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Before you start

Before creating an experiment, run through this checklist:
  • All funnels you plan to use are published.
  • You’ve tested each funnel individually and confirmed it works end to end.
  • You’ve decided which funnel is the control — your current baseline.
  • You’ve chosen a primary metric to optimize for (e.g. CR: Paywall → Purchase, ARPU).
  • You have a clear hypothesis — what you’re changing and why you expect it to improve results.

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Launch experiments

Create, launch, and validate an experiment in FunnelFox. Learn how to avoid the most common setup mistakes.

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1. Create experiment

Go to the Experiments page and click Create experiment.
1

Name the experiment

Enter a descriptive name.
2

Set the alias

The alias becomes the experiment URL: your-project.fnlfx.com/{alias}. It’s auto-generated from the name, but you can edit it.
3

Select the control funnel

Choose the funnel that serves as your baseline. All variant results are measured against this funnel.
4

Add variants

Choose the funnel that serves as your variant. You can click Add funnel variant and add up to 3 variants — 4 funnels total (Control + B + C + D). Each funnel can only be used once across all variants in the same experiment.
5

Set the traffic split

Use the slider to distribute traffic across variants. All splits must total 100%.
Equal splits give you the fastest path to statistical significance.
6

Create

Click Create. The experiment is saved and receives traffic.

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2. Validate the setup

Do a quick check to confirm everything is working:
  • Open the experiment URL in your browser (your-project.fnlfx.com/{alias}). You should land on one of the variant funnels.
  • After a few visits, open the experiment analytics and confirm users are being recorded.
Open the experiment URL in several incognito windows to simulate different users.

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3. Finish experiment

Finish the experiment when your results are statistically reliable. Look for a Confidence level of ≥95% and an Observed power of ≥80% on the winning variant, sustained consistently over time — not just a single good day. If both are green and the trend is stable, you have enough evidence to pick a winner. When you’re ready to pick a winner, follow the steps below to finish the experiment.
The winning funnel automatically takes over the experiment URL. If you have a conflicting alias, go to that funnel’s settings and change its alias to something else before you finish the experiment.
1

Open the experiment settings

Go to the A/B testing page, click your experiment name, then click Settings at the top right.
2

Finish experiment

Click Finish experiment at the bottom.
3

Select the winning variant

Choose the funnel that performed best. This funnel will take over the experiment URL.
4

Confirm

Click Finish to confirm your selection. The experiment is disabled and the winning funnel is now live at the experiment URL.After that, the same URL that previously ran the A/B test points directly to the winning funnel, with no experiment routing involved.

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Experiment results

Once your experiment collects data, you can view results on the experiment overview page. Go to the Experiments page and either click any experiment name, or click Experiment overview under Actions on the right. The overview shows a table with one row per variant. The first row is always the control (your baseline). All other variants are compared against it.
Here’s what each column means:
  • Variation: Variant label (A, B, C, D).
  • Funnel: The funnel assigned to this variant.
  • Users: Unique sessions that entered this variant.
  • Purchases: Number of completed purchases.
  • CR: Start → Purchase: Percentage of users who started the funnel and completed a purchase. Color-coded against the control — green for improvement, red for decline.
  • CR: Start → Sign Up: Percentage of users who started the funnel and completed sign-up.
  • CR: Start → Paywall: Percentage of users who started the funnel and reached the paywall.
  • CR: Paywall → Purchase: Percentage of users who reached the paywall and completed a purchase.
  • ARPU: Average revenue per user (total revenue divided by total sessions).
  • ARPPU: Average revenue per paying user (total revenue divided by number of purchases).
  • Confidence level: How statistically significant the result is. Green (≥95%) = significant, neutral (80–94%) = inconclusive, red (<80%) = likely noise. For non-control variants only.
  • CR: Confidence interval: The plausible range for the true conversion rate. Color-coded by whether the interval is fully above (green), fully below (red), or overlapping (neutral) the control’s CR. For non-control variants only.
  • Observed power: Whether the test had enough data to detect a real difference. Green (≥80%) = sufficient, below 80% = the test may have missed a real effect. For non-control variants only.
You can also use a chart below the table to track any metric over time per variant.

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Filters

To narrow results to those that matter for your decisions, data on experiment results can be filtered by:
  • Currency
  • Country

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Experiment settings

To edit an experiment, go to the Experiments page, click your experiment name, then click Settings at the top right.
Editing an experiment or its variants resets all analytics data for the experiment — previous results will no longer be available.
You can update the following fields:
  • Name
  • Alias
  • Variants
  • Traffic split
You can also delete the experiment from the settings page. Deletion is permanent and removes the experiment and all its variants.

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Experiment event metadata

FunnelFox attaches experiment context to all webhooks. Look for these fields:
  • experiment_id — unique identifier for the experiment.
  • experiment_title — human-readable experiment name.
  • experiment_alias — the URL alias the experiment runs on.
See the Webhooks reference for the full list of events that carry this data. Analytics integrations (such as Amplitude or Mixpanel) also receive experiment context as event properties:
  • experiment — the experiment alias.
  • experimentId — the experiment ID.