Will you Buy it Now?: Predicting Passengers that Purchase Premium Promotions Using the PAX Model

Abstract

Upselling is often a critical factor in revenue generation for businesses in the tourism and travel industry. Utilizing passenger data from a major international airline company, we develop the PAX (Passenger, Airline, eXternal) model to predict passengers that are most likely to accept an upgrade offer from economy to premium. Formulating the problem as an extremely unbalanced, cost-sensitive, supervised binary classification, we predict if a customer will take an upgrade offer. We use a feature vector created from the historical data of 3 million passenger records from 2017 to 2019, in which passengers received approximately 635,000 upgrade offers worth more than $422,000,000 U.S. dollars. The model has an F1-score of 0.75, outperforming the airline's current rule-based approach. Findings have several practical applications, including identifying promising customers for upselling and minimizing the number of indiscriminate emails sent to customers. Accurately identifying the few customers who will react positively to upgrade offers is of paramount importance given the airline ‘industry's razor-thin margins. Research results have significant real-world impacts because there is the potential to improve targeted upselling to customers in the airline and related industries.

Keywords

upselling price elasticity aviation company data prediction airline travel

Get full access to this article

View all access options for this article.

References

Abdella

J. A.

Zaki

Shuaib

, & Khan

(2019). Airline ticket price and demand prediction: A survey. Journal of King Saud University - Computer and Information Sciences.

Abdollahi

Khaleghi

, & Yang

(2020). An integrated feature learning approach using deep learning for travel time prediction. Expert Systems with Applications, 139, 112864.

Aboelmaged

, & Mouakket

(2020). Influencing models and determinants in big data analytics research: A bibliometric analysis. Information Processing & Management, 57 (4), 102234.

, & Noh

(2009). Airline customer satisfaction and loyalty: Impact of in-flight service quality. Service Business, 3 (3), 293–307.

Aydin

, & Ziya

(2008). Pricing promotional products under upselling. Manufacturing & Service Operations Management, 10 (3), 360–376.

Benesty

Chen

Huang

, & Cohen

(2009). Pearson correlation coefficient. In Cohen

Huang

Chen

& Benesty

(Eds.), Noise reduction in speech processing (pp. 1–4). Berlin: Springer.

Bodrunova

S. S

. (2018). Internet science. Proceedings of the 5th International Conference, INSCI 2018, St. Petersburg, Russia, October 24–26, 2018. Springer.

Borenstein

, & Rose

N. L

. (1994). Competition and price dispersion in the U.S. airline industry. Journal of Political Economy, 102, (4), 653–683.

Bradley

A. P

. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 30 (7), 1145–1159.

10.

Breiman

(2001). Random forests. Machine Learning, 45 (1), 5–32.

11.

Bryan

D. L.

, & O'Kelly

M. E

. (1999). Hub-and-spoke networks in air transportation: An analytical review. Journal of Regional Science, 39, (2), 275–295.

12.

Caruana

Lou

Gehrke

Koch

Sturm

, & Elhadad

(2015). Intelligible models for HealthCare: Predicting pneumonia risk and hospital 30-day readmission. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1721–1730.

13.

Castillo

J. C.

Knoepfle

, & Weyl

(2017). Surge pricing solves the wild goose chase. Proceedings of the 2017 ACM Conference on Economics and Computation, 241–242.

14.

Chen

Y. L.

Yeh

Y. H.

, & Ma

M. R

. (2021). A movie recommendation method based on users’ positive and negative profiles. Information Processing & Management, 58 (3), 102531.

15.

Cheng

, & Hüllermeier

(2009). Combining instance-based learning and logistic regression for multilabel classification. Machine Learning, 76 (2), 211–225.

16.

Chiang

C. T.

Yang

M. H.

Koo

T. L.

, & Liao

C. H

. (2020). What drives customer engagement behavior? The impact of user participation from a sociotechnical perspective. Journal of Electronic Commerce Research, 21, (3), 197–214.

17.

Clemes

M. D.

, & Choong

(2008). An empirical analysis of customer satisfaction in international air travel. Innovative Marketing, 4 (2), 15.

18.

Cui

Orhun

A. Y.

, & Duenyas

(2018). How price dispersion changes when upgrades are introduced: Theory and empirical evidence from the airline industry. Management Science, 65 (8), 3835–3852.

19.

Dai

B. T

. (2014). How can consumer preferences be leveraged for targeted upselling in cable T.V. services? Pacific Telecommunications Council (PTC'14). Singapore: Research Collection School of Information Systems.

20.

Denizci Guillet

(2020). Online upselling: Moving beyond offline upselling in the hotel industry. International Journal of Hospitality Management, 84, 102322.

21.

Duda

R. O.

Hart

P. E.

, & Stork

D. G

. (2012). Pattern classification. Hoboken: John Wiley & Sons.

22.

Eti

, & Mızrak

(2020). Analysing customer satisfaction of civil aviation companies of Turkey with text mining. In Dincer

& Yüksel

(Eds.), Strategic outlook for innovative work behaviours: Interdisciplinary and multidimensional perspectives (pp. 21–41). New York: Springer International Publishing.

23.

Friedman

J. H

. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29, (5), 1189–1232.

24.

Manogaran

Cao

Jiang

, & Yang

(2020). Intelligent decision-making of online shopping behavior based on internet of things. International Journal of Information Management, 50, 515–525.

25.

Greenstein-Messica

, & Rokach

(2020). Machine learning and operation research-based method for promotion optimization of products with no price elasticity history. Electronic Commerce Research and Applications, 40, 100914.

26.

Gupta

(2018). Influence of demographic variables: Customers’ perception about cross-selling and up-selling for eBanking. International Journal of Electronic Customer Relationship Management, 11, (2), 126–141.

27.

Ham

Koo

, & Chung

(2020). Configurational patterns of competitive advantage factors for smart tourism: An equifinality perspective. Current Issues in Tourism, 23, (9), 1066–1072.

28.

Hamari

Hanner

, & Koivisto

(2020). “Why pay premium in freemium services?” A study on perceived value, continued use and purchase intentions in free-to-play games. International Journal of Information Management, 51, 102040.

29.

Hanley

J. A.

, & McNeil

B. J

. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143 (1), 29–36.

30.

Hanrahan

B. V.

Willamowski

J. K.

Swaminathan

, & Martin

D. B

. (2015). TurkBench: Rendering the market for Turkers. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, 1613–1616.

31.

Hara

Adams

Milland

Savage

Callison-Burch

, & Bigham

J. P

. (2018). A data-driven analysis of workers’ earnings on Amazon Mechanical Turk. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 1–14.

32.

Havlicek

L. L.

, & Peterson

N. L

. (1976). Robustness of the Pearson correlation against violations of assumptions. Perceptual and Motor Skills, 43 (3_suppl), 1319–1334.

33.

Hlee

Lee

Koo

, & Chung

(2020). Will the relevance of review language and destination attractions be helpful? A data-driven approach. Journal of Vacation Marketing, 27, (1), 61–81.

34.

W. H.

Lin

B. T.

F. S.

, & Jeng

J. Y

. (2016). An upselling pricing model using SDP-based rating mechanism with dynamic weight. Proceedings of the The 3rd Multidisciplinary International Social Networks Conference on SocialInformatics 2016, Data Science 2016, 1–6.

35.

Huang

Loy

C. C.

, & Tang

(2016). Learning deep representation for imbalanced classification. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 5375–5384.

36.

Hussain

Al Nasser, A., & Hussain, Y. K. (2015). Service quality and customer satisfaction of a UAE-based airline: An empirical investigation. Journal of Air Transport Management, 42, 167–175.

37.

Johnson

J. S.

, & Friend

S. B

. (2015). Contingent cross-selling and up-selling relationships with performance and job satisfaction: An MOA-theoretic examination. Journal of Personal Selling & Sales Management, 35, (1), 51–71.

38.

Kleinbaum

D. G.

Dietz

Gail

Klein

, & Klein

(2002). Logistic regression. Berlin: Springer.

39.

Kubiak

B. F.

, & Weichbroth

(1970). Cross and up-selling techniques in e-commerce activities. The Journal of Internet Banking and Commerce, 15, (3), 1–7.

40.

Lengerich

Tan

Chang

C. H.

Hooker

, & Caruana

(2020). Purifying interaction effects with the functional ANOVA: An efficient algorithm for recovering identifiable additive models. Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020, Palermo, Italy, 2402–2412.

41.

Lou

Caruana

, & Gehrke

(2012). Intelligible models for classification and regression. Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 150–158.

42.

Lou

Caruana

Gehrke

, & Hooker

(2013). Accurate intelligible models with pairwise interactions. Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 623–631.

43.

Lundberg

S. M.

, & Lee

S. I

. (2017). A unified approach to interpreting model predictions. In Guyon

Luxburg

U. V.

Bengio

Wallach

Fergus

Vishwanathan

& Garnett

(Eds.), Advances in neural information processing systems 30 (pp. 4765–4774). Red Hook, NY: Curran Associates, Inc.

44.

Luttmann

(2019). Evidence of directional price discrimination in the U.S. airline industry. International Journal of Industrial Organization, 62, 291–329.

45.

N. F.

Yuan

C. W.

Ghafurian

, & Hanrahan

B. V

. (2018). Using stakeholder theory to examine drivers’ stake in Uber. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems -CHI ‘18, 1–12.

46.

Mao

, & Li

(2020). Location recommendation by combining geographical, categorical, and social preferences with location popularity. Information Processing & Management, 57 (4), 102251.

47.

Manchanayake

S. M. A. M.

Samarasinghe

D. P.

Perera

L. P. J.

Bandara

H. M. M. T.

Kumaradasa

K. C.

Premadasa

, & Samarasinghe

A. P

. (2019). Potential upselling customer prediction through user behavior analysis based on CDR data. 2019 14th Conference on Industrial and Information Systems (ICIIS), 46–51.

48.

Mayer

V. F.

Santos

G. E

. de

, & Marques

O. R. B

. (2020). Option framing for upselling tourism services: Does cognitive availability prevent irrational choices? Tourism Economics.

49.

Murphy

K. P

. (2012). Machine learning: A probabilistic perspective. Cambridge, MA: MIT Press.

50.

Nadeau

, & Turney

P. D

. (2005). A supervised learning approach to acronym identification. In Kégl

& G. Lapalme (Eds.), Advances in artificial intelligence (pp. 319–329). Berlin: Springer.

51.

Nicolini

, & Salini

(2006). Customer satisfaction in the airline industry: The case of British Airways. Quality and Reliability Engineering International, 22, (5), 581–589.

52.

Nori

Jenkins

Koch

, & Caruana

(n.d.). InterpretML: A unified framework for machine learning interpretability. Ithaca, NY: Cornell University.

53.

Norvell

Kumar

, & Contractor

(2018). Assessing the customer-based impact of up-selling versus down-selling. Cornell Hospitality Quarterly, 59, (3), 215–227.

54.

Parks

S. C.

, & Demicco

F. J

. (2002). Age- and gender-based market segmentation. International Journal of Hospitality & Tourism Administration, 3, (1), 1–20.

55.

Opitz

, & Maclin

(1999). Popular ensemble methods: An empirical study. Journal of Artificial Intelligence Research, 11, 169–198.

56.

Ostrowski

P. L.

O'Brien

T. V.

, & Gordon

G. L

. (1993). Service quality and customer loyalty in the commercial airline industry. Journal of Travel Research, 32, (2), 16–24.

57.

Potdar

S., Pardawala, T. S., & Pai, C. D. (2017). A comparative study of categorical variable encoding techniques for neural network classifiers. International Journal of Computer Applications, 175, (4), 7–9.

58.

Renjith

Sreekumar

, & Jathavedan

(2020). An extensive study on the evolution of context-aware personalized travel recommender systems. Information Processing & Management, 57 (1), 102078.

59.

Ribeiro

M. T.

Singh

, & Guestrin

(2016). “Why Should I Trust You?”: Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–1144.

60.

Royston

(2004). Multiple imputation of missing values. The Stata Journal, 4, (3), 227–241.

61.

Safavian

S. R.

, & Landgrebe

(1991). A survey of decision tree classifier methodology. IEEE Transactions on Systems, Man, and Cybernetics, 21, (3), 660–674.

62.

Sarker

I. H.

, & Kayes

A. S. M

. (2020). ABC-RuleMiner: User behavioral rule-based machine learning method for context-aware intelligent services. Journal of Network and Computer Applications, 168, 102762.

63.

Song

W. K.

, & Lee

H. C

. (2020). An analysis of traveler need for and willingness to purchase airline dynamic packaging: A Korean case study. Journal of Air Transport Management, 82, 101735.

64.

Southern

Cheng

Zhang

, & Abowd

G. D

. (2017). Understanding the cost of driving trips. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 430–434.

65.

Squires

Wilder

D. A.

Fixsen

Hess

Rost

Curran

, & Zonneveld

(2007). The effects of task clarification, visual prompts, and graphic feedback on customer greeting and up-selling in a restaurant. Journal of Organizational Behavior Management, 27, (3), 1–13.

66.

Stavins

(2001). Price discrimination in the airline market: The effect of market concentration. Review of Economics and Statistics, 83, (1), 200–202.

67.

Steffen

Weibel

Stämpfli

A. E.

, & von Arx, W. (2020). Upselling by default: The effect of default options on travelers’ board and lodging choices. Journal of Travel Research, 59, (7), 1253–1267.

68.

Stein

Chen

A. S.

, & Hua

K. A

. (2005). Decision tree classifier for network intrusion detection with GA-based feature selection. Proceedings of the 43rd Annual Southeast Regional Conference - Volume 2, 136–141.

69.

Steinberg

(2009). CART: Classification and regression trees. In The top ten algorithms in data mining (pp. 193–216). Boca Raton, FL: Chapman and Hall/CRC.

70.

Svangren

M. K.

Skov

M. B.

, & Kjeldskov

(2018). Passenger trip planning using ride-sharing services. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 1–12.

71.

Tan

Caruana

Hooker

, & Lou

(2018). Distill-and-compare: Auditing black-box models using transparent model distillation. Proceedings of the 2018 AAAI/ACM Conference on A.I., Ethics, and Society, 303–310.

72.

Van Buuren

(2018). Flexible imputation of missing data. Boca Raton, FL: CRC Press.

73.

Wiesman

D. W

. (2006). The effects of performance feedback and social reinforcement on up-selling at fast-food restaurants. Journal of Organizational Behavior Management, 26, (4), 1–18.

74.

Yang

Wang

Zhang

, & Gao

(2020). Contextual bandits with hidden features to online recommendation via sparse interactions. IEEE Intelligent Systems, 35, (5), 62–72.

75.

Zhu

Wang

Cao

, & Cao

(2019). Online purchase decisions for tourism e-commerce. Electronic Commerce Research and Applications, 38, 100887.

76.

Zins

A. H

. (2001). Relative attitudes and commitment in customer loyalty models: Some experiences in the commercial airline industry. International Journal of Service Industry Management, 12, (3), 269–294.