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Definition

Yield optimization is a technique utilized by ad servers to improve the performance of a given advertiser creative. In this technique the ad server tries to identify publisher impressions which are working well as per campaign parameters from the impressions that are not. It then tries to place more and more creatives on the impressions that are working and less on the ones which are not. Eventual goal is to place all creatives on the impressions which are working well. Yield optimization could be as rudimentary as tracking CTR (click through rate) for a given site and optimizing creatives based on it. On the other hand it could be as sophisticated as feeding a host of campaign specific parameters, like time of the day, publisher, ad size, geographical location, channel, price etc, into a machine learning system and letting the machine make the decision based on all those parameters about creative placement.

What you need to know about yield optimization: There are a couple of things you need to know about yield optimization:

Definition

What is the best way of targeting the users on the web? There are theories after theories about what the best way is and how other theories are incorrect. I would say - target the users who have the maximum propensity to buy your product and you are in business. The question is how do you find such users? One low hanging fruit to targeting users who might be interested in your product is to retarget them. This simply means that you target these users based on the fact that you have ‘seen’ them somewhere before and you can infer something about them  based on where and when you saw them before. For example, if a user was ‘seen’ on a business related site in the morning they might be interested in business related products and offerings.  Another example could be that a user was ‘seen’ on Apple’s online store configuring a MacBook, this user did not eventually complete the transaction and exited the site. Yet another example could be of a user who partially completed a credit card application and then exited the site. If you have ‘seen’ these users before, you can target them again on the web and you have a pool of users which are more likely to buy something from you then the rest of the users online. Anecdotally speaking, retargeting comprises of 80% of performance for most campaigns.