Pre-provision matched users before tracker import
Before importing work from another tracker, provision users in the new system with the same email addresses and linked accounts so ownership and comments survive the move cleanly.
Why this can grow a startup
Migrations feel unsafe when the imported work loses authorship, mentions, or assignee context. Pre-provisioning users is not glamorous, but it removes one of the fastest ways for a switch to look broken. The buyer sees the old work land with recognizable owners, which makes the new system feel like continuity instead of a partial copy.
Source-backed example
Linear's migration guide recommends provisioning users ahead of time and matching them with the same email address plus linked accounts so creators, assignees, mentions, and comments import properly.
Source-reported evidence
Source: Linear · Browse Linear tactics
Creators, assignees, mentions, and comments import properly when users are pre-matched
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 25, 2026
When to use it
Use this when Onboarding, Product, Sales is relevant to migration, switcher intent, activation and you can run a bounded test with a free 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 Onboarding and Product, using evidence from Linear as context—not as a promised result.
- Record a baseline for: Share of target users completing the product's first-value action. 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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