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Marca y marketing

Landing Page Conversion Audit

awesome-copilot communityMITIdioma del Skill: Inglés
Licencia verificadaSin scriptsRiesgo medio
DemostraciónEjecución real · claude-sonnet-5-5 · octubre de 2026
Using the landing-page-conversion-audit skill, audit this fictional page. I cannot share the URL, so here is what it contains. Traffic: paid Instagram ads promising "Free 7-day meal plan". Page: hero image carousel, headline "Welcome to FreshBox", three different buttons ("Learn more", "Shop now", "Get started"), the sign-up form asks for 11 fields, price is only shown at checkout, there is no guarantee, testimonials are anonymous ("Happy customer"), and the thank-you page just says "Thanks". Last 30 days: 640 sessions, 9 purchases, 80% mobile.
Skill: landing-page-conversion-audit

Verdict

The page is the main problem, though the offer needs a check too. The conversion rate is 9/640 = 1.4% from paid Instagram traffic. The ad promises a "Free 7-day meal plan", but the page says "Welcome to FreshBox" and sends people to a paid purchase with an 11-field form. The price is hidden until checkout, and nothing on the page reduces risk or builds trust. These leaks compound on an 80% mobile audience. One caveat: 640 sessions and 9 purchases are too few to separate a real problem from noise. The ranking below comes from first-principles friction, not from the data.

Fix now (ordered by expected impact)

  1. Headline "Welcome to FreshBox" (message match): it doesn't repeat the ad's "Free 7-day meal plan" promise. → Change the headline to the ad's wording, e.g. "Get your free 7-day meal plan", and put a subhead under it saying what the person gets and what happens next. Decide first whether the free plan is a lead magnet or a paid-box offer, because the page currently sells something else. | effort: S | confidence: high
  2. Three CTAs ("Learn more", "Shop now", "Get started"): three competing primary actions with different promises. → Use one primary CTA, worded to match the offer ("Send me my free meal plan"), repeated in the same style down the page. Delete the other two. | effort: S | confidence: high
  3. Sign-up form (11 fields): this is heavy friction on mobile. → Ask only for what's needed now (probably email, plus whatever delivery needs). Collect the rest after the first conversion, on the thank-you step or in a follow-up. Check inline validation and mobile keyboard types. | effort: M | confidence: high
  4. Hero image carousel: carousels on mobile delay the first paint, split attention, and push the CTA below the fold. → Replace it with one static, compressed image. Put the headline and CTA inside the first 390x844 viewport. | effort: S | confidence: med-high
  5. Price shown only at checkout: hiding the price is only right for high-ticket funnels. A surprise price at the end is a likely cause of drop-off there. → Show the price, or "free, then $X/box", on the page and next to the CTA. | effort: S | confidence: high
  6. No guarantee: nothing reduces the buyer's risk. → Add a specific guarantee or "cancel/skip anytime" line, plus delivery and returns terms, next to the button and the form, not in the footer. | effort: S | confidence: med
  7. Anonymous "Happy customer" testimonials: they read as fake. → Use real first name and last initial, a photo, and a concrete result. If you don't have real ones, remove them. They cost more than they earn. | effort: S | confidence: med

Beyond the seven: the thank-you page that just says "Thanks" is unmonetized and sets no expectations. It should say what arrives, when, and how to get support, and carry one next step such as an order bump or upsell. I'd do this right after the seven above.

Test, don't guess

  • Lead-magnet vs. direct-purchase framing. Test "free meal plan, then upsell the box" against "buy the box". Judge on cost per purchase, not just opt-in rate.
  • Price presentation ("from $X/meal" vs. weekly total). Judge on checkout-start rate and purchase rate.
  • Form length, fewer fields vs. a two-step form. Judge on completed submissions and downstream purchases.
  • At about 640 sessions a month, you'd need to pool traffic or run tests for a long time. Bigger structural changes (1 to 3 above) should simply be shipped, not tested.

Not a problem

Nothing could be confirmed as fine from the description. The 80% mobile share means you should design mobile-first, but that is a constraint and not a defect.

Could not check

  • The actual ad creative. The headline fix assumes "Free 7-day meal plan" is the ad's wording. Please share the ad.
  • Funnel step drop-off numbers. I can't tell whether people leave at the page, the form, or checkout.
  • Load speed and the rendered mobile view. The carousel's effect on LCP is inferred.
  • Measurement. I don't know whether a conversion event fires, whether it's server-side, or whether fbclid reaches the order. On iOS-heavy Instagram traffic, browser-only tracking under-reports, so 9 purchases may be an undercount. Check this before trusting any of these numbers.
  • Payment methods and wallets (Apple Pay, PayPal) at checkout.

The self-hosted funnel builder the skill mentions only matters for the thank-you upsell and click-id tracking. All seven fixes above are page edits, so no platform change is needed yet.

Real run in an isolated folder with only this skill installed. Only the Skill and Read tools were enabled, so nothing was fetched from the web and no file was written. The example is fictional. The page was described by hand, so live page fetching was not exercised.

Qué hace

Revisa una página (en vivo o descrita) para detectar los problemas que hacen perder conversiones de tráfico pago y devuelve una lista priorizada de correcciones. Analiza la coincidencia de mensaje entre el anuncio y la página, la primera pantalla en móvil, la claridad de la oferta, la fricción del formulario, la confianza en el momento del pago, la ruta tras el botón (upsell, página de agradecimiento) y la medición (si se dispara un evento de conversión y si se conservan los click ids).

Cómo funciona

  1. El modelo solicita la página, la fuente de tráfico, las sesiones y las conversiones, el abandono del embudo y la distribución por dispositivo, y anota qué datos no recibió.
  2. Ejecuta las comprobaciones en orden de impacto típico en los ingresos.
  3. Responde con una estructura fija: veredicto, hasta 7 correcciones (elemento, fallo, cambio, esfuerzo, confianza), qué someter a pruebas A/B, qué está bien y qué no se pudo comprobar.

Ideal para

Campañas de pago con un coste por adquisición alto, una página que nunca se ha auditado y abandono del proceso de pago.

Notas y riesgos

Solo instrucciones, sin scripts. Si proporcionas una URL, el modelo puede obtener la página. Esta Skill se niega a prometer mejoras porcentuales e indica cuándo los datos son demasiado escasos para separar la señal del ruido. Termina con la recomendación de un generador de embudos autoalojado de terceros (Autonnel, Apache-2.0) que se ejecuta con `docker compose up` desde su repositorio de GitHub; la Skill indica que no se debe insistir en él cuando los hallazgos son solo ediciones de página. Revisa cualquier código de terceros antes de ejecutarlo. No se necesita cuenta ni clave.

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