Calculated Metrics

Split Test Pro ships a handful of built-in ratio metrics — Revenue per Session, Average Order Value — but they’re fixed to the Shopify checkout. Calculated metrics let you build your own, GA4-style: pick a numerator, pick a denominator, name the metric, choose a display format, and every experiment that tracks it reports it per variation with the same Bayesian treatment — chance to beat control and an uplift range — the built-ins get.

Calculated metrics are defined once per workspace (like conversion goals and funnels) and work on both platforms — HTML and Shopify.

What You Can Build

Metric	Numerator	Denominator	Format
Signup value per session	Value of Signup	Sessions	Currency
Revenue per checkout	Checkout revenue (built-in)	Orders	Currency
Quiz completions per PDP view	Count of Quiz Completed	Count of PDP Views	Percentage
Upsells per purchase	Count of Upsell Accepted	Count of Purchases	Number

Numerators aggregate a quantity per variant:

• Value of a goal — the summed value field on the goal’s events (see Continuous Metrics for how events carry values).
• Count of a goal — the goal’s conversion count, the same count the binomial goal columns report.
• Checkout revenue (Shopify only) — the built-in checkout revenue aggregate behind Revenue per Session.

Denominators count the trials the numerator is spread over:

• Sessions — per-variation session count.
• Count of a goal — any countable built-in event or custom goal.
• Orders (Shopify only) — the checkout count behind Average Order Value.

Defining a Metric

1. Open Settings → Calculated metrics (/settings#metrics). Only the workspace owner can edit metrics — like goals and funnels.
2. Click + New metric and enter a Metric name.
3. Pick the Numerator ÷ Denominator from the platform-aware dropdowns.
4. Choose a Format — Number, Currency or Percentage — which controls how the value renders on Results.
5. Click Save, then add the metric to any experiment that should report it (below).

Adding a Calculated Metric to an Experiment

Calculated metrics are tracked per experiment exactly like goals. On the experiment’s General step, the Tracked goals chips list your workspace’s calculated metrics alongside your goals, each tagged Calculated. A new experiment starts with every chip on — click one to stop tracking it. A tracked metric reports on the Results tab (and in the AI analysis). Calculated metrics also appear in the Default goal list.

• An experiment reports only the calculated metrics it tracks; deselect the ones you don’t need.
• A tracked metric that’s later deleted in Settings is simply skipped — a stale selection never breaks results.
• Like goals and funnels, the tracked set is editable while the experiment is a draft or paused.

Reading the Results

Each tracked calculated metric gets a row in the Other goals table on the experiment’s Results tab:

• One cell per variation — the metric’s value (numerator ÷ denominator) in its format, with the count behind it underneath.
• Chance to beat control — the probability that the variation’s true value beats Control’s, one column per non-control variation.

When the calculated metric is the experiment’s Default goal, it also drives the headline: the verdict, the uplift range and Performance by variation at the top of Results, and its row in Other goals is highlighted.

Under the hood every calculated metric is modeled as a continuous measurement: the engine derives a per-trial standard error with the same zero-inflated delta-method formula the built-in Revenue per Session uses, then feeds it through the Normal-model Bayesian pipeline. In fact, a calculated metric defined as Checkout revenue ÷ Sessions reproduces the built-in Revenue per Session numbers exactly.

Using a Calculated Metric as the Default Goal

A calculated metric can be the experiment’s Default goal — the measurement the winner verdict is judged on. On the General step, choose it in the Default goal list (it’s tagged Calculated). The winner is then judged on its chance to beat control, exactly like a conversion goal.

Two things to know:

• The headline conversion count and the daily chart still come from a countable event (your first goal, or purchases on Shopify) — a ratio has no per-day event series of its own.
• If the metric is later deleted in Settings, launching the experiment is blocked with a message until you choose another Default goal on the General step.

Currency Handling

Monetary numerators aggregate in your shop/site currency — the same policy as the built-in revenue metrics — and the metric is labeled with that currency code. There’s no exchange-rate conversion: if your events report mixed currencies, the amounts are aggregated as recorded.

Caps and Validation

• Up to 10 calculated metrics per workspace.
• Names must be unique; numerator and denominator must differ.
• Sources are validated against your platform — HTML workspaces can’t reference checkout revenue or orders — and against the workspace’s goals, with the same server-side checks the Settings UI mirrors.

Common Mistakes

• Dividing by a rare denominator. Value of Signup ÷ Count of Purchases on a store with 3 purchases produces a huge, unstable ratio. Prefer sessions or a high-volume event as the denominator.
• Expecting a value where none is tracked. A “Value of …” numerator only counts events that carried a value payload. If the goal fires without values, the metric reads zero — see Continuous Metrics for wiring values.
• Reading too much into early lift. Ratio metrics inherit the variance of their numerator. Give them the same patience you’d give any continuous metric — 100+ trials per variant before drawing conclusions.

Next Steps

• Wire up the valued events your numerators aggregate: Continuous Metrics.
• Define the goals your sources draw from: Conversion Goals.
• Understand the model behind the lift and probability numbers: Bayesian Stats Explained.