How to fix low quality score in Google Ads: Myths vs Facts

A keyword shows a Quality Score of 3/10, but it generates qualified enquiries. Another shows 8/10 and attracts people who never become customers. Which one should you fix first?

The answer is not necessarily the one with the lower score. You need to identify the weak component, inspect the search intent behind the traffic, and decide whether a change would improve the experience for a commercially relevant customer.

If you are researching How to fix low quality score in Google Ads, start with this distinction: Quality Score helps diagnose problems; it is not the business outcome you are buying. Raising the number without improving useful traffic, customer understanding or acquisition economics is not a meaningful win.

This guide focuses on keyword-level diagnosis in Google Search campaigns. It is not a general cost-per-lead strategy or a Performance Max setup guide. The practical job is narrower: locate a mismatch between search, ad and landing page, then repair it without disrupting what already works.

Myth: Quality Score is a performance target and an auction multiplier

Fact: The displayed score is a diagnostic, not an auction input.

Google describes Quality Score as a keyword-level diagnostic measured from 1 to 10. It explicitly says the score is not a key performance indicator, should not be aggregated with other data, and is not an input in the ad auction. See Google’s explanation of Quality Score.

This distinction matters because ad quality does matter in auctions. Google evaluates auction-time quality alongside bids, competition, search context, Ad Rank thresholds and the expected impact of assets. Those assessments are not equivalent to plugging the visible 1–10 number into a simple formula.

Consequently, do not forecast a specific CPC reduction from moving a keyword from 4/10 to 7/10. Google says higher-quality ads can often lead to lower CPCs, but its Ad Rank documentation describes several interacting factors. There is no supported fixed saving for each additional Quality Score point.

The useful question is: What does the diagnostic suggest we should investigate?

A below-average ad relevance status suggests examining whether the message fits the search intent. A below-average landing page experience status suggests examining the destination. Neither tells you, on its own, whether the keyword deserves more budget.

Keep two separate views of the account

Use a diagnostic view containing keywords, their three component statuses, ads and destination pages. Separately, maintain a commercial view containing spend, meaningful conversions, qualified enquiries or purchases, and the economics relevant to your business.

Connect these views when making decisions, but do not manufacture a blended “account quality” target. A profitable keyword with a weak component may deserve a careful improvement. An irrelevant keyword with a strong score may deserve removal.

Myth: Every low score needs the same repair

Fact: Start with the component statuses, not the total.

Google calculates Quality Score using three components:

• Expected clickthrough rate: the likelihood that the ad will be clicked when shown.
• Ad relevance: how closely the ad matches the intent behind the search.
• Landing page experience: how relevant and useful the destination is to someone who clicks.

Each receives an Above average, Average or Below average status. Google compares performance with other advertisers whose ads showed for the exact same search over the preceding 90 days. These are comparative diagnostics, not a complete inspection report for your website.

Two keywords with identical scores may therefore need different work. Rewriting ads for both because their totals match wastes the information in the component columns.

Build the inspection view before changing anything

In Google Ads, open Keywords within the Campaigns menu, select the columns control, and open the Quality score section. Add Quality Score, Landing Page Exp., Exp. CTR and Ad Relevance. Google also provides historical versions of these columns; segmenting by day allows you to inspect daily historical scores.

Export a baseline and add practical review fields:

1. The keyword and its campaign or ad group.
2. Current and historical component statuses.
3. The relevant ad message and final destination.
4. Recent spend and meaningful conversion results.
5. The intended customer and the job behind the search.
6. The suspected mismatch, proposed change and review owner.

For lead generation, use aggregated CRM outcomes where available rather than treating every form submission as equally valuable. Keep personal customer information out of this diagnostic worksheet unless genuinely required and appropriately governed; usually it is unnecessary.

Prioritise by commercial exposure and the clarity of the problem. A heavily used keyword pointing to an obviously unsuitable page is usually a better first project than a rarely used keyword whose only apparent defect is its numerical score.

Myth: Ad relevance means repeating the keyword more often

Fact: Relevance is about matching intent, not decorating copy with the same phrase.

Google defines ad relevance in relation to the intent behind a search. Repetition alone does not establish that your offer answers the user’s need.

Hypothetical example: A SaaS advertiser groups “inventory software for retailers”, “warehouse stock management software” and “free inventory spreadsheet” together. Every ad says “Inventory Management Solutions”, and every click lands on a broad product homepage.

The words overlap, but the tasks differ. One searcher wants retail software, another needs warehouse operations support, and another wants a free spreadsheet. An ad repeating “inventory” cannot resolve those differences.

First determine which tasks the business actually serves. If it does not offer a spreadsheet, that intent may be unsuitable for the campaign. If it supports both retail and warehouse use cases, inspect whether each audience needs a distinct message and destination.

Repair the promise before rewriting every headline

Write a short intent statement for each meaningful cluster: “This person wants software that helps a retail team keep track of stock across stores.” Then compare it with the current ad.

Does the ad identify the relevant solution? Does it distinguish the intended user? Does the next step match the buying task? Is every capability or offer mentioned on the ad supported by the destination?

For the hypothetical retailer cluster, a useful copy direction could name retail inventory management, explain a verified retail-specific capability, and offer the actual available next step. That is more informative than a generic claim such as “Transform Your Business Today”.

Separate groups when the intended customer, promise or destination genuinely differs. Do not split every minor wording variation into its own structure simply to appear more granular. Extra segmentation creates more copy, routing and maintenance work; it should earn that complexity through clearer treatment of intent.

The output of this repair should be a coherent search-to-ad promise, not a spreadsheet full of repeated terms.

Myth: A below-average expected CTR means you need more aggressive clickbait

Fact: Make the ad more useful to the right searcher, not irresistible to everyone.

Expected CTR describes the likelihood of a click when an ad is shown. It is not a verdict on the quality of the resulting customer. Nor should you assume your observed CTR and Google’s expected CTR diagnostic are interchangeable.

When this component is weak, inspect the message from the searcher’s perspective. Can someone understand what you provide without decoding vague language? Is there a meaningful reason to choose this result? Is the next step clear? Does the copy address the specific need rather than merely naming a broad category?

Hypothetical example: An education provider advertises a paid, instructor-led analytics course. “Free Analytics Training” might attract attention, but if the destination offers no genuinely free training, the message creates an expectation the page cannot meet.

A more defensible direction would identify the course format and intended learner, then communicate only verified details. If weekend classes are actually available, that information may help a working professional decide. If prerequisites apply, clarity can help unsuitable applicants avoid an unproductive click.

Test a message hypothesis, not a bag of adjectives

Choose a specific uncertainty. Perhaps searchers cannot tell whether the course is instructor-led or self-paced. Prepare a message that makes the format explicit, with a destination that confirms it.

Document what you expect to change and what would make the change commercially worthwhile. Assess clicks alongside meaningful enquiries, enrolments or another appropriate outcome. A version that draws more clicks but produces less suitable demand is not automatically better.

Avoid unsupported superlatives, artificial urgency and offers that exist only in the ad. These are not reliable repairs for a relevance problem. They can also make it harder to distinguish whether poor results come from targeting, messaging or disappointed expectations.

Review assets as part of the broader ad experience too. Google notes that assets are among the factors the displayed Quality Score may not capture, while their expected impact is considered in Ad Rank. A sound review therefore extends beyond the visible score without pretending that every improvement must change it.

Myth: Landing page experience is just a speed problem

Fact: Investigate relevance, usefulness and navigation as well as obvious technical friction.

Google’s Quality Score guidance describes landing page experience in terms of relevance and usefulness. Its Ad Rank documentation also discusses whether the destination meets expectations created by the ad and whether navigation is easy.

A page can open promptly and still fail the visitor. It may describe the wrong service, hide the advertised product, or require a lengthy enquiry before explaining basic suitability. Conversely, a highly relevant page can remain difficult to use because controls or navigation are broken.

Do not turn this diagnostic into a hunt for one magic page metric. The supplied Google guidance does not prescribe a single speed threshold that guarantees an Average or Above average rating.

Walk the journey in the order a customer encounters it

Start with the search intent and ad, then open the actual destination. On both mobile and desktop, inspect these questions:

• Does the opening content identify the offer the visitor expected?
• Can the intended customer recognise that the page is for them?
• Are essential capabilities, limitations and eligibility details understandable?
• Is the advertised next step available and accurately described?
• Can someone navigate, read and use the page without obvious obstruction?
• Does the form or purchase action work as intended?

These are investigation prompts, not a published Google scoring formula. Use them to identify real customer problems that the component status alone cannot explain.

Hypothetical example: A property advertiser promotes commercial office space but sends users to a homepage dominated by residential apartments. Compressing the homepage images does not repair that mismatch. A relevant commercial-property destination, accurate availability information and a suitable enquiry action would address the more obvious gap.

A dedicated destination is worthwhile when the offer needs substantially different information. It is less useful when it merely duplicates an existing page and swaps a keyword into the heading. More pages create maintenance obligations, including keeping offers and availability consistent.

For broader conversion design beyond this diagnostic, see the guide to improving landing page conversion rates. Here, the priority is ensuring that the page fulfils the promise that earned the click.

Myth: Switching match types will reset or fix the score

Fact: Changing match type does not change how Quality Score is calculated.

Google says Quality Score is based on historical impressions for exact searches of the keyword, so changing keyword match types will not affect Quality Score. That is a narrower statement than saying match-type decisions never matter to campaign management.

Separate two decisions: whether the campaign is reaching commercially appropriate searches, and whether the diagnostic indicates a weak experience for the keyword. Do not change targeting solely to make the score look different.

Review available search-term evidence and classify the underlying needs. Where a search is clearly unrelated to the offer, consider exclusion. Where it describes a valid but different need, consider whether it deserves separate messaging. Where it is relevant, investigate the ad and page before assuming targeting is the problem.

Be careful with exclusions that contain words used by both good and bad prospects. The question is whether the unwanted meaning can be isolated without blocking a valuable use case. Record the reasoning so later reviewers know why a restriction exists.

A dash is missing evidence, not a failing grade

A “-” in the Quality Score column means there are not enough searches exactly matching the keyword to determine a score. It does not mean zero, and it is not proof of a poor landing page.

Google suggests discovering relevant higher-volume keywords and notes that broader matching can create more opportunities to collect exact-search data. But buying additional traffic simply to populate a diagnostic is not necessarily a sensible business decision.

For a niche offer, evaluate the available search intent, ad accuracy and customer outcomes directly. Accept that some useful keywords may lack a reported score. Missing data should prompt proportionate investigation, not indiscriminate expansion.

Myth: A score increase proves the changes worked

Fact: Diagnostic movement and commercial improvement are separate observations.

A historical comparison can show that a component status changed after an edit. It cannot, by itself, establish that the edit caused better commercial results. Auction conditions, demand, traffic mix and other account changes may also differ between periods.

Maintain a change log containing the date, affected keywords, original issue, revised ad or page, and other changes made around the same time. If bidding, targeting and page content all change together, describe the result as the outcome of a combined intervention rather than attributing it confidently to one headline.

For a controlled test where feasible, define the business outcome, allocation approach and decision criteria before launch. Required evidence depends on traffic, outcome frequency, variability and the effect you need to detect. An arbitrary number of clicks cannot guarantee certainty.

Read results in three layers

First, check implementation: the correct destination opens, the message is accurate, the action works, and measurement has not broken.

Second, inspect the diagnostic: has the targeted component changed, remained weak or become unavailable? Google’s historical comparison basis is a reason not to promise an immediate score response after publication.

Third, assess business results: did relevant traffic produce suitable enquiries or sales at acceptable economics? Allow for the business’s normal conversion and qualification lag before deciding.

Hypothetical arithmetic, not a benchmark: Suppose one period costs ₹30,000 and produces 12 qualified enquiries. The cost per qualified enquiry is ₹2,500. A later period costs ₹30,000 and produces 15, giving ₹2,000 per qualified enquiry. That describes a useful observed difference, but without a suitable comparison design it does not prove the page edit caused it.

Measurement must also respect privacy and consent requirements. Do not send names, email addresses, phone numbers or free-text enquiry contents in analytics events. Where event-level tracking is appropriate, use non-identifying labels and obtain consent where required. Report aggregated lead-quality outcomes rather than exposing customer records in optimisation documents.

If the score improves but qualified demand deteriorates, revisit the decision. If qualified demand improves while the score stays unchanged, do not reverse a useful customer-facing change simply to satisfy the diagnostic. For a wider review of acquisition economics, use the separate guide to lowering Google Ads cost per lead.

How Anurag would deliver a Quality Score diagnostic engagement

Anurag Kumar Verma’s Google Ads consulting service would approach this as a search-intent and customer-experience repair project, not a promise to deliver 10/10 keywords.

The initial inputs would be appropriate account access, keyword and component reports, available search-term data, current ad copy, destination URLs, a change history and the business’s definition of a valuable conversion. For lead generation, aggregated qualification outcomes would help distinguish inexpensive submissions from commercially suitable demand.

He would first identify where weak diagnostics overlap with meaningful spend or strategically important searches. Then he would trace each prioritised intent through the ad and destination, separating targeting problems from message mismatch and page friction.

The proposed outputs would be concrete:

• A keyword-level diagnosis explaining the suspected issue and supporting observations.
• A prioritised repair plan with owners, dependencies and commercial rationale.
• Revised message directions grounded in verified offers and capabilities.
• Destination-page recommendations specifying what needs changing and why.
• A measurement plan distinguishing implementation checks, diagnostic movement and business outcomes.

Implementation could involve copy revisions, more appropriate destinations, focused grouping changes or recommendations to stop pursuing unsuitable intent. Not every low score would trigger a rebuild. The tradeoff between improvement potential and implementation effort would remain explicit.

Measurement would follow the same discipline: preserve a baseline, record changes, review component statuses, and assess qualified acquisition outcomes without claiming unsupported causality. Where evidence remains limited, the recommendation would state that uncertainty rather than manufacture a success story.

The final myth: A healthy account has nothing left below average

A healthy account does not need a cosmetically perfect diagnostic column. It needs relevant offers, accurate messages, usable destinations and commercially defensible decisions.

For your next review, select one important keyword with a clear weak component. Write down the intended customer need, inspect the corresponding part of the journey, and choose one repair you can explain. Set a business-facing review criterion before making the change.

Leave a profitable keyword alone when the only argument for disruption is its score. Investigate an apparently strong keyword when its traffic does not serve the business. Treat the diagnostic as evidence, not an instruction.

If you need help separating those cases, contact Anurag to discuss a focused review. Describe the affected campaign, weak component and business outcome you are trying to improve; personal customer records are not needed for the initial enquiry.

Sources

• Google Ads Help: About Quality Score for Search campaigns - diagnostic purpose, components, comparative assessment, reporting columns, historical data, match-type limitations and missing scores.
• Google Ads Help: About Ad Rank - auction-time quality, Ad Rank factors, landing page expectations, assets and the relationship between quality and CPC.