Facebook's Ad Targeting allows advertisers to pinpoint who they want to target their ads to. These targets can be very specific and have been useful in business and political campaigns. However Facebook does not give data on which targets were most effective. After doing some research on Russian Facebook Ads that used this feature I think one can 'hack' this it to build ones own model and optimize who should see the ad.
The general idea is that one can create a dataset by randomly selecting different demographics / interests and randomly assigning the message. With this one can build kind of an ecological (since we don't se user level response, just aggregates) - uplift model and target to people with more control than Facebook might give you. I've written about uplift modeling here and here before and those techniques can be applied to this as well.
The general idea is that one can create a dataset by randomly selecting different demographics / interests and randomly assigning the message. With this one can build kind of an ecological (since we don't se user level response, just aggregates) - uplift model and target to people with more control than Facebook might give you. I've written about uplift modeling here and here before and those techniques can be applied to this as well.
For example, suppose you have two different advertisements to give to people but you are unsure which people should see which ad. One can randomly pick demographics, likes or interests and assign one of the two ads. Repeating this over many times will yield a dataset with performance metrics of each ad (costs, clicks, impressions) along with the targeted demos, and the ad assigned. Then one can build models on each metric on the ad and demos/like features to predict counterfactuals on which ad to assign a particular group.
This has 3 potential benefits
1) The first is that you may get better performance in some regards than facebook. This seems somewhat unlikely because Facebook is known to have good data. But it is a possibility.
2) The second is that you may be more efficient in ad expenditures than facebooks algorithm. This seems more plausible since we don’t know what facebook algorithm does under the hood and it seems likely to they bias it for their own profits, not yours.
3) Finally, One thing that the advertiser will most certainly get is knowledge on which demographics or targets are most susceptible to a particular kind of ad. This can be helpful for a number of reasons. As stated before facebook does not give much data on demographics that are susceptible to it. This can be used to target demo / interests outside of the Facebook platform.
One would have to run a lot of experiments but companies and political campaigns do that. Bloomberg reportedly runs experiments on 160 campaigns and spends millions of dollars on digital advertisement. I wonder if they try anything like this...
One would have to run a lot of experiments but companies and political campaigns do that. Bloomberg reportedly runs experiments on 160 campaigns and spends millions of dollars on digital advertisement. I wonder if they try anything like this...
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