Does Google Ads Ad Strength Predict Better Conversions?

What does the reported research show?

Search Engine Journal’s November 3, 2026 report attributes these findings to research by Brad Geddes. These are study findings as summarized by the publication, not forecasts for an individual advertiser.

What can a 5,900-account study tell your business?

The practical news is a research finding, not an announced change to how Google Ads works. According to the Search Engine Journal report, Google serves higher-rated responsive search ads more often, but the higher rating does not consistently identify the stronger converter. That is a reason to question score-driven optimization—not a reason to deliberately write worse ads.

A study spanning 5,900 accounts raises a useful business question: are your reporting and approval processes rewarding the right thing? If a monthly report celebrates improved Ad Strength without discussing qualified leads, purchases, or acquisition costs, it may be emphasizing a diagnostic over an outcome.

However, account count alone does not establish that the result applies equally to a Boulder home-service company, a national online retailer, and a B2B software provider. They have different searches, buying cycles, conversion definitions, and budgets. A broad finding can identify a pattern worth testing without supplying your account’s answer.

The supplied report summary does not specify the full comparison method, account mix, treatment of ties, or statistical uncertainty. It also does not establish that a higher rating causes poorer conversion performance. Those limitations matter before converting a research headline into an account-wide rule.

Use the finding to review your decision process. Do not use it as permission to pause every Excellent-rated ad or preserve every Poor-rated ad. Neither decision follows from the evidence.

What does the 45% finding actually mean?

The reported 45% figure describes how often higher-rated ads converted better in the study’s comparisons. It is not a conversion rate. It does not mean those ads converted 45% of visitors, improved conversions by 45%, or wasted the remaining share of spend.

It also does not mean your lower-rated ad has a predictable probability of winning. Without the detailed methodology, you should not assume every comparison had equal traffic, equal conversion volume, or identical search intent. The remaining comparisons should not automatically be described as decisive lower-rated-ad wins; the summary does not explain how ties or inconclusive results were handled.

The business interpretation is narrower and more useful: Ad Strength is not a dependable stand-alone substitute for measured conversion performance.

Even conversion rate is only one part of the decision. An ad can convert a larger share of clicks while generating fewer total qualified customers. Another can produce more form submissions but attract prospects outside your service area. A retailer can gain purchases while shifting demand toward lower-margin products.

Before deciding which ad is better, specify what better means. For lead generation, that may be cost per qualified opportunity with sufficient opportunity volume. For eCommerce, it may be profitable revenue after accounting for acquisition costs, product economics, and returns. Click-through rate and raw lead volume can help explain the result, but they should not silently replace the business objective.

How is Ad Strength different from actual ad performance?

A responsive search ad, or RSA, uses a collection of headlines and descriptions that Google can assemble into different combinations. Ad Strength provides feedback on that creative setup, including the range and relevance of the assets supplied. It is useful for spotting opportunities to make an ad more complete or less repetitive.

It is not a measurement of your profit, sales-team acceptance rate, or customer lifetime value. It is also distinct from Quality Score. Treating these different concepts as interchangeable makes account reviews confusing and can lead to changes that solve the wrong problem.

Consider a hypothetical commercial cleaning company that serves offices but not homes. Broad headlines about affordable cleaning may create a flexible collection of assets. Yet a clear phrase such as “Commercial Office Cleaning” helps residential searchers recognize that the service is not for them. That qualification may matter more commercially than adding another generic benefit.

The useful response to a weak rating is therefore an inspection, not blind compliance. Are headlines genuinely different? Do they describe the service customers searched for? Are important benefits missing? Does the landing page support the offer? Can assembled combinations repeat themselves or imply something inaccurate?

Improve assets where the diagnosis is sound. Preserve necessary specificity where the suggestion conflicts with the business. A stronger rating can accompany a better ad, but the rating alone does not establish that the change helped.

Why should pinning be part of the investigation?

Pinning restricts an RSA asset to a specified position. It gives advertisers more control over message order while reducing the combinations Google can assemble. The Search Engine Journal summary identifies pinning as an explanation to investigate, but the supplied information does not provide enough detail to quantify its effect or establish a universal pinning rule.

There are legitimate business reasons to constrain an ad. A service qualifier may need prominent placement. An offer may need wording that prevents a misleading interpretation. A regulated advertiser may need legal review of the combinations that can appear. In these situations, creative freedom is not the only consideration.

There are also weak reasons to pin. A stakeholder may prefer a fixed sentence order because it resembles a traditional text ad. That preference is not evidence that customers respond better to it. Unnecessary restrictions can remove useful combinations without improving accuracy or relevance.

For the hypothetical cleaning company, a useful test might compare a flexible asset set with a version that keeps the commercial-service qualifier in the first headline position. The hypothesis would be that clearer qualification improves the quality of inquiries—not that pinning itself is inherently better.

Keep the distinction between a messaging test and a pinning test explicit. If you replace the offer, rewrite the headlines, and change pinning together, the result may identify a better overall ad treatment, but it will not isolate which change caused the difference. Required disclosures should remain protected in both versions; compliance is not an experimental variable.

Which number should change your reporting?

The same reported finding deserves a second look through a reporting lens. If a rating does not consistently identify the stronger converter, moving that rating upward cannot, by itself, prove an optimization succeeded. Source: Search Engine Journal’s report on Brad Geddes’ study.

What benchmarks should you use instead of a rating target?

Use your own business economics and comparable account history. The reported study does not supply universal cost or return benchmarks, so assigning a single numeric target would be misleading.

How should you test a higher-rated ad against a more controlled version?

Start with a commercial hypothesis. “Improve Ad Strength” describes a platform label. “Make commercial-only eligibility clearer to reduce residential inquiries” describes a business problem you can investigate. Write down the expected benefit and the trade-off you are willing to accept before editing anything.

Next, verify measurement. A lead-generation account should distinguish successful submissions from button clicks and, where feasible, connect inquiries with qualified leads or sales in the CRM. An eCommerce account should verify transaction values and duplicate prevention. A creative test cannot rescue an unreliable outcome definition.

Choose a focused campaign or ad group with enough activity to support a meaningful comparison. Use Google Ads experiment functionality where it supports the intended change, and confirm how traffic will be divided. Merely placing two ads in an ad group does not guarantee equal or random exposure.

Keep targeting, offers, landing pages, bidding strategy, and conversion definitions as consistent as practical. Do not announce a winner immediately after a strong day. Decide on the evaluation approach in advance, account for conversion delay, and review uncertainty rather than treating every difference as real.

Evaluate the main outcome alongside guardrails. A lower cost per qualified lead is encouraging, but not if useful lead volume falls below business needs. Higher purchase value is encouraging, but not if profitability deteriorates. Review search terms and lead feedback to understand the mechanism, while avoiding confident conclusions from tiny segments.

For a low-volume account, the honest result may be inconclusive. Preserve the business-critical message, make obvious clarity improvements, and avoid repeatedly resetting the test. Statistical uncertainty is a reason to limit the claim—not a reason to promote Ad Strength into a substitute for evidence.

What should your next ad review include?

Before changing creative to improve a rating, make the review decision-ready.

• Name the primary business outcome: qualified opportunities, acquired customers, or profitable sales.
• Confirm that conversion tracking records the intended action without obvious duplication.
• Separate rating feedback from observed performance in the report.
• Check every headline and description for relevance, unsupported claims, and repetitive messaging.
• Document why each pinned asset needs a fixed position.
• Define a specific hypothesis and identify which variables must stay stable.
• Record the test setup, launch date, conversion lag, and evaluation criteria.
• Use lead-quality or order-quality evidence before declaring the winning treatment.
• Keep an unchanged reference version and a clear rollback plan.