Personalized Mobile Targeting with User Engagement Stages: Combining a Structural Hidden Markov Model and Field Experiment

Published Online:https://doi.org/10.1287/isre.2018.0831

References

  • Abhishek V, Fader P, Hosanagar K (2012) Media exposure through the funnel: A model of multi-stage attribution. Working paper, University of California, Irvine, Irvine.Google Scholar
  • Adler B (2014) Mobile apps: What's a good conversion rate? Localytics (blog). Accessed March 5, 2019, http://info.localytics.com/blog/mobile-apps-whats-a-good-conversion-rate.Google Scholar
  • Aguirregabiria V, Mira P (2007) Sequential estimation of dynamic discrete games. Econometrica 75(1):1–53.CrossrefGoogle Scholar
  • An Y, Hu Y, Hopkins J, Shum M (2013) Identifiability and inference of hidden Markov models. Working paper, University of Connecticut, Mansfield.Google Scholar
  • Andrews M, Luo X, Fang Z, Ghose A (2015) Mobile ad effectiveness: Hyper-contextual targeting with crowdedness. Marketing Sci. 35(2):218–233.LinkGoogle Scholar
  • Arcidiacono P, Miller RA (2011) Conditional choice probability estimation of dynamic discrete choice models with unobserved heterogeneity. Econometrica 79(6):1823–1867.CrossrefGoogle Scholar
  • Ascarza E, Hardie BGS (2013) A joint model of usage and churn in contractual settings. Marketing Sci. 32(4):570–590.LinkGoogle Scholar
  • Bakos Y, Brynjolfsson E (1999) Bundling information goods: Pricing, profits, and efficiency. Management Sci. 45(12):1613–1630.LinkGoogle Scholar
  • Bhattacharya D, Dupas P (2012) Inferring welfare maximizing treatment assignment under budget constraints. J. Econometrics 167(1):168–196.CrossrefGoogle Scholar
  • Brodie RJ, Hollebeek LD, Jurić B, Ilić A (2011) Customer engagement: Conceptual domain, fundamental propositions, and implications for research. J. Service Res. 14(3):252–271.CrossrefGoogle Scholar
  • Chernozhukov V, Hansen C (2005) An iv model of quantile treatment effects. Econometrica 73(1):245–261.CrossrefGoogle Scholar
  • Choudhary V (2010) Use of pricing schemes for differentiating information goods. Inform. Systems Res. 21(1):78–92.LinkGoogle Scholar
  • Choudhary V, Ghose A, Mukhopadhyay T, Rajan U (2005) Personalized pricing and quality differentiation. Management Sci. 51(7):1120–1130.LinkGoogle Scholar
  • ComScore (2016) The 2016 U.S. mobile app report. Accessed September 13, 2016, https://www.comscore.com/Insights/Presentationsand-Whitepapers/2016/The-2016-US-Mobile-App-Report.Google Scholar
  • Connault B (2014) Hidden rust models. Working paper, University of Pennsylvania, Philadelphia.Google Scholar
  • Dubé JP, Luo X, Fang Z (2017b) Self-signaling and prosocial behavior: A cause marketing experiment. Marketing Sci. 36(2):161–186.LinkGoogle Scholar
  • Dubé JP, Fang Z, Fong N, Luo X (2017a) Competitive price targeting with smartphone coupons. Marketing Sci. 36(6):944–975.LinkGoogle Scholar
  • Firpo S (2007) Efficient semiparametric estimation of quantile treatment effects. Econometrica 75(1):259–276.CrossrefGoogle Scholar
  • Fong NM, Fang Z, Luo X (2015) Geo-conquesting: Competitive locational targeting of mobile promotions. J. Marketing Res. 52(5):726–735.CrossrefGoogle Scholar
  • Fudenberg D, Villas-Boas JM (2007) Behavior-based price discrimination and customer recognition. Economics and Information Systems: Handbooks in Information Systems, vol. 1 (Elsevier, Amsterdam), 377–436.Google Scholar
  • Ghose A, Han SP (2014) Estimating demand for mobile applications in the new economy. Management Sci. 60(6):1470–1488.LinkGoogle Scholar
  • Han SP, Park S, Oh W (2015) Mobile app analytics: A multiple discrete-continuous choice framework. Management Inform. Systems Quart. 40(4):983–1008.CrossrefGoogle Scholar
  • Hotz VJ, Miller RA, Sanders S, Smith J (1994) A simulation estimator for dynamic models of discrete choice. Rev. Econom. Stud. 61(2):265–289.CrossrefGoogle Scholar
  • Huang Y, Vir Singh P, Ghose A (2015) A structural model of employee behavioral dynamics in enterprise social media. Management Sci. 61(12):2825–2844.LinkGoogle Scholar
  • Kim YH, Kim DJ, Wachter K (2013) A study of mobile user engagement (MoEN): Engagement motivations, perceived value, satisfaction, and continued engagement intention. Decision Support Systems 56:361–370.CrossrefGoogle Scholar
  • Kumar V, Sriram S, Luo A, Chintagunta PK (2011) Assessing the effect of marketing investments in a business marketing context. Marketing Sci. 30(5):924–940.LinkGoogle Scholar
  • Kwon HE, So H, Han SP, Oh W (2016) Excessive dependence on mobile social apps: A rational addiction perspective. Inform. Systems Res. 27(4):919–939.LinkGoogle Scholar
  • Laxman S, Tankasali V, White RW (2008) Stream prediction using a generative model based on frequent episodes in event sequences. Proc. 14th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 453–461.CrossrefGoogle Scholar
  • Li C, Luo X, Zhang C, Wang X (2017) Sunny, rainy, and cloudy with a chance of mobile promotion effectiveness. Marketing Sci. 36(5):762–779.LinkGoogle Scholar
  • Li H (2015) Intertemporal price discrimination with complementary products: E-books and e-readers. Working paper, Carnegie Mellon University, Pittsburgh.Google Scholar
  • Localytics (2016) An even better recipe for perfect push notifications. Accessed September 2017, http://ebooks.localytics.com/an-even-better-recipe-for-perfect-push-notifications.Google Scholar
  • Luo X, Andrews M, Fang Z, Phang CW (2013) Mobile targeting. Management Sci. 60(7):1738–1756.LinkGoogle Scholar
  • MacDonald IL, Zucchini W (1997) Hidden Markov and Other Models for Discrete-Valued Time Series, vol. 110 (CRC Press, Boca Raton, FL).Google Scholar
  • Magnac T, Thesmar D (2002) Identifying dynamic discrete decision processes. Econometrica 70(2):801–816.CrossrefGoogle Scholar
  • Miller RA (1984) Job matching and occupational choice. J. Political Econom. 92(6):1086–1120.CrossrefGoogle 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.LinkGoogle Scholar
  • Netzer O, Lattin JM, Srinivasan V (2008) A hidden Markov model of customer relationship dynamics. Marketing Sci. 27(2):185–204.LinkGoogle Scholar
  • Punera K, Merugu S (2010) The anatomy of a click: Modeling user behavior on web information systems. Proc. 19th ACM Internat. Conf. Inform. Knowledge Management (Association for Computing Machinery, New York), 989–998.CrossrefGoogle Scholar
  • Qiu L, Kumar S (2017) Understanding voluntary knowledge provision and content contribution through a social-media-based prediction market: A field experiment. Inform. Systems Res. 28(3):529–546.LinkGoogle Scholar
  • Rust J (1987) Optimal replacement of GMC bus engines: An empirical model of Harold Zurcher. Econometrica 55(5):999–1033.CrossrefGoogle Scholar
  • Shiller B (2016) Personalized price discrimination using big data. Working paper, Brandeis University, Waltham, MA.Google Scholar
  • SWRVE (2016) Monetization report 2016: Lifting the lid on player spend patterns in mobile. Accessed March 5, 2019, https://www.swrve.com/images/uploads/whitepapers/swrvemonetization-report-2016.pdf.Google Scholar
  • ThaiTech (2015) One in four mobile apps not used more than once. Accessed March 5, 2019, https://tech.thaivisa.com/app-retention/11662/.Google Scholar
  • Wager S, Athey S (2018) Estimation and inference of heterogeneous treatment effects using random forests. J. Amer. Statist. Assoc. 113(523):1228–1242.CrossrefGoogle Scholar
  • Weisberg HI, Pontes VP (2015) Post hoc subgroups in clinical trials: Anathema or analytics? Clinical Trials 12(4):357–364.CrossrefGoogle Scholar
  • Xu K, Chan J, Ghose A, Han SP (2017) Battle of the channels: The impact of tablets on digital commerce. Management Sci. 63(5):1469–1492.LinkGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.