Keyword Research Checklist

How has keyword research evolved beyond simple syntax matching?

Effective PPC Keyword Research has transitioned from building exhaustive lists of exact-match phrases to defining broad Search Themes and managing semantic signals. In an environment where exact match now signifies same meaning rather than same text, strategies must pivot to training AI models with first-party data and high-quality creative inputs to capture intent-driven, conversational queries effectively.

From Keywords to Signals

The industry is currently facing Semantic Drift, a phenomenon where the algorithm’s interpretation of a keyword’s meaning deviates from business goals over time. This requires frequent auditing, not of the words themselves, but of the intent they attract. We have moved away from manual list building, a core concept in The Google Ads Keyword Guide, toward Search Themes, the replacement for traditional keyword lists in Performance Max campaigns. These themes allow advertisers to provide up to 50 specific inputs per asset group, guiding the AI targeting without restricting it to rigid syntax.

The Shift in Matching and Discovery

Intent-Based Matching has fundamentally changed the auction dynamics. The Exact Match setting now triggers ads for queries sharing the same meaning or intent, not just the same syntax. This expansion is necessary due to the rise of Zero-Click Searches and AI Overviews, where informational queries are answered directly on the SERP. Traditional organic clicks for simple definitions are vanishing, forcing strategies to focus on complex, high-intent queries that machines cannot easily summarize.

The Experience Angle

We challenge the industry obsession with automation by arguing that while matching is automated, the input requires more human expertise than ever. In our analysis, we found that meaning-based matching can fail spectacularly without human guardrails. We managed a B2B campaign where a niche technical term was interpreted by the AI as a broad consumer query, wasting 20% of the budget in 48 hours. This only stopped when we applied negative keyword guardrails to force the algorithm back into its lane. The machine provides the scale, but the human provides the context.

How can AI tools identify high-value search themes?

While basics covered in How To Use Google Keyword Planner provide a foundation, modern discovery relies on agentic AI tools like Ads Advisor and AI Max to uncover opportunities manual research misses. By scanning landing pages and historical account performance, these tools identify keywordless opportunities and conversational queries. Marketers must now function as editors, validating AI suggestions against business objectives rather than generating lists from scratch.

Agentic Discovery Tools

Ads Advisor operates as an agentic conversational tool, scanning websites and account history to generate personalized keyword and asset ideas. It moves beyond static recommendations by engaging in a dialogue with the strategist. This pairs with AI Max for Search, an optimization layer that utilizes Search Term Matching and broad match technology to find incremental, high-performing queries that human research often overlooks.

Performance Through Keywordless Tech

The integration of Keywordless Technology within AI Max allows ads to match based on landing page content and user intent rather than explicit keywords. This is critical for capturing conversational queries that do not fit standard keyword patterns. Data indicates that AI-powered improvements have boosted Broad Match Performance by 10% for advertisers using Smart Bidding, validating the shift away from granular control toward broad signal capture.

The Experience Angle

Our team utilizes a specific workflow with Ads Advisor to brainstorm initial themes, but we strictly limit its autonomy. We manually vet every theme against a client’s specific UVP. In a recent test, we used specific prompt engineering to force the AI to differentiate between “informational” and “commercial” intent keywords. The AI initially suggested high-volume educational terms for a sales-focused campaign. By refining the prompt to focus on bottom-of-funnel transaction intent, we filtered out 40% of the fluff before it ever entered the account.

What is the role of match types in an intent-driven ecosystem?

The distinct lines between match types have blurred, with Exact Match now capturing semantic variants and Broad Match becoming the default partner for Smart Bidding. The strategy is no longer about syntax control but about signal density; Broad Match requires robust conversion data to function, while Search Themes in Performance Max act as soft signals to guide the algorithm toward relevant audiences.

The Smart Bidding Power Pair

Smart Bidding Exploration allows algorithms to bid aggressively on lower-ROAS opportunities to discover new conversion pockets. This works best when paired with Broad Match, creating a Power Pair capable of capturing unique searches. Google reports that 15% of daily queries are new. Strategies focused on How To Find Low Competition Keywords manually often miss these, but Broad Match covers this blind spot by matching to the intent behind the new query rather than the words used.

Controlling the Wide Net

To manage this reach, Search Themes Reporting in PMax provides insights into which categories of queries the AI is prioritizing, allowing us to refine inputs cyclically. Additionally, Brand Inclusions offer a necessary control mechanism, allowing Broad Match campaigns to restrict traffic solely to searches related to specific brands. This ensures that the wide net of broad match does not dilute brand equity by appearing on competitor terms or irrelevant generic searches.

The Experience Angle

We advise readers never to switch to Broad Match without first having the Broad Match Fail-Safe in place. This means having offline conversion tracking or Enhanced Conversions active. In our testing, Broad Match campaigns running on maximize clicks or standard conversion data frequently optimize for cheap, low-quality traffic. Without value-based data, the algorithm interprets a junk lead the same as a high-value sale. We only unlock Broad Match once we can feed the system revenue data, ensuring it optimizes for profit rather than volume.

How do negative keywords protect budget in automated campaigns?

As matching broadens, negative keywords become the primary lever for targeting precision. A layered architecture, applying exclusions at the Account, Campaign, and Ad Group levels, is essential to prevent budget waste on irrelevant or low-intent queries. Unlike positive keywords, negative keywords do not match to close variants, requiring exhaustive lists of misspellings and variations.

Precision in a Broad Match World

With broad match and PMax, you cannot control what you target, only what you exclude. We utilize Account-Level Negatives to create a global shield against terms like “free,” “jobs,” or “tutorial” that never convert. For PMax specifically, utilizing the 10,000 Negative Keyword Limit at the campaign level is mandatory to sculpt traffic, as you lack ad group granular control. This allows for blocking specific product lines or irrelevant service areas without restricting the entire account.

The Syntax Gap

A critical oversight is assuming negative keywords work like positive ones. They do not. Close Variants Limitation means if you exclude “shoe,” ads might still show for “shoes.” You must build Intent Clustering lists that cover every variation, plural, and misspelling. Grouping these negatives by theme, such as Price Shoppers or DIY Researchers, simplifies management and ensures you are blocking the intent, not just a single word.

The Experience Angle

We frequently see Cannibalization destroy account efficiency. In our audits, we often find Non-Brand campaigns flooded with branded search terms, inflating ROAS artificially. We implement strict negative keyword lists to force the Google algorithm to respect campaign structure. For example, we add Brand negatives to Non-Brand campaigns to ensure they are truly prospecting for new users, rather than poaching easy traffic that would have converted organically or via a cheaper brand campaign.

How does landing page quality influence keyword performance?

Keyword research is ineffective without GEO-Ready content. Features like Final URL Expansion and Dynamic Search Ads rely entirely on the structural integrity and semantic clarity of the website to match user intent. Landing pages must be optimized for Answer Engine queries, providing direct, structured answers to complex questions, to secure visibility in AI Overviews and ensure high Quality Scores.

Feeding the Asset Matching Engine

Tools like Final URL Expansion remove the need for manual landing page selection, but they require clear signals. The AI scans your page for relevance. If your H1 and H2 tags are vague, the system won’t match your page to high-value queries. Text Customization features further rely on this, dynamically rewriting headlines based on your page content. A page with generic copy results in generic ads, lowering relevance and increasing costs.

Optimizing for Speed and Answers

Beyond content, technical factors like Mobile Speed Score are direct ranking signals; a one-second delay can drop conversion rates by 20%. Furthermore, to capture traffic from AI Overviews (AIO), content must be structured to answer specific user questions immediately, rather than burying the lead. The algorithms prioritize content that functions as a direct answer, rewarding clarity over word count.

The Experience Angle

We have shifted our On-Page strategy from Keyword Stuffing to Answer Structuring. Instead of repeating a target keyword five times, we analyze the Search Terms Report for “People Also Ask” style questions. We then rewrite landing page H2s to explicitly ask those questions and provide concise answers immediately below. This structure triggers dynamic matching and featured snippets far more effectively than density-based optimization.

How do we measure the impact of keyword strategies beyond clicks?

Success metrics must shift from Click-Through Rate to Incremental Revenue. Utilizing frameworks like Meridian (MMM) allows advertisers to measure the causal impact of paid search and its interplay with other channels. Integrating first-party data via tools like Data Manager ensures the AI optimizes for business value, or profit, rather than vanity metrics, training the system to find high-value keywords automatically.

Holistic Measurement

Attribution is no longer linear. We use Meridian, an open-source Marketing Mix Model, to understand how search volume contributes to total incrementality. This helps verify if paid search is driving new business or just claiming credit for existing demand. It moves the conversation from “How many clicks did we get?” to “How much net-new revenue did this strategy generate?”

Data as the New Targeting

The quality of your keywords is now determined by the quality of your data. Using Data Manager, we unify first-party data to calculate Enhanced Conversions. This recovers lost signals and trains the bidding algorithm on actual user value. When the system knows which users spend the most, it automatically prioritizes keywords that attract similar high-value customers, effectively automating keyword qualification.

The Experience Angle

We warn clients against the Vanity Metric trap of optimizing for blended ROAS in isolation. A high ROAS often masks a failure in growth if it is driven entirely by branded keywords. We switch the primary KPI to New Customer Acquisition Goals or Profit on Ad Spend (POAS). In one case, accepting a lower ROAS on a non-brand campaign actually doubled total company profit because it was bringing in net-new customers rather than recycling existing ones.

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