More Quality. Less Quantity.
The purpose of Pareto.io 's automations is to increase your performance and productivity. That's why we work with machine learning/statistical algorithms and don't just use rule systems.
Thus, our algorithms depend on:
a) Minimum sample volume
The minimum sample is used to guarantee that the optimizations are statistically based, i.e. that they will really deliver the expected performance. Algorithms without a minimum sample simply work by chance, generating a certain amount of data and conversions, but without being sure of the result.
b) Statistical confidence ranging from 95% to 99%
The profile has a direct impact on the volume of actions found. An algorithm can understand that one ad performs better than another with 88% statistical confidence and we still won't generate a card. We prefer to wait longer, generate less quantity and more quality/certainty with each recommendation.
Today, this system allows us to optimize accounts to a high degree, from small to large advertisers.
How does it look in my reality?
The general trend is that the greater your investment, the more one-clicks will be created. More investment means more data. More data means more algorithms reaching the minimum sample. More minimum sample means more algorithmic definitions, generating MORE ONE-CLICKS.
If your investment is low, don't worry about the volume of cards or if we're not generating all types of cards. The most important thing is that the algorithm will suggest optimization when the time is right. And that time could be next month. Or today. Or it could have been yesterday. Pareto.io will learn from each piece of data, generating completely different patterns in each account. Discover Pareto's software for automating Google Ads and Meta Ads with AI.
But don't worry. The important thing is the quality of the optimizations. Not the quantity.
