Journal of the Academy of Marketing Science

, Volume 44, Issue 4, pp 440–453 | Cite as

Paths to and off purchase: quantifying the impact of traditional marketing and online consumer activity

  • Shuba Srinivasan
  • Oliver J. RutzEmail author
  • Koen Pauwels
Original Empirical Research


This study investigates the effects of consumer activity in online media (paid, owned, and earned) on sales and their interdependencies with the traditional marketing mix elements of price, advertising and distribution. We develop an integrative conceptual framework that links marketing actions to online consumer activity metrics along the consumer’s path to purchase (P2P). Our framework proposes that the path to purchase has three basic stages–learning (cognitive), feeling (affective), behavior (conative)—and that these can be measured with novel online consumer activity metrics such as clicking on a paid search ads (cognitive) or Facebook likes and unlikes of the brand (affective). Our empirical analysis of a fast moving consumer good supports a know–feel–do pathway for the low–involvement product studied. We find, for example, that earned media can drive sales. However, we find that the news is not all good as it relates to online consumer activity: higher consumer activity on earned and owned media can lead to consumer disengagement in the form of unlikes. While traditional marketing such as distribution (60%) and price (20%) are the main drivers of sales variation for the studied brand, online owned (10%), (un)earned (3%), and paid (2%) media explain a substantial part of the path to purchase. It is noteworthy that TV advertising (5%) explains significantly less than online media in our case. Overall, our study should help strengthen marketers’ case for building share in consumers’ hearts and minds, as measured through consumer online activity and engagement.


Paid, owned, and earned media VAR FMCG Path to purchase Advertising 



The authors thank Google WPP for supporting this work with data and funding and Randy Bucklin for insightful comments and suggestions.


  1. Achrol, R. S., & Kotler, P. (2011). Frontiers of the marketing paradigm in the third millennium. Journal of the Academy of Marketing Science, 40, 35–52.CrossRefGoogle Scholar
  2. Ambler, T. (2003). Marketing and the bottom line. Harlow: Prentice Hall.Google Scholar
  3. Anderson, S. P., & Renault, R. (2006). Advertising content. The American Economic Review, 39(1), 305–326.Google Scholar
  4. Benkwitz, A., Lutkepohl, H., & Wolters, J. (2001). Comparison of bootstrap confidence intervals for impulse responses of German monetary systems. Macroeconomic Dynamics, 5, 81–100.CrossRefGoogle Scholar
  5. Court, D., Elzinga, D., Mulder, S., &Vetvik, O.J. (2009). The Consumer Decision Journey. McKinsey Quarterly, June.Google Scholar
  6. Danaher, P. J., & Dagger, T. S. (2013). Comparing the relative effectiveness of advertising channels: a case study of a multimedia blitz campaign. Journal of Marketing Research, 50(4), 517–534.CrossRefGoogle Scholar
  7. De Matos, C. A., & Rossi, C. A. V. (2008). Word-of-mouth communications in marketing: a meta-analytic review of the antecedents and moderators. Journal of the Academy of Marketing Science, 36, 578–596.CrossRefGoogle Scholar
  8. Dekimpe, M.G., & Hanssens, D.M. (1995). Empirical Generalizations About Market Evolution and Stationarity. Marketing Science, 14(summer, part 2), 109–121.Google Scholar
  9. Dekimpe, M. G., & Hanssens, D. M. (1999). Sustained spending and persistent response: A new look at long-term marketing profitability. Journal of Marketing Research, 36(4), 397-412.Google Scholar
  10. Evans, L., & Wells, G. M. (1983). An alternative approach to simulating VAR models. Economic Letters, 12(1), 23–29.CrossRefGoogle Scholar
  11. Godes, D., Mayzlin, D., Chen, Y., Das, S., Dellarocas, C., Pfeiffer, B., Libai, B., Sen, S., Shi, M., & Verlegh, P. (2005). Firm’s management of social interactions. Marketing Letters, 16(3/4), 415–428.Google Scholar
  12. Hanssens, D.M. (2009). Advertising Impact Generalizations in a Marketing Mix Context. Journal of Advertising Research, June, 127–129.Google Scholar
  13. Hanssens, D.M., Parsons, L.J., & Schultz, R.L. (2001). Market Response Models: Econometric and Time Series Analysis. 2nd Edition, Kluwer Academic Publishers.Google Scholar
  14. Hanssens, D., Pauwels, K., Srinivasan, S., Vanhuele, M., & Yildirim, G. (2014). Consumer attitude metrics for guiding marketing Mix decisions. Marketing Science, 33(4), 534–550.CrossRefGoogle Scholar
  15. Hirschman, A. O. (1970). Organizations, and States. Harvard: University press. Exit, Voice, and Loyalty: Responses to Decline in Firms.Google Scholar
  16. Horváth, C. (2003). Dynamic Analysis of Marketing Systems. Doctoral Thesis, University of Groningen, Alblasserdam: Labyrinth Publication.Google Scholar
  17. Internet Live Stats (2014). Accessed Nov 2014.
  18. Keller, K., & Lehmann, D. (2006). Brands and Branding: Research Findings and Future Priorities. Marketing Science, 25(November-December), 740–759.Google Scholar
  19. Kotler, P., & Keller, K. (2012). Marketing management (14th ed.). Upper Saddle: Pearson Prentice-Hall.Google Scholar
  20. Lautman, M. R., & Pauwels, K. (2009). What is important? identifying metrics that matter. Journal of Advertising Research, 49(3), 339–359.CrossRefGoogle Scholar
  21. Lecinski, J. (2011). Zero Moment of Truth. Accessed 2014.
  22. Li, H., & Kannan, P. K. (2014). Attributing conversions in a multichannel online marketing environment: an empirical model and a field experiment. Journal of Marketing Research, 51(1), 40–56.CrossRefGoogle Scholar
  23. Moe, W., & Schweidel, D. (2012). Online product opinions: Incidence, evaluation, and evolution. Marketing Science, 31(3), 372–386.CrossRefGoogle Scholar
  24. Naik, P., & Peters, K. (2009). A hierarchical marketing communications model of online and offline media synergies. Journal of Interactive Marketing, 23(4), 288–299.CrossRefGoogle Scholar
  25. Nielsen (2012). State of the Media: Advertising and Audience Part 2. Accessed April 2014.
  26. Ofcom (2013). Communications Market Report 2013. White paper.Google Scholar
  27. Onishi, H., & Manchanda, P. (2012). Marketing activity, blogging, and sales. International Journal of Research in Marketing, 29(3), 221–234.CrossRefGoogle Scholar
  28. Pauwels, K., & Hanssens, D. M. (2007). Performance regimes and marketing policy shifts. Marketing Science, 26(3), 293–311.CrossRefGoogle Scholar
  29. Pauwels, K., & van Ewijk, B. (2014). Do Online Behavior Tracking or Attitude Survey Metrics Drive Brand Sales? An Integrative Model of Attitudes and Actions on the Consumer Boulevard. Marketing Science Institute, Working Paper #13-118.Google Scholar
  30. Pauwels, K., & Weiss, A. M. (2008). Moving from free to Fee: How marketing can stimulate gains and stem losses for an online content provider. Journal of Marketing, 72(3), 14–31.CrossRefGoogle Scholar
  31. Pesaran, M. H., & Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics Letters, 58, 17–29.CrossRefGoogle Scholar
  32. Sonnier, G. P., McAlister, L., & Rutz, O. J. (2011). A dynamic model of the effect of online communications on firm sales. Marketing Science, 30(4), 702–716.CrossRefGoogle Scholar
  33. Srinivasan, S., Vanhuele, M., & Pauwels, K. (2010). Mind-set metrics in market response models: an integrative approach. Journal of Marketing Research, 47(4), 672–684.CrossRefGoogle Scholar
  34. Stephen, A. T., & Galak, J. (2012). The effect of traditional and social earned media on sales: an application to a microlending marketplace. Journal of Marketing Research, 49(5), 624–639.CrossRefGoogle Scholar
  35. Tellis, G. (2004). Effective advertising: How, when, and Why advertising works. Thousand Oaks: Sage Publications.Google Scholar
  36. Trusov, M., Bucklin, R. E., & Pauwels, K. (2009). Effects of word-of-mouth versus traditional marketing: findings from an internet social networking site. Journal of Marketing, 73(September), 90–102.CrossRefGoogle Scholar
  37. Vakratsas, D., & Ambler, T. (1999). How advertising works: what Do We really know? Journal of Marketing, 63(January), 26–43.CrossRefGoogle Scholar
  38. Wiesel, T., Pauwels, K., & Arts, J. (2011). Marketing’s profit impact: quantifying online and Off-line funnel progression. Marketing Science, 30(4), 604–611.CrossRefGoogle Scholar
  39. Zigmond, D., & Stipp, H. (2011). Multitaskers may be advertisers’ best audience. Harvard Business Review, 12(1/2), 32–33.Google Scholar

Copyright information

© Academy of Marketing Science 2015

Authors and Affiliations

  • Shuba Srinivasan
    • 1
  • Oliver J. Rutz
    • 2
    Email author
  • Koen Pauwels
    • 3
  1. 1.School of ManagementBoston UniversityBostonUSA
  2. 2.Foster School of BusinessUniversity of WashingtonSeattleUSA
  3. 3.Ozyegin UniversityIstanbulTurkey

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