> For the complete documentation index, see [llms.txt](https://docs.intelligems.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.intelligems.io/analytics/overview/experiment-analytics/metric-definitions/post-test-metrics.md). # Post-Test Metrics Post-Test Metrics is a view available from the **Key Metrics** dropdown in the **Results** tab of a test. It helps you understand what happened *after* your test ended, not just during it. Where standard test results measure performance within the experiment window, Post-Test Metrics tracks the downstream purchase behavior of customers who converted during the experiment, split by variant.\ \ This makes it possible to ask: **which variant drove more valuable customers over time?** #### How It Works Post-Test Metrics looks at customers who converted during the test and tracks their purchase activity after that conversion, broken out by variant. A few important details: * Tracking is tied to **customer ID / customer email**, not browser cookie * Data is available for up to **one year** from test start * Existing test filters affect which in-test customers are analyzed, but **do not affect post-test orders** (e.g., filtering to orders containing a specific product narrows the set of in-test customers, but post-test orders from those customers reflect all purchases) * The **Include in-test orders** toggle controls whether the first (in-test) purchase is counted in downstream metrics #### Post-Test Metrics * **In-Test Customers:** The number of customers who converted in each variant during the experiment window. * **Post-Test Orders per Customer:** Average number of orders placed after the test by customers who converted during the experiment, per variant. * **Post-Test Revenue per Customer:** Average revenue generated after the test by customers who converted during the experiment, per variant. * **Post-Test Profit per Customer:** Average profit generated after the test by customers who converted during the experiment, per variant. * **% of Customers with Post-Test Order:** The share of in-test customers who placed at least one additional order after the experiment. * **Post-Test Revenue per Visitor:** Post-test revenue normalized by the total number of in-test visitors for each variant. This allows fair comparison across variants regardless of size. * **Post-Test Profit per Visitor:** Post-test profit normalized by the total number of in-test visitors for each variant. With the **Include in-test orders** toggle enabled, this is Intelligems' best approximation of an LTV estimate compared head-to-head between test groups. #### Include In-Test Orders Toggle The **Include in-test orders** toggle (checked by default) controls what counts as "post-test" behavior: * **Checked:** Post-test metrics include the original in-test purchase plus any subsequent orders. This produces an LTV-style view of customer value from the moment of acquisition. * **Unchecked:** Post-test metrics reflect only orders placed after the test ended, excluding the initial conversion. This isolates repeat purchase behavior. #### When to Use Post-Test Metrics Post-Test Metrics is most useful for tests where long-term customer value matters more than immediate conversion, particularly: * **Pricing tests:** did a lower price attract lower-value customers? * **Shipping tests:** did free shipping drive more repeat purchases? * **Subscription tests:** how did variant groups behave after initial sign-up? * **Merchandising tests:** did one variant drive better repeat behavior? It's also useful retroactively. Historical tests can be revisited to evaluate downstream impact, as long as the required customer-level data is available. {% hint style="info" %} **Note:** Long-term customer behavior can be influenced by many factors after a test ends, especially for brands that run frequent tests or make significant changes between experiments. Post-Test Metrics is most reliable when interpreted in context rather than as a standalone measure. {% endhint %} --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.intelligems.io/analytics/overview/experiment-analytics/metric-definitions/post-test-metrics.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. 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