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Measuring the Lifetime Value of Customers Acquired from Google Search Advertising

Author

Listed:
  • Tat Y. Chan

    () (Olin Business School, Washington University in St. Louis, St. Louis, Missouri 63130)

  • Chunhua Wu

    () (Olin Business School, Washington University in St. Louis, St. Louis, Missouri 63130)

  • Ying Xie

    () (Olin Business School, Washington University in St. Louis, St. Louis, Missouri 63130)

Abstract

Our main objective in this paper is to measure the value of customers acquired from Google search advertising accounting for two factors that have been overlooked in the conventional method widely adopted in the industry: (1) the spillover effect of search advertising on customer acquisition and sales in off-line channels and (2) the lifetime value of acquired customers. By merging Web traffic and sales data from a small-sized U.S. firm, we create an individual customer-level panel that tracks all repeated purchases, both online and off-line, and tracks whether or not these purchases were referred from Google search advertising. To estimate the customer lifetime value, we apply the methodology in the customer relationship management literature by developing an integrated model of customer lifetime, transaction rate, and gross profit margin, allowing for individual heterogeneity and a full correlation of the three processes. Results show that customers acquired through Google search advertising in our data have a higher transaction rate than customers acquired from other channels. After accounting for future purchases and spillover to off-line channels, the calculated value of new customers using our approach is much higher than the value obtained using conventional method. The approach used in our study provides a practical framework for firms to evaluate the long-term profit impact of their search advertising investment in a multichannel setting.

Suggested Citation

  • Tat Y. Chan & Chunhua Wu & Ying Xie, 2011. "Measuring the Lifetime Value of Customers Acquired from Google Search Advertising," Marketing Science, INFORMS, vol. 30(5), pages 837-850, September.
  • Handle: RePEc:inm:ormksc:v:30:y:2011:i:5:p:837-850
    DOI: 10.1287/mksc.1110.0658
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    File URL: http://dx.doi.org/10.1287/mksc.1110.0658
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    References listed on IDEAS

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    Cited by:

    1. Kannan, P.K. & Li, Hongshuang “Alice”, 2017. "Digital marketing: A framework, review and research agenda," International Journal of Research in Marketing, Elsevier, vol. 34(1), pages 22-45.
    2. Bernd Skiera & Nadia Abou Nabout, 2013. "Practice Prize Paper ---PROSAD: A Bidding Decision Support System for Profit Optimizing Search Engine Advertising," Marketing Science, INFORMS, vol. 32(2), pages 213-220, March.
    3. Ashwin Aravindakshan & Olivier Rubel & Oliver Rutz, 2015. "Managing Blood Donations with Marketing," Marketing Science, INFORMS, vol. 34(2), pages 269-280, March.
    4. Klapdor, Sebastian & Anderl, Eva M. & von Wangenheim, Florian & Schumann, Jan H., 2014. "Finding the Right Words: The Influence of Keyword Characteristics on Performance of Paid Search Campaigns," Journal of Interactive Marketing, Elsevier, vol. 28(4), pages 285-301.
    5. Weijia (Daisy) Dai & Michael Luca, 2016. "Effectiveness of Paid Search Advertising: Experimental Evidence," Harvard Business School Working Papers 17-025, Harvard Business School.
    6. Chunhua Wu, 2015. "Matching Value and Market Design in Online Advertising Networks: An Empirical Analysis," Marketing Science, INFORMS, vol. 34(6), pages 906-921, November.

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