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Metrics & Success Framework

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Overview

This framework defines how we measure product success across discovery, delivery, and outcomes. We use metrics to inform decisions, validate hypotheses, and demonstrate impact.

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Metrics Hierarchy

North Star Metric
      ↓
Product Metrics (leading indicators)
      ↓
Feature Metrics (specific success criteria)

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North Star Metric

Definition: The single metric that best captures the core value we deliver to customers.

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Example North Star Metrics (Financial Services)

For Wealth Management Platform: 
Assets Under Management (AUM) actively managed per advisor
Why: Captures both platform adoption and advisor productivity For Broker Services Platform: 
Monthly Active Trading Value
Why: Reflects platform usage, liquidity, and customer engagement Characteristics of a good North Star:
  • Reflects customer value (not just vanity metrics)
  • Measurable and trackable
  • Drives business outcomes
  • Aligns entire organisation
  • Actionable (we can influence it)

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Product Metrics Framework

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AARRR (Pirate Metrics)

Acquisition - How do users find us?
  • New user sign-ups
  • Marketing qualified leads (MQLs)
  • Trial starts
  • Cost per acquisition (CPA)
Activation - Do users have a great first experience?
  • Onboarding completion rate
  • Time to first value
  • First transaction/action completed
  • Percentage completing key setup steps
Retention - Do users come back?
  • Daily Active Users (DAU)
  • Monthly Active Users (MAU)
  • DAU/MAU ratio (stickiness)
  • Cohort retention curves
  • Churn rate
Revenue - How do we make money?
  • Monthly Recurring Revenue (MRR)
  • Average Revenue Per User (ARPU)
  • Customer Lifetime Value (LTV)
  • LTV:CAC ratio
  • Revenue churn vs logo churn
Referral - Do users tell others?
  • Net Promoter Score (NPS)
  • Referral rate
  • Viral coefficient
  • Word-of-mouth attribution

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Financial Services Specific Metrics

Engagement Metrics:
  • Active accounts per advisor
  • Transactions per day/week/month
  • AUM growth rate
  • Portfolio reviews completed
  • Reports generated
Efficiency Metrics:
  • Time to complete key workflows
  • Manual process automation rate
  • Error rate reduction
  • Support ticket volume
Business Impact:
  • Revenue per user
  • Account retention rate
  • Cross-sell/upsell rate
  • Operational cost savings
Quality & Compliance:
  • Transaction accuracy rate
  • SLA adherence (uptime, performance)
  • Compliance incidents
  • Audit findings
  • Security incidents

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Feature Success Criteria

For every feature, define success before building:

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1. Usage Metrics

How many users will adopt this feature? Examples:
  • 60% of wealth managers use automated rebalancing within 90 days
  • 80% of brokers enable real-time alerts
  • 40% of clients access mobile app weekly

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2. Outcome Metrics

What impact will it have on user behaviour or business? Examples:
  • Reduce portfolio review time from 45 min to 15 min (66% reduction)
  • Increase client meetings per advisor from 8 to 12 per week (+50%)
  • Decrease trade execution errors by 80%

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3. Satisfaction Metrics

Do users like it? Examples:
  • Feature satisfaction score >4/5
  • NPS increase of 10+ points
  • feature opt-out rate

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4. Business Metrics

Does it drive business value? Examples:
  • Increase AUM per advisor by 25%
  • Reduce operational costs by $100K/year
  • Increase user retention by 15%

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Setting Feature Success Criteria

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Template

For feature: [Feature Name] Target Users: [Who will use this?] Success Metrics:
  1. Adoption: [X%] of [user segment] will use this feature within [timeframe]
  2. Outcome: [Metric] will improve from [baseline] to [target] ([% change])
  3. Satisfaction: Feature will achieve [score] satisfaction rating
  4. Business Impact: [Business metric] will improve by [% or $]
Measurement Plan:
  • Track via [analytics tool/dashboard]
  • Review frequency: [daily/weekly/monthly]
  • Owner: [person responsible]
Timeline:
  • Launch: [date]
  • 30-day review: [date]
  • 90-day assessment: [date]

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Example

For feature: Automated Portfolio Rebalancing Target Users: Wealth managers managing 50+ client portfolios Success Metrics:
  1. Adoption: 60% of wealth managers will use automated rebalancing within 90 days
  2. Outcome: Portfolio rebalancing time will decrease from 2 hours to 15 minutes (87% reduction)
  3. Satisfaction: Feature will achieve 4.5/5 satisfaction score
  4. Business Impact: AUM per advisor will increase by 20% due to capacity gains
Measurement Plan:
  • Track via Amplitude + internal admin dashboard
  • Review frequency: Weekly for first 30 days, then monthly
  • Owner: CPO
Timeline:
  • Launch: Q2 2026
  • 30-day review: Check adoption trending toward 20%
  • 90-day assessment: Validate all success criteria met

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Measurement Stack

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Analytics Tools

  • Product Analytics: Amplitude, Mixpanel, or similar
  • User Behavior: Session recordings, heatmaps
  • Business Intelligence: Internal data warehouse
  • Customer Feedback: NPS surveys, in-app feedback
  • Support Metrics: Support ticket system

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Dashboards

Executive Dashboard (CEO, Board):
  • North Star Metric
  • MRR/ARR growth
  • Customer acquisition and churn
  • NPS score
Product Dashboard (CPO, Product Team):
  • Feature adoption rates
  • Key user flows and funnels
  • A/B test results
  • Success metrics for in-flight features
Operational Dashboard (CTO, Ops Team):
  • System uptime and performance
  • Error rates and incidents
  • Support ticket volume
  • Technical debt metrics

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Metrics Review Cadence

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Daily

  • Critical system metrics (uptime, errors)
  • Revenue and transaction volume
  • Major incident alerts

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Weekly

  • Feature adoption trends
  • User engagement metrics
  • A/B test progress
  • Support ticket themes

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Monthly

  • Product metrics deep dive
  • Feature success criteria review
  • Cohort retention analysis
  • NPS trends

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Quarterly

  • North Star Metric review
  • Product-market fit assessment
  • OKR progress review
  • Strategic metrics alignment

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Product-Market Fit Metrics

Signals of Product-Market Fit:
  • 40% rule: >40% of users would be “very disappointed” if product went away
  • Retention curve flattens: Cohorts stabilize after initial drop-off
  • Organic growth: >40% of new users come from word-of-mouth
  • High engagement: Users exceed expected usage patterns
  • NPS >50: Strong customer advocacy
Measurement:
  • Sean Ellis PMF survey (quarterly)
  • Cohort retention analysis (monthly)
  • Customer acquisition source tracking (ongoing)
  • Usage benchmarking (quarterly)

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A/B Testing Framework

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When to A/B Test

  • Uncertain which approach is better
  • Controversial change
  • Significant risk if wrong
  • Want to measure incremental impact

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Test Design

  1. Hypothesis: “We believe [change] will result in [outcome]”
  2. Success Metric: Primary metric to measure
  3. Sample Size: Calculate for statistical significance
  4. Duration: Typically 1-2 weeks minimum
  5. Traffic Split: Usually 50/50, sometimes 90/10 for risky changes

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Example

Hypothesis: We believe adding a portfolio health score
will increase engagement with rebalancing tools

Variant A (Control): Current experience
Variant B (Test): Add health score widget

Success Metric: % of users who initiate a rebalancing action
Secondary Metrics: Time on page, feature discovery rate

Sample Size: 1,000 users per variant
Duration: 2 weeks
Traffic Split: 50/50

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Avoiding Metrics Pitfalls

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Don’t

  • Vanity metrics - Metrics that look good but don’t drive decisions
  • Too many metrics - Focus on what matters most
  • Lack of context - Always compare (vs. target, vs. last period, vs. cohort)
  • Ignoring segments - Average can mask important variations
  • Short-term thinking - Balance short-term wins with long-term health

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Do

  • Lead with outcomes - Focus on customer and business impact
  • Segment your data - Analyze by user type, cohort, feature usage
  • Track leading indicators - Predict problems before they become critical
  • Set targets - Every metric needs a goal
  • Tell stories - Use metrics to inform narratives, not replace them

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Financial Services Metric Considerations

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Regulatory Metrics

Track for compliance and audit:
  • Transaction accuracy rate (target: 99.99%)
  • System uptime (SLA: 99.9%+)
  • Data retention compliance
  • Audit trail completeness
  • Security incident response time

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Risk Metrics

Monitor for operational risk:
  • Failed transaction rate
  • Data discrepancy rate
  • Unauthorized access attempts
  • System capacity utilization
  • Disaster recovery test results

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Client Trust Metrics

Critical for financial services:
  • Net Promoter Score (NPS)
  • Customer satisfaction (CSAT)
  • Assets under management (AUM) growth
  • Client retention rate
  • Referral rate

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OKR Framework

Align metrics with quarterly Objectives and Key Results:

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Example OKR

Objective: Improve wealth manager productivity Key Results:
  1. Reduce average portfolio review time from 45 min to 20 min
  2. Increase client meetings per advisor from 8 to 12 per week
  3. Achieve 4.5+ satisfaction score from wealth managers
Metrics Tracking:
  • Weekly: Check progress on time savings
  • Monthly: Survey advisor satisfaction
  • Quarterly: Assess overall objective achievement

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Templates

  • Feature success criteria template: /templates/success-criteria-template.md
  • A/B test plan template: /templates/ab-test-template.md
  • OKR template: /templates/okr-template.md
  • Metrics dashboard spec: /templates/dashboard-spec-template.md