Template

Impact Measurement Plan

An interactive template to define outcome metrics, attribution strategy, data sources, reporting cadence, thresholds, owners, and learning questions for every deployed innovation. Submissions are saved to the platform so measurement plans travel with deployments and support repeatable handoffs.

Interactive Tool

Impact Measurement Plan

Use this plan to make the intended impact concrete, show how outcomes will be measured and attributed, and name the people and data sources that will own ongoing measurement. Attach or link this plan to each deployment so teams avoid orphaned pilots and ensure learning informs scale decisions.

Write a concise statement of the change you expect (for example: 'Reduce time to onboard a customer from 5 days to 2 days' or 'Increase first‑week retention from 40% to 55%').
Name the specific metric(s) that will indicate success (e.g., 'avg onboarding days', '7-day retention %', 'defects per 1,000 units'). Include units where relevant.
Record the current baseline, how it was measured, and any calculations or filters used. Example: 'Baseline = 5.2 days, measured from system X using completed-onboarding event timestamps.'
Select the primary method you'll use to attribute observed changes to this deployment and provide specifics below if needed.
If using a control, describe group selection, randomization method, expected size, or matching strategy. If not using a control, explain why and how you will address alternative explanations.
Describe expected sample size calculations, target confidence/alpha, power assumptions, or practical thresholds if statistical testing isn't applicable. Example: 'Target 80% power to detect a 10% lift at alpha=0.05; estimated N=1,200 users per arm.'
List each data source (system, table, report), the data owner or steward, and how data access will be granted. Example: 'Events table in Analytics DB (owner: Data Team); CRM outcomes (owner: Sales Ops).'
List key validation rules, expected data refresh cadence, and who will monitor data quality. Example: 'Daily ingestion sanity check (row counts), de-duplication rule, and missing-value threshold of 5%.'
How often reports will run and who receives them (dashboards, weekly emails, steering committee). Example: 'Weekly dashboard for delivery team; monthly exec summary for product leadership.'
Provide links to dashboards or describe the visualizations needed (charts, cohort views, segmentation) and who will build them.
Specify the numeric or qualitative thresholds that indicate readiness to scale (for example, 'Sustained >8% lift for 6 weeks and no increase in complaint rates').
Define clear trigger conditions and the immediate action if they occur (e.g., 'If key metric drops >5% relative to control for two consecutive weeks, pause rollout and investigate').
Describe automated alerts, who is notified, and the escalation path. Include runbook references where available.
Note personal data usage, consent requirements, retention policies, and any regulatory constraints. Identify the compliance owner.
Name a single accountable person who will ensure the plan is executed and reported on.
List roles or individuals who need visibility into results (product, ops, legal, customers, partners).
Capture unresolved hypotheses and follow-up questions that should inform future experiments or data collection.
Paste links to the deployment plan, experiment runbook, dashboards, or relevant tickets so reviewers can find context quickly.
Choose whether this plan should be linked into the project's RACI or governance artifacts.
You can explore this tool now. Sign in or create an account to save your responses and return to them later.
Sign In to Save Create an Account
Make this tool part of your work

Save a personal copy, bring it to your team, or tailor the questions and workflow to fit what you are hungry to improve.

Member customization and team collaboration are coming soon.
Discuss Ask the Librarian

Discussion

Comments and conversation will live here.