Dynamic pricing of ancillaries using machine learning: one step closer to full offer optimization

被引:8
|
作者
Kummara, MadhuSudan Rao [1 ]
Guntreddy, Bhaskara Rao [1 ]
Vega, Ines Garcia [1 ]
Tai, Yun Hsuan [1 ]
机构
[1] Etihad Airways, RM Solut & Innovat, Abu Dhabi, U Arab Emirates
关键词
Offer optimization; Dynamic pricing; New distribution capability (NDC); Customer segmentation; Personalization Offer management;
D O I
10.1057/s41272-021-00347-6
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
Today airlines' ancillary pricing decision-making is mostly manual, where prices are generally determined by analysts through competitor benchmarking and historical data analysis. After manual computation, ancillary prices are filed in ATPCO (Airline Tariff Publishing Company) or Merchandising systems and these prices can be further tailored to the characteristics of the ancillary request through merchandising rules. Using airline ancillary and itinerary data, we built a gradient boosting machine algorithm that can understand the intricate relations between numerous attributes such as passenger type, itinerary, aircraft type, ancillary product, or season and can make an automated pricing decision based on science. The analysts are relieved from manual work and have the flexibility to change the machine learning (ML) algorithm's input and output to suit business strategies. The ML algorithm learns the new trends and patterns as part of its training, and analysts can track its performance periodically. The output of the ML algorithm seamlessly integrates with merchandising platforms to implement the dynamic pricing of ancillaries and offers in the direct, indirect, and channels with new distribution capability. The ML algorithm is extendable to airline and third-party ancillary products and ticket bundles. It can suggest an optimal mix of products and price points that have the highest propensity to purchase for a given customer and travel itinerary.
引用
收藏
页码:646 / 653
页数:8
相关论文
共 50 条
  • [21] Optimal Pricing of Configurable Products using Machine Learning
    Hernandez, Angel C.
    Masaryk, David
    Mecir, Juraj
    Saliminejad, Siamak
    2024 11TH IEEE SWISS CONFERENCE ON DATA SCIENCE, SDS 2024, 2024, : 99 - 106
  • [22] Policy Optimization Using Semiparametric Models for Dynamic Pricing
    Fan, Jianqing
    Guo, Yongyi
    Yu, Mengxin
    JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2024, 119 (545) : 552 - 564
  • [23] Correction: Sustainable regional rail system pricing using a machine learning-based optimization approach
    Ilgin Gokasar
    Ahmet Karakurt
    Yusuf Kuvvetli
    Muhammet Deveci
    Dursun Delen
    Dragan Pamucar
    Annals of Operations Research, 2024, 332 : 1315 - 1316
  • [24] From One-off Machine Learning to Perpetual Learning: A STEP Perspective
    Zhang, Du
    2018 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC), 2018, : 17 - 23
  • [25] ML-descent: An optimization algorithm for full-waveform inversion using machine learning
    Sun, Bingbing
    Alkhalifah, Tariq
    GEOPHYSICS, 2020, 85 (06) : R477 - R492
  • [26] Webform Optimization using Machine Learning
    Hanmandla, Akshaykumar
    Ranoliya, Jaydeep
    Ojha, Dhananjaykumar
    Kulkarni, Saurabh
    2021 6TH INTERNATIONAL CONFERENCE FOR CONVERGENCE IN TECHNOLOGY (I2CT), 2021,
  • [27] Dynamic pricing under competition using reinforcement learning
    Alexander Kastius
    Rainer Schlosser
    Journal of Revenue and Pricing Management, 2022, 21 : 50 - 63
  • [28] Dynamic pricing under competition using reinforcement learning
    Kastius, Alexander
    Schlosser, Rainer
    JOURNAL OF REVENUE AND PRICING MANAGEMENT, 2022, 21 (01) : 50 - 63
  • [29] Dynamic Population on Bio-Inspired Algorithms Using Machine Learning for Global Optimization
    Caselli, Nicolas
    Soto, Ricardo
    Crawford, Broderick
    Valdivia, Sergio
    Chicata, Elizabeth
    Olivares, Rodrigo
    BIOMIMETICS, 2024, 9 (01)
  • [30] Atrial fibrillation classification using step-by-step machine learning
    Goodfellow, Sebastian D.
    Goodwin, Andrew
    Greer, Robert
    Laussen, Peter C.
    Mazwi, Mjaye
    Eytan, Danny
    BIOMEDICAL PHYSICS & ENGINEERING EXPRESS, 2018, 4 (04):