Combining multiple campaigns into a single AI-driven strategy can dramatically improve performance, increase resource efficiency, and simplify your campaign management. This approach enables the AI to optimize ad spend across various campaigns with similar objectives, ensuring better results, faster optimization, and more effective budget utilization.
In this article, we'll take a look at the benefits, scenarios, and outcomes of using the AI multi-campaign approach, along with the conditions where it works best 🚀
When you group similar campaigns under a single AI Bid Optimization strategy, the AI can optimize ad spend more effectively, bringing multiple benefits:
💥 Maximize AI Coverage: By combining smaller or less optimized campaigns with larger, more efficient ones, you ensure more of your ad spend is optimized by AI. Even if some campaigns may not perform well individually, combining them lets AI optimize across the full set, leading to better results overall.
💥 Simplify Campaign Management: Instead of managing individual campaigns with separate budgets and KPIs, you can consolidate them into one portfolio. This streamlined approach makes it easier to monitor, control, and optimize your campaigns from a single dashboard, saving time and effort.
💥 Better Performance Through Cohesive Optimization: AI-driven bid strategies work best when the system can optimize across a unified portfolio. Avoid local optimizations that might be applied in isolated campaigns and let AI determine the best bid strategy across all campaigns, ensuring better overall performance and consistency.
❗ We recommend the AI multi-campaign approach for multiple campaigns united with the SAME business objective and KPI.
The AI multi-campaign approach is especially valuable in the following situations:
🚀 Overcoming Lack of Time and Control (Multiple Geos)
If your brand campaigns are highly focused in tier 1 geos, but other geos lack sufficient optimization and control, grouping similar campaigns from those geos can help bring uniformity and control, allowing the AI to optimize across all regions effectively.
🚀 Breaking Down Siloed Campaigns
Many campaigns may be operating independently, even though they share similar KPIs. By grouping these campaigns together in one AI strategy, AI can provide holistic optimization, which ensures more cohesive and efficient performance.
🚀 Resource Optimization
Managing numerous campaigns separately can be resource-intensive. Combining campaigns with similar objectives allows you to allocate resources more efficiently, improve scalability, and reduce the overhead associated with handling multiple individual campaigns.
When campaigns are aligned by similar KPIs, the AI is able to manage the budget and optimization in a way that maximizes return on investment. Here’s how:
💡 Scalable Performance
As you scale your campaigns, AI optimizes resources efficiently, ensuring minimal additional costs and maximizing your performance.
💡 Increased Speed & Efficiency
Consolidating campaigns speeds up the optimization cycle, allowing AI to make adjustments and improvements faster.
💡 Better ROI
A unified portfolio enables better ad spend allocation, leading to a more optimized strategy and a higher return on investment.
Here are some scenarios where the multi-campaign approach is ideal:
Example 1. By Campaign Type
For example, you might have Competitor and Generic campaigns in India, both with a target CPA of <$10. Instead of managing them separately, you can group them into a single AI strategy for unified optimization.
Example 2. By Language & Geo
If you have campaigns in Canada and Australia, both targeting a CPA of <$20, you can combine them into a single AI strategy portfolio, benefiting from shared optimization across the regions.
Example 3. By Performance KPI
If multiple campaigns share the same target CPA (<$5), and your goal is volume maximization (with no prioritization across geos), consolidating them into one AI strategy (one portfolio) allows for the most efficient resource allocation, regardless of campaign type.
🚫 Exclusions: Discovery, Probing, & Broad Match campaigns must remain under separate AI strategies.
When you unify campaigns with similar objectives, you can expect the following outcomes:
Increased Speed & Efficiency
Faster optimization cycles, as AI can process multiple campaigns together and adjust more quickly.
Improved productivity, as the system can focus on a unified goal without having to manage separate campaign silos.
Improved Scalability
Consolidating campaigns allows you to scale more effectively without significantly increasing costs or resource needs.
AI optimizes your overall portfolio, making it easier to grow without losing efficiency.
Higher ROI
With better-ad spend allocation and cohesive optimization, your campaigns will deliver improved performance, driving better returns on your investments.
The AI multi-campaign approach is a powerful way to optimize campaigns across different geos, types, and KPIs, providing more efficient resource allocation and faster optimization. By consolidating campaigns with similar objectives, you allow AI to make smarter decisions and boost your overall campaign performance.
Ready to scale efficiently with the power of a unified portfolio? ➡️ Follow our step-by-step guide and set up your first AI-powered bid optimization strategy.