In order to achieve the ultimate goal of running profitable campaigns, one needs to learn to identify the right audience for the offer. Audiences are separated by different parameters such as country, device, OS and browser. Different audiences have very different tastes for banners, landers and offers. The thorough and scientific approach to identify the winner audience is to exhaust all possible combinations. This approach will easily produce ten over campaigns under each campaign group. If we are testing 10 offers in 20 countries, we are looking at hundreds of campaigns. This number of campaigns will make manual optimization very difficult. Unless you are highly trained to look at hundreds of rows of data to make precise and rational decisions, otherwise it is close to impossible to manage these campaigns. Even if you are trained in this, wouldn’t it better to invest your time and energy strategizing on optimization rather than doing monotonous manual work? Hence, we introduce auto-optimization to maximize personal creativity on strategizing. With auto-optimization, you will be freed from the tedious work to observe data and formulate hypothesis.
The optimization strategy is made of various conditions that we set in the auto-optimization rules. You may refer to how to set it up here. For each condition you may set a threshold value to trigger a certain action like blacklist or whitelist. Afilter Tracker will read these conditions and trigger actions when the specified conditions are met. There is one thing worth noting is that each variable can only be used once in all conditions under the same auto-optimization rule. Namely, if you have used the variable LP CTR(%) in one condition, you shall not use it again in other conditions.
The most commonly used conditions are blacklist and whitelist sub-placements. When you buy traffic from a traffic source, there are many sub-placements or also known as widgets. They are the individual websites or apps that the traffic source buys traffic from. Each of these sub-placements has their own audience profile, hence different tastes for ads. The same campaign might perform differently in different sub-placements. For most traffic sources they usually source from thousands of sub-placements. Efficient automatic blacklisting will help to distribute the budget evenly across different sub-placements and avoid budget being wasted on un-performing sub-placements, especially when there are some sub-placements are notorious for their using of bot traffic. Whitelisted sub-placements are where the gold lies. These are your profitable audience that you can scale up.
Try to use auto-optimization as far as possible for more precise and efficient decision-making.