Basis Spotlight: Put Optimization in the Driver’s Seat for your Campaigns
There’s an endless number of options for creating a digital media campaign, but rarely does your client’s budget afford the luxury of an endless bankroll. So driving performance is key and the key to driving performance is optimization. Optimization is defined as the action of making the best or most effective use of a situation or resource and in the case of your campaign that resource equates to money.
In the generation of evolving technology, making guesses in order to optimize your campaign is no longer a viable option. There are too many risks involved, too much time required with manual analysis and the chances of success cannot be guaranteed. You want to achieve your campaign KPI as efficiently and effectively as possible. Enter machine learning optimization.
Our model-based technology leverages machine learning and a unique algorithm to maximize a tactic’s performance. Basis collects data from 30+ tactics parameters, at the brand level, to dynamically create models in real time to optimize towards a desired CPC goal. And it is constantly refining its model to best achieve that goal. By determining a unique price per impression based on the likelihood of meeting goals, the optimizer maximizes spend and saves time for buyers.
The optimization capabilities in Basis started with algorithmic optimization and with the introduction of machine learning optimization, media buyers can now opt for optimization with granular control or they can put intelligence in the driver’s seat.
With two cool optimization options in Basis, how do you know which to use?
Want the option for granular control?
|Machine Learning Optimization:
Want to put intelligence in the driver’s seat?
|Ideal to help you monitor the inventory in which your campaigns are buying.||Ideal for campaigns where you need multiple variables to be evaluated.|
|Choose from five different options to optimize against: CTR, eCPC, eCPA, eCPCV, and VCR.||Let the technology evaluate over 30 different targeting parameters to create an ideal optimization model for your campaigns.|
|Determine how the algorithm will work for you by setting the objectives and advanced control values.||Allow the optimizer to submit smart bids based on the probability of the impressions won, giving you the results you expect.|
|Work with the optimizer by modifying individual domain, exchange, or placement bids or status.||Permit your campaigns to learn from each other and aggregate success results to increase performance.|
There is an incredible amount of data available to media buyers and this level intelligence wouldn’t be possible for humans to produce on their own, let alone in real-time. Machine learning delivers a greater depth of knowledge and the ability to make buying and placement decisions instantly.The most significant benefit of machine learning for media buyers is its ability to continuously learn from data produced by any campaign. This allows the programmatic technology to react to changes in campaign performance and continuously improve results.
No matter how we’ve described machine learning optimization and Basis here, the best experience is to see it in action.