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
  • [1] Dynamic pricing of ancillaries using machine learning: one step closer to full offer optimization
    MadhuSudan Rao Kummara
    Bhaskara Rao Guntreddy
    Ines Garcia Vega
    Yun Hsuan Tai
    Journal of Revenue and Pricing Management, 2021, 20 : 646 - 653
  • [2] Emerging research on airline ancillaries: a review of offer management and dynamic pricing literature
    Mumbower, Stacey
    JOURNAL OF REVENUE AND PRICING MANAGEMENT, 2025,
  • [3] PELICAN: One step closer to the solution of the pricing problem
    Hardy, R.L.
    Oppenheim, C.
    Rubbert, I.
    Information Services and Use, 2001, 21 (3-4): : 157 - 164
  • [4] Airbnb Dynamic Pricing Using Machine Learning
    Wang, Yuhan
    NEW PERSPECTIVES AND PARADIGMS IN APPLIED ECONOMICS AND BUSINESS, ICAEB 2023, 2024, : 37 - 51
  • [5] Dynamic offer creation for airline ancillaries using a Markov chain choice model
    Kevin K. Wang
    Michael D. Wittman
    Thomas Fiig
    Journal of Revenue and Pricing Management, 2023, 22 : 103 - 121
  • [6] Dynamic offer creation for airline ancillaries using a Markov chain choice model
    Wang, Kevin K. K.
    Wittman, Michael D.
    Fiig, Thomas
    JOURNAL OF REVENUE AND PRICING MANAGEMENT, 2023, 22 (02) : 103 - 121
  • [7] Voice Analysis with Machine Learning: One Step Closer to an Objective Diagnosis of Essential Tremor
    Suppa, Antonio
    Asci, Francesco
    Saggio, Giovanni
    Di Leo, Pietro
    Zarezadeh, Zakarya
    Ferrazzano, Gina
    Ruoppolo, Giovanni
    Berardelli, Alfredo
    Costantini, Giovanni
    MOVEMENT DISORDERS, 2021, 36 (06) : 1401 - 1410
  • [8] Analysis of Routine Computed Tomographic Scans With Radiomics and Machine Learning One Step Closer to Clinical Practice
    Farwell, Michael D.
    Mankoff, David A.
    JAMA ONCOLOGY, 2022, 8 (03) : 393 - 394
  • [9] Multi-Step Look-Ahead Optimization Methods for Dynamic Pricing With Demand Learning
    Elreedy, Dina
    Atiya, Amir F.
    Shaheen, Samir, I
    IEEE ACCESS, 2021, 9 : 88478 - 88497
  • [10] Option pricing using Machine Learning
    Ivascu, Codrut-Florin
    EXPERT SYSTEMS WITH APPLICATIONS, 2021, 163