You are building an ad campaign. You’ve completed vast amounts of research, worked hard on the creative, media plan and set your goals. Now it’s time to figure out frequency -- how many times a person should see each ad. This task is extremely important. If your ad gets either too many or too few views, the whole campaign can derail.
And once you’ve set the frequency, what is the actual probability of meeting it? Most premium publishers and ad exchanges claim they’ll help advertisers achieve the perfect frequency target, by ensuring that the average frequency meets a set frequency cap. The key word here is average. But is a frequency cap what you need? If you advertise with different premium publishers, how do you choose the cap for each site? How do you avoid overexposure to people visiting more than one of those sites -- or underexposure to those visiting just one?
Achieving average frequency can mean you are not necessarily controlling ad exposure to those people reached. For example, a campaign reaching 1 million people with an average frequency of 10 impressions could mean either of the following:
In the second case, there are again 10 million total impressions, and again an average frequency of 10 impressions. But while the first scenario is successful, the second one scenario misses the target frequency with both under and over exposure.
So what can you do to avoid such a scenario? Instead of looking at average frequency, you can look at frequency buckets: the number of people exposed to an exact number of impressions. In the example above, for example, you’d want to know how many people fall into frequency bucket 10 – that is, how many saw the ad the desired 10 times? In the first scenario, the answer is 1 million; in the second scenario, zero – a total failure. Some advertisers would also consider any user exposed to the ad nine or 11 times a success; they would look at a frequency bucket of 9, 10 or 11 impressions. And again, in the example above, the first scenario would result in the desired 1 million, the second in zero.
The question advertisers should be asking is, “How many people actually had the proper amount of exposure?” Not, “What is the average exposure?”
So avoid averages. And raise the bucket!