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A/B test your landing pages

A landing-page A/B test is an Experiment whose variants are Pages. You point one ad at one URL; Erdo serves different versions of that page to different visitors, keeps each visitor on the version they first saw, tags every lead and analytics event with the version that produced it, and — once the evidence is in — the measurement loop calls the winner. The point of wiring it this way is that nothing about the split leaks into the ad or the URL. The ad’s destination is a single page’s share link. A visitor who lands there is quietly assigned a variant and stays on it; the address bar never changes, so click attribution, canonical URLs, and retargeting all keep working.

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Setting one up

You don’t configure this by hand — you ask Erdo in a conversation:
“A/B test this landing page. Make a variant with a punchier hero, split traffic 50/50, and tell me which gets more leads.”
The agent builds the variant pages, creates an Experiment with those pages as its variants, gives each an allocation (the share of traffic it should get), and arms the measurement loop against the dataset your lead form writes to. Going live is the one step that waits for you: starting the experiment — the moment your page’s traffic comes under its control — raises an approval card, as does swapping the variants of one that’s already running. Approve it and the split is live.

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How traffic is split

Each variant carries an allocation percent — the slice of visitors it should receive. The rules that turn those numbers into a real split are deliberately forgiving, so a half-configured experiment still serves sensibly:
  • Explicit weights are used as given and normalised by their total, so they need not sum to 100 — [30, 30] is a 50/50 split, not “30% each and 40% nowhere”.
  • A variant left without a weight takes an equal share of whatever the explicit weights leave under 100. So control at 60 plus two un-weighted variants gives 60 / 20 / 20.
  • If nothing adds up (every weight zero or missing), traffic is split evenly rather than dropped.
The control is whichever variant the ad’s URL points at. Serving a different variant renders that variant’s content at the control’s URL — there is no redirect.

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Stickiness

A visitor is assigned once and kept there. The first time someone hits the page, Erdo does a weighted pick and stores the result in a cookie scoped to that experiment; every later visit reads the cookie and serves the same variant. A variant that’s since been removed from the experiment falls back to a fresh pick, so a stale cookie never shows a dead page. Assignment happens in the visitor’s browser, which is what lets the page stay fast: the shared page shell is cached and served to everyone, and only the per-visitor choice runs live. The experiment never slows down the common, non-experiment page.

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Attribution: every lead and event carries its variant

The whole point is being able to say “variant B produced more leads,” so the assigned variant is stamped automatically onto the two things you measure with:
  • Leads. When the page’s form submits, the variant the visitor was actually served is written onto the lead row — you don’t have to build the page to copy it, and it can’t be faked by the page’s own markup. The lead lands in your dataset with a variant column alongside the form fields, so per-variant conversion is a plain query.
  • Analytics. If you’ve enabled page analytics, the assigned variant is attached to every event (pageviews, clicks, conversions) as a property, so your funnels split by variant with no extra tagging.
Because attribution is stamped from the variant the visitor was served — not from anything the generated page declares about itself — it stays correct even if a page’s own code is wrong about which variant it is.

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Deciding the winner

Measurement is the same deterministic loop every Experiment uses: a scheduled recipe reads your lead dataset, records one observation per variant per metric, and writes a decision-check breadcrumb. When you tell the agent which metric decides the test (say, lead-submit rate, higher is better) and how big a lead you’ll accept as real, the loop does one more thing once the evidence gate clears and a clear winner has emerged: it signals the decision. That moves the experiment from running to reading, and Erdo writes up the outcome — the winning variant, the numbers, and the learning — and marks it decided. You can also just read the observations and make the call yourself; the automatic signal only fires when you’ve configured a decision metric and margin. Either way the experiment ends with a recorded decision you can point back to.

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What you see

In your workspace the experiment lives under Activity, usually inside the Workstream that hosts the campaign. You watch the per-variant lead counts and conversion rates accrue, see the decision check flip to “gate passed,” and read the final decision narrative when the loop (or you) calls it.