How can I find out why my SaaS landing page is not converting?

How do I define the conversion leak?

Choose one primary conversion, such as starting a trial, booking a demo, or creating an account. Write down the sequence from landing-page visit to CTA click, form start, form completion, and final activation or qualified demo. Measure each step separately instead of judging the page from one overall conversion rate. The largest change between two adjacent steps identifies the part of the funnel that needs investigation first. A low traffic-to-CTA rate points toward positioning, trust, or clarity, while strong CTA clicks followed by weak form completion point toward friction.

The check marks what stops sign-ups on your page, measured on a phone and a desktop, and writes the fix for each problem.

How do I check whether traffic and message match are the problem?

Segment the conversion path by traffic source, device, geography, and new versus returning visitors before changing the page. Compare paid search, social, organic, and email traffic when those sources are part of the acquisition mix. Look for differences in conversion rate, bounce rate, and session duration between the segments. Check whether the ad, keyword, or referral promise uses the same meaning as the landing-page hero. Unqualified traffic and a message mismatch can create a low conversion rate that the page alone cannot fix.

• Compare the audience implied by the acquisition source with the audience named on the page.
• State the promised outcome in the hero.
• Separate a source-specific problem from a page-wide problem before rewriting the page.

This is a manual investigation, not an automatic analytics integration. LandingBoost does not import customer analytics, ad accounts, traffic sources, heatmaps, or session replays for scoring. Its Relevance assessment uses the audience and intent implied by the captured page. Compare the acquisition promise yourself, using your ad copy, search intent, and analytics.

How do I find out why visitors leave?

Use quantitative funnel data to locate the leak, then use behavior evidence to investigate why visitors hesitate or leave. Session recordings can show the path taken by individual visitors, while heatmaps show scrolling and interaction patterns across visitors. Filters and funnels help connect those patterns to a page, device, source, or step in the conversion path. Look for hesitation, shallow scrolling, rage clicks, repeated clicks on inactive elements, and form abandonment. Treat behavior evidence as a way to test a suspected cause, not as a replacement for defining the conversion sequence.

• Use recordings to inspect hesitation and unexpected navigation.
• Use heatmaps to inspect scrolling and interaction concentration.
• Use filters and funnels to connect behavior with the suspected conversion step.

LandingBoost does not provide heatmaps or session replay, so it marks page-level problems before enough visitors exist for behavioral analysis. If visitors start and abandon, the recommended sequence is to remove friction and then watch behavior in Clarity or Hotjar once enough people arrive.

How do I check the page's decision path?

Review the page in the order a visitor makes a decision. Start with message match, then check whether a first-time visitor can explain the offer after five seconds. Finally, inspect friction such as slow speed, poor mobile usability, and problems that prevent visitors from completing the action. Write down whether the visitor understands the offer, believes it, knows the next step, and can complete it. Keep the review focused on the decision path instead of collecting an undifferentiated list of possible CRO issues.

• Check whether the hero states the relevant outcome for the intended audience.
• Check whether the CTA states the next step and whether proof appears near the decision point.
• Check whether the visitor can complete the action without unnecessary friction.

Hero → CTA → proof is a useful manual review order, not a fixed execution sequence inside LandingBoost. It ranks the problems it measures by what a first-time visitor meets first. It also does not complete your signup, log in to your app, or test a payment. Check those journeys separately with a test account and a safe test payment flow.

How do I decide what to fix first?

Choose the lowest-confidence part of the decision path, not the section with the longest list of possible issues. Use the evidence from the funnel, traffic segments, behavior patterns, and page review to identify the broken decision. If visitors do not understand the offer, fix clarity. If visitors understand the offer but do not click, fix the CTA or proof. If visitors click but do not finish, fix friction.

• Record the funnel step and segment that support the selected diagnosis.
• State the proposed change as a focused response to the broken decision.
• Keep the diagnosis and the implementation change separate when evaluating the result.

LandingBoost ranks page-level problems by what a first-time visitor meets first, not from your measured funnel data. Every problem shows where it is on your page, why it matters and the change to make. After you publish, check the page again for free; daily checks then flag a page that stops loading, a changed main button or prices that disagree.

When should I use an audit, analytics, or testing tool?

Use a page audit before more traffic when you need to identify the first leak from the page itself. Use analytics and behavior tools after visitors arrive to observe what people do across the funnel. Use an A/B-testing platform after you have named a hypothesis and need to validate it with traffic. Keep page building separate from diagnosis because builders publish pages rather than identify the first edit on an already shipped page. The practical sequence is page diagnosis, visitor observation, then controlled validation.

LandingBoost is not Microsoft Clarity and does not replace your funnel analytics. A page audit can produce an edit hypothesis before traffic arrives. Once visitors exist, compare that hypothesis with their behavior rather than assuming the audit identified the only reason they left.

What does LandingBoost check?

LandingBoost gives no score. The four questions below are still a useful way to read any landing page. LandingBoost measures the parts it can (a covered button, a plan with no button, two prices, broken links, a first screen that never says what the product is) the same way on every site, and AI writes new wording from your own text.

• Clarity: Can a new visitor understand what the product does, who it is for, and the outcome it offers? An attractive but abstract headline can be hard to interpret even when the interface looks polished.
• Relevance: Do the headline, offer, and page content fit the audience and intent implied by the page? This is not a reading of your actual traffic segments or the ads that brought someone there.
• Trust: Is there concrete, credible evidence near the decision point? Check both proof and important commitments: the terms, risks, and limits a buyer needs to understand before proceeding.
• Action: Is the next step visible, specific, and easy to understand? A clear button should also set the right expectation about what happens after the click.

Fixing what LandingBoost marks does not guarantee more signups or revenue. Use analytics or a controlled test to measure the actual business outcome. Read the methodology for the distinction between page evidence and conversion results.

What should I record before making the edit?

Keep a short diagnostic note instead of changing everything at once. Record the public URL, the date, the target visitor, the intended conversion, and the exact page element you intend to change. Add the supporting evidence: a captured phrase, a missing next-step explanation, or a funnel observation from your own analytics. Write the proposed edit as a hypothesis, not as a promise of uplift.

For example, “the CTA does not explain whether a card is required” is a page observation. “Visitors abandon because they fear entering a card” is a hypothesis that needs more evidence. If no card is required, stating that fact beside the button could be a reasonable test. If a card is required, do not use a no-card promise to make the score look better. Keep the page accurate even when a more persuasive claim would be tempting.

After shipping, inspect the actual published page, rescan the changed element, and confirm that the promised next step still works. Then observe the selected funnel step. If you changed the hero, form, pricing, and traffic source together, you will have a harder time attributing the result to one edit.

What does the free scan include?

A free account gets one AI scan a month (up to 2 pages) and a daily check of 1 site. Every problem is shown, with the first fix and the first new wording in full. Reading saved results uses no scan.

$9 once unlocks every fix in one report. Pro ($12 a month for 1 product), Growth ($49 a month for up to 5) and Lifetime ($99 once) open every fix in every report and watch more sites and competitors. Check the current pricing before relying on an allowance.

Frequently asked questions

Does LandingBoost score my landing page?

No. LandingBoost stopped scoring pages in October 2026. It marks the problems it can measure on your page (a main button under a cookie banner, a plan with no button, two prices for one plan, broken links, text off a phone screen) and writes the fix; AI writes new wording from your own text and it is checked against your site.

Can LandingBoost scan a private or inaccessible page?

Submit a publicly accessible live HTTP or HTTPS landing-page URL. LandingBoost reads rendered content, not private dashboards, localhost, or private networks. If the capture is inaccessible or degraded, that is not proof your page fails to convert. There is no guaranteed scan duration; page response, capture, and AI processing affect timing.

What evidence supports a fix?

Each item says how it was found: measured on your page in a real browser, or written by AI from your page's own words and checked against your site. Wording that adds a price, a name or a promise your pages don't make is dropped. If real customer proof is missing, the fix asks for your input rather than inventing a testimonial, logo, or result.