Competitive advertising strategies for programmatic television

Abstract

Programmatic television advertising technologies allow advertisers to detect the placement of competitor ads and schedule their own ads almost in real time. This paper investigates how managers can improve the effectiveness of their ad schedules by considering the relative placement of their ads with respect to competitor ads. By analyzing a dataset of more than 43,000 own-brand and 49,000 competitor TV ad insertions, we propose and estimate the effects of four ad scheduling strategies on online conversions. The best strategy is to place ads in isolation, either when competitors are not advertising at all or advertising on other stations; this avoidance strategy results in the greatest effectiveness of own-brand ads and delivers conversions from competitor ads. If an avoidance strategy is not possible, brands should advertise more heavily than their competitors. Doing so mitigates the substitution effect of competitive advertising, which occurs when competitor ads outnumber own-brand ads. Our analyses show that adopting programmatic television technology would have led the focal firm to increase the conversions from television advertising by 59%.

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Notes

  1. 1.

    The traditional ad purchase and insertion process generally involves several manual interactions such as requests for proposals, budget approvals, insertion orders, ad trafficking, managing emails, spreadsheets and phone calls among advertisers, media agencies and television stations (Google Marketing Platform 2019).

  2. 2.

    Spending in programmatic advertising is expected to reach 3.8 billion in the U.S. in 2019, a six-fold jump from the 2016 spending of $640 million (eMarketer 2017).

  3. 3.

    http://www.mediamonitors.com.my/services.aspx.

  4. 4.

    https://www.ispot.tv/.

  5. 5.

    Advertising elasticity is commonly used to assess the impact of advertising on demand and it is defined as the percentage increase in demand due to a 1% increase in advertising spending.

  6. 6.

    The only exception is Danaher et al. (2008), although they use weekly data.

  7. 7.

    In addition, brands could decide to place ads before or after competitor ads. We analyzed this case with our data and found no difference in the effects of advertising for ads that were placed before and after competitor ads.

  8. 8.

    To determine whether our results were the same if we considered all products of the brand, we also ran the analysis using website visits. This variable captured the number of visits for all products of the brand (including the car insurance comparison product). The results, shown in the Robustness Checks section, agree with those of our main analysis.

  9. 9.

    We refer to the conditions as “strategies” to emphasize that brands could strategically decide to place their ads. However, the focal brand did not intentionally pursue any of the identified “strategies.”

  10. 10.

    We added 1 to the number of brand ads in Equation 1 to account for the focal ad k.

  11. 11.

    Mason and Perreault (1991) show that problems associated with multicollinearity decrease as the explained variance and the sample size increase. They show that multicollinearity problems are minimal when the R2 is 0.75 and the sample size is 300, even with correlations as high as 0.95 between independent variables. Our main model is calibrated on 31,391 observations and yields an R2 of 0.57 when estimated with OLS.

  12. 12.

    We also ran a robustness check using a longer time window of 240 min and obtained the same results.

  13. 13.

    The augmented Dickey-Fuller test (α = −38.29, p < .01), Perron test (α = −41.49, p < .01), and Elliott et al. (1996) GLS-ADF test (α = −47.22, p < .01) all rejected the null hypothesis that the online conversions variable has a unit root.

  14. 14.

    We can also calculate the 90% duration interval of the advertising effect considering that the cumulative elasticity, τ hours after it is broadcast, is given by δτ = δτ − 1 + γ(βC − δτ − 1), with δ0 = βI.

  15. 15.

    The Ljung-Box portmanteau test indicated that we could not reject the null hypothesis that the residuals of the model in Equations 4 and 5 were serially uncorrelated (Q = 4.22, p = .24). Therefore, the model accurately accounted for autocorrelation.

  16. 16.

    Robustness checks indicated that the focal results did not change with more complex dynamic structures for the error term.

  17. 17.

    In the U.S., brands can pay for exclusive advertising rights during entire broadcasts to avoid their ads being shown together with competitor ads (Dukes and Gal-Or 2003). In the French market, exclusivity agreements generally involve that brand and competitor ads are not shown together within the same break. The focal firm did not engage in this practice during the period of analysis.

  18. 18.

    Importantly, the correlation between own and competitive ad spending does not generate endogeneity problems because both are included in the model. Correlation among independent variables can only lead to multicollinearity issues but, as explained before, this should not be a problem in our application.

  19. 19.

    In the robustness checks, we run alternative analyses, including a richer set of fixed effects and an indicator of online advertising spending. The results of these additional robustness checks agree with our main results.

  20. 20.

    We use the delta method to test all the hypotheses.

  21. 21.

    As previously noted, in this condition the focal brand has a higher SOV than the competition.

  22. 22.

    Park and Gupta (2012, pp. 583–584) show that when the Hausman test detects endogeneity the copulas are significant, and that when the Hausman rejects endogeneity the copulas are non-significant.

  23. 23.

    Web Appendix 2 shows the results of the model using the calibration sample.

  24. 24.

    The MAD/Mean ratio is defined as \( \frac{\sum_{\mathrm{t}=1}^{\mathrm{T}}\left|{\mathrm{y}}_{\mathrm{t}}-{\hat{\mathrm{y}}}_{\mathrm{t}}\right|}{\sum_{\mathrm{t}=1}^{\mathrm{T}}{\mathrm{y}}_{\mathrm{t}}} \), where yt and \( {\hat{\mathrm{y}}}_{\mathrm{t}} \) represent the actual and predicted values of the series in time t respectively, and T is the total number of periods considered in the prediction. The MAD/Mean ratio is preferred over other forecast accuracy statistics when working with intermittent and low-volume data (in our application, the conversion series reaches low values during the night).

  25. 25.

    If the brand engaged in a combination of strategies, the economic returns would lie somewhere in between the results from the considered individual strategies shown in Table 5.

  26. 26.

    In our case, the modal category contains the zeroes, so the PMC is equal to 1 – percentage of observations with advertisement. The PRE is calculated as \( \mathrm{PRE}=\frac{\mathrm{PCP}-\mathrm{PMC}}{1-\mathrm{PMC}} \).

  27. 27.

    We ran these analyses using our data but mostly found insignificant effects. We speculate that these effects are not noticed in our data because it is aggregated at the hourly level. Future research could explore if these effects can be detected using data at the minute level or taking the lift in conversions after ad occurrence as the dependent variable.

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Guitart, I.A., Hervet, G. & Gelper, S. Competitive advertising strategies for programmatic television. J. of the Acad. Mark. Sci. 48, 753–775 (2020). https://doi.org/10.1007/s11747-019-00691-5

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Keywords

  • Programmatic television
  • Real-time competitor tracking
  • Competitive advertising
  • Advertising scheduling
  • Advertising effectiveness
  • Television advertising