Improving Penetration Forecasts Using Social Interactions Data

Published Online:https://doi.org/10.1287/mnsc.2014.1954

References

  • Ansari A, Koenigsberg O, Stahl F (2011) Modeling multiple relationships in online social networks. J. Marketing Res. 48(4):713–728.CrossrefGoogle Scholar
  • Barabási AL, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–512.CrossrefGoogle Scholar
  • Bass FM (1969) A new product growth model for consumer durables. Management Sci. 15(5):215–227.LinkGoogle Scholar
  • Bass FM, Krishnan TV, Jain DC (1994) Why the Bass model fits without decision variables. Marketing Sci. 13(3):203–223.LinkGoogle Scholar
  • Bass FM, Gordon K, Ferguson TL, Githens ML (2001) DIRECTV: Forecasting diffusion of a new technology prior to product launch. Interfaces 31(3):S82–S93.LinkGoogle Scholar
  • Chevalier JA, Mayzlin D (2006) The effect of word of mouth on sales: Online book reviews. J. Marketing Res. 43(3):345–354.CrossrefGoogle Scholar
  • Coleman JS, Katz E, Menzel H (1966) Medical Innovation: A Diffusion Study (Bobbs-Merrill Company, Indianapolis, IN).Google Scholar
  • Dellarocas C, Zhang X, Awad NF (2007) Exploring the value of online product reviews in forecasting sales: The case of motion pictures. J. Interactive Marketing 21(4):23–44.CrossrefGoogle Scholar
  • Dover Y, Goldenberg J, Shapira D (2012) Network traces on penetration: Uncovering degree distribution from adoption data. Marketing Sci. 31(4):689–712.LinkGoogle Scholar
  • Du RY, Kamakura WA (2011) Measuring contagion in the diffusion of consumer packaged goods. J. Marketing Res. 48(1):28–47.CrossrefGoogle Scholar
  • Dubé JP, Hitsch GJ, Chintagunta P (2010) Tipping and concentration in markets with indirect network effects. Marketing Sci. 29(2):216–249.LinkGoogle Scholar
  • Dubé JP, Hitsch GJ, Jindal P (2011) Estimating durable goods adoption decisions from stated preference data. Working paper, University of Chicago, Chicago.Google Scholar
  • Duan W, Gu B, Whinston AB (2008) Do online reviews matter?—An empirical investigation of panel data. Decision Support Systems 45(4):1007–1016.CrossrefGoogle Scholar
  • Easingwood CJ, Mahajan V, Muller E (1983) A nonuniform influence innovation diffusion model of new product acceptance. Marketing Sci. 2(3):273–295.LinkGoogle Scholar
  • East R, Hammond K, Lomax W (2006) Measuring the impact of positive and negative word of mouth on brand purchase probability. Internat. J. Res. Marketing 25(3):215–224.CrossrefGoogle Scholar
  • Garber T, Jacob G, Libai B, Eitan M (2004) From density to destiny: Using spatial analysis for early prediction of new product success. Marketing Sci. 23(3):419–429.LinkGoogle Scholar
  • Godes D, Mayzlin D (2004) Using online conversations to study word-of-mouth communication. Marketing Sci. 23(4):545–560.LinkGoogle Scholar
  • Godes D, Mayzlin D (2009) Firm-created word-of-mouth communication: Evidence from a field test. Marketing Sci. 28(4):721–739.LinkGoogle Scholar
  • Goldenberg J, Libai B, Muller E (2002) Riding the saddle, how cross-market communications creates a major slump in sales. J. Marketing 66(2):1–16.CrossrefGoogle Scholar
  • Goldenberg J, Lowengart O, Shapira D (2009) Zooming in: Self-emergence of movements in new product growth. Marketing Sci. 28(2):274–292.LinkGoogle Scholar
  • Gordon BR (2009) A dynamic model of consumer replacement in the PC processor industry. Marketing Sci. 28(5):846–867.LinkGoogle Scholar
  • Hahn M, Park S, Krishnamurthi L, Zoltners AA (1994) Analysis of new product diffusion using a four-segment trial-repeat model. Marketing Sci. 13(3):224–247.LinkGoogle Scholar
  • Hauser J, Tellis GJ, Griffin A (2006) Research on innovation: A review and agenda for Marketing Science. Marketing Sci. 25(6):687–717.LinkGoogle Scholar
  • Horsky D, Simon LS (1983) Advertising and the diffusion of new products. Marketing Sci. 2(1):1–17.LinkGoogle Scholar
  • Iyengar R, Van den Bulte C, Valente TW (2011) Opinion leadership and social contagion in new product diffusion. Marketing Sci. 30(2):195–212.LinkGoogle Scholar
  • Kalish S, Sen SK (1986) Diffusion models and the marketing mix for single products. Mahajan V, Wind Y, eds. Innovation Diffusion Models of New Product Acceptance (Ballinger, Cambridge, MA), 87–115.Google Scholar
  • Katz E, Lazarsfeld P (1955) Personal Influence (Free Press, New York).Google Scholar
  • Keller E, Fay B (2012) The Face-to-Face Book: Why Real Relationships Rule in a Digital Marketplace (Free Press, New York).Google Scholar
  • Lehmann DR, Esteban-Bravo M (2006). When giving some away makes sense to jump start the diffusion process. Marketing Lett. 17(4):243–254.CrossrefGoogle Scholar
  • Lenk PJ, Rao AG (1990) New models from old: Forecasting product adoption by hierarchical Bayes procedures. J. Marketing Res. 9(1):42–53.AbstractGoogle Scholar
  • Leskovec J, Adamic LA, Huberman BA (2007) The dynamics of viral marketing. ACM Trans. on the Web (TWEB) 1(1):1–39.CrossrefGoogle Scholar
  • Liu Y (2006) Word of mouth for movies: Its dynamics and impact on box office revenue. J. Marketing 70(3):74–89.CrossrefGoogle Scholar
  • Mahajan V, Peterson RA (1985) Models of Innovation (Sage, Newbury Park, CA).CrossrefGoogle Scholar
  • Mahajan V, Muller E, Kerin R (1984) Introduction strategy for new products with positive and negative word-of-mouth. Management Sci. 30(12):1389–1404.LinkGoogle Scholar
  • Mahajan V, Muller E, Bass FM (1990) New product diffusion models in marketing: A review and directions for research. J. Marketing 54(1):1–26.CrossrefGoogle Scholar
  • Manchanda P, Xie Y, Youn N (2008) The role of targeted communication and contagion in product adoption. Marketing Sci. 27(6):961–976.LinkGoogle Scholar
  • Mansfield E (1961) Technical change and the rate of imitation. Econometrica 29(4):741–766.CrossrefGoogle Scholar
  • Muller E, Yogev G (2006) When does the majority become a majority? Empirical analysis of the time at which main market adopters purchase the bulk of our sales. Technological Forecasting Soc. Change 73(9):1107–1120.CrossrefGoogle Scholar
  • Nair HS (2007) Intertemporal price discrimination with forward-looking consumers: Application to the US market for console video-games. Quant. Marketing Econom. 5(3):239–292.CrossrefGoogle Scholar
  • Nair HS, Manchanda P, Bhatia T (2010) Asymmetric social interactions in physician prescription behavior: The role of opinion leaders. J. Marketing Res. 47(5):883–895.CrossrefGoogle Scholar
  • Nam S, Manchanda P, Chintagunta PK (2010) The effect of signal quality and contiguous word of mouth on customer acquisition for a video-on-demand service. Marketing Sci. 29(4):690–700.LinkGoogle Scholar
  • Roberts JR, Nelson CJ, Morrison PD (2005) A prelaunch diffusion model for evaluating market defense strategies. Marketing Sci. 24(1):150–164.LinkGoogle Scholar
  • Robinson B, Lakhani C (1975) Dynamic price models for new product planning. Management Sci. 21(10):1113–1122.LinkGoogle Scholar
  • Rossi PE, Allenby GM (2003) Bayesian statistics and marketing. Marketing Sci. 22(3):304–328.LinkGoogle Scholar
  • Schmittlein DC, Mahajan V (1982) Maximum likelihood estimation for an innovation diffusion model of new product acceptance. Marketing Sci. 1(1):57–78.LinkGoogle Scholar
  • Shaikh NI, Rangaswamy A, Balakrishnan A (2007) Modeling the diffusion of innovations through small-world networks. Working paper, Pennsylvania State University, University Park.Google Scholar
  • Shriver SK (2015) Network effects in alternative fuel adoption: Empirical analysis of the market for ethanol. Marketing Sci. Forthcoming.LinkGoogle Scholar
  • Sinha RK, Chandrashekaran M (1992) A split hazard model for analyzing the diffusion of innovations. J. Marketing Res. 29(1):116–127.CrossrefGoogle Scholar
  • Song I, Chintagunta PK (2003) A micromodel of new product adoption with heterogenous and forward-looking consumers: Application to the digital camera category. Quant. Marketing Econom. 1(4):371–407.CrossrefGoogle Scholar
  • Sood A, James GM, Tellis GJ (2009) Functional regression: A new model for predicting market penetration of new products. Marketing Sci. 28(1):36–51.LinkGoogle Scholar
  • Srinivasan V, Mason CH (1986) Nonlinear least squares estimation of new product diffusion models. Marketing Sci. 5(2):169–178.LinkGoogle Scholar
  • Strang D (1991) Adding social structure to diffusion models: An event study framework. Sociol. Methods Res. 19(3):324–353.CrossrefGoogle Scholar
  • Sultan F, Farley JU, Lehmann DR (1990) A meta-analysis of applications of diffusion models. J. Marketing Res. 27(1):70–77.CrossrefGoogle Scholar
  • Talukdar D, Sudhir K, Ainslie A (2002) Investigating new product diffusion across products and countries. Marketing Sci. 21(1):97–114.LinkGoogle Scholar
  • Trusov M, Bodapati AV, Bucklin RE (2010) Determining influential users in Internet social networks. J. Marketing Res. 47(4):643–658.CrossrefGoogle Scholar
  • Trusov M, Bucklin RE, Pauwels K (2009) Effects of word-of-mouth versus traditional marketing: Findings from an Internet social networking site. J. Marketing 73(5):90–102.CrossrefGoogle Scholar
  • Trusov M, Rand W, Joshi YV (2013) Improving prelaunch diffusion forecasts: Using synthetic networks as simulated priors. J. Marketing Res. 50(6):675–690.CrossrefGoogle Scholar
  • Van den Bulte C, Joshi YV (2007) New product diffusion with influentials and imitators. Marketing Sci. 26(3):400–421.LinkGoogle Scholar
  • Van den Bulte C, Lilien GL (1997) Bias and systematic change in the parameter estimates of macro-level diffusion models. Marketing Sci. 16(4):338–353.LinkGoogle Scholar
  • Van den Bulte C, Lilien GL (2001) Medical innovation revisit ed. Social contagion versus marketing effort. Amer. J. Sociol. 106(5):1409–1435.CrossrefGoogle Scholar
  • van der Lans R, van Bruggen G, Eliashberg J, Wierenga B (2010) A viral branching model for predicting the spread of electronic word of mouth. Marketing Sci. 29(2):348–365.LinkGoogle Scholar
  • Watts DJ, Strogatz SH (1998) Collective dynamics of “small world” networks. Nature 393(4):440–442.CrossrefGoogle 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.