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Investigating cross-media effects in a multichannel marketing environment: the role of email, catalog, television, and radio

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Abstract

We empirically examine the cross-media effects of personalized and mass media on consumers’ purchase incidence in a multichannel shopping environment. We capture the cross-media effect as the combined impact of two distinct marketing communications on consumers’ purchase behavior. Our data consists of individual-level transaction data and information on consumers’ exposure to multiple marketing media consisting of personalized (catalog and email) and mass (television and radio) media. We find that personalized (mass) media are more influential in driving consumers’ online (offline) purchases in a multichannel shopping environment. Our analysis of cross-media effects reveals synergistic (attenuating) effects between media components across (within) personalized and mass media. Furthermore, our examination of media elasticities demonstrates that discounting such cross-media effects between personalized and mass media components can bias a firm’s assessment of the effectiveness of media components in a multichannel-multimedia marketing environment. Results from our model can help marketing managers in the optimal planning of integrated marketing communication in a multichannel-multimedia shopping environment.

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Notes

  1. The retailer has a consumer relationship management (CRM) system using loyalty card program

  2. In this dataset, we find that about 88.74% of consumers purchase at least twice.

  3. \({\upbeta }^{Online}_{Email}\) - \({\upbeta }^{Offline}_{Email}\) = 0.6872, p< 0.01; \({\upbeta }^{Online}_{Catalog}\) - \({\upbeta }^{Offline}_{Catalog}\) = 0.1597, p< 0.01

  4. \({\upbeta }^{Online}_{Television}\) - \({\upbeta }^{Offline}_{Television}\) = -0.2268, p< 0.01; \({\upbeta }^{Online}_{Radio}\) - \({\upbeta }^{Offline}_{Radio}\) = − 0.1584, n.s.

  5. We employ simulation techniques (e.g., Allenby and Lenk 1994) for computing the elasticities. We use parameter estimates to compute base channel incidence probabilities and the change in channel incidence probabilities due to a 5%, 10%, 15%, and 20% bump respectively for each independent variable. These are then averaged to obtain the final elasticies and corresponding standard error. Since media component disbursement frequencies are different—emails can be sent daily while catalogs are sent less frequently, managers should interpret the media effectiveness accordingly.

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Authors and Affiliations

  1. Allen G. Aaronson Department of Marketing and International Business, Zicklin School of Business, Baruch College-The City University of New York, New York, NY, USA

    Karthik Sridhar & Ram Bezawada

  2. School of Economics, Finance and Marketing, College of Business and Law, RMIT University, Melbourne, Australia

    Ashish Kumar

Authors
  1. Karthik Sridhar
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  2. Ashish Kumar
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  3. Ram Bezawada
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Correspondence to Ashish Kumar.

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All authors contributed equally.

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The study uses data from a third party that precludes from making data publicly available due to the non-disclosure agreement signed with the firm.

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Sridhar, K., Kumar, A. & Bezawada, R. Investigating cross-media effects in a multichannel marketing environment: the role of email, catalog, television, and radio. Mark Lett 33, 189–201 (2022). https://doi.org/10.1007/s11002-021-09592-6

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