Excessive online shopping cart abandonment rates constitute a major challenge for e-commerce companies and can inhibit their success within their competitive environment. Simultaneously, the emergence of the Internet’s commercial usage results in steadily growing volumes of data about consumers’ online behavior. Thus, data-driven methods are needed to extract valuable knowledge from such big data to automatically identify online shopping cart abandoners. Hence, this contribution analyzes clickstream data of a leading German online retailer comprising 821,048 observations to predict such abandoners by proposing different machine learning approaches. Thereby, we provide methodological insights to gather a comprehensive understanding of the practicability of classification methods in the context of online shopping cart abandonment prediction: our findings indicate that gradient boosting with regularization outperforms the remaining models yielding an F1-Score of 0.8569 and an AUC value of 0.8182. Nevertheless, as gradient boosting tends to be computationally infeasible, a decision tree or boosted logistic regression may be suitable alternatives, balancing the trade-off between model complexity and prediction accuracy.
Abu-NimehS.NappaD.WangX.NairS. (2007, October). A comparison of machine learning techniques for phishing detection. In Proceedings of the Anti-Phishing Working Groups 2nd Annual eCrime Researchers Summit (pp. 60–69). ACM.
2.
BengioY. (2009). Learning deep architectures for AI. Now Publishers.
3.
BoginaV.MokrynO.KuflikT. (2019). Will this session end with a purchase? Inferring current purchase intent of anonymous visitors. Electronic Commerce Research and Applications, 34, Article 100836.
4.
BoroujerdiE. G.MehriS.Sadeghi GarmaroudiS.PezeshkiM.MehrabadiF. R.MalakoutiS.KhadiviS. (2014, May27–29). A study on prediction of user’s tendency toward purchases in websites based on behavior models [Conference session]. 2014 6th Conference on Information and Knowledge Technology (IKT), Shahrood, Iran.
5.
BoserB. E.GuyonI. M.VapnikV. N. (1992, July). A training algorithm for optimal margin classifiers. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory (pp. 144–152). ACM.
6.
BradleyA. P. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(7), 1145–1159.
BreimanL. (2001). Random Forests. Machine Learning, 45(1), 5–32.
9.
BreimanL.FriedmanJ.OlshenR.StoneC. (1984). Classification and regression trees. Chapman & Hall.
10.
BucklinR. E.SismeiroC. (2003). A model of web site browsing behavior estimated on clickstream data. Journal of Marketing Research, 40(3), 249–267.
11.
CaruanaR.Niculescu-MizilA. (2006). An empirical comparison of supervised learning algorithms. In CohenW. (Ed.), Proceedings of the 23rd international conference on Machine learning (pp. 161–168). ACM.
12.
ChatterjeeP.HoffmanD. L.NovakT. P. (2003). Modeling the clickstream: Implications for web-based advertising efforts. Marketing Science, 22(4), 520–541.
13.
ChenH.ChiangR. H. L.StoryV. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165–1188.
14.
CheungK.-W.KwokJ. T.LawM. H.TsuiK.-C. (2003). Mining customer product ratings for personalized marketing. Decision Support Systems, 35(2), 231–243.
15.
ChipmanH. A.GeorgeE. I.McCullochR. E. (1998). Bayesian CART model search. Journal of the American Statistical Association, 93(443), 935–948.
CholletF.AllaireJ. J. (2018). Deep learning with R. Manning.
18.
ClimentF.MomparlerA.CarmonaP. (2019). Anticipating bank distress in the Eurozone: An extreme gradient boosting approach. Journal of Business Research, 101, 885–896.
19.
CloseA. G.Kukar-KinneyM. (2010). Beyond buying: Motivations behind consumers’ online shopping cart use. Journal of Business Research, 63(9–10), 986–992.
CoverT.HartP. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27.
22.
CuiG.WongM. L.LuiH.-K. (2006). Machine learning for direct marketing response models: Bayesian networks with evolutionary programming. Management Science, 52(4), 597–612.
23.
DavisJ.GoadrichM. (2006, June). The relationship between precision-recall and ROC curves. In Proceedings of the 23rd International Conference on Machine learning (pp. 233–240). ACM.
24.
DengL. (2011, October). An overview of deep-structured learning for information processing. In Proceedings of Asian-Pacific Signal & Information Processing Annual Summit and Conference (APSIPA-ASC) (pp. 1–14). NIPS.
25.
DietterichT. G. (2002). Ensemble learning. In ArbibM. A. (Ed.), The handbook of brain theory and neural networks (pp. 110–125). The MIT Press.
26.
Doshi-VelezF.KimB. (2017). Towards a rigorous science of interpretable machine learning. arXiv: 1702.08608.
27.
DudaR. O.HartP. E.StorkD. G. (1973). Pattern classification and scene analysis. Wiley.
28.
FawcettT. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874.
29.
FayyadU.Piatetsky-ShapiroG.SmythP. (1996). From data mining to knowledge discovery in databases. AI Magazine, 17(3), 37–54.
30.
FreundY.SchapireR. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55, 119–139.
31.
FriedmanJ. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5), 1189–1232.
FriedmanJ.HastieT. H.TibshiraniR. (2000). Additive logistic regression: A statistical view of boosting. The Annals of Statistics, 28(2), 337–407.
34.
GeisserS. (1975). The predictive sample reuse method with applications. Journal of the American Statistical Association, 70(350), 320–328.
35.
GuidottiR.MonrealeA.RuggieriS.TuriniF.GiannottiF.PedreschiD. (2019). A survey of methods for explaining black box models. ACM Computing Surveys, 51(5), 1–42.
36.
HandD. J. (2009). Measuring classifier performance: A coherent alternative to the area under the ROC curve. Machine Learning, 77(1), 103–123.
37.
HintonG. E.OsinderoS.TehY.-W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527–1554.
38.
HochreiterS.SchmidhuberJ. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
39.
HuangG.-H.KorfiatisN.ChangC.-T. (2018). Mobile shopping cart abandonment: The roles of conflicts, ambivalence, and hesitation. Journal of Business Research, 85, 165–174.
40.
JainA. K.MaoJ.MohiuddinK. M. (1996). Artificial neural networks: A tutorial. Computer, 29(3), 31–44.
41.
JamesG.WittenD.HastieT.TibshiraniR. (2013). An introduction to statistical learning. Springer.
42.
KellyS.NoonanC. (2017). The doing of datafication (and what this doing does): Practices of edification and the enactment of new forms of sociality in the Indian public health service. Journal of the Association for Information Systems, 18(2), 872–899.
43.
KohaviR. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In International Joint Conference on Artificial Intelligence (IJCAI), 2, 1137–1145.
44.
KohaviR.ProvostF. (2001). Applications of data mining to electronic commerce. In ProvostF.KohaviR. (Eds.), Applications of data mining to electronic commerce (pp. 5–10). Springer.
45.
Kukar-KinneyM.CloseA. G. (2010). The determinants of consumers’ online shopping cart abandonment. Journal of the Academy of Marketing Science, 38(2), 240–250.
46.
L’HeureuxA.GrolingerK.ElyamanyH. F.CapretzM. A. M. (2017). Machine learning with big data: Challenges and approaches. IEEE Access, 5, 7776–7797.
47.
LiawA.WienerM. (2002). Classification and regression by random forest. R News, 2(3), 18–22.
48.
LycettM. (2013). “Datafication”: Making sense of (big) data in a complex world. European Journal of Information Systems, 22(4), 381–386.
49.
MoeW. W. (2003). Buying, searching, or browsing: Differentiating between online shoppers using in-store navigational clickstream. Journal of Consumer Psychology, 13(1–2), 29–39.
50.
MoeW. W.FaderP. S. (2004a). Capturing evolving visit behavior in clickstream data. Journal of Interactive Marketing, 18(1), 5–19.
51.
MoeW. W.FaderP. S. (2004b). Dynamic conversion behavior at E-commerce sites. Management Science, 50(3), 326–335.
52.
MontgomeryA. L. (2001). Applying quantitative marketing techniques to the Internet. Interfaces, 31(2), 90–108.
53.
MontgomeryA. L.LiS.SrinivasanK.LiechtyJ. C. (2004). Modeling online browsing and path analysis using clickstream data. Marketing Science, 23(4), 579–595.
54.
MoroS.CortezP.RitaP. (2014). A data-driven approach to predict the success of bank telemarketing. Decision Support Systems, 62, 22–31.
55.
MuhammadI.YanZ. (2015). Supervised machine learning approaches: A survey. ICTACT Journal on Soft Computing, 5(03), 946–952.
56.
OpitzD.MaclinR. (1999). Popular ensemble methods: An empirical study. Journal of Artificial Intelligence Research, 11, 169–198.
57.
ProvostF.FawcettT. (1997). Analysis and visualization of classifier performance: Comparison under imprecise class and cost distributions. In Proceeding of the 3rd International Conference on Knowledge Discovery and Data Mining (pp. 43–48). ACM.
RajammaR. K.PaswanA. K.HossainM. M. (2009). Why do shoppers abandon shopping cart? Perceived waiting time, risk, and transaction inconvenience. Journal of Product & Brand Management, 18(3), 188–197.
60.
RaziM. A.AthappillyK. (2005). A comparative predictive analysis of neural networks (NNs), nonlinear regression and classification and regression tree (CART) models. Expert Systems with Applications, 29(1), 65–74.
RumelhartD. E.HintonG. E.WilliamsR. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533–536.
63.
SakarC. O.PolatS. O.KatirciogluM.KastroY. (2019). Real-time prediction of online shoppers’ purchasing intention using multilayer perceptron and LSTM recurrent neural networks. Neural Computing and Applications, 31(10), 6893–6908.
64.
SamuelA. L. (1959). Some studies in machine learning using the game of checkers. IBM Journal of Research and Development, 3(3), 210–229.
65.
SchapireR. E.FreundY.BartlettP.LeeW. S. (1998). Boosting the margin: A new explanation for the effectiveness of voting methods. The Annals of Statistics, 26(5), 1651–1686.
66.
SchmidhuberJ. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85–117.
67.
SismeiroC.BucklinR. E. (2004). Modeling purchase behavior at an E-commerce web site: A task-completion approach. Journal of Marketing Research, 41(3), 306–323.
68.
SrivastavaN.HintonG.KrizhevskyA.SutskeverI.SalakhutdinovR. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15, 1929–1958.
69.
StoneM. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society: Series B (Methodological), 36(2), 111–133.
70.
SuykensJ. A. K.VandewalleJ. (1999). Least squares support vector machine classifiers. Neural Processing Letters, 9(3), 293–300.
71.
TambeP.CappelliP.YakubovichV. (2019). Artificial intelligence in human resources management: Challenges and a path forward. California Management Review, 61(4), 15–42.
72.
TibshiraniR. J.TibshiraniR. (2009). A bias correction for the minimum error rate in cross-validation. The Annals of Applied Statistics, 3(2), 822–829.
73.
VafeiadisT.DiamantarasK. I.SarigiannidisG.ChatzisavvasK. (2015). A comparison of machine learning techniques for customer churn prediction. Simulation Modelling Practice and Theory, 55, 1–9.
74.
Van den PoelD.BuckinxW. (2005). Predicting online-purchasing behaviour. European Journal of Operational Research, 166(2), 557–575.
75.
VapnikV. N. (1982). Estimation of dependences based on empirical data. Springer.
76.
WilliamsN.ZanderS.ArmitageG. (2006). A preliminary performance comparison of five machine learning algorithms for practical IP traffic flow classification. ACM SIGCOMM Computer Communication Review, 36(5), 5–16.
77.
ZhangG.PatuwoB. E.HuM. Y. (1998). Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, 14(1), 35–62.
78.
ZhangP. (1993). Model selection via multifold cross validation. The Annals of Statistics, 21(1), 299–313.
79.
ZhangQ.YangL. T.ChenZ.LiP. (2018). A survey on deep learning for big data. Information Fusion, 42, 146–157.
80.
ZhengB.LiuB. (2018, January12–14). A scalable purchase intention prediction system using extreme gradient boosting machines with browsing content entropy [Conference session]. 2018 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, United States.
