Precision in digital advertising used to mean building highly granular campaigns. Marketers created many layers, separating each variable—like match types or device bids—into their own silos. This approach gave them control and, for a while, seemed to guarantee results. However, as artificial intelligence transforms advertising, these detailed structures might now stand in your way.
Takeaways 🚀
✅Simplify campaigns for AI optimization
✅Focus on early-stage intent
✅Consolidate data for better insights
✅Embrace curiosity-driven strategies
✅Transition from control to strategy
Because of these changes, artificial intelligence has pushed many advertisers to rethink campaign structure. In fact, in the latest Ads Decoded podcast, Ads Product Liaison Ginny Marvin and Search Ads Product Management Director Brandon Ervin discuss why simplified campaign structures now outperform complicated ones. They reveal how consolidating campaigns can boost results and why marketers need to adopt a fresh mindset.
The Granularity Paradox
In the past, granular campaign structures worked because manual oversight handled every scenario. For example, if you wanted unique bids for mobile users in Chicago and desktop users in New York, you had to create separate groups or campaigns.
Today, things have changed dramatically. AI loves data, and modern machine learning needs lots of it to spot patterns, predict outcomes, and optimize bids in real-time. Yet, when you split up campaigns too much, you scatter the data across too many buckets.
How Fragmentation Limits AI
Try teaching a computer to spot a “cat” by using only one pixel at a time. Clearly, it will never see the whole picture. In the same way, when you break campaigns into tiny pieces, the AI misses crucial information and makes less effective decisions.
As Brandon Ervin points out, simplified structures help AI look at the complete picture. When you merge ad groups or campaigns, you give the system access to richer data. As a result, AI can:
- Detect important patterns in user behavior, timing, and intent.
- Better predict who will convert and how valuable each lead is.
- Optimize bids more accurately with larger pools of conversion data.
Ultimately, trying to control every detail ends up limiting the AI. This prevents you from improving key measures, such as ROI or profit.
Capturing Early-Stage Search Intent
Meanwhile, Ginny Marvin and Brandon Ervin also highlight the need to focus on early-stage search intent. Classic campaign structures tend to zero in on “bottom-of-funnel” keywords—terms that signal someone is about to buy. While valuable, this approach ignores much of the user journey.
Understanding the Buyer’s Path
These days, people rarely move straight from searching to purchasing. For instance, they often explore, research, and compare before they decide. For example, someone might look up “best running shoes” or “how to train for a marathon” before searching for a specific shoe brand.
Therefore, simpler campaign structures, especially when working with broad match keywords and Smart Bidding, let advertisers reach people during initial discovery. Instead of guessing every keyword, you can rely on AI to figure out the intent—even if the exact keyword is new.
With this approach, campaign setups become flexible nets, allowing you to catch interested users throughout their journey, not just at the end.
From Setup to Strategy: The Curiosity Mindset
Letting go of detailed control can feel strange for experienced advertisers. Nevertheless, instead of micromanaging, you need to guide and strategize. Marvin and Ervin explain that adopting curiosity helps with this shift.
Rather than wondering, “How do I control these bids?” try asking “How can I help the AI drive my business goals?” You can do this by taking several important steps:
- First, feed the AI solid data. Make sure conversion tracking is correct and always include key first-party data.
- Second, choose the right goals. Move the focus from CPC to bigger metrics like ROAS (Return on Ad Spend) or profit.
- Finally, test ideas. Use campaign controls to run experiments and see what works best.
Ervin says new campaign controls serve as levers, not switches. By setting boundaries, like a target CPA or ROAS, you guide the AI and then let the system optimize. This gives you more time to improve creativity, understand your audience, and refine your business strategy.
Conclusion
All things considered, manual, granular campaign management is on the way out. The most successful advertisers now partner with AI, instead of trying to control every detail. If you simplify your campaign structures, focus on early-stage intent, and stay curious, you unlock more growth and efficiency.
To sum up, the Ads Decoded podcast offers a key lesson. In an AI-first world, simplicity does not just save time—it delivers stronger performance.
