The Five Dimensions of MVP Success
Measuring MVP success is not one number. It is a set of signals across five dimensions that, together, tell you whether you have found product-market fit. Dimension 1: Activation. Did users complete the core action that delivers the first value? Activation measures the percentage of users who reach the 'aha moment' - the point where they understand why your product is useful. Dimension 2: Engagement. Are users doing the thing your product is built for? Engagement measures how deeply and frequently users interact with your core feature. Dimension 3: Retention. Are users coming back? Retention is the most important MVP metric. A product that users return to unprompted has something valuable. A product that users try once and abandon does not. Dimension 4: Revenue. Are users paying? Or if you have not launched paid plans yet, are they indicating willingness to pay? Revenue signals real commitment that no NPS score or survey can replicate. Dimension 5: Learning. What did you learn that changes your understanding of the problem or the user? Learning metrics are often overlooked but are the most important for MVP stage: if you launched and learned nothing, the MVP failed even if the metrics look good.
MVP Success Metrics Template (Blank Version)
--- MVP SUCCESS METRICS --- Document version: [v1.0] Date: [DD/MM/YYYY] Product name: [Product name] MVP launch date (actual or planned): [DD/MM/YYYY] Measurement period: [e.g. 30 days / 60 days / 90 days post-launch] Prepared by: [Name, Role] SECTION 1: NORTH STAR METRIC 1.1 North Star Metric (NSM) The single metric that best captures whether your MVP is delivering core value. Choose one. NSM: [Metric name] Definition: [Precise definition of how this metric is measured] Target for [measurement period]: [Specific number] Current baseline (if any): [Current value or 'zero - pre-launch'] Why this is the NSM: [1-2 sentences explaining why this metric captures core value delivery] Examples of good North Star Metrics: - Marketplace: Gross merchandise value (GBP transacted per month) - SaaS productivity tool: Tasks completed per active user per week - Communication tool: Messages sent per day per active team - B2B analytics: Reports generated and shared per month SECTION 2: ACTIVATION METRICS 2.1 Activation event definition What is the single action that indicates a new user has experienced first value? Activation event: [e.g. 'User creates and saves their first project'] 2.2 Activation metrics | Metric | Definition | Target | Timeframe | Priority | |--------|-----------|--------|-----------|----------| | Activation rate | % of sign-ups who complete activation event | [X%] | First 7 days | P1 | | Time to activation | Median time from sign-up to activation event | [< X hours] | - | P1 | | Activation funnel drop-off | Step in onboarding where most users abandon | [Identify top 2 drop-off points] | - | P2 | | [Custom metric] | [Definition] | [Target] | [Timeframe] | [P1/P2/P3] | 2.3 Activation baseline and target Baseline (pre-launch): [If known from beta or waitlist data, otherwise N/A] Target (acceptable): [Minimum acceptable activation rate] Target (good): [Target activation rate] Target (excellent): [Excellent activation rate] SECTION 3: ENGAGEMENT METRICS 3.1 Core engagement action What is the primary action that indicates a user is engaging with the core value of the product? Core action: [e.g. 'User processes a job sheet'] 3.2 Engagement metrics | Metric | Definition | Target | Timeframe | Priority | |--------|-----------|--------|-----------|----------| | DAU/MAU ratio | Daily active users / Monthly active users | [X%] | Ongoing | P1 | | Core action frequency | Average core actions per active user per [week/month] | [X per week] | - | P1 | | Feature adoption rate | % of activated users who use [key feature] | [X%] | 30 days | P2 | | Session depth | Average pages/screens visited per session | [X] | - | P3 | | [Custom metric] | [Definition] | [Target] | [Timeframe] | [Priority] | 3.3 Engagement health indicator For B2B SaaS - a healthy early-stage product typically shows: DAU/MAU > 20%, or weekly active usage among paying customers > 3 sessions per week. Define what 'healthy engagement' looks like for your specific product and user type. [Write your product-specific engagement health definition here] SECTION 4: RETENTION METRICS 4.1 Retention definition What does 'retained' mean for your product? A user is retained if they: [e.g. 'Return to the product and complete at least one core action within a given week'] Retention period: [ ] Daily [ ] Weekly [ ] Monthly (choose based on expected usage frequency) 4.2 Retention metrics | Metric | Definition | Target | Timeframe | Priority | |--------|-----------|--------|-----------|----------| | Week 1 retention | % of new users who return in week 1 after sign-up | [X%] | - | P1 | | Week 4 retention | % of new users who return in week 4 after sign-up | [X%] | - | P1 | | Month 3 retention | % of month-1 cohort still active in month 3 | [X%] | - | P1 | | Churn rate (paid) | % of paying customers who cancel per month | [< X%] | Monthly | P1 | | Resurrection rate | % of churned users who return | [X%] | Monthly | P3 | 4.3 Retention benchmarks by product type Consumer app (high frequency, daily): Healthy day-7 retention is 25-40%; day-30 is 10-20% B2B SaaS (weekly usage): Healthy week-4 retention is 40-60%; month-3 is 30-50% B2B SaaS (monthly usage): Healthy month-3 retention is 60-80% Marketplace: Varies significantly by category; buyer return rate within 90 days is a leading indicator SECTION 5: REVENUE METRICS 5.1 Revenue model [ ] Subscription (monthly/annual) [ ] Usage-based [ ] Transactional / marketplace [ ] Freemium with paid upgrade [ ] Not yet monetised (indicate willingness to pay proxy metric below) 5.2 Revenue metrics | Metric | Definition | Target | Timeframe | Priority | |--------|-----------|--------|-----------|----------| | MRR | Monthly recurring revenue | GBP [amount] | By day [X] | P1 | | Paying customers | Number of active paying customers | [X] | By day [X] | P1 | | Average revenue per user (ARPU) | MRR / paying customers | GBP [amount] | - | P2 | | Conversion rate (free-to-paid) | % of free users who upgrade to paid | [X%] | - | P2 | | Customer acquisition cost (CAC) | Total marketing + sales spend / new customers | GBP [amount] | Monthly | P2 | | LTV:CAC ratio | Customer lifetime value / CAC | [X:1] | Month 3 | P3 | 5.3 Pre-revenue proxy metrics (if not yet monetised) If you have not launched paid plans, use these proxy metrics to indicate revenue potential: - Waitlist sign-ups willing to pay: [Target number] - Pilot agreements signed (committed to pay): [Target number] - LOIs from prospective customers: [Target number] SECTION 6: LEARNING METRICS Learning metrics measure what you discovered, not what you achieved. They are qualitative or quantitative signals that update your understanding of the problem, user, or solution. 6.1 Primary learning hypothesis Complete this sentence: We believe [assumption]. We will test this by [method]. We will know we are right if [evidence]. Hypothesis 1: We believe [assumption]. We will test this by [method]. We will know we are right if [evidence]. Hypothesis 2: We believe [assumption]. We will test this by [method]. We will know we are right if [evidence]. Hypothesis 3: We believe [assumption]. We will test this by [method]. We will know we are right if [evidence]. 6.2 Learning methods [ ] User interviews (target: [X] interviews in [timeframe]) [ ] Usage analytics (which features are used vs not used) [ ] Support tickets and feedback themes [ ] NPS survey (target score: [X], minimum responses: [X]) [ ] A/B tests (list specific tests planned) [ ] Churn exit surveys 6.3 Decision triggers Pre-define the metric values that will trigger a strategic decision (pivot, persevere, or stop). Pivot signal: If [metric] is below [value] after [timeframe], we will [specific action]. Persevere signal: If [metric] reaches [value] by [timeframe], we will [specific action - e.g. raise next round, hire sales]. Stop signal: If [metric] is below [value] after [timeframe] with no clear path to improvement, we will [specific action]. SECTION 7: INSTRUMENTATION PLAN 7.1 Analytics stack Primary analytics: [e.g. PostHog, Mixpanel, Amplitude] Revenue tracking: [e.g. Stripe Dashboard, ChartMogul] Error monitoring: [e.g. Sentry] Customer feedback: [e.g. Intercom, Typeform] 7.2 Events to track at launch (minimum viable instrumentation) List the specific events your analytics tool must track from day 1. - user_signed_up: Fired when user completes registration - activation_event_completed: Fired when user completes [activation event] - [core_action]_completed: Fired when user completes core action - subscription_started: Fired when user starts a paid subscription - subscription_cancelled: Fired when user cancels - [Add all events needed to measure the metrics above] 7.3 Dashboard and reporting cadence Daily: [Which metrics reviewed daily? e.g. sign-ups, activation events, errors] Weekly: [Which metrics reviewed weekly? e.g. retention cohorts, MRR, NPS] Monthly: [Which metrics reviewed monthly? e.g. LTV:CAC, full funnel, cohort curves] --- END OF TEMPLATE ---
Filled Example: FieldPulse (B2B SaaS, Field Service Management)
NORTH STAR METRIC: Jobs completed through FieldPulse per month Target (month 3): 500 jobs processed across all active accounts Why: This metric captures genuine adoption of the core workflow. A job completed through FieldPulse means a technician used the mobile app on-site, a job sheet was created, and an invoice was generated. It is a direct measure of whether we have replaced the existing paper-based process. ACTIVATION: Activation event: Manager creates first job AND at least one technician completes it on the mobile app. Target activation rate: 60% of accounts activate within 14 days of sign-up Target time to activation: < 48 hours from sign-up for accounts with motivated setup RETENTION: Week 4 retention target: 70% of activated accounts still processing at least 5 jobs per week Month 3 retention target: 55% of paying accounts still active Churn rate target: < 5% monthly for paying accounts Rationale for targets: Field service teams have high switching costs once technicians adopt a new app. If we achieve good activation, retention should be strong. The risk is at activation - teams that do not get all technicians using the app in week 1 rarely persist. LEARNING HYPOTHESES: Hypothesis 1: We believe the primary blocker to activation is getting technicians to download and use the mobile app. We will test this by tracking mobile app installs per account vs jobs completed. We will know we are right if accounts with < 50% technician mobile adoption show activation rates below 30%. Hypothesis 2: We believe the invoice generation feature is the primary retention driver. We will test this by tracking invoice feature usage in retained vs churned accounts at month 1. We will know we are right if 90%+ of retained accounts have generated at least one automated invoice. DECISION TRIGGERS: Pivot signal: If month-3 retention falls below 35% despite good activation, we will conduct 10 exit interviews to identify what value we are failing to deliver and consider a pivot to a different workflow area. Stop signal: If we have fewer than 10 paying customers by day 90 with less than GBP 500 MRR, we will pause development and do a full customer discovery reset.
How to Set Realistic Metric Targets
The hardest part of completing this template is choosing targets that are ambitious but realistic. Common benchmarks to guide your targets: For B2B SaaS MVPs, typical early-stage benchmarks are activation rate 40-60%, month-1 retention 50-70%, month-3 churn < 10% monthly, and free-to-paid conversion 2-8%. For consumer apps, activation (day 1 return) 25-40%, day-7 retention 15-30%, day-30 retention 5-15%. For marketplaces, supply-side acquisition to first transaction 30-50%, buyer repeat purchase within 90 days 20-40%. Set targets at three levels: acceptable (the minimum to continue), good (the target for a healthy early-stage product), and excellent (the signal to accelerate). This prevents the common mistake of treating any positive number as success. Your targets should also be calibrated to your channel and ICP: a product targeting CTOs at Series A companies should expect lower volume but higher revenue per customer than a product targeting individual developers.