{"id":577,"date":"2023-02-01T15:02:47","date_gmt":"2023-02-01T12:02:47","guid":{"rendered":"https:\/\/acua.qcri.org\/blog\/?p=577"},"modified":"2023-02-01T15:02:47","modified_gmt":"2023-02-01T12:02:47","slug":"will-they-take-this-offer-a-machine-learning-price-elasticity-model-for-predicting-upselling-acceptance-of-premium-airline-seating","status":"publish","type":"post","link":"https:\/\/acua.qcri.org\/blog\/will-they-take-this-offer-a-machine-learning-price-elasticity-model-for-predicting-upselling-acceptance-of-premium-airline-seating\/","title":{"rendered":"Will They Take This Offer? A Machine Learning Price Elasticity Model for Predicting Upselling Acceptance of Premium Airline Seating"},"content":{"rendered":"
\"Will
Will They Take This Offer? A Machine Learning Price Elasticity Model for Predicting Upselling Acceptance of Premium Airline Seating<\/a><\/figcaption><\/figure>\n

Employing customer information from one of the world’s largest airline companies, we develop a\u00a0price elasticity model\u00a0(PREM) using machine learning to identify customers likely to purchase an upgrade offer from economy to premium class and predict a customer’s acceptable price range.<\/p>\n

A simulation of 64.3 million flight bookings and 14.1 million email offers over three years mirroring actual data indicates that PREM implementation results in approximately 1.12 million (7.94%) fewer non-relevant customer email messages, a predicted increase of 72,200 (37.2%) offers accepted, and an estimated $72.2 million (37.2%) of increased revenue.<\/p>\n

Our results illustrate the potential of automated pricing information and targeting marketing messages for upselling acceptance.<\/p>\n

We also identified three customer segments: (1)\u00a0Never Upgrades\u00a0are those who never take the upgrade offer, (2)\u00a0Upgrade Lovers\u00a0are those who generally upgrade, and (3)\u00a0Upgrade Lover Lookalikes\u00a0have no historical record but fit the profile of those that tend to upgrade.<\/p>\n

We discuss the implications for airline companies and related travel and tourism industries.<\/p>\n

(Open Access Gold)<\/p>\n

Thirumuruganathan, S., Al Emadi, N., Jung, S.G., Salminen, J., Ramirez Robillos, D., and Jansen, B. J. (2023) Will They Take This Offer? A Machine Learning Price Elasticity Model for Predicting Upselling Acceptance of Premium Airline Seating<\/a>. Information & Management<\/span>.<\/p>\n","protected":false},"excerpt":{"rendered":"

Employing customer information from one of the world’s largest airline companies, we develop a\u00a0price elasticity model\u00a0(PREM) using machine learning to identify customers likely to purchase an upgrade offer from economy to premium class and predict a customer’s acceptable price range. A simulation of 64.3 million flight bookings and 14.1 million email offers over three years… Continue reading Will They Take This Offer? A Machine Learning Price Elasticity Model for Predicting Upselling Acceptance of Premium Airline Seating<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[26,32,33,12,21,47],"tags":[],"yoast_head":"\nWill They Take This Offer? 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