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:
- result summary with denominator, baseline, window, and confidence;
- what changed and what did not;
- plausible mechanisms and alternative explanations;
- Keep, drop, test decision;
- next experiment with one intentional change;
- 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.
1---2name: measure-growth3description: 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.4---56# Measure growth outcomes78Connect measurement to a decision. Do not create dashboards without an operator action.910## Define the decision system1112Specify:1314- business outcome and decision owner;15- user behavior that represents value;16- primary signal and why it predicts the outcome;17- diagnostic signals for reach, attention, comprehension, belief, motivation, friction, and value;18- guardrails for quality, cost, retention, and harm;19- segments and observation window;20- decision date and keep, revise, stop, or scale thresholds.2122Define event names, properties, identity rules, source of truth, and QA steps when implementation detail23is requested. Distinguish leading, lagging, and diagnostic measures.2425## Read results carefully2627Check:2829- exposure and opportunity volume;30- baseline and comparable period;31- audience, channel, device, geography, and customer mix;32- instrumentation changes and missing data;33- seasonality, releases, promotions, outages, and competitor movement;34- downstream quality, revenue, or retention;35- qualitative objections and customer language.3637Do not turn correlation into causation. Prefer a comparison or test that creates different predictions38for competing explanations. State uncertainty and accept inconclusive results.3940## Produce a decision4142Return:43441. result summary with denominator, baseline, window, and confidence;452. what changed and what did not;463. plausible mechanisms and alternative explanations;474. **Keep, drop, test** decision;485. next experiment with one intentional change;496. durable learning record.5051Write each durable learning as:5253- observation;54- audience, offer, channel, and time boundary;55- evidence and confidence;56- implication;57- where it must not be generalized.5859Promote a learning only from observed behavior or a documented test. Never invent unavailable60analytics or silently treat missing observations as zero.6162Use the narrowest permitted data access. Keep tracking changes, experiment activation, messages, and63external writes behind explicit approval.
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