Google exact match degradation expands to semantically unrelated terms

Brian Lasonde, founder and CEO of PPC Boost, shared documentation on November 12, 2025, showing that exact match keywords in Google Ads now trigger advertisements for search queries that lack the core semantic elements of the original keywords. The screenshot demonstrates that an exact match keyword "[best hypoallergenic food for dogs]" matched 13 search queries, none of which contained the term "hypoallergenic."

According to the documentation posted by Lasonde, search terms triggering the advertisement included "dog food for skin allergies," "dog food for allergies," "best dog food for allergies and skin issues," and "best food for dogs with allergies." All queries matched as exact match close variants despite the absence of the keyword's defining attribute. The keyword "hypoallergenic" represents a specific product characteristic that differentiates these dog foods from standard allergy-focused options.

The data shows that search queries such as "best dog food for itchy skin," "dog allergen reducing food," "allergy sensitive dog food," and "top food for dogs with allergies" all triggered advertisements through the exact match keyword containing "hypoallergenic." Each search term displayed "Exact match (close variant)" as the match type designation in Google Ads reporting. The campaign assigned all matched queries to the "Hypoallergeni Dog Food" ad group.

"Crazy how bad exact match has gotten w/ the advent of close variants," according to Lasonde's assessment shared with 95 engagement reactions on LinkedIn. "This screenshot shows that exact match terms including hypoallergenic aren't even matching to terms with hypoallergenic in them at all."

The performance patterns raise questions about semantic matching precision. When advertisers specify "hypoallergenic" in exact match keywords, Google's system interprets this as equivalent to broader allergy-related searches. This interpretation extends exact match functionality beyond matching semantic intent to matching topical categories. The distinction matters for advertisers promoting products with specific attributes that justify premium pricing or represent distinct value propositions.

Google's documentation on keyword matching states that exact match requires semantic alignment with keyword meaning. The platform's eight-step matching sequence interprets user input using spell-corrections, synonyms, and related concepts. Exact match theoretically maintains more restrictive eligibility requirements compared to broad match, which utilizes comprehensive signal analysis including landing pages, previous searches, and user location.

The practical implementation appears more expansive. According to Lasonde, "for smaller spenders, you often don't even need to build out phrase or broad match types, because exact is well, pretty broad." This observation suggests that exact match now covers traffic volumes traditionally associated with phrase match or broad match modified keywords. Advertisers who previously maintained distinct match type strategies to control traffic quality and volume may find these distinctions collapsing.

Industry professionals responding to the documentation emphasized the erosion of campaign control mechanisms. Multiple commenters noted that exact match functions increasingly like phrase match, while phrase match behaves like broad match, and broad match combined with AI Max delivers unpredictable query matching. The consensus suggests a systematic reduction in targeting precision across all match type options.

"Exact match is basically 'smart broad' at this point," according to one industry professional's response. "Feels like Google's nudging everyone toward automated intent matching instead of manual precision." This characterization reflects concerns that Google prioritizes conversion optimization over advertiser control of traffic composition. The platform's machine learning systems determine relevance based on predicted conversion likelihood rather than literal keyword alignment.

The challenges intensify for campaigns with limited budgets. When exact match keywords trigger advertisements for semantically distant queries, smaller advertisers face disproportionate impacts. These businesses lack the budget flexibility to accommodate broad traffic exploration and depend on precise targeting to maximize limited resources. Budget constraints make negative keyword management increasingly critical as exact match expands its matching scope.

Google made significant changes to exact match behavior in 2018, when the platform updated close variants to include the meaning of searcher intent rather than literal keyword matching. This modification represented a fundamental shift from syntactic matching to semantic interpretation. The 2018 changes eliminated SKAG (Single Keyword Ad Group) strategies that advertisers had used to maintain granular control over keyword targeting and quality score optimization.

The current documentation suggests close variant interpretation has expanded beyond semantic equivalence to topical proximity. When "hypoallergenic dog food" matches searches for "dog food for allergies" without any hypoallergenic terminology, the system operates on categorical assumptions about user intent rather than semantic alignment with keyword meaning. This represents a qualitative difference from matching "best hypoallergenic food for dogs" to "top hypoallergenic dog food" where semantic elements remain consistent.

Several technical factors contribute to this expansion. Google's BERT technology enables semantic understanding that connects keywords with searches expressing similar intent using different terminology. The system recognizes equivalent meanings despite different word choices. However, the transition from semantic equivalence to categorical matching represents an extension of this capability. Allergy-related searches constitute a category that may or may not require hypoallergenic products specifically.

One industry professional suggested that using longer phrases in exact match keywords tends to broaden match scope. "I've noticed that when adding long phrases as exact match it tends to broaden match scope," according to this analysis. "This isn't documented anywhere but more words = more opportunities to match stuff I guess." The observation suggests that keyword length correlates with matching flexibility, contradicting traditional expectations that specific, detailed keywords would narrow targeting.

The documentation shows that words like "best" potentially broaden matching scope significantly. This term appears in high-volume generic searches that may trigger matches across semantically distant queries. Advertisers using superlatives in exact match keywords may experience broader traffic than those using more neutral terminology. The recommendation from some practitioners involves testing simpler keyword formulations like "[hypoallergenic dog food]" rather than "[best hypoallergenic food for dogs]" to potentially restore matching precision.

Negative keyword management emerges as the primary control mechanism available to advertisers facing expanded exact match behavior. Multiple industry professionals responding to the documentation characterized negative keywords as "the new targeting" mechanism. Rather than positive keyword selection determining traffic composition, negative keyword exclusions increasingly define campaign boundaries. This inversion of traditional targeting logic requires more reactive campaign management approaches.

Search query report monitoring becomes essential from the first day of campaign operation. According to responses to Lasonde's documentation, "the only way to really keep on top of this is by monitoring the SQR from day 1 of the campaign going live." This recommendation suggests that exact match keywords no longer provide predictable traffic patterns that advertisers can anticipate. Instead, continuous monitoring and adjustment replace upfront targeting precision.

The challenges compound with Google's systematic automation expansion across its advertising platform. AI Max for Search campaigns, introduced on May 6, 2025, combines search term matching with text customization and final URL expansion. Independent testing has shown AI Max delivering conversions at 90% higher costs than phrase match in some cases. The platform's automation initiatives prioritize machine learning optimization over manual campaign control, with exact match erosion representing one component of this broader strategic direction.

Some industry professionals question whether Google will phase out exact match entirely. "It's really weakened over the past year or so, and I can't help but think that they are going to phase it out altogether," according to one response to the documentation. "It is looking more and more like broad match every day." This perspective suggests that maintaining multiple match type options serves diminishing practical purpose if all match types converge toward broad matching behavior.

The timing coincides with other platform changes affecting advertiser control. Google modified campaign setup flows in September 2025 to default to Performance Max when advertisers select all available channels. The interface restructuring represents another step toward automated campaign types that minimize manual targeting decisions. These changes collectively suggest Google's strategic preference for automation over advertiser-directed campaign management.

For businesses promoting products with specific attributes like hypoallergenic formulations, the expanded matching creates particular challenges. Product differentiation depends on consumers recognizing distinct benefits that justify different purchasing decisions. When exact match keywords blur these distinctions by matching generic allergy-related searches, advertisers lose the ability to target consumers specifically seeking hypoallergenic options. This affects both campaign efficiency and the ability to communicate distinct product value propositions.

Some advertisers reported experimenting with broad match exclusively to determine if maintaining match type distinctions provides any value. "Have you tried to go Broad just for the test?" one professional asked in response to the documentation. The question reflects growing skepticism about whether investing effort in match type strategy delivers meaningful differences in traffic composition. If exact match performs like broad match, maintaining multiple match types may represent unnecessary campaign complexity.

The Search Partner Network adds another dimension to these matching concerns. Analysis shows that Search Partner placements consistently underperform Google Search proper, delivering 37% lower return on ad spend according to research from June 2025. When exact match keywords already trigger semantically distant queries on Google Search, the additional expansion onto Search Partner sites compounds targeting imprecision and performance degradation.

Microsoft advertising platforms face similar issues with match type precision. One professional noted that "last year I was complaining that phrase match on Microsoft meant any b******* they want to serve an ad on that might be somewhat related to the industry that you might possibly be thinking about advertising for." This suggests platform-wide trends toward looser matching interpretation rather than Google-specific behavior. The advertising technology ecosystem appears to prioritize conversion optimization over targeting precision across multiple platforms.

Google's internal performance claims contrast with advertiser experiences documented in real-world testing. While Google promotes conversion improvements from broad match adoption, practitioners report that exact match now captures traffic that would have triggered broad match keywords in previous years. The disconnect between platform messaging and advertiser documentation suggests different perspectives on what constitutes successful targeting and campaign performance.

The documentation shared by Lasonde generated substantial engagement from advertising professionals managing campaigns across budget ranges and verticals. The 95 reactions and extensive comment thread demonstrate widespread recognition of the issue across the industry. Professionals from various specializations including performance marketing, digital advertising, and programmatic buying acknowledged experiencing similar patterns in their campaigns.

Several responses emphasized that the changes benefit Google financially while complicating advertiser campaign management. "We all know who benefits from this," according to one comment, suggesting that expanded matching increases impression volumes and click-through rates that drive platform revenue. This perspective frames match type expansion as serving platform business objectives rather than advertiser efficiency or user experience improvement.

The technical specifications of exact match remain unchanged in Google's documentation. The platform still describes exact match as requiring semantic alignment with keyword meaning. However, the practical implementation demonstrated in advertiser accounts shows semantically unrelated terms triggering advertisements through exact match keywords. This gap between documented behavior and observed behavior creates uncertainty for advertisers attempting to understand how their campaigns will perform based on keyword selection.

Campaign optimization strategies must adapt to accommodate expanded exact match behavior. Traditional approaches that relied on match type layering to control traffic quality and volume require fundamental reconsideration. Advertisers now face choices between accepting broader traffic through exact match keywords or implementing extensive negative keyword lists to restore targeting precision. Neither option replicates the control available through previous exact match implementations.

The documentation demonstrates that fundamental changes to keyword matching affect day-to-day campaign management rather than representing abstract technical modifications. Advertisers promoting hypoallergenic dog food face direct business impacts when their exact match keywords trigger searches from consumers seeking any allergy-related products. These consumers may have different needs, price sensitivities, and conversion likelihoods compared to those specifically researching hypoallergenic formulations.

Timeline

• 2018 - Google updated exact match close variants to include searcher intent meaning, eliminating SKAG strategy viability
• December 26, 2024 - Google Ads bidding switch silently enabled broad match keywords when converting to conversion-based bidding strategies
• May 6, 2025 - Google announced AI Max for Search campaigns combining search term matching with text customization features
• August 30, 2025 - Google published comprehensive documentation outlining AI-powered keyword matching improvements with broad match enhancements
• September 26, 2025 - Google restructured campaign creation interface to default Performance Max when selecting all channels
• November 6, 2025 - Independent testing showed AI Max delivered conversions at 90% higher costs than phrase match
• November 12, 2025 - Brian Lasonde documented exact match keywords containing "hypoallergenic" matching searches without any hypoallergenic terminology

Summary

Who: Brian Lasonde, founder and CEO of PPC Boost, documented the issue affecting Google Ads advertisers using exact match keywords for campaigns promoting products with specific attributes like hypoallergenic dog food.

What: Exact match keywords containing the term "hypoallergenic" triggered advertisements for 13 search queries that did not contain any hypoallergenic terminology, all matching as exact match close variants despite semantic distance from the original keyword.

When: The documentation appeared on November 12, 2025, on LinkedIn, though the pattern represents ongoing erosion of exact match precision that accelerated following 2018 close variant policy changes and 2025 AI-powered matching implementations.

Where: The issue affects Google Ads Search campaigns globally, with particular impact on advertisers promoting products with specific differentiating attributes who depend on targeting precision to reach qualified consumers.

Why: Google's semantic matching systems interpret exact match keywords based on topical categories and predicted conversion likelihood rather than literal semantic alignment, prioritizing automated optimization over advertiser control of traffic composition.