Excluded internal domain list for clean customer views
Block your own company domain and other throwaway domains from auto-creating customer records so request views stay about buyers instead of your team.
Why this can grow a startup
Customer evidence gets noisy fast when internal testing and staff requests masquerade as real accounts. An exclusion list keeps the request graph honest. That matters for roadmap calls, reporting, and anything public-facing because the company can trust that counts and account views reflect actual customers rather than internal traffic.
Source-backed example
Linear excludes its own domain from Customer Requests so internal issue creation does not generate fake customer records, and also supports broader excluded-domain rules.
Source-reported evidence
Source: Linear Docs · Browse Linear Docs tactics
Excluded domains prevent internal requests from creating customer records
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 27, 2026
When to use it
Use this when Product, Support, Analytics is relevant to data hygiene, customer evidence, b2b 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 Product and Support, using evidence from Linear Docs as context—not as a promised result.
- Record a baseline for: Users who resolve the issue and complete the next useful 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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