Given a pool of messages, how can you maximize CTR?
The problem of maximizing CTR reduces to the problem of estimating the probability that a person in a specific context will click on each of the messages. Once you have the probabilities, all you need to do is apply the max operator and show the message with the highest probability. Technically, you don’t need to get the point estimates right—you just need to get the ranking right.
Abstracting out, there are four levers for increasing CTR:
- Better models and data: Posed as a supervised problem, we are aiming to learn clicks as a function of a) the kind of content, b) the kind of context, and c) the kinds of people. (And, of course, interactions between all three are included.) To learn preferences well, we need to improve your understanding of the content, context, and kinds of people. For instance, to understanding content more finely, you may need to code font size, font color, etc.
- Modeling externalities (user learning): It sounds funny when you say that CTR of a system that shows no messages to some people some of the time can be better than a system that shows at least some message to everyone every time they log in. But it can be true. If you need to increase CTR over longer horizons, you need to be able to model the impact of showing one message on a person opening another message. If you do that, you may realize that the best option is to not even show a message this time. (The other way you could ‘improve’ CTR is by losing people—you may lose people you bombard with irrelevant messages and the only people who ‘survive’ are those who like what you send.)
- Experimenting With How to Present a Message: Location on the webpage, the font, etc. all may matter. Experiment to learn.
- Portfolio: This let’s go of the fixed portfolio. Increase your portfolio of messages so that you have a reasonable set of things for everyone. It is easy enough to mistake people dismissing a message with disinterest in receiving messages. Don’t make the mistake. If you want to learn where you are failing, find out for which kinds of people you have the lowest (calibrated) probability scores for and think hard about what kinds of messages will appeal to these kinds of people.