Welcome. This applet is designed to allow you to explore the robustness of the results of Shapiro, Hitsch & Tuchman (2019) to various modeling choices. Updated results will be loaded in as they become available.
Please cite any use of the figures from this applet both to the URL (https://advertising-effects.chicagobooth.edu/) and the paper:Shapiro, Bradley and Hitsch, Günter J. and Tuchman, Anna, Generalizable and Robust TV Advertising Effects (September 17, 2019). Available at SSRN: https://ssrn.com/abstract=3273476 or http://dx.doi.org/10.2139/ssrn.3273476
This tab shows our results for ROI (return of investments). All calculations are based on estimated advertising effect from border-strategy with carry-over parameter set to 0.9.
Average ROI of Weekly Advertising: Set one week's advertising equal to zero and leave all other weeks unchanged. Compute ROI of going from zero ads in that week to the observed level of ads. Average across weeks where we observe positive advertising.
Average ROI of All Observed Advertising: Compute ROI of going from all weeks with zero advertising to all weeks having the observed level of advertising.
Maximum ROI: Set all weeks to zero advertising. Compute ROI of going from zero advertising to 1 GRP in a single week and zero ads in all other weeks. Average across weeks where we observe positive advertising.
Marginal ROI: Start with observed level of advertising. Add 1 additional GRP to a single week. Compute ROI of this 1 GRP increase in advertising. Average across weeks where we observe positive advertising.
We analyze how much larger the ad effect need to be in order for the ROI to be equal to zero.
The numbers in the histogram represent by how much you would have to multiply the estimated ad effect in order for the ROI to be equal to zero. Only brands with positive estimated ad effect are included. Observations in blue indicate that the break-even ad effect is within the confidence interval of the estimated ad effect.
This tab is specific only to specifications where we estimate the best delta.
This tab shows the residual variation in advertising stock, net of market/store, month, and week-of-year FEs and price, promotions, and feature/display controls for different main specifications and assumed delta. The coefficient of variation is computed as the ratio of the residual variation to the average DMA-level weekly GRPs for that brand. The residual SD is computed as the standard deviation of the residuals.
This tab shows our main results estimated using different functional forms. We choose to estimate a cubic B-spline with knots placed at the 10th, 25th, 50th, 75th and 90th percentile of observed advertising stock for each brand, and compare the results with our original log(1 + Adstock) function. The comparison is shown in a scatter plot, where the predicted quantities are on the vertical axis and ad stock is on the horizontal axis.
The predicted quantities at Adstock = 0 are equal for both log and spline functions. To compare the results at similar level, We adjusted the mean value of predicted quantities to be the same for some brands.