Dynamic Pricing for Airline Ancillaries with Customer Context

Naman Shukla · Arinbjörn Kolbeinsson · Ken Otwell · Lavanya Marla · Kartik Yellepeddi

53 citations0/0 files storedACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)

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Supply Chain and Inventory Management Consumer Market Behavior and Pricing Aviation Industry Analysis and Trends

Abstract

Ancillaries have become a major source of revenue and profitability in the travel industry. Yet, conventional pricing strategies are based on business rules that are poorly optimized and do not respond to changing market conditions. This paper describes the dynamic pricing model developed by Deepair solutions, an AI technology provider for travel suppliers. We present a pricing model that provides dynamic pricing recommendations specific to each customer interaction and optimizes expected revenue per customer. The unique nature of personalized pricing provides the opportunity to search over the market space to find the optimal price-point of each ancillary for each customer, without violating customer privacy.

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References

1979Prospect Theory: An Analysis of Decision under Risk 2006Shrouded Attributes, Consumer Myopia, and Information Suppression in Competitive Markets 1985Discrete Choice Analysis 2015Dynamic pricing and learning: Historical origins, current research, and new directions 2005A Model of Add-On Pricing 2012Add-on Pricing by Asymmetric Firms 2018Customized Regression Model for Airbnb Dynamic Pricing 2013An investigation into traveler preferences and acceptance levels of airline ancillary revenues

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