Post-Launch Optimization

REGISTRATION FLOW OPTIMIZATION

TYPICAL REGISTRATION FUNNEL:

Step 1: Download app / Visit site
├── Drop-off: ~30%
├── Reasons: Slow download, app store friction
└── Optimize: App size, store listing

Step 2: Create account (email/phone)
├── Drop-off: ~20%
├── Reasons: Form friction, privacy concerns
└── Optimize: Social login, minimal fields

Step 3: Verify identity (KYC)
├── Drop-off: ~40% (biggest drop)
├── Reasons: Document requirements, technical issues
└── Optimize: Tiered KYC, better UX, async processing

Step 4: Link bank account
├── Drop-off: ~15%
├── Reasons: Trust concerns, technical issues
└── Optimize: Clear messaging, fallback options

Step 5: Fund wallet
├── Drop-off: ~20%
├── Reasons: Friction, lack of urgency
└── Optimize: Incentives, streamlined flow

Overall conversion: ~30% of starters complete
Target: 50%+ completion rate

1. KYC flow (highest drop-off)
2. Account creation (early funnel)
3. Funding flow (last mile)
4. Trust messaging throughout

A/B TESTING FOR CBDC

WHAT TO TEST:

High-impact areas:
├── Onboarding flow variations
├── KYC step simplification
├── First transaction prompts
├── Re-engagement messaging
├── Feature discovery
└── Support interventions

TESTING METHODOLOGY:

Sample size:
├── Minimum: 1,000 users per variant
├── Statistical significance: 95%
├── Duration: 1-2 weeks typically
└── Avoid: Too short tests, too small samples

Traffic split:
├── 50/50 for major tests
├── 90/10 for risky changes
├── Control always included
└── Random assignment

Metrics:
├── Primary: The thing you're trying to improve
├── Secondary: Things you don't want to break
├── Guardrails: Metrics that shouldn't degrade
└── Example: Primary = completion rate, Guardrail = fraud rate

INTERPRETATION:
├── Wait for statistical significance
├── Check for segment differences
├── Consider practical significance
├── Document learnings
└── Roll out winner

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CBDC CHURN ANALYSIS

CHURN DEFINITION:
├── No transaction in 60 days = at-risk
├── No transaction in 90 days = churned
├── Reactivation possible until wallet closed
└── Monitor: Weekly churn rate trend

CHURN REASONS:

Low utility:
├── Signal: Few transactions before churn
├── Reason: Nothing to buy, inconvenient
├── Intervention: Merchant network expansion
└── Prevention: Better onboarding expectations

Bad experience:
├── Signal: Support tickets before churn
├── Reason: Problems not resolved
├── Intervention: Better support, proactive outreach
└── Prevention: Quality improvements

Competitive alternative:
├── Signal: Similar usage to others, then stops
├── Reason: Found better option
├── Intervention: Feature parity, differentiation
└── Prevention: Competitive monitoring

Life circumstance:
├── Signal: Gradual decline
├── Reason: Changed habits, moved, etc.
├── Intervention: Limited
└── Acceptance: Some churn is natural
```

CHURN PREVENTION STRATEGIES

PREDICTIVE INDICATORS:
├── Declining transaction frequency
├── Decreasing balance
├── No transactions in 14 days
├── Support tickets without resolution
├── App not opened in 7 days
└── Model: Combine into churn risk score

INTERVENTION TIERS:

Tier 1: At-risk (risk score 0.3-0.5)
├── Trigger: Early warning signals
├── Action: In-app prompt, value reminder
├── Timing: Automated, immediate
└── Example: "Complete a transaction this week for [incentive]"

Tier 2: High-risk (risk score 0.5-0.7)
├── Trigger: Clear disengagement signals
├── Action: Push notification, email outreach
├── Timing: Within 48 hours
└── Example: "We've improved [feature]—try it today"

Tier 3: Imminent churn (risk score 0.7+)
├── Trigger: Near-certain churn
├── Action: Personal outreach, special offer
├── Timing: Immediate
└── Example: "Is there something we can help with?"

RE-ENGAGEMENT:
├── 30-day inactive: Reminder email
├── 60-day inactive: Win-back offer
├── 90-day inactive: Survey + incentive
└── Beyond: Periodic reminders, accept loss

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FEATURE USAGE FRAMEWORK

USAGE METRICS BY FEATURE:

Core features (must work perfectly):
├── P2P transfer: X% use, Y frequency
├── Balance check: X% use, Y frequency
├── Transaction history: X% use, Y frequency
├── Merchant payment: X% use, Y frequency
└── Target: 80%+ of users use core features

Secondary features (nice to have):
├── Bill payment: X% use
├── Request money: X% use
├── Split bill: X% use
├── Budgeting: X% use
└── Target: 30%+ of users use 2+ secondary features

Advanced features (power users):
├── Recurring payments: X% use
├── Export statements: X% use
├── Multiple wallets: X% use
└── Target: 10%+ of users use advanced features

USAGE PATTERNS:
├── Power users (10%): Use most features heavily
├── Regular users (30%): Use core + 1-2 secondary
├── Light users (40%): Use only core features
├── Dormant (20%): Rarely or never use
└── Goal: Move users up the engagement ladder
```

FEATURE PRIORITIZATION FRAMEWORK

PRIORITIZATION MATRIX:

High Impact + Low Effort = DO FIRST
├── Quick wins
├── Immediate value delivery
├── High ROI
└── Examples: UX fixes, performance improvements

High Impact + High Effort = PLAN
├── Strategic investments
├── Require planning and resources
├── Worth the investment
└── Examples: Major new features, infrastructure

Low Impact + Low Effort = MAYBE
├── Nice to have
├── Do if time permits
├── Don't prioritize
└── Examples: Minor enhancements

Low Impact + High Effort = DON'T DO
├── Resource waste
├── Avoid
├── Deprioritize
└── Examples: Features no one asked for

PRIORITIZATION CRITERIA:
├── User demand: How many users want this?
├── Business impact: How does this affect adoption?
├── Effort: How long to build?
├── Risk: What could go wrong?
├── Strategic fit: Does this align with vision?
└── Score each 1-5, calculate weighted priority

---

SUSTAINABILITY METRICS

ADOPTION HEALTH:
├── Net user growth: New users - Churned users
├── Target: Positive and accelerating
├── Warning: Zero or negative growth
└── Measure: Monthly

ENGAGEMENT HEALTH:
├── Active user ratio: MAU / Total registered
├── Target: 40%+ active
├── Warning: <20% active
└── Measure: Weekly

ECONOMIC HEALTH:
├── Transaction value growth: MoM % change
├── Target: Growing
├── Warning: Declining
└── Measure: Monthly

OPERATIONAL HEALTH:
├── Cost per user: Total ops cost / Active users
├── Target: Declining over time
├── Warning: Increasing
└── Measure: Quarterly

ECOSYSTEM HEALTH:
├── Merchant net growth: New - Churned
├── Target: Positive
├── Warning: Negative
└── Measure: Monthly

SATISFACTION HEALTH:
├── NPS trend: Quarter-over-quarter
├── Target: Improving
├── Warning: Declining
└── Measure: Quarterly
```

PATH TO SUSTAINABILITY

PHASE 1: SUBSIDIZED GROWTH (Year 1)
├── Heavy investment in acquisition
├── Incentive-driven adoption
├── Building critical mass
├── Metrics: User count, transaction volume
└── Investment: High

PHASE 2: ORGANIC GROWTH EMERGING (Year 2)
├── Reduced incentives
├── Word-of-mouth increasing
├── Network effects beginning
├── Metrics: Organic %, retention
└── Investment: Moderate

PHASE 3: SELF-SUSTAINING (Year 3+)
├── Minimal acquisition spending
├── Organic growth dominant
├── Network effects strong
├── Metrics: Organic %, efficiency
└── Investment: Maintenance

SUSTAINABILITY INDICATORS:
├── >50% of new users from organic channels
├── Retention stable without incentives
├── Positive NPS
├── Growing without proportional spending
└── Unit economics improving

IF NOT ACHIEVING SUSTAINABILITY:
├── Reassess value proposition
├── Consider scope reduction
├── Evaluate continuation decision
└── Document lessons for future
```

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✅ Data-driven optimization works: Systematic analysis and testing improve adoption outcomes.

✅ Churn is predictable: Leading indicators can identify at-risk users before they churn.

✅ Onboarding is critical: First-time experience heavily influences long-term retention.

⚠️ Whether optimization can overcome fundamental value proposition issues: No amount of optimization fixes a product people don't want.

⚠️ Long-term sustainability for retail CBDC: No retail CBDC has demonstrated self-sustaining adoption at scale.

🔴 Optimizing vanity metrics: Improving numbers that don't correlate with real adoption.

🔴 Endless pivoting without learning: Changing constantly without understanding what works.

🔴 Ignoring negative signals: Optimizing while ignoring fundamental problems.

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Assignment: Design a post-launch optimization dashboard for CBDC.

Time investment: 2-3 hours

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Q1: Which step typically has the highest drop-off in CBDC registration?
A) Download app B) Create account C) Identity verification (KYC) D) Fund wallet
Answer: C

Q2: What is a healthy 90-day retention target?
A) 20% B) 35% C) 50%+ D) 80%
Answer: C

Q3: What does cohort analysis reveal?
A) Total users B) Whether retention is improving over time C) Daily transactions D) Revenue
Answer: B

Q4: What is an indicator of approaching sustainability?
A) High marketing spend B) >50% organic user acquisition C) Declining NPS D) Increasing incentives
Answer: B

Q5: What should trigger churn intervention?
A) Annual review B) Predictive indicators showing risk C) After user leaves D) Random selection
Answer: B

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End of Lesson 18