# Channel Stop Or Scale Decision Sheet

Canonical page: https://waitlist.crany.ai/marketing-templates/channel-stop-scale-decision

By Tangenix. Published 2026-10-24. Category: Acquisition measurement.

Decide whether to hold, repair, stop or expand a marketing channel using comparable outcomes, full effort and explicit evidence limits.

## Task

Make a bounded channel-allocation decision using several evidence sources rather than one experiment result.

## When to use

Use this after a channel has produced enough task-relevant observations to review its role. Assess data quality, audience fit, actual capacity and remaining obligations before committing more effort.

## Choose another approach when

You want to scale from impressions alone, hide recurring maintenance costs, or treat a weak uncontrolled comparison as proof that a channel can never work.

## Inputs

- The channel’s actual role, relevant audience and supported offer.
- Outcome reports with stable definitions, mature windows and known gaps.
- Total preparation, publication and follow-up effort plus current capacity.

## Template

    Channel / review window / owner: [bounded scope]
    Original purpose and audience: [what success would mean]
    What actually shipped: [verified placements or work]
    Evidence quality gate: [tracking, eligibility and complete observation window]
    Qualified outcomes: [actual definition, count and uncertainty]
    Task/audience learning: [separate qualitative evidence]
    Full resource use: [setup, recurring effort and any real spend]
    Remaining obligations / constraints: [support and policy limits]
    Alternative use of the same effort: [realistic option]
    Decision: hold for evidence / repair / stop / bounded expansion
    Reason and strongest contrary evidence: [show uncertainty]
    Expansion cap or repair scope: [explicit manageable limit]
    Stop/review trigger: [predeclared condition or date]
    Owner / next action: [work that can actually be completed]
    Do not remove necessary follow-up or claim causal profitability merely to make a channel look scalable.

## Illustrative example

This is not a Crany customer result. Replace example details with verified facts.

    Fictional example: a founder’s tutorial has generated relevant product questions, while a high-impression placement has no reliable task-fit evidence. The review does not rank the placement higher simply because its visible counter is larger.
    The tutorial still requires answering questions and keeping the demonstration current. Those obligations are included before considering a second tutorial. A tracking gap in the placement is assigned a bounded repair rather than interpreted as proof that nobody cared.
    The founder chooses a limited next action with a review point. The sheet records the strongest reason that decision might be wrong and preserves the option to stop or change it when better evidence arrives.

## How to use

1. Check evidence quality and the channel’s actual purpose before comparing results.
2. Include full effort, current obligations and a realistic alternative allocation.
3. Choose a bounded decision with contrary evidence and explicit review conditions.

## Checks

- Unmeasured outcomes are not converted into either success or zero demand.
- A larger activity counter is not used as a substitute for relevant outcomes.
- Expansion includes the capacity required for continuing follow-up.

## What to measure

Track completion of the chosen action, new evidence and whether review conditions are met. Compare relevant outcomes under consistent definitions and observation windows. This allocation record is not a validated profitability model or evidence of a randomized channel effect.

## Research and sources

Microsoft emphasizes measurement and data quality for experiments; Atlassian addresses activity and capacity planning. This original channel-allocation sheet uses those principles without treating an observational review as a controlled experiment.

- [Microsoft Research: Patterns of Trustworthy Experimentation, Pre-Experiment Stage](https://www.microsoft.com/en-us/research/articles/patterns-of-trustworthy-experimentation-pre-experiment-stage/) — checked 2026-10-24. Clear hypotheses, appropriate measurement, assignment units and data-quality checks before a controlled experiment.
- [Atlassian Team Playbook: Capacity planning](https://www.atlassian.com/team-playbook/plays/capacity-planning) — checked 2026-10-24. Observe activities, include overhead, distinguish projects and review time allocation. Team exercise adapted to founder planning, not a validated productivity benchmark.

Prepared with AI assistance. Sources support the stated principles; they do not endorse Crany or guarantee performance.

## About Crany

Crany is an AI marketing co-founder in development for software founders and vibe coders. It is designed to strategize, execute approved marketing and improve based on results. These free resources are usable independently of app access. The public waitlist is at https://waitlist.crany.ai/.