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


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.

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  1. We use both the Augmented Dickey-Fuller test, which maintains evolution as the null hypothesis and the KPSS test which maintains stationarity as the null hypothesis (e.g., Pauwels and Weiss 2008).

  2. We use the one standard error criterion to judge the statistical significance of each impulse response coefficient (Pesaran and Shin 1998). Standard errors are calculated using a Monte Carlo simulation approach with 1,000 runs in each case (see Horváth 2003).

  3. To evaluate the accuracy of our GFEVD estimates, we obtain standard errors using Monte Carlo simulations (see Benkwitz et al. 2001).

  4. Our non-disclosure agreement with the data provider does not allow us to specifically name the product category and brand.

  5. Because only consumers that like a brand, can unlike it later, this causal relation is similar to that of marriage Granger causing divorce.

  6. Each equation requires estimation of 9 parameters (the intercept and 1 lag of each endogenous variable). While we have 40 observations, we need to use 1 to take first differences and 1 to include lags, leaving 38 observations for parameter estimation.


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

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Correspondence to Oliver J. Rutz.

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The authors are listed reverse alphabetically, all authors contributed equally

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Srinivasan, S., Rutz, O.J. & Pauwels, K. Paths to and off purchase: quantifying the impact of traditional marketing and online consumer activity. J. of the Acad. Mark. Sci. 44, 440–453 (2016).

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