A quick look at machine learning algorithms behind Leadza
The idea of creating Leadza was to simplify the campaign management and optimization process for those of you who run ad campaigns by yourself. That's a taught goal because simplification means less dealing with understanding of your funnel, calculating metrics and setting the rules. There are plenty of different solutions to optimize Facebook ads, but mostly they are supposed to be rule-based automation systems where you still do many things manually.
Rule-based approach assumes that you as a marketer want something specific. They say: you might want to increase the budget by 10% every day OR you might want to turn off ad sets with CPI lower than 2$. In other words you ask your Facebook campaign to behave the way you put in the rule. The more rules you create the less potential you have to achieve your marketing goals because: rules limit your opportunities, they are based mostly on feelings and need to be changed.
We changed the way you may optimize your Facebook campaigns. How? We developed a system based on machine learning algorithms which can suggest many different combinations of your campaigns' settings to achieve maximum results. The system analyzes your previous statistics, make a prediction for the next day and recommends how to change the settings of your campaign. So you can adjust your marketing tactics to achieve better results by implementing suggested optimization tips and knowing how recommended action will affect the campaign performance.
Here is the list of the features you may like and better understand how the system works: Predictive budget allocation
This feature allows to predict to what level you can increase your budget to maintain good performance of a particular ad set and to understand for which ad sets the budget should be decreased due to low performance. Finally Leadza
recommends new budget proportions to all ad sets in your campaign.
For you that means no more guesswork with budgets. You may save time on allocating budgets manually and scale effectively. Default recommendations are made for active ad sets with both daily and lifetime budget settings.