We help Meta outperform Meta

No. Amanda AI always creates campaigns from scratch on Meta. Existing campaigns are not taken over or managed.

Yes.

With Cross-Channel Budget Allocation enabled, the budget can be distributed between Google and Meta based on comparative performance signals.

Instead of fixed splits, allocation is evaluated based on expected marginal return across platforms.

This feature operates at the total budget level and does not increase the advertiser’s overall spend.

Yes. Meta’s AI is still responsible for auction-level optimization. Amanda AI does not replace Meta’s learning phase — it structures campaigns so Meta can learn more effectively.

Amanda AI does not automatically downgrade optimization events just to exit learning. If an ad set performs well but shows “learning limited,” it may be allowed to continue rather than being reset unnecessarily.

In short: Meta handles auction learning. Amanda AI structures the system to improve learning.

Placement settings follow Meta’s recommended best practices by default, but you can adjust placement preferences based on your strategy. This includes placements across Facebook, Instagram, Messenger, and Audience Network, depending on your setup.

Account history provides Meta with behavioral signals about who converts, when they convert, and under what conditions. Amanda AI is designed to preserve and build on that historical signal rather than frequently resetting it.

By maintaining a consistent main objective and stable campaign structure, historical conversion data remains usable and accumulative over time. The more consistent the signal, the better Meta’s algorithm can identify high-intent users.

Frequent objective changes or structural rebuilds can weaken that signal and reduce delivery stability. Amanda AI treats historical data as an asset — not something to reset in pursuit of short-term adjustments.

An ad set stays in learning when it doesn’t generate enough conversion events. Meta typically requires around 50 optimization events per week for an ad set to fully exit the learning phase.

Common reasons ad sets get stuck include:

• Budgets spread across too many ad sets

• Frequent edits (budget changes, targeting changes, new ads)

• Low conversion volume

• Optimizing toward events that rarely occur

   

When data is fragmented or limited, Meta struggles to stabilize delivery.

Historical data remains in your Meta account. Past performance signals, pixel data, and conversion history are not deleted. That data continues to inform delivery and optimization where relevant.

Temporary campaigns are useful for seasonal promotions, product launches, limited-time offers, and short-term awareness pushes. These campaigns can support the always-on structure but should not replace it. Turning core campaigns on and off too frequently can weaken performance stability.

Meta often recommends structural changes to accelerate learning, such as switching optimization events or adjusting campaign objectives. While those recommendations can increase short-term event volume, they may disrupt long-term signal quality.

Amanda AI prioritizes consistent optimization toward the main business objective and builds on historical conversion data instead of frequently resetting the system. This supports stronger, more stable performance over time.

The goal is signal quality — not just faster exits from learning.

Learning resets are often caused by frequent objective changes, large daily budget shifts, major targeting edits, and constant structural rebuilds.

Amanda AI reduces resets by keeping objectives consistent, avoiding unnecessary optimization event switches, limiting aggressive structural changes, and allowing stable ad sets to continue running even if labeled “learning limited.”

The goal is stability. A stable structure means stronger signals and more predictable performance.