Customer-ready completion triggered by release production
Mark work done when the release reaches production, not when the pull request merges, so customer-facing updates fire at the moment the change is usable.
Why this can grow a startup
Merged code and customer-available code are different events, but many teams still talk about them as if they happen together. That creates false shipped signals for support, success, and launch messaging. Production-triggered completion fixes the timing. The issue stays open until the right release actually lands, which means customer-facing updates, help-center changes, and roadmap movement start from a moment the buyer can verify.
Source-backed example
Linear's Releases docs recommend moving issues into a started Merged status on merge and letting release automations mark them done once the release hits production, which also helps tools like Asks and Intercom react when the change is available to customers.
Source-reported evidence
Source: Linear Docs · Browse Linear Docs tactics
Release automations can mark issues Done only when a chosen pipeline reaches production.
Attributed to the cited source; GrowthDex has not independently verified it. Use this as context, not as a forecast, benchmark, or recommended target.
Evidence scope: claims and metrics are attributed to the linked source; GrowthDex has not independently replicated the reported result. Check the original context and validate fit before applying it. Read the evidence methodology.
Source discovered: May 28, 2026
When to use it
Use this when Lifecycle, Product, Support is relevant to release communication, customer-ready shipping, retention and you can run a bounded test with a low budget.
When not to use it
Do not use it as a substitute for customer evidence, a clear owner, or a measurable stop condition. Local platform rules and market behavior still need checking.
How to do it
- Choose one target segment and define the exact next action this tactic should prompt.
- Adapt the tactic to Lifecycle and Product, using evidence from Linear Docs as context—not as a promised result.
- Record a baseline for: Share of the target cohort repeating the core action within the chosen period. Set a guardrail that would make you pause the test.
- Run a narrow pilot and choose a test window that fits the normal decision or use cycle; do not scale before you have observed the full cycle.
- Compare the signal with its baseline. Scale only if the intended action improves without crossing the guardrail; otherwise revise one variable or stop and record what you learned.
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