product Importance: 7/10

Minimum Viable Effect Sizes

Minimum Viable Effect Sizes (MVE) define the smallest meaningful improvement threshold for your specific product metrics that justifies implementation effort and resource allocation. Unlike generic effect size guidelines, MVE is tailored to your business context, user base, and implementation constraints.

The Core Problem

Why Generic Effect Sizes Fall Short

Generic effect size guidelines (small = 0.2, medium = 0.5, large = 0.8) ignore critical business factors:

  • Implementation costs: A 0.3 effect size might be worth it for a simple UI change but not for a complete backend rewrite
  • User base size: A 0.2 effect size matters more with 1M users than 1K users
  • Revenue impact: Same effect size has different business value depending on user monetization
  • Opportunity costs: Resources spent on small improvements could be used elsewhere

MVE bridges this gap by calculating effect size thresholds based on your specific business context.

Calculating Minimum Viable Effect Sizes

Basic Formula

MVE = Implementation Cost / (User Base × Revenue Impact Per User)

Step-by-Step Calculation

1. Define Implementation Costs

  • Development time: Hours × hourly rate
  • Design resources: UI/UX work, user research
  • Testing and QA: Quality assurance effort
  • Deployment and monitoring: Infrastructure and operational costs
  • Opportunity cost: What else could you build instead?

2. Calculate Revenue Impact Per User

  • Direct revenue: Subscription fees, transaction fees, ad revenue
  • Indirect value: Customer lifetime value, referral value
  • Cost savings: Support reduction, operational efficiency
  • Strategic value: Competitive advantage, market positioning

3. Determine User Base

  • Total active users: Monthly active users (MAU)
  • Affected users: Users who will experience the change
  • Conversion funnel: Users at the relevant stage

Real-World Examples

E-commerce Checkout Optimization

Context: Online retailer with 100K monthly users

  • Implementation cost: $15,000 (2 weeks dev + design)
  • Revenue per user: $50 average order value
  • Conversion rate: 2.5% baseline
  • Affected users: 100K (all checkout users)

Calculation:

MVE = $15,000 / (100,000 × $50 × 0.025)
MVE = $15,000 / $125,000
MVE = 0.12 (12% improvement needed)

Interpretation: Need at least 12% conversion rate improvement to justify implementation.

SaaS Feature Adoption

Context: B2B SaaS with 10K paid users

  • Implementation cost: $50,000 (1 month dev + testing)
  • Revenue per user: $200/month subscription
  • Feature adoption: 30% baseline
  • Affected users: 10K (all users)

Calculation:

MVE = $50,000 / (10,000 × $200 × 0.30)
MVE = $50,000 / $600,000
MVE = 0.083 (8.3% improvement needed)

Interpretation: Need at least 8.3% feature adoption improvement to justify implementation.

Mobile App Engagement

Context: Freemium app with 500K DAU

  • Implementation cost: $25,000 (3 weeks dev + A/B testing)
  • Revenue per user: $2/month (freemium conversion)
  • Engagement metric: 40% daily active usage
  • Affected users: 500K (all DAU)

Calculation:

MVE = $25,000 / (500,000 × $2 × 0.40)
MVE = $25,000 / $400,000
MVE = 0.0625 (6.25% improvement needed)

Interpretation: Need at least 6.25% engagement improvement to justify implementation.

Context-Specific Considerations

Industry Benchmarks

E-commerce

  • Conversion rate: 0.1-0.5% improvements often meaningful
  • Average order value: 5-15% improvements typically worthwhile
  • Cart abandonment: 2-8% reduction in abandonment rates
  • Customer satisfaction: 0.2-0.5 Cohen’s d for satisfaction scores

SaaS Products

  • Feature adoption: 5-20% improvement in adoption rates
  • User retention: 2-10% improvement in monthly retention
  • Support tickets: 10-30% reduction in ticket volume
  • Time to value: 15-40% reduction in onboarding time

Content Platforms

  • Engagement metrics: 5-15% improvement in time on site
  • Sharing behavior: 10-25% increase in social shares
  • Content consumption: 8-20% improvement in completion rates
  • User-generated content: 15-40% increase in UGC creation

Business Model Considerations

Freemium Models

  • Conversion to paid: Small improvements (2-5%) can have large revenue impact
  • Engagement metrics: Medium improvements (10-20%) drive conversion
  • Feature usage: Large improvements (20-40%) differentiate from free tier

Subscription Models

  • Retention rates: Small improvements (2-8%) compound over time
  • Feature adoption: Medium improvements (10-25%) increase stickiness
  • Customer satisfaction: Medium improvements (0.3-0.6 Cohen’s d) reduce churn

Marketplace Models

  • Supply-side metrics: Large improvements (20-50%) needed for network effects
  • Demand-side metrics: Medium improvements (10-30%) drive marketplace health
  • Transaction volume: Small improvements (5-15%) can be highly profitable

Implementation Framework

Pre-Experiment Planning

1. Define MVE Thresholds

  • Primary metric: Most important business outcome
  • Secondary metrics: Supporting evidence metrics
  • Guardrail metrics: Metrics that shouldn’t degrade
  • Implementation criteria: What triggers rollout decision

2. Calculate Sample Size Requirements

  • Power analysis: Ensure ability to detect MVE
  • Minimum detectable effect: Statistical power for MVE
  • Duration planning: Time needed to reach statistical significance
  • Resource allocation: Budget for experiment duration

3. Set Success Criteria

  • Statistical significance: p < 0.05 (or appropriate threshold)
  • Effect size: Exceeds MVE threshold
  • Practical significance: Business impact justifies implementation
  • Guardrail protection: No significant negative impacts

During Experiment Execution

1. Monitor Progress

  • Interim analysis: Check if MVE is achievable
  • Early stopping: Stop if clearly not meeting MVE
  • Resource adjustment: Increase sample size if needed
  • Quality assurance: Ensure data quality and experiment integrity

2. Adaptive Decision Making

  • Bayesian updating: Incorporate new information
  • Prior adjustment: Update MVE based on new data
  • Context changes: Adjust for external factors
  • Stakeholder communication: Keep team informed of progress

Post-Experiment Analysis

1. Effect Size Evaluation

  • Point estimate: Best estimate of true effect
  • Confidence intervals: Uncertainty around estimate
  • Practical significance: Does it exceed MVE?
  • Business impact: Revenue and user experience implications

2. Implementation Decision

  • Go/No-go criteria: Clear decision framework
  • Risk assessment: Potential negative consequences
  • Rollout strategy: Gradual vs. full deployment
  • Success metrics: How to measure implementation success

Advanced Considerations

Dynamic MVE Adjustment

Market Conditions

  • Competitive landscape: Adjust for competitive pressure
  • Economic factors: Recession vs. growth periods
  • Seasonal patterns: Holiday vs. regular periods
  • User behavior changes: Platform migration, usage patterns

Business Evolution

  • Growth stage: Startup vs. mature company priorities
  • Resource constraints: Budget and team capacity changes
  • Strategic shifts: Pivot in product direction
  • Technology changes: New capabilities and constraints

Portfolio-Level Optimization

Multiple Experiments

  • Resource allocation: Distribute budget across experiments
  • Priority ranking: Which experiments to run first
  • Dependency management: Experiments that affect each other
  • Learning accumulation: Building knowledge across experiments

Cross-Experiment Learning

  • Effect size patterns: Typical improvement ranges
  • Implementation cost patterns: Common cost structures
  • Success factors: What drives larger effects
  • Failure patterns: Common reasons for small effects

Common Pitfalls and Solutions

Calculation Errors

Underestimating Costs

  • Hidden costs: QA, monitoring, rollback planning
  • Opportunity costs: Alternative uses of resources
  • Long-term costs: Maintenance and support
  • Solution: Include all cost categories in MVE calculation

Overestimating Revenue Impact

  • Attribution errors: Credit for improvements not caused by change
  • Time horizon: Short-term vs. long-term impact
  • User segment differences: Not all users equally valuable
  • Solution: Conservative revenue impact estimates

Implementation Challenges

Setting Unrealistic MVEs

  • Too high: Never achievable, demotivating
  • Too low: Implement everything, resource waste
  • Too rigid: Doesn’t account for learning and adaptation
  • Solution: Regular MVE review and adjustment

Ignoring Context Changes

  • Market evolution: Competitive landscape changes
  • User behavior shifts: Platform and usage changes
  • Business model changes: Revenue structure evolution
  • Solution: Quarterly MVE review and update

Tools and Templates

MVE Calculator Template

Implementation Cost: $_______
User Base: _______ users
Revenue Per User: $_______
Baseline Metric: _______%
MVE Threshold: _______%

Break-even Analysis:
- Minimum improvement needed: _______%
- Revenue impact at MVE: $_______
- ROI at MVE: _______%
- Payback period: _______ months

Decision Framework

Go Decision Criteria

  • Effect size exceeds MVE threshold
  • Statistical significance achieved
  • No negative guardrail impacts
  • Implementation resources available
  • Strategic alignment confirmed

No-Go Decision Criteria

  • Effect size below MVE threshold
  • Statistical significance not achieved
  • Negative guardrail impacts detected
  • Implementation resources unavailable
  • Strategic priorities changed

See Also

Further Reading

  • Product Management: Cagan, M. “Inspired: How to Create Tech Products Customers Love”
  • Experimentation: Kohavi, R. et al. “Trustworthy Online Controlled Experiments”
  • Business Analysis: Hubbard, D. “How to Measure Anything”

Minimum Viable Effect Sizes transform abstract statistical concepts into concrete business decisions. The most successful product teams use MVE to focus their optimization efforts on changes that truly matter for their users and business.