AdStock is a simple mathematical model of how advertising builds and decays. It is invented by Simon Broadbent as he studied Milward Brown’s ad awareness data.
AdStock helps to:
- Optimize your advertisement scheduling
- Used in marketing-mix modelling to come up with advertising ROIs, etc.
- Helps you decide when to be off-air and when to be on-air
- Helps you understand the advertising decay behaviour
How advertising builds and decays?
Let us take awareness as a parameter to understand the concept of AdStock. As a consumer watches an advertisement for the first time, let us assume that consumer gains certain awareness of the brand, category, etc. Now, when the same consumer watches the advertisement for the second time, the advertisement builds on the awareness. The advertisement hopefully will strengthen the awareness, recall, preferences, etc. So, advertising builds on itself and that is why we call it as a campaign building.
Similar to the way it builds, an advertisement also decays in similar fashion. If a consumer has seen an advertisement A1 10 times in a week and the same consumer has seen an advertisement A2 only once in a week, then the way the consumer forgets the advertisements is very different. The decay rate of an advertisement depends on various parameters such as: the strength of the advertisement itself, media plan, media vehicles chosen, category involvement of the consumer, etc.
The normal GRP data doesn’t take into account the build and decay rates. So it doesn’t take into account the residual effect of advertising, though a company doesn’t advertise in a specific period. AdStock is nothing but the GRP data taking into account of the build and decay of advertising, which is more sensible in marketing applications.
Optimize your advertisement scheduling
As explained, the AdStock GRPs are the GRPs weighted for the advertising build and decay rates.
Let us look at case to optimize the scheduling strategy for an advertisement. For this case, the advertisement is assumed to have a half-life of 6 weeks (hypothetical). This will come out for a decay rate of 12.24% as shown in the table below.
We have four options of scheduling, each using roughly the same (1200-1500 GRPs) amount of GRPs. Once we translate these raw GRPs into AdStock GRPs, it will help us decide which scheduling strategy is the most optimum as explained below.
The AdStock GRPs are adjusted based on the decay rate. For example, the number 469 in Wk 2 is arrived by: (250 of Wk2) plus (250*87.8) (decayed GRPs of Week 1) = 469.
Similarly, 662 in Week 3 is arrived by: (250 of Wk 3) + (250*87.8) (decay of Wk 2) +(250*77.0)(decay of Wk 1)= 469
From the above, it is clear that Option 1 gives the maximum ROI. The other parameter important for selection of an option is the off-air time. Which of the above options gives me the maximum off-air time (when you don’t air the advertisement)?
From the above table, it is clear that Option 1 gives the maximum off-air time for the advertisement by still maintaining more than 500 GRPs. In the above example, 500 GRPs is considered as the threshold and if it goes below, then the advertisement has to come on-air.
To sum it up, AdStock helps marketers understand ‘When to advertise‘? AdStock is commonly used in scheduling, marketing-mix modelling, etc.
Any comments on this regard are most welcome.
3 thoughts on “AdStock GRPs”
Is it possible for you to show in Excel, how the decay rate is found..guessing probably from regression..and how to convert weekly adstock to monthly? Thanks
Since you don’t define ROI then your first example does not make sense. Your option 1 costs more to run (Total GRP 1,500) and it generates a lower Adstock GRP score (4,301) whereas option 4 costs less (Total GRP 1,200) and has a higher Adstock GRP score (4,671). So without more information it seems like your conclusion is actually wrong …. And I wonder why …. So either clarify or change your recommendation.
Also half-life scores we normally use in practice is 3-4 weeks and not 6.
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