Paid Landing Page Audit

Landing Page Audit

Audit the page paid traffic lands on, and return a fix list the media buyer can actually ship. Every paid click costs money whether or not the page converts, so every leak found here is a direct spend leak.

Experienced auditors do not start with the page - they start with the money trail:

1. The ad-to-page click path.
2. The analytics funnel.
3. Qualitative evidence.
4. A heuristic read of the page itself, last.

A beautiful page that breaks message match wastes spend regardless of on-page craft.

The output is a prioritised fix list where every finding declares its evidence class and its severity. Never a promised percentage lift: most CRO changes do not win when tested (roughly 10-33% of experiments improve their target metric, per Kohavi's Microsoft/Google data and Optimizely's 127,000-experiment corpus), so any specific lift promise is almost certainly wrong.

This skill owns everything past the click and nothing before it. Ad-account root cause (tracking, structure, targeting, creative, bidding) belongs to mbfinotti/advertising-skills@ad-account-diagnostic, which hands off here when its evidence points downstream. The boundary is reciprocal.

If the evidence during this audit points upstream of the click (fatigued creative, wrong audience, broken conversion tracking, a broken offer no page can rescue), refuse the page verdict, say exactly which upstream signal you saw, and hand off. Do not let a page audit quietly become an account diagnostic.

This skill never implements fixes: it names them, ranks them, and assigns them.

Interview

Ask before opening anything. One question per message; offer multiple-choice answers where possible; skip anything already supplied.

• What is the one action this page must drive? (purchase / trial signup / demo or call booking / lead form / download)
• B2B or B2C/ecommerce?
• Which platform sends the paid traffic, and can you share the actual ad - copy, creative or thumbnail, and the keyword or audience behind it? (Without the ad, the highest-impact check runs blind.)
• How can I access the page? (live URL / pasted copy or HTML / screenshots - desktop and mobile / a mix)
• Sessions and conversions on this page over the last 14-30 days? (Decides whether statistics or first principles carry the audit - see Volume Floor.)
• Device split, and geo mix - does one page serve several countries, languages, or currencies?
• Target CPA or ROAS, and where does the page's traffic currently sit against it? (The gap is what the fix list gets ranked against.)
• What counts as a good outcome downstream? (B2B: MQL/SQL/pipeline, not form fills; B2C: revenue, not add-to-carts.)
• What evidence exists beyond the page - analytics funnel, session recordings, heatmaps, survey or support verbatims?
• Has anything on this page been changed or tested before? What happened?
• Is there a date the result has to land by - a launch, a budget review, a seasonal peak - or no deadline?
• Do you want the fastest recovery on this campaign, or a page that keeps paying across the next ones? (One-off win or compounding asset.)
• Who implements the fixes - you, a designer, a dev team - and what is the effort ceiling: hours of copy edits, a sprint of build, or a redesign you would have to sell internally?

Those last three interview answers re-rank the fix list before it is written; the report says which answer moved what.

• A hard date promotes what lands in hours: message match, headline and CTA copy, reusing the ad's key visual. It demotes anything needing a build or a stakeholder.
• A compounding mandate promotes offer clarity and structural work that survives the next campaign, even though it is the slowest family to ship.
• A low effort ceiling deletes rows rather than reordering them: name what fell outside the ceiling under the report's "Ruled out" section instead of dropping it silently, so nobody re-discovers it next quarter.

Workflow

1. Run the Interview; collect every answer before touching the page.
2. Rule out upstream causes first. A CPA chart cannot tell a page problem from an ad problem. Check before blaming the page:

• CTR declining or frequency creeping (creative fatigue): hand to mbfinotti/advertising-skills@ad-creative-fatigue.
• The conversion event firing and deduplicated: if not, hand to mbfinotti/advertising-skills@ad-conversion-tracking, and treat every downstream number in this audit as suspect.
• Targeting or the offer changed when performance did: hand to mbfinotti/advertising-skills@ad-account-diagnostic.

Only proceed once the page is plausibly the problem.

1. Walk the click path. Open the ad, the keyword or audience, and the landing page side by side. Answer from the clicker's perspective: what did the ad promise, and does the first screen confirm it?

Check four match axes:

• Verbal: the headline echoes the ad's promise in its own words.
• Visual: the hero echoes the ad's creative or opening frame - almost every audit skips this.
• Offer: same offer, same price, same CTA verb.
• Geo: currency, language, region-valid offer for every geo the campaign targets.

If the page swaps headlines by UTM or ad set, load it with the real ad parameters: dynamic personalisation silently serving the generic page is a paid-traffic-specific bug class. Confirm click IDs survive the landing and any redirects.

1. Pull the analytics funnel, segmented by device and traffic source: land → engage → form start or add-to-cart → completion. This is where opinion turns into observation. Apply the Volume Floor before reading anything, and never mix platform-reported and on-site conversion counts - they differ by attribution window and view-through, and are not comparable totals.
2. Gather qualitative evidence where it exists: session recordings (rage clicks, dead clicks, form hesitation), scroll and heatmaps (does anyone reach the proof? the CTA?), and voice-of-customer verbatims. Skip without apology if unavailable - but then say so in "Could not check".
3. Run the heuristic page review using references/page-checks.md, in its order: the checks are sequenced by revenue impact for paid traffic, not by ease of checking.

If you can fetch and render the page, verify each check against the rendered result at a phone-sized viewport and a desktop viewport. Otherwise, work from the pasted copy or screenshots and mark every claim that needed rendering (load, layout, dynamic content) as "Could not check".

Everything found here is an opinion until analytics or an experiment upgrades it.

1. Prioritise (section below). Rank by efficiency - the funnel step each fix unblocks per unit of effort - weighted by evidence and re-ranked against the deadline, mandate and effort ceiling from the Interview. Cap the fix list at seven items - a thirty-item list never gets built.
2. Write the report in the shape below (full template and worked example in references/report-template.md). Run the Report Integrity checks and iterate until the report passes all of them.
3. Log a prediction and a re-check date. Each shipped fix names the funnel step that should move and in which direction. Re-check one full business cycle later, at matched attribution-lag maturity.

If your harness has persistent memory, memorise the page baseline, findings, shipped fixes, and predictions so the next run starts from history. Otherwise, emit a short state block the user can paste into the next session.

Evidence Classes

Every finding carries one of three claim classes, and the report never blurs them:

• Opinion - judgeable from the page alone: message match, value-proposition clarity, CTA hierarchy, visible friction, trust-cue presence. Informed hypotheses, not proof.