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Synergistic effects of social media and traditional marketing on brand sales: capturing the time-varying effects

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Abstract

As social media gains more importance, managers are challenged to quantify its return on sales. The academic understanding in the effectiveness of social media is limited, and in fact the synergistic effects between social media and traditional marketing efforts have rarely been investigated. Despite the dynamics in marketing effectiveness on sales, the time-varying effectiveness of social media has never been studied either. In this study, we capture the time-varying effects of social media and the time-varying synergistic effects of social media and traditional marketing with a time-varying effect model (TVEM) approach. The empirical analyses of a large U.S. ice-cream brand sales reveal that a) the effectiveness of social media and traditional marketing vary over time, b) the synergistic effects vary over time for social media with product sampling and with in-store promotions, c) the proposed TVEM approach has a higher predictive accuracy than the benchmark models, and d) the proposed TVEM approach saves marketing costs by $0.4 million per year, compared to the time-invariant benchmark model. Overall, this study enables managers to not only better understand the synergistic effects of social media marketing and traditional marketing, but also the time-varying effectiveness of their marketing efforts with TVEM approach for better resource allocation.

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Notes

  1. Runge’s phenomenon states that when using higher-order polynomials for approximation, the interpolated polynomial will oscillate strongly at the boundaries of an interval and increase in error between the original function and the approximation (De Boor 2001).

  2. A quadratic spline (q = 2) has a continuous first derivative and its second derivative is a step function (Walls and Schafer 2005).

  3. We find that our results are highly robust to the selection of number of knots. We have conducted the analysis with different number of knots ranging from 5 to 35 and found that the results are very similar but K = 12 provides the best fit with the lowest AIC.

  4. Wand (2003) recommends treating the coefficients of truncated power functions as random variables with normal distributions, subject to the constraint that these penalty coefficients have finite variance, where α3 + k ~ N(0, η1) and b3 + k ~ N(0, η2), k = 1,…,K. The finite variance constraint allows the variance parameters, η1 and η2, to shrink the penalty coefficients to zero and provide optimal degree of smoothness (Stremersch and Lemmens 2009). We can use the restricted maximum likelihood (REML) estimates of the variance parameters (Wand 2003).

  5. We do not reveal the name of the company due to preserving the confidential agreement.

  6. We find that the weekly level analysis is more robust than at aggregated monthly or yearly level analysis.

  7. Since in-store promotion does not include the cost of product sampling events and product sampling is conducted only a few weeks in year, we do not find multicollinearity between these two variables.

  8. We find little variation in price across time, brand, and distribution channels; therefore, we do not include price as a main variable in our time-varying effect analysis.

  9. In our robustness check, we explored the benefit of allowing all parameters to vary over time; however, we only found a slight improvement in the model fit and prediction at the cost of degrees of freedom. In this study, we are more interested in managerially controllable marketing variable that has high financial benefits. Hence, we do not specify the parameters of control variables, denoted as δ, as time varying.

  10. We do not use the IV approach for product sampling and price for the following reasons: Product sampling typically occurs in the beginning of the season, and we capture that effect by including seasonality as a control variable. The focal firm does not change the price of their products much. Due to the lack of variation in price, we do not account for endogeneity issue.

  11. As described in Kumar et al. (2011), we capture the time-varying effect of the marketing variables in a monotonic way. We specify the monotonic time-varying coefficient as: (tij)monotonic=β 0 +β 1*t, which assumes the coefficients of marketing to be in a linear function of time. We compared the model fit with other functional forms of t and found the linear form of t to provide the best model fit.

  12. We found the interaction variables among different traditional marketing to be insignificant. Consequently, we do not include those in our reporting of results.

  13. RAE ranges from 0 to 1 if the proposed model performed same or better than the naïve approach. If RAE closer to 1 suggests that the focal model performed very similar to the naïve prediction. While, RAE farther from 1 indicates that the focal model predicted much better than the naïve model (Kumar et al. 1995).

  14. We thank the area editor for recommending the relative marketing elasticity analysis.

  15. We obtained an estimated cost for increasing television GRP from the company representative. In our hypothetical analysis, we have used the average price of $20,000 for 1 GRP per week for three exposures of 20 weeks of television advertisements per year.

  16. We present the benefit on sales instead because we do not know the cost of social media impressions to compute cost savings.

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Acknowledgements

We sincerely thank the editor, the AE, and the reviewers for their valuable feedback. We thank Alok Saboo and Insu Park for their comments. We thank the firm that provided us with this unique dataset for conducting the study. We also thank Renu for copy editing the manuscript.

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Authors and Affiliations

  1. Georgia State University, 3348 Peachtree Rd, NE, Suite 204, Atlanta, GA, 30326, USA

    V. Kumar & JeeWon Brianna Choi

  2. Otsuka Pharmaceutical Development & Commercialization, 508 Carnegie Center, Princeton, NJ, 08540, USA

    Mallik Greene

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  1. V. Kumar
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Correspondence to V. Kumar.

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Shrihari Sridhar served as Area Editor for this article.

V. Kumar (VK) is the Regents Professor, Richard and Susan Lenny Distinguished Chair, & Professor in Marketing, and Executive Director of the Center for Excellence in Brand and Customer Management, and the Director of the PhD Program in Marketing at the J. Mack Robinson College of Business, Georgia State University in Atlanta; the Chang Jiang Scholar at Huazhong University of Science and Technology in Wuhan, China; the Lee Kong Chian Fellow at Singapore Management University in Singapore; and the ISB Senior Fellow, Indian School of Business, India. JeeWon Brianna Choi is a doctoral student in Marketing at the Center for Excellence in Brand and Customer Management at J. Mack Robinson College of Business, Georgia State University, Atlanta, GA. Mallik Greene is a Director and Head of Mental Health PortfolioHealth Economics and Outcomes Research at Otsuka Pharmaceutical Development & Commercialization.

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Kumar, V., Choi, J.B. & Greene, M. Synergistic effects of social media and traditional marketing on brand sales: capturing the time-varying effects. J. of the Acad. Mark. Sci. 45, 268–288 (2017). https://doi.org/10.1007/s11747-016-0484-7

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