# Measure Growth

> Design growth measurement or learn from marketing results. Use for analytics and tracking plans, KPI trees, campaign measurement, attribution boundaries, experiment readouts, performance reviews, launch retrospectives, cohort or funnel analysis, deciding what to keep or stop, or converting observed results into bounded reusable learning.

- Skill: `forsvn-labs/measure-growth` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add forsvn-labs/measure-growth`
- Raw SKILL.md: https://api.skillmd.com/api/skills/forsvn-labs/measure-growth/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Marketing & Growth
- Author: forsvn-labs (https://skillmd.com/u/forsvn-labs)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/forsvn-labs/measure-growth

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# Measure growth outcomes

Connect measurement to a decision. Do not create dashboards without an operator action.

## Define the decision system

Specify:

- business outcome and decision owner;
- user behavior that represents value;
- primary signal and why it predicts the outcome;
- diagnostic signals for reach, attention, comprehension, belief, motivation, friction, and value;
- guardrails for quality, cost, retention, and harm;
- segments and observation window;
- decision date and keep, revise, stop, or scale thresholds.

Define event names, properties, identity rules, source of truth, and QA steps when implementation detail
is requested. Distinguish leading, lagging, and diagnostic measures.

## Read results carefully

Check:

- exposure and opportunity volume;
- baseline and comparable period;
- audience, channel, device, geography, and customer mix;
- instrumentation changes and missing data;
- seasonality, releases, promotions, outages, and competitor movement;
- downstream quality, revenue, or retention;
- qualitative objections and customer language.

Do not turn correlation into causation. Prefer a comparison or test that creates different predictions
for competing explanations. State uncertainty and accept inconclusive results.

## Produce a decision

Return:

1. result summary with denominator, baseline, window, and confidence;
2. what changed and what did not;
3. plausible mechanisms and alternative explanations;
4. **Keep, drop, test** decision;
5. next experiment with one intentional change;
6. durable learning record.

Write each durable learning as:

- observation;
- audience, offer, channel, and time boundary;
- evidence and confidence;
- implication;
- where it must not be generalized.

Promote a learning only from observed behavior or a documented test. Never invent unavailable
analytics or silently treat missing observations as zero.

Use the narrowest permitted data access. Keep tracking changes, experiment activation, messages, and
external writes behind explicit approval.