How Google Ads Have Changed (And Why You Need To Adapt Your PPC Strategy)
If you’re still building Google Ads campaigns around keyword lists and match types, you’re designing for a system that no longer exists. Google’s auction doesn’t match keywords to queries anymore; it matches your offering to inferred user needs based on conversational context. Here’s how to reformat your strategy around intent instead of search terms.
1. Map campaigns to user goals, not search term buckets
Stop organizing ad groups by keyword clusters. Instead, structure campaigns around the problem your customer is trying to solve. The same intent can surface through dozens of different queries, and the same query can reflect multiple intents depending on context. “Best CRM” could mean “I need feature comparisons” or “I’m ready to buy and want validation.” Google’s AI reads that difference now. Your campaign structure should too. Group keywords by intent state, not match type.
2. Shift landing pages from “what” to “why”
Product spec sheets don’t win ad auctions these days. If your landing page explains why and how someone should use your product, not just what it is, you’re more likely to get served. Google’s reasoning layer rewards contextual alignment. When the AI builds an answer about solving a problem and your page directly addresses that problem, you’re in. This isn’t about SEO keyword density. It’s about giving the system enough context to understand what job your product does.
3. Use broad match strategically to unlock AI placements
If you want to show up inside AI Overviews or AI Mode, you need broad match keywords, Performance Max, or AI Max for Search campaigns. Exact and phrase match still work for brand defense and high-visibility placements above AI summaries, but they won’t get you into the conversational layer where exploration happens. This doesn’t mean abandoning control; it means testing broad match in limited, monitored ways while the algorithm learns your audience.
4. Feed the system rich metadata and first-party data
The algorithm prioritizes multiple high-quality images, optimized shopping feeds with every relevant attribute filled in, and Customer Match lists that teach the AI which user segments represent the highest value. That training affects how aggressively it bids for similar users. Volume matters here: AI-powered campaigns need meaningful conversion data to scale effectively, often 30 conversions in 30 days at a minimum. Smaller budgets or longer sales cycles face a gap where they lack the data needed to train algorithms and compete in automated auctions.
5. Redefine success metrics for exploratory behavior
AI Mode attracts high-funnel, exploratory searches. Conversion rates won’t match bottom-of-funnel branded searches, and that’s expected if you’re planning for it. The problem happens when you want a fast return on ad spend without adjusting how you define success for these placements. You’re monitoring overall cost-per-conversion and hoping high-funnel clicks convert downstream, but Google doesn’t provide visibility into how ads perform specifically in AI Mode versus traditional search. Plan your measurement framework accordingly.
The shift to intent-first isn’t a tactic. It’s a lens. And it’s the most durable way to structure paid search as Google keeps introducing new AI-driven formats.
ASTRALCOM helps brands restructure PPC campaigns around intent, not outdated keyword strategies. Learn how we approach paid search in an AI-driven auction.
