Predicting Stages in Omnichannel Path to Purchase: A Deep Learning Model

References

• Acquisti A, Brandimarte L, Loewenstein G (2015) Privacy and human behavior in the age of information. Science 347(6221):509–514.Crossref, Google Scholar
• Adamopoulos P, Todri V (2015) The effectiveness of marketing strategies in social media: Evidence from promotional events. Proc. 21th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining, 1641–1650. https://doi.org/10.1145/2783258.2788597.Google Scholar
• Adamopoulos P, Tuzhilin A (2014) Estimating the value of multi-dimensional data sets in context-based recommender systems. Poster Proc 8th ACM Conf. Recommender Systems (Association for Computing Machinery, New York).Google Scholar
• Adamopoulos P, Ghose A, Todri V (2018) The impact of user personality traits on word of mouth: Text-mining social media platforms. Inform. Systems Res. 29(3):612–640.Google Scholar
• Adamopoulos P, Ghose A, Tuzhilin A (2021a) Heterogeneous demand effects of recommendation strategies in a mobile application: Evidence from econometric models and machine-learning instruments. Management Inform. Systems Quart., Forthcoming.Google Scholar
• Adamopoulos P, Todri V, Ghose A (2021b) Demand effects of the Internet-of-things sales channel. Inform. Systems Res. 32(1):238–267.Link, Google Scholar
• Adomavicius G, Zhang J (2016) Classification, ranking, and top-K stability of recommendation algorithms. INFORMS J. Comput. 28(1):129–147.Link, Google Scholar
• Andrews M, Luo X, Fang Z, Ghose A (2016) Mobile ad effectiveness: Hyper-contextual targeting with crowdedness. Marketing Sci. 35(2):218–233.Link, Google Scholar
• Barry TE, Howard DJ (1990) A review and critique of the hierarchy of effects in advertising. Internat. J. Advertising 9(2):121–135.Crossref, Google Scholar
• Bell D, Gallino S, Moreno A (2015) Showrooms and information provision in omni‐channel retail. Production Oper. Management 24(3):360–362.Crossref, Google Scholar
• Bergmeir C, Benítez JM (2012) On the use of cross-validation for time series predictor evaluation. Inform. Sci. 191:192–213.Crossref, Google Scholar
• Chandukala SR, Dotson JP, Brazell JD, Allenby GM (2011) Bayesian analysis of hierarchical effects. Marketing Sci. 30(1):123–133.Link, Google Scholar
• Chen T, Guestrin C (2016) XGboost: A scalable tree boosting system. Proc. 22nd ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining, 785–794.Google Scholar
• Cui HT, Ghose A, Halaburda H, Iyengar G, Pauwels K, Sriram S, Tucker C, and Venkataraman S (2021) Informational challenges in omnichannel marketing: remedies and future research. J. Marketing 85(1):103–120.Google Scholar
• Ding AW, Li S, Chatterjee P (2015) Learning user real-time intent for optimal dynamic web page transformation. Inform. Systems Res. 26(2):339–359.Link, Google Scholar
• Fisher ML, Gallino S, Xu JJ (2019) The value of rapid delivery in omnichannel retailing. J. Marketing Res. 56(5):732–748.Crossref, Google Scholar
• Geva T, Oestreichersinger G, Efron N, Shimshoni Y (2017) Using forum and search data for sales prediction of high-involvement projects. Management Inform. Systems Quart. 41(1):65–82.Crossref, Google Scholar
• Ghose A (2017) TAP: Unlocking the Mobile Economy (MIT Press, Cambridge, MA).Crossref, Google Scholar
• Ghose A, Todri V (2016) Toward a digital attribution model: Measuring the impact of display advertising on online consumer behavior. MIS Quart. 40(4):889–910.Crossref, Google Scholar
• Ghose A, Goldfarb A, Han SP (2012) How is the mobile Internet different? Search costs and local activities. Inform. Systems Res. 24(3):613–631.Link, Google Scholar
• Ghose A, Li B, Liu S (2019) Mobile targeting using customer trajectory patterns. Management Sci. 65(11):5027–5049.Link, Google Scholar
• Ghose A, Singh PV, Todri V (2017) Got Annoyed? Examining the Advertising Effectiveness and Annoyance Dynamics.Google Scholar
• Ghose A, Li B, Macha M, Sun C, Foutz NZ (2021) Trading privacy for the greater social good: How did America react during COVID-19? Preprint, submitted June 2020, https://dx.doi.org/10.2139/ssrn.3624069.Google Scholar
• Goodfellow I, Yoshua B, Aaron C (2016) Deep Learning (MIT Press, Cambridge, MA).Google Scholar
• Gopalakrishnan A, Park YH (2021) The impact of coupons on the visit-to-purchase funnel. Marketing Sci. 40(1):48–61.Link, Google Scholar
• Guo H, Tang R, Ye Y, Li Z, He X (2017) DeepFM: A factorization-machine based neural network for CTR prediction. Proc. Twenty-Sixth Internat. Joint Conf. Artificial Intelligence (IJCAI-17), 1725–1731.Google Scholar
• Hasanzadeh K, Kajosaari A, Häggman D, Kyttä M (2020) A context sensitive approach to anonymizing public participation GIS data: From development to the assessment of anonymization effects on data quality. Comput. Environment Urban Systems 83:101513.Google Scholar
• Huang N, Sun T, Chen P, Golden JM (2019) Word-of-mouth system implementation and customer conversion: A randomized field experiment. Inform. Systems Res. 30(3):805–818.Link, Google Scholar
• Hui SK, Fader PS, Bradlow ET (2009) Path data in marketing: An integrative framework and prospectus for model building. Marketing Sci. 28(2):320–335.Link, Google Scholar
• Hui SK, Inman JJ, Huang Y, Suher J (2013) The effect of in-store travel distance on unplanned spending: Applications to mobile promotion strategies. J. Marketing 77(2):1–16.Crossref, Google Scholar
• Kelleher JD, Mac Namee B, D’arcy A (2020) Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (MIT Press, Cambridge, MA).Google Scholar
• Kumar A, Mehra A, Kumar S (2019) Why do stores drive online sales? Evidence of underlying mechanisms from a multichannel retailer. Inform. Systems Res. 30(1):319–338.Link, Google Scholar
• Lavidge RJ, Steiner GA (1961) A model for predictive measurements of advertising effectiveness. J. Marketing 25(6):59–62.Crossref, Google Scholar
• Lee D, Hosanagar K (2021) How do product attributes and reviews moderate the impact of recommender systems through purchase stages? Management Sci. 67(1):524–546.Link, Google Scholar
• Lee D, Hosanagar K, Nair HS (2018) Advertising content and consumer engagement on social media: Evidence from Facebook. Management Sci. 64(11):5105–5131.Link, Google Scholar
• Lemon KN, Verhoef PC (2016) Understanding customer experience throughout the customer journey. J. Marketing 80(6):69–96.Crossref, Google Scholar
• Lian J, Zhou X, Zhang F, Chen Z, Xie X, Sun G (2018) xDeepFM: Combining explicit and implicit feature interactions for recommender systems. Proc. 24th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining, 1754–1763.Google Scholar
• Liaukonyte J, Teixeira T, Wilbur KC (2015) Television advertising and online shopping. Marketing Sci. 34(3):311–330.Link, Google Scholar
• Luo X, Andrews M, Fang Z, Phang CW (2013) Mobile targeting. Management Sci. 60(7):1738–1756.Link, Google Scholar
• Luo X, Zhang Y, Zeng F, Qu Z (2020) Complementarity and cannibalization of offline-to-online targeting: A field experiment on omnichannel commerce. MIS Quart. 44(2):957–982.Google Scholar
• Malik N, Singh PV (2019) Deep learning in computer vision: Methods, interpretation, causation, and fairness. Oper. Res. Management Sci. in the Age of Analytics. INFORMS. 2019:73–100.Google Scholar
• Molitor D, Spann M, Ghose A, Reichhart P (2020) Effectiveness of location-based advertising and the impact of interface design. J. Management Inform. Systems 37(2):431–456.Crossref, Google Scholar
• Moe WW (2003) Buying, searching, or browsing: Differentiating between online shoppers using in-store navigational clickstream. J. Consumer Psych. 13(1-2):29–39.Crossref, Google Scholar
• Montgomery AL, Li S, Srinivasan K, Liechty JC (2004) Modeling online browsing and path analysis using clickstream data. Marketing Sci. 23(4):579–595.Link, Google Scholar
• Montes R, Sand-Zantman W, Valletti T (2019) The value of personal information in online markets with endogenous privacy. Management Sci. 65(3):1342–1362.Link, Google Scholar
• Neumann N, Tucker CE, Whitfield T (2019) Frontiers: How effective is third-party consumer profiling? Evidence from field studies. Marketing Sci. 38(6):918–926.Abstract, Google Scholar
• Park CH, Park YH (2016) Investigating purchase conversion by uncovering online visit patterns. Marketing Sci. 35(6):894–914.Link, Google Scholar
• Provost F, Fawcett T (2013) Data Science for Business: What You Need to Know About Data Mining and Data Analytic Thinking (O’Reilly Media, Inc., Sebastopol, CA).Google Scholar
• Ray J, Menon S, Mookerjee V (2020) Bargaining over data: When does making the buyer more informed help? Inform. Systems Res. 31(1):1–15.Link, Google Scholar
• Rudin C, Carlson D (2019) The secrets of machine learning: Ten things you wish you had known earlier to be more effective at data analysis. Operations Research & Management Science in the Age of Analytics. INFORMS. 44-72.Google Scholar
• Sayedi A, Jerath K, Srinivasan K (2014) Competitive poaching in sponsored search advertising and its strategic impact on traditional advertising. Marketing Sci. 33(4):586–608.Link, Google Scholar
• Shukla AD, Gao G, Agarwal R (2021) How digital word-of-mouth affects consumer decision making: Evidence from doctor appointment booking. Management Sci. 67(3):1546–1568.Link, Google Scholar
• Song T, Huang J, Tan Y, Yu Y (2019) Using user-and marketer-generated content for box office revenue prediction: Differences between microblogging and third-party platforms. Inform. Systems Res. 30(1):191–203.Link, Google Scholar
• Strong EK (1925) Theories of selling. J. Appl. Psych. 9(1):75.Crossref, Google Scholar
• Tashman LJ (2000) Out-of-sample tests of forecasting accuracy: an analysis and review. Internat. J. Forecasting 16(4):437–450.Crossref, Google Scholar
• Tellis GJ (2003) Effective Advertising: Understanding When, How, and Why Advertising Works (Sage Publications, Thousand Oaks, CA).Google Scholar
• Todri V, Ghose A, Singh PV (2020) Trade-offs in online advertising: Advertising effectiveness and annoyance dynamics across the purchase funnel. Inform. Systems Res. 31(1):102–125.Link, Google Scholar
• Ursu RM (2018) The power of rankings: Quantifying the effect of rankings on online consumer search and purchase decisions. Marketing Sci. 37(4):530–552.Link, Google Scholar
• Ursu RM, Wang Q, Chintagunta PK (2020) Search duration. Marketing Sci. 39(5):849–871.Link, Google Scholar
• Verhoef PC, Kannan PK, Inman JJ (2015) From multichannel retailing to omnichannel retailing: introduction to the special issue on multichannel retailing. J. Retailing 91(2):174–181.Crossref, Google Scholar
• Verhoef P, van Ittersum K, Kannan PK, Inman J (2021) Omnichannel retailing: A consumer perspective. Kahle L, Lowrey T, Huber J, eds. APA Handbook of Consumer Psychology (American Psychological Association).Google Scholar
• Wang P, Sun F, Wang D, Tao J, Guan X, Bifet A (2017) Inferring demographics and social networks of mobile device users on campus from AP-trajectories. Proc. 26th Internat. Conf. on World Wide Web Companion, 139–147.Google Scholar
• Yoganarasimhan H (2020) Search personalization using machine learning. Management Sci. 66(3):1045–1070.Link, Google Scholar
• Zhang D, Dai H, Dong L, Wu Q, Guo L, Liu X (2019) The value of pop-up stores in driving online engagement in platform retailing: Evidence from a large-scale field experiment with Alibaba. Management Sci. 65(11):5142–5151.Link, Google Scholar