Ad targeting is often useful when you have multiple things to sell (opportunity cost) or when the cost of running an ad is non-trivial or when an irrelevant ad reduces your ability to reach the user later or any combination of the above. (For a more formal treatment, see here.)

But say that you want proof—you want to estimate the benefit of targeting. How would you do it?

When there is one product to sell, some people have gone about it as follows: randomize to treatment and control, show the ad to a random subset of respondents in the control group and an equal number of respondents picked by a model in the treatment group, and compare the outcomes of the two groups (it reduces to comparing subsets unless there are spillovers). This experiment can be thought off as a way to estimate how to spend a fixed budget optimally. (In this case, the budget is the number of ads you can run.) But if you were interested in finding out whether a budget allocated by a model would be more optimal than say random allocation, you don’t need an experiment (unless there are spillovers). All you need to do is show the ad to a random set of users. For each user, you know whether or not they would have been selected to see an ad by the model. And you can use this information to calculate payoffs for the respondents chosen by the model, and for the randomly selected group.

Let me expand for clarity. Say that you can measure profit from ads using CTR. Say that we have built two different models for selecting people to whom we should show ads—Model A and Model B. Now say that we want to compare which model yields a higher CTR. We can have four potential scenarios for selection of respondents by the model:

model_a, model_b

0, 0

1, 0

0, 1

1, 1

For CTR, 0-0 doesn’t add any information. It is the conditional probability. To measure which of the models is better, draw a fixed size random sample of users picked by model_a and another random sample of the same size from users picked by model_b and compare CTR. (The same user can be picked twice. It doesn’t matter.)

Now that we know what to do, let’s understand why experiments are wasteful. The heuristic account is as follows: experiments are there to compare ‘similar people.’ When estimating allocative efficiency of picking different sets of people, we are tautologically comparing different people. That is the point of the comparison.

All this still leaves the question of how would we measure the benefit of targeting. If you had only one ad to run and wanted to choose between showing an advertisement to everyone versus fewer people, then show the ad to everyone and estimate profits based on the rows selected in the model and profits from showing the ad to everyone. Generally, showing an ad to everyone will win.

If you had multiple ads, you would need to randomize. Assign each person in the treatment group to a targeted ad. In the control group, you could show an ad for a random product. Or you could show an advertisement for any one product that yields the maximum revenue. Pick whichever number is higher as the one to compare against.