range of pricing scenarios and provide valuable input in the decision-making process.

SOLUTION

A multi-layered machine learning model.

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The first step was to run a series of workshops with key stakeholders across Revenue Management, Pricing and Customer Relations to identify available flight, pricing and customer data.

We then trained a multi-layered machine learning model that on one hand clustered customers based on their characteristics (incl. transactional data) and on the other analysed price elasticity for each of these individual clusters across different routes, seasons and cabins.

Our model was put to use, testing out each pricing strategy, predicting demand and a range of financial metrics against the business plan. The scenario identified as best was implemented and announced by the airline group a few months later.

Actual performance was within 5% of the model prediction and we were asked to use our model again a few months later to examine the impact of a new series of pricing changes.

Sounds interesting?

Get in touch now and we can schedule a free, one hour, consultation. No strings attached.

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