# Growth Dashboard > Weekly growth dashboard: acquisition, activation, retention, revenue, referral metrics with trend analysis and commentary for growth teams - Skill: `uitbreidenos/growth-dashboard` (Agent Skill) - Install (CLI): `npx skillmds@latest add uitbreidenos/growth-dashboard` - Raw SKILL.md: https://api.skillmd.com/api/skills/uitbreidenos/growth-dashboard/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Marketing & Growth - Author: UitbreidenOS (https://skillmd.com/u/uitbreidenos) - Updated: 2026-09-22 - Page: https://skillmd.com/skills/uitbreidenos/growth-dashboard --- # Growth Dashboard Skill ## When to activate - Building the weekly growth report for leadership or the team - Pulling together AARRR metrics across acquisition, activation, retention, revenue, and referral - Diagnosing which growth lever is broken this week vs. last week - Writing the narrative commentary around your metrics — not just the numbers - Designing a new growth dashboard in Looker Studio, Metabase, or Notion - Tracking experiment portfolio health alongside business metrics ## When NOT to use - Deep-dive analysis on a single metric — this is a synthesis skill, not a debugging skill - Setting up analytics infrastructure — use `/analytics-tracking` - Designing individual experiments — use `/experiment-tracker` - Financial modelling for investors — use the financial-model workflow ## Instructions ### Weekly growth dashboard prompt ``` Build my weekly growth dashboard for the week of [DATE]. Product: [describe — SaaS / marketplace / mobile app / ecommerce] Stage: [pre-PMF / post-PMF / scaling] North Star Metric: [the one number that captures business health] This week's data: ACQUISITION - New visitors: [N] (vs. [N] last week, [N] this time last month) - New signups / leads: [N] (vs. [N] LW) - Signup rate (visitors → signups): [X%] - CAC by channel this week: [Google: $X | Meta: $X | Organic: $X | Referral: $X] - Paid spend: $[X] (vs. $[X] LW) ACTIVATION - New users who completed activation event: [N] / [N] new signups = [X%] activation rate - Activation definition: [what counts as activated — e.g., "created first project"] - Time to activation (median): [X hours/days] RETENTION - DAU / WAU / MAU: [N] / [N] / [N] - DAU/MAU ratio (stickiness): [X%] - 7-day retention (of cohorts from 7 days ago): [X%] - 30-day retention: [X%] - Churn this week: [N] customers / $[X] MRR REVENUE - MRR: $[X] (vs. $[X] last week) - New MRR: $[X] - Expansion MRR: $[X] - Churned MRR: $[X] - Net new MRR: $[X] - LTV:CAC ratio: [X:1] - Payback period: [X months] REFERRAL - Referral signups this week: [N] - Referral rate: [referral signups / total signups]: [X%] - NPS (if measured this week): [X] EXPERIMENTS RUNNING [List active A/B tests, days running, current lift vs. control] --- Produce the weekly growth dashboard with: 1. Headline numbers (this week vs. last week vs. 4-week average) 2. Traffic light status for each metric (green / yellow / red vs. targets) 3. Top 3 observations — what changed materially and why 4. One hypothesis for each negative trend 5. Recommended actions for next week 6. Experiment portfolio: which tests to conclude, which to extend ``` ### AARRR framework builder ``` Design a complete AARRR metrics framework for [product]. Product stage: [pre-launch / early / growth / scale] Business model: [subscription / transactional / usage-based / freemium] Primary channel: [content / paid / PLG / sales] Build metrics for each stage: ACQUISITION — How are people finding us? Primary metrics: - Total new visitors / leads / signups by channel - CAC by channel (spend / new customers from that channel) - Organic vs. paid split - Channel efficiency score: [conversion rate × avg LTV / CAC] Benchmarks: - Good CAC payback: < 12 months for SMB, < 18 months for mid-market - Organic should be growing as % of total over time (not flat or shrinking) ACTIVATION — Are new users getting value? Primary metrics: - Activation rate: % of signups who complete [aha moment event] - Time to activation (median days) - Aha moment completion by acquisition channel (organic users activate differently than paid) Benchmarks: - < 20% activation rate: major onboarding problem - 20-40%: improvement opportunity - 40-60%: healthy for complex products - > 60%: strong for simple tools RETENTION — Are users coming back? Primary metrics: - D1 / D7 / D30 retention (% of users who return on that day) - Weekly / monthly cohort retention curves - DAU/MAU stickiness ratio - Feature adoption depth (users using 1 feature vs. 3+ features) Benchmarks: - D7 retention > 25%: viable (B2C), > 40%: viable (B2B SaaS) - D30 > 15% (B2C), > 35% (B2B) - DAU/MAU > 20%: engaged product REVENUE — Are we monetising effectively? Primary metrics: - MRR / ARR and growth rate (WoW, MoM) - ARPU / ARPA (per user / per account) - LTV (average contract × gross margin / churn rate) - LTV:CAC ratio - Payback period - Net Revenue Retention (NRR): [expansion - churn] / beginning MRR Benchmarks: - LTV:CAC > 3:1: healthy - Payback period < 12 months: good, < 18 months: acceptable - NRR > 100%: expansion offsets churn (best-in-class > 120%) REFERRAL — Are users telling others? Primary metrics: - Viral coefficient: new users per existing user per period - Referral rate: % of signups from referral - NPS and promoter percentage - Reviews / case studies generated Benchmarks: - Viral coefficient > 0.5: meaningful word-of-mouth - > 1.0: virality (rare, often short-lived) - NPS > 40: promoter-dominated base Produce a dashboard template for [product] with all 15 core metrics, targets, and data sources. ``` ### Growth narrative generator ``` Write the growth commentary for my weekly/monthly report. Audience: [CEO / board / growth team / whole company] Tone: [analytical / executive summary / conversational] This period's performance: - [Key metric]: [result vs. target — was it above/below/on target?] - [Key metric]: [result] - [Key metric]: [result] Context to weave in: - What external factors affected results? [seasonality / competitor action / macro] - What internal changes happened? [campaigns launched / product changes / pricing changes] - What experiments concluded? [results] - What's going right? [1-2 things working well] - What's the risk? [1 thing that concerns you] - Next week's focus: [the one lever you're pulling] Write a 200-300 word narrative that: 1. Leads with the North Star Metric movement — positive or negative, name it 2. Attributes the movement to 1-2 specific causes (not vague — "paid CAC rose 18% because iOS 18 changes reduced Meta signal quality") 3. Identifies the one metric that matters most this week and why 4. Gives a concrete action — not "we will monitor" but "we will do X by Friday" 5. Ends with the outlook: are we on track for the month? Do not write: "We saw mixed results." Name the results and own them. ``` ### Cohort revenue analysis ``` Analyse my revenue cohorts to understand LTV and payback. Product: [subscription SaaS / transactional] Cohort definition: [month of first payment] Data available: [months of history] Cohort table format: Month | Starting MRR | Month 1 MRR | Month 3 | Month 6 | Month 12 | LTV estimate Provide data for each cohort: [paste CSV or table] Analyse: 1. Retention by cohort — which cohorts are retaining best and why? (Ask: what changed in acquisition, activation, or product around that cohort's start date?) 2. Expansion revenue — are surviving customers expanding? NRR = (beginning MRR + expansion - churn - contraction) / beginning MRR NRR > 100%: each cohort is worth more over time (best-in-class: 120-140%) 3. LTV calculation: Average monthly revenue per customer: $[X] Average customer lifespan: 1 / monthly churn rate = [X months] LTV = avg monthly revenue × avg lifespan × gross margin % LTV = $[X] × [X] × [X%] = $[X] 4. Payback period: CAC / (ARPU × gross margin %) = [X months] Compare to your average customer lifespan — if payback > lifespan, you're underwater 5. Which channel produces the highest LTV customers? Break LTV by acquisition channel: [paid / organic / referral / sales] This tells you where to double down on CAC investment Produce: LTV by cohort chart, payback analysis, and channel LTV comparison table. ``` ### Channel mix optimisation ``` Optimise my marketing channel mix for growth. Current channel performance: | Channel | Spend | New Customers | CAC | Avg LTV | LTV:CAC | Payback | |---|---|---|---|---|---|---| | Google Ads | $[X] | [N] | $[X] | $[X] | [X:1] | [X mo] | | Meta Ads | $[X] | [N] | $[X] | $[X] | [X:1] | [X mo] | | Content/SEO | $[X] | [N] | $[X] | $[X] | [X:1] | [X mo] | | Referral | $[X] | [N] | $[X] | $[X] | [X:1] | [X mo] | | Outbound sales | $[X] | [N] | $[X] | $[X] | [X:1] | [X mo] | Total budget: $[X]/month Growth target: [X new customers/month or $X new MRR] Analysis: 1. Rank channels by LTV:CAC — allocate more to channels with ratio > 3:1 2. Identify saturated channels — if CAC is rising MoM on a channel, you're hitting scale ceiling 3. Identify underinvested channels — organic/referral often has best LTV but lowest investment 4. Marginal economics: at 2x spend on [channel], what happens to CAC? (typically rises) 5. Minimum viable spend — some channels (e.g., SEO, community) require threshold investment to produce results Budget reallocation recommendation: - Increase: [channel] — highest LTV:CAC, not yet saturated - Maintain: [channel] — performing at target, stable CAC - Reduce: [channel] — CAC rising, LTV:CAC below 2:1 - Experiment: [channel] — untested, low cost to validate New budget allocation: [table showing before vs. after] Projected impact: [X more customers/month at current conversion rates] ``` ## Example **User:** Here's our weekly numbers. Tell me what's happening and what to do. Signups: 342 (down from 410 last week). Activation: 31% (down from 35%). MRR: $48,200 (up $1,100 from last week). Churn: 2 customers ($400 MRR). Paid spend: $12,000. **Claude's dashboard output:** **Week of [DATE] — Growth Dashboard** **North Star: MRR $48,200 (+$1,100 WoW) — on track** | Metric | This Week | Last Week | Trend | |---|---|---|---| | Signups | 342 | 410 | -17% | | Activation rate | 31% | 35% | -4pp | | New MRR | $1,500 | $1,800 | -17% | | Churned MRR | $400 | $300 | +33% | | Net new MRR | $1,100 | $1,500 | -27% | | CAC (blended) | $35 | $29 | +21% | **Top 3 observations:** 1. Signup volume dropped 17% — with spend flat at $12K, CAC rose from $29 to $35. This is a paid acquisition signal quality problem, not a budget problem. Check your Google Ads search term reports and Meta ad frequency — likely hitting audience fatigue. 2. Activation dropped 4 points — 342 signups × 4pp drop = ~14 fewer activations than expected. At our typical conversion from activation to paid, that's ~$700 at risk in next 30 days. Something in the onboarding flow changed or broke this week — check if any UI/product changes deployed. 3. MRR is still positive because expansion held — customers upgrading offset new customer slowdown. This is fragile; expansion can't carry the business if new user flow keeps declining. **Actions for next week:** - Paid: pull Meta frequency report; if frequency > 4 per person, launch new creative set - Activation: run session recordings on sign-up → aha moment path (Hotjar/FullStory) — find the drop - Churn: call the 2 churned customers this week; understand if product, price, or competitor ---