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:
- Not everybody supports it. If you are an advertiser and have bought some inventory from a publisher and are using an ad server, Atlas for example, it will not optimize your creatives for you. The reason for this is that Atlas was not built to do so. It assumes that you know what inventory you are buying and have already done so, thus it makes no sense for it to decide whether it should place a creative on the impression or not. It assumes that you have made the decision already when you did the buy. It just does the placements for you.
Typically, ad exchanges are places where you could see really sophisticated yield optimization. This is so because you are trying to buy the inventory on the fly and it makes sense for you to know whether a given impression has any probability of doing well for your campaign or not. RightMedia’s yieldmanager is one such ad server which supports very robust yield optimization. You can use yieldmanager to run your own campaigns even if you did not want to participate in their ad exchange. Although there is a queue to get on to RightMedia exchange.
Regardless of the fact whether you are buying inventory in bulk from an advertiser or from an exchange you could be smarter about how your place your ads on it if the server you are using supports yield optimization. Here is a scenario – you have a couple of campaign running on a couple of buys from different publishers. Instead of placing your creatives evenly or per your media plan you can try to let them ‘compete’ with each other and let the ad server do the optimization for you. Certain creatives might do well for a given publisher and their user audience. Lots of agencies and advertisers are either unaware of this feature or do not utilize it fully. - It needs to learn. When you employ an ad server which can do yield optimization based on machine learning, you will end up losing some impressions to learning. When the campaign starts, the ad server will try to place the creatives on all publishers and then it will slowly weed out the ones which are not performing and zone in the ones which are. The more you let it learn the better it will perform. In yieldmanager, for example, you can specify how many impressions you want it to use for learning.
Yield optimization is a great tool at the disposal of an advertiser that they can utilize to improve the performance of their campaigns and specifically their creatives. Creatives play an important role in the success of your campaign. Some creatives work well for one scenario while others work for another. If your ad server can pick and choose these scenario and creative combination smartly, your campaigns will tend to be more successful. As an advertiser you should try to find out how you can use this feature of your ad server.
