Apple Ads optimizationpowered by reinforcement learning

Our proprietary RL model is pre-trained on millions of simulated bidding scenarios across different auction dynamics, keyword competition levels, and budget constraints. This offline training phase allows the model to learn general bidding strategies without risking client budgets.

Once deployed on your campaigns, the model continuously adapts to your specific performance data in real-time. It balances exploration (testing new bid levels) with exploitation (scaling proven strategies), optimizing for your target KPIs while learning from every auction outcome. The model never stops learning, becoming more effective as it gathers campaign-specific data.

The RL system begins optimizing immediately, but performance improvements typically become measurable within 2-3 weeks. The learning curve depends on your campaign data volume and conversion frequency.

For optimal results, we recommend at least 20-50 conversions per campaign per day. Lower volumes work, but the model requires sufficient data to learn effectively and may take longer to converge.

Campaigns with higher conversion volumes see faster optimization, as the model can test hypotheses and learn patterns more quickly.

Very little. The RL system handles bid optimization, keyword prioritization, and budget allocation autonomously. However, you retain strategic control over key decisions.

You set overall campaign goals, define target KPIs (CPA, ROAS), approve total budgets, and manage creative assets. You can also pause campaigns, exclude keywords, or override system decisions at any time.

Think of it as setting the strategy while Catchbase handles the execution. The platform provides full transparency into all actions taken, so you always understand what is being optimized and why.

Yes. When integrated with your MMP (AppsFlyer, Adjust, etc.), Catchbase can optimize for any post-install event you track, including subscriptions, in-app purchases, trial starts, or custom conversion events.

The RL model learns which keywords and bid levels drive the highest quality users who convert to your target event. This allows for true revenue optimization, not just install volume.

Keep in mind that lower-funnel optimization requires sufficient conversion volume for the model to learn effectively. If conversion rates are very low, we may recommend optimizing for an intermediate event first.

No. Your campaign data remains completely private and is never shared across clients or used to improve models for other accounts.

Each client has an isolated RL model instance that learns exclusively from their campaigns. Your competitive advantage stays yours.

We are ISO 27001:2022 and GDPR compliant, with strict data governance policies. Campaign data is encrypted in transit and at rest, and we only access the minimum data necessary for optimization.

The RL system includes an exploration mechanism that continuously probes for high-potential keywords.

New keywords are tested with conservative bids initially. The model monitors early performance signals and either scales promising keywords or deprioritizes underperformers, allocating budget efficiently.

This exploration-exploitation balance is automatic. The system ensures you are always discovering new growth opportunities without overspending on unproven keywords.