Metrics Dashboard Growth

metrics-dashboard-growth

Agent: Growth Engineer

L2 growth engineer (Nx) responsible for growth instrumentation, metrics dashboards, funnel analysis, and growth loop activation.

Department ethos: ideal-data-growth.md

Skill Description

The growth dashboard builder designs and implements dashboards that visualize the full AARRR funnel — acquisition volume and CAC by channel, activation rate by cohort, retention curves, revenue per user, and referral loop throughput — enabling the growth team to monitor performance and make experiment decisions from a single view.

When to Use

• When the growth model is finalized and its metrics need a monitoring dashboard.
• When the growth team reviews metrics via ad-hoc queries or spreadsheets instead of a live view.
• When a new experiment launches and the team needs a results dashboard showing variant performance.
• When existing growth dashboards do not cover a new channel, loop, or funnel stage.

Workflow

1. Define dashboard scope: Determine which AARRR stages, channels, experiments, and loops the dashboard must cover. Identify the primary audience (growth lead, growth engineer, leadership).
2. Design layout: Structure the dashboard in AARRR order — acquisition at the top, referral at the bottom. Include a summary scorecard with the 5 most important growth numbers.
3. Build acquisition views: Visualize daily signups by channel, CAC by channel, and channel-level conversion rates. Include UTM-based attribution breakdowns.
4. Build activation and retention views: Show activation rate by cohort, retention curves (Day 1/7/30/90), and time-to-activation distribution.
5. Build experiment views: Display active experiment status — variant sample sizes, conversion rates, confidence intervals, and days to significance. Link to the significance tracker.
6. Build loop views: Show loop throughput metrics — viral coefficient, referral conversion rate, cycle time, and loop-generated users as a percentage of total acquisition.
7. Validate and publish: Cross-check dashboard numbers against raw queries. Publish with access permissions and a documentation page explaining each metric.

Anti-Patterns

• Growth dashboard without experiment views: Omitting experiment results from the growth dashboard forces the team to check a separate tool, reducing experiment visibility. Why: experiments are the primary mechanism of growth improvement; they should be front-and-centre on the growth dashboard.
• No channel-level breakdown: Showing aggregate acquisition without per-channel data prevents CAC optimization. Why: aggregate CAC hides that one channel may be 5x more expensive than another.
• Stale retention curves: Displaying retention curves that do not update with each new cohort gives the team outdated information. Why: retention changes with product updates and user mix; stale curves mask recent improvements or regressions.

Output

Success:

• A growth dashboard with AARRR views, channel-level acquisition metrics, cohort retention curves, active experiment results, loop throughput metrics, and a documentation page.

Failure:

• Dashboard metrics do not match raw query results. Report the discrepancy, root cause, and corrective action.

Related Skills

• metrics-dashboard-builder -- the data analyst's dashboard builder covers product KPIs; this skill focuses on growth-specific AARRR metrics.
• growth-model-designer -- the growth model defines the metrics this dashboard visualizes.
• funnel-analyser-growth -- funnel analysis results are displayed on the growth dashboard.
• statistical-significance-tracker -- experiment significance data feeds the experiment views on this dashboard.