landing-page-experimentation

name	landing-page-experimentation
description	Sets up conversion measurement and A/B testing for a landing page - server/edge variant assignment (no flicker), A/B vs multi-armed bandit, statistical significance, filtering bot/AI traffic, analytics. Use when asked to A/B test, run an experiment, measure conversions, check significance, or set up landing-page analytics. Not for writing copy, page structure, or Core Web Vitals.

landing-page-experimentation

Assign variants on the server, respect the sample-size floor, and distrust the numbers until the bots are filtered.

The failure this fixes

Teams bolt a client-side A/B script onto a page; it swaps the DOM after render (flash of original content, 200-500ms delay), and the anti-flicker snippet that hides the body to mask it directly tanks LCP/CLS — so the test suppresses the conversion it measures. Then they run tests that can never reach significance (only ~13% of tests produce a winner; low-traffic pages can't clear the ~300 conversions/variant floor), and trust analytics corrupted by bot/AI traffic (0-second sessions, some ad-driven tests >98% bots) and ad-blockers that eat 10-30% of client events. The deepest scar: tooling isn't the lever — the same AI CRO tools deliver a few % lift DIY vs several-fold more expert-guided, because human hypothesis quality is what moves the number.

When to use / when NOT to use

Use for measurement and testing: variant assignment, A/B vs bandit, sample size & significance, bot filtering, event instrumentation, and consent.

Not for: writing the variants (landing-page-copywriting), section design (landing-page-structure), the page's own Core Web Vitals (web-vitals-and-seo — though this skill's edge-assignment protects them), palette (visual-design-system), or building the page (nextjs-landing-page).

Workflow

1. Assign the variant server-side / at the edge (Next.js middleware, Cloudflare Workers) and render the correct HTML — no client DOM swap, no anti-flicker snippet. Persist the assignment in a cookie so a visitor always sees the same variant; log exposure server-side at assignment time (ad-blockers drop client events). Use the precompute pattern (encode the variant into a URL segment) so each variant stays statically cached — don't call cookies()/headers() in a root layout. Details: references/experimentation-guide.md.
2. Size the test before running it. ~300+ conversions/variant floor; ≥2 weeks covering multiple weekday/weekend cycles; run an A/A test first to catch tooling bugs; pre-commit the sample size and never peek / stop early.
3. Pick A/B vs bandit deliberately. Fixed-split A/B for permanent structural decisions (pricing, core UX) needing clean causal certainty; multi-armed bandit (Thompson Sampling) for many-variant, short-lived, high-traffic optimization to minimize regret. Bandits need a conversion floor before they route meaningfully.
4. Filter bots before trusting any delta. Exclude known crawler UAs server-side; watch for 0-second-session / ~100%-bounce spikes and metronome hit patterns. Unfiltered, a bandit "wins" by optimizing for scraper behavior.
5. Instrument real events, not vanity counts. GA4 generate_lead / purchase / add_to_cart plus custom scroll-depth (GA4's default scroll event fires only at 90%; bounce is now the inverse of engagement). Heatmaps/session recordings generate hypotheses; A/B validates them.
6. Know the honest limit. Most pages lack the traffic to test the levers that matter — below the floor, use qualitative review (heatmaps, replay, user testing), not underpowered tests that produce false winners faster.

The rules

• Server/edge variant assignment + server-rendered HTML = zero flicker, neutral-to-positive CWV, no anti-flicker snippet.
• Server-side exposure logging — client events are lost to ad-blockers.
• ~300 conversions/variant, ≥2-week multi-cycle, A/A first, no peeking.
• A/B for causal/permanent; bandit for many-variant/short-lived; bandits need a conversion floor.
• Bot/AI traffic is real and must be filtered before trusting deltas.
• Real-event instrumentation over pageview vanity metrics.
• Human hypothesis quality is the lever, not the tool — more automation without discipline just accelerates false winners.
• Every lift % in the research is directional, never a promise (the 13% win rate baseline means published wins are survivors, not the average).

Output

Server/edge variant assignment with server-side exposure logging; a pre-committed sample size honoring the ~300/variant floor and multi-cycle duration; an explicit A/B-vs-bandit choice; bot filtering; real-event instrumentation; and every tool name, pricing, and lift number version-gated or flagged directional — with a clear "use qualitative review instead" path for low-traffic pages.

References

• references/experimentation-guide.md - server/edge assignment & the precompute pattern, sample-size and significance discipline, A/B-vs-bandit decision, bot filtering, the tooling landscape (version-gated), and the fact ledger.