Constructing bundled offers for airline customers

Madireddy, Manini; Sundararajan, Ramasubramanian; Doreswamy, Goda; Nia, Meisam Hejazi; Mital, Amod

doi:10.1057/s41272-017-0096-y

Cited by 11 publications

(5 citation statements)

References 16 publications

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“…One approach consists of identifying one of several predefined segments an incoming requests originates from, and then offering a segment-specific preassembled offer set (which may also include the prebundling of seat and ancillaries, see Madireddy et al 2017). With regard to the choice-based framework introduced in §2.2, this logic would mean that we assume a number of segments K = {1, ..., K}, with each customer originating exactly from one segment k ∈ K. Each segment comes along with its individual choice behavior which is expressed by the probabilities P (k) j (S t ).…”

Section: Mathematical Modelmentioning

confidence: 99%

A review of revenue management: Recent generalizations and advances in industry applications

Klein

Koch

Steinhardt

et al. 2020

European Journal of Operational Research

108

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Section: Mathematical Modelmentioning

confidence: 99%

A review of revenue management: Recent generalizations and advances in industry applications

Klein

Koch

Steinhardt

et al. 2020

European Journal of Operational Research

108

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show abstract

“…These papers are often quite conceptual in nature with limited focus on the specific models used in dynamic offer creation. For example, Madireddy et al (2017) described a piecewise approach for offer optimization consisting of separate modules that estimate customer willingness-to-pay, perform segmentation, and generate sets of possible offers, based on the information available from RMS and e-commerce platforms. Fiig et al (2018) described an end-to-end flow to dynamically price flights and ancillary services and discuss how this system would interact with current distribution practices.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic offer creation for airline ancillaries using a Markov chain choice model

Wang

Wittman²,

Fiig³

2023

J Revenue Pricing Manag

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Customers have become accustomed to a highly streamlined and personalized experience when shopping online. While tech giants such as Apple, Amazon, and Netflix are experts in using customer information and shopping context to deliver relevant offers, airlines are falling behind in this regard with their static content and one-size-fits-all retailing approach. To meet the growing expectations of their customers, the airline industry has expressed a vision for dynamic offer creation, which will allow airlines to dynamically bundle and price a set of offers that is customized to the context of the shopping request. Realizing this vision requires significant advancements in both distribution and science. On the distribution side, these advancements will come with the adoption of the New Distribution Capability. On the science side, which is the focus of this paper, little progress has been made despite years of research. In particular, airlines still lack a tractable scientific model to dynamically create and price offer sets at scale. In this paper, we present a novel approach to solve the airline dynamic offer creation problem using a Markov chain choice model. Our model displays attractive qualitative properties—the resulting offers and prices are chosen in such a way as to discourage purchases of unprofitable offers and nudge customers towards more profitable ones. Our model naturally proposes offers that are relevant to the customer, as including irrelevant offers in the offer set leads to a reduction in revenue and ancillary purchase rates. In a simulation study with two customer segments, we find that our model significantly increases ancillary revenue over a naïve, unsegmented pricing model that mimics current state-of-the-art practice. While our studies are conducted under several idealized assumptions, they demonstrate a substantial revenue potential from dynamic offer creation in both unsegmented and segmented applications.

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“…One example includes dynamic product bundling, where optimal custom product bundles are offered to customers and dynamically priced. These bundles include a combination of à la carte and bundled flight and ancillary services (Madireddy et al 2017 ; Vinod et al 2018 ; Wang et al 2021 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Highly debated but still unbundled: The evolution of U.S. airline ancillary products and pricing strategies

Mumbower

Hotle

Garrow

2022

J Revenue Pricing Manag

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This paper investigates how airline pricing strategies and revenues have evolved over time for unbundled flight products, using the eight largest U.S. airlines. Findings indicate that ticket exchange fees are being eliminated by network and low-cost carriers. On the other hand, baggage and seat reservation fees have grown more complex, with some carriers moving towards dynamic pricing. Ancillary revenues from unbundled flight products represent an increasing portion of airlines’ total operating revenues, especially for ultra low-cost carriers. We estimate that the product unbundling trend has decreased revenues to the U.S. Airport and Airway Trust Fund by 4.0%–4.6% due to untaxed baggage and seat reservation fees alone. We also discuss government policies on ancillary revenue disclosure and pricing transparency, and the global COVID-19 pandemic’s impact on ancillaries.

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Constructing bundled offers for airline customers

Cited by 11 publications

References 16 publications

A review of revenue management: Recent generalizations and advances in industry applications

A review of revenue management: Recent generalizations and advances in industry applications

Dynamic offer creation for airline ancillaries using a Markov chain choice model

Highly debated but still unbundled: The evolution of U.S. airline ancillary products and pricing strategies

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