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Data-driven models for itinerary preferences of air travelers and application for dynamic pricing optimization

Author

Listed:
  • Thierry Delahaye

    (Amadeus S.A.S.)

  • Rodrigo Acuna-Agost

    (Amadeus S.A.S.)

  • Nicolas Bondoux

    (Amadeus S.A.S.)

  • Anh-Quan Nguyen

    (Amadeus S.A.S.)

  • Mourad Boudia

    (Amadeus S.A.S.)

Abstract

There is an increasing interest within the travel industry in better understanding customer behavior, particularly the way customers choose between itinerary alternatives when searching for flights. Such an understanding can help travel providers (e.g., airlines) adapt better to market conditions and customer needs, thus increasing their revenue. In this paper, we deal with the problem of modeling air passenger choice between flight itineraries. We describe a two-stage approach to predict travelers’ choice behavior by combining machine learning and discrete choice-modeling techniques. The applicability of these models is then illustrated by employing them for dynamic pricing optimization. We conduct experiments on a dataset extracted from searches and bookings on several European markets, aiming at assessing both the accuracy of our customer models and the effect of price optimization. The proposed approach seems to be effective on both dimensions: (a) improved accuracy when predicting choice, and (b) increased expected revenue of shopping sessions. The experiments show that 42 percent of actual choices fall within the three highest estimated probabilities among 50 alternatives in each shopping session. Moreover, the results also show more than 20 per cent of additional revenue compared with a baseline approach.

Suggested Citation

  • Thierry Delahaye & Rodrigo Acuna-Agost & Nicolas Bondoux & Anh-Quan Nguyen & Mourad Boudia, 2017. "Data-driven models for itinerary preferences of air travelers and application for dynamic pricing optimization," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 16(6), pages 621-639, December.
  • Handle: RePEc:pal:jorapm:v:16:y:2017:i:6:d:10.1057_s41272-017-0095-z
    DOI: 10.1057/s41272-017-0095-z
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    References listed on IDEAS

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    Cited by:

    1. Lhéritier, Alix & Bocamazo, Michael & Delahaye, Thierry & Acuna-Agost, Rodrigo, 2019. "Airline itinerary choice modeling using machine learning," Journal of choice modelling, Elsevier, vol. 31(C), pages 198-209.
    2. Ruben van de Geer & Arnoud V. den Boer, 2022. "Price Optimization Under the Finite-Mixture Logit Model," Management Science, INFORMS, vol. 68(10), pages 7480-7496, October.
    3. Mourad Boudia & Suraj Mohamed & Nicolas Bondoux & Thierry Delahaye, 2021. "Traveler centric airline offer design and optimization," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(6), pages 634-645, December.
    4. Thomas Loots & Arnoud V. den Boer, 2023. "Data‐driven collusion and competition in a pricing duopoly with multinomial logit demand," Production and Operations Management, Production and Operations Management Society, vol. 32(4), pages 1169-1186, April.
    5. Ahmed Abdelghany & Khaled Abdelghany & Ching-Wen Huang, 2021. "An integrated reinforced learning and network competition analysis for calibrating airline itinerary choice models with constrained demand," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(3), pages 227-247, June.
    6. Yong-Wu Zhou & Chuanying Chen & Yuanguang Zhong & Bin Cao, 2020. "The allocation optimization of promotion budget and traffic volume for an online flash-sales platform," Annals of Operations Research, Springer, vol. 291(1), pages 1183-1207, August.

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