BidTheatre ASX can automatically adjust bid levels to optimize against a given campaign KPI. The optimization models are continually updated using Machine Learning algorithms fueled by previous buying activity.
How do I activate ML on my campaign?
Use a Filter with an ML option selected.
Use Machine Learning: Will optimize without sacrificing campaign delivery goals
Use Machine Learning - Strict: Will optimize and risk sacrificing campaign delivery goals.
As always, Filters can be used on specific bid strategies, or as campaign wide default.
In addition to using a Filter with activated ML, the campaign will need to have a set KPI. The set KPI will affect which model to use.
|Engagement / Clicks||Yes|
|Action / Conversions||Est. December 2018|
|Awareness / Reach||Est. August 2018|
Things to think about when using ML
If you're targeting deals, keep in mind that the ML optimization may place many bids under the deal floor.
Also, keep in mind that the optimization rarely excludes poor performance completely, but rather places low bids.
Each bid strategy can have an assigned optimization strategy that governs how bids are optimized. To set the optimization strategy, click the ID of an existing bid strategy. Optimization evaluates the performance of a specific ad on a specific RTB Site, and adjusts individual ad/site bids according to the optimization strategy selected.
A few standard optimization strategies are provided for use by BidTheatre. You can also create your own optimization strategies in Assets / Optimization Strategies.
Currently optimization can be made on two functions
|Min CTR||CTR (0-1)||Optimize on this minimum click-through-rate CTR||Suitable for optimization of branding campaigns|
|Max eCPC||eCPC||Optimize towards this maxmium effective cost per click||Suitable for optimization of performance campaigns|
|Min Inscreen Rate||Inscreen Rate (0 -1)||Optimize towards a minimum inscreen rate||Suitable both for branding and performance campaigns|
Optimization Type - Initial Learning
Currently there exists one type of optimization strategies - Initial Learning.
In Initial Learning, a fixed part of the the budget is devoted to a learning phase where Optimization Type Value number of impressions will be bought from each site. After the learning phase, sites that performed worse than the Optimization Function Value of the the Optimization Function will be placed on the campaign exclusion list.
Pros: No risk of underdelivery unless target function value is set too tight. Conserves best performers onto a new sitelist and bid strategy.
Cons: No optimization is performed after the learning phase.