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skills/chain-estimation-decision-storytelling/resources/examples/build-vs-buy-analytics-platform.md

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+# Decision: Build Custom Analytics Platform vs. Buy SaaS Solution
+
+**Date:** 2024-01-15
+**Decision-maker:** CTO + VP Product
+**Audience:** Executive team
+**Stakes:** Medium ($500k-$1.5M over 3 years)
+
+---
+
+## 1. Decision Context
+
+**What we're deciding:**
+Should we build a custom analytics platform in-house or purchase a SaaS analytics solution?
+
+**Why this matters:**
+- Current analytics are manual and time-consuming (20 hours/week analyst time)
+- Product team needs real-time insights to inform roadmap decisions
+- Sales needs usage data to identify expansion opportunities
+- Engineering wants to reduce operational burden of maintaining custom tools
+
+**Alternatives:**
+1. **Build custom**: Develop in-house analytics platform with our exact requirements
+2. **Buy SaaS**: Purchase enterprise analytics platform (e.g., Amplitude, Mixpanel)
+3. **Hybrid**: Use SaaS for standard metrics, build custom for proprietary analysis
+
+**Key uncertainties:**
+- Development cost and timeline (historical variance ±40%)
+- Feature completeness of SaaS solution (will it meet all needs?)
+- Usage growth rate (affects SaaS costs which scale with volume)
+- Long-term flexibility needs (will we outgrow SaaS or need custom features?)
+
+**Constraints:**
+- Budget: $150k available in current year, $50k/year ongoing
+- Timeline: Need solution operational within 6 months
+- Requirements: Must support 100M events/month, 50+ team members, custom dashboards
+- Strategic: Prefer minimal vendor lock-in, prioritize time-to-value
+
+**Audience:** Executive team (need bottom-line recommendation + risks)
+
+---
+
+## 2. Estimation
+
+### Alternative 1: Build Custom
+
+**Costs:**
+- **Initial development**: $200k-$400k (most likely $300k)
+  - Base estimate: 6 engineer-months × $50k loaded cost = $300k
+  - Range reflects scope uncertainty and potential technical challenges
+  - Source: Similar internal projects averaged $280k ±$85k (30% std dev)
+
+- **Annual operational costs**: $40k-$60k per year (most likely $50k)
+  - Infrastructure: $15k-$25k (based on 100M events/month)
+  - Maintenance: 0.5 engineer FTE = $25k-$35k per year
+  - Source: Current analytics tools cost $45k/year to maintain
+
+- **Opportunity cost**: $150k
+  - Engineering team would otherwise work on core product features
+  - Estimated value of deferred features: $150k in potential revenue impact
+
+**Benefits:**
+- **Cost savings**: $0 subscription fees (vs $120k/year for SaaS)
+- **Perfect fit**: 100% feature match to our specific needs
+- **Flexibility**: Full control to add custom analysis
+- **Strategic value**: Build analytics competency, own our data
+
+**Probabilities:**
+- **Best case (20%)**: On-time delivery at $250k, perfect execution
+  - Prerequisites: Clear requirements, no scope creep, experienced team available
+
+- **Base case (50%)**: Moderate delays and cost overruns to $350k over 8 months
+  - Typical scenario based on historical performance
+
+- **Worst case (30%)**: Significant delays to $500k over 12 months, some features cut
+  - Risk factors: Key engineer departure, underestimated complexity, changing requirements
+
+**Key assumptions:**
+- Engineering team has capacity (currently 70% utilized)
+- No major technical unknowns in data pipeline
+- Requirements are stable (< 10% scope change)
+- Infrastructure costs scale linearly with events
+
+### Alternative 2: Buy SaaS
+
+**Costs:**
+- **Initial implementation**: $15k-$25k (most likely $20k)
+  - Setup and integration: 2-3 weeks consulting
+  - Data migration and testing
+  - Team training
+  - Source: Vendor quote + reference customer feedback
+
+- **Annual subscription**: $100k-$140k per year (most likely $120k)
+  - Base: $80k for 100M events/month
+  - Users: $2k per user × 20 power users = $40k
+  - Growth buffer: Assume 20% event growth per year
+  - Source: Vendor pricing confirmed, escalates with usage
+
+- **Switching cost** (if we change vendors later): $50k-$75k
+  - Data export and migration
+  - Re-implementing integrations
+  - Team retraining
+
+**Benefits:**
+- **Faster time-to-value**: 2 months vs. 8 months for build
+  - 6-month head start = earlier insights = better decisions sooner
+  - Estimated value: $75k (half of opportunity cost avoided)
+
+- **Proven reliability**: 99.9% uptime SLA
+  - Reduces operational risk
+  - Frees engineering for core product
+
+- **Feature velocity**: Continuous improvements from vendor
+  - New capabilities quarterly (ML-powered insights, predictive analytics)
+  - Estimated value: $30k/year in avoided feature development
+
+- **Lower risk**: Predictable costs, no schedule risk
+  - High confidence in timeline and total cost
+
+**Probabilities:**
+- **Best case (40%)**: Perfect fit, seamless implementation, $100k/year steady state
+  - Vendor delivers on promises, usage grows slower than expected
+
+- **Base case (45%)**: Good fit with minor gaps, standard implementation, $120k/year
+  - 85% of needs met out-of-box, workarounds for remaining 15%
+
+- **Worst case (15%)**: Poor fit requiring workarounds or supplemental tools, $150k/year
+  - Missing critical features, need to maintain some custom tooling
+
+**Key assumptions:**
+- SaaS vendor is stable and continues product development
+- Event volume growth is 20% per year (manageable)
+- Vendor lock-in is acceptable (switching cost is reasonable)
+- Security and compliance requirements are met by vendor
+
+### Alternative 3: Hybrid
+
+**Costs:**
+- **Initial investment**: $100k-$150k (most likely $125k)
+  - SaaS implementation: $20k
+  - Custom integrations and proprietary metrics: $100k-$130k development
+
+- **Annual costs**: $80k-$100k per year (most likely $90k)
+  - SaaS subscription (smaller tier): $60k-$70k
+  - Maintenance of custom components: $20k-$30k
+
+**Benefits:**
+- **Balanced approach**: Standard analytics from SaaS, custom analysis in-house
+- **Reduced risk**: Less development than full build, more control than pure SaaS
+- **Flexibility**: Can shift balance over time based on needs
+
+**Probabilities:**
+- **Base case (60%)**: Works reasonably well, $125k + $90k/year
+- **Integration complexity (40%)**: More overhead than expected, $150k + $100k/year
+
+**Key assumptions:**
+- Clean separation between standard and custom analytics
+- SaaS provides good API for custom integrations
+- Maintaining two systems doesn't create excessive complexity
+
+---
+
+## 3. Decision Analysis
+
+### Expected Value Calculation (3-Year NPV)
+
+**Discount rate:** 10% (company's cost of capital)
+
+#### Alternative 1: Build Custom
+
+**Year 0 (Initial):**
+- Best case (20%): -$250k development - $150k opportunity cost = -$400k
+- Base case (50%): -$350k development - $150k opportunity cost = -$500k
+- Worst case (30%): -$500k development - $150k opportunity cost = -$650k
+
+**Expected Year 0:** ($-400k × 0.20) + ($-500k × 0.50) + ($-650k × 0.30) = -$525k
+
+**Years 1-3 (Operational):**
+- Annual cost: $50k/year
+- PV of 3 years at 10%: $50k × 2.49 = $124k
+
+**Total Expected NPV (Build):** -$525k - $124k = **-$649k**
+
+*Note: Costs are negative because this is an investment. Focus is on minimizing cost since benefits (analytics capability) are equivalent across alternatives.*
+
+#### Alternative 2: Buy SaaS
+
+**Year 0 (Initial):**
+- Implementation: $20k
+- No opportunity cost (fast implementation)
+
+**Years 1-3 (Operational):**
+- Best case (40%): $100k/year × 2.49 = $249k
+- Base case (45%): $120k/year × 2.49 = $299k
+- Worst case (15%): $150k/year × 2.49 = $374k
+
+**Expected annual cost:** ($100k × 0.40) + ($120k × 0.45) + ($150k × 0.15) = $116.5k/year
+**PV of 3 years:** $116.5k × 2.49 = $290k
+
+**Total Expected NPV (Buy):** -$20k - $290k = **-$310k**
+
+**Benefit adjustment for faster time-to-value:** +$75k (6-month head start)
+**Adjusted NPV (Buy):** -$310k + $75k = **-$235k**
+
+#### Alternative 3: Hybrid
+
+**Year 0 (Initial):**
+- Development + implementation: $125k
+- Partial opportunity cost: $75k (half the custom build time)
+
+**Years 1-3 (Operational):**
+- Expected annual: $90k/year × 2.49 = $224k
+
+**Total Expected NPV (Hybrid):** -$125k - $75k - $224k = **-$424k**
+
+### Comparison Summary
+
+| Alternative | Expected 3-Year Cost | Risk Profile | Time to Value |
+|-------------|---------------------|--------------|---------------|
+| Build Custom | $649k              | **High** (30% worst case) | 8 months |
+| Buy SaaS     | $235k              | **Low** (predictable) | 2 months |
+| Hybrid       | $424k              | **Medium** | 5 months |
+
+**Cost difference:** Buy SaaS saves **$414k** vs. Build Custom over 3 years
+
+### Sensitivity Analysis
+
+**What if development cost for Build is 20% lower ($240k base instead of $300k)?**
+- Build NPV: -$577k (still $342k worse than Buy)
+- **Conclusion still holds**
+
+**What if SaaS costs grow 40% per year instead of 20%?**
+- Year 3 SaaS cost: $230k (vs. $145k base case)
+- Buy NPV: -$325k (still $324k better than Build)
+- **Conclusion still holds**
+
+**What if we need to switch SaaS vendors in Year 3?**
+- Additional switching cost: $65k
+- Buy NPV: -$300k (still $349k better than Build)
+- **Conclusion still holds**
+
+**Break-even analysis:**
+At what annual SaaS cost does Build become cheaper?
+- Build 3-year cost: $649k
+- Buy 3-year cost: $20k + (X × 2.49) - $75k = $649k
+- Solve: X = $282k/year
+
+**Interpretation:** SaaS would need to cost $282k/year (2.4x current estimate) for Build to break even. Very unlikely.
+
+### Robustness Check
+
+**Conclusion is robust if:**
+- Development cost < $600k (currently $300k base, $500k worst case ✓)
+- SaaS annual cost < $280k (currently $120k base, $150k worst case ✓)
+- Time-to-value benefit > $0 (6-month head start valuable ✓)
+
+**Conclusion changes if:**
+- SaaS vendor goes out of business (low probability, large incumbents)
+- Regulatory requirements force on-premise solution (not currently foreseen)
+- Custom analytics become core competitive differentiator (possible but unlikely)
+
+---
+
+## 4. Recommendation
+
+### **Recommended option: Buy SaaS Solution**
+
+**Reasoning:**
+
+Buy SaaS dominates Build Custom on three dimensions:
+
+1. **Lower expected cost**: $235k vs. $649k over 3 years (saves $414k)
+2. **Lower risk**: Predictable subscription vs. 30% chance of 2x cost overrun on build
+3. **Faster time-to-value**: 2 months vs. 8 months (6-month head start enables better decisions sooner)
+
+The cost advantage is substantial ($414k savings) and robust to reasonable assumption changes. Even if SaaS costs double or we need to switch vendors, Buy still saves $300k+.
+
+The risk profile strongly favors Buy. Historical data shows 30% of similar build projects experience 2x cost overruns. SaaS has predictable costs with 99.9% uptime SLA.
+
+Time-to-value matters: getting analytics operational 6 months sooner means better product decisions sooner, worth approximately $75k in avoided opportunity cost.
+
+**Key factors:**
+1. **Cost**: $414k lower expected cost over 3 years
+2. **Risk**: Predictable vs. high uncertainty (30% worst case for Build)
+3. **Speed**: 2 months vs. 8 months to operational
+4. **Strategic fit**: Analytics are important but not core competitive differentiator
+
+**Tradeoffs accepted:**
+- **Vendor dependency**: Accepting switching cost of $65k if we change vendors
+  - Mitigation: Choose stable, market-leading vendor (Amplitude or Mixpanel)
+
+- **Some feature gaps**: SaaS may not support 100% of custom analysis needs
+  - Mitigation: 85% coverage out-of-box, workarounds for remaining 15%
+  - Can supplement with lightweight custom tools if needed ($20k-$30k vs. $300k+ full build)
+
+- **Less flexibility**: Can't customize as freely as in-house solution
+  - Mitigation: Most SaaS platforms offer extensive APIs and integrations
+  - True custom needs can be addressed incrementally
+
+**Why not Hybrid?**
+Hybrid ($424k) is $189k more expensive than Buy with minimal additional benefit. The complexity of maintaining two systems outweighs the incremental flexibility.
+
+---
+
+## 5. Risks and Mitigations
+
+### Risk 1: SaaS doesn't meet all requirements
+
+**Probability:** Medium (15% worst case scenario)
+
+**Impact:** Need workarounds or supplemental tools
+
+**Mitigation:**
+- Conduct thorough vendor evaluation with 2-week pilot
+- Map all requirements to vendor capabilities before committing
+- Budget $30k for lightweight custom supplements if needed
+- Still cheaper than full Build even with supplements
+
+### Risk 2: Vendor lock-in / price increases
+
+**Probability:** Low-Medium (vendors typically increase 5-10%/year)
+
+**Impact:** Higher ongoing costs
+
+**Mitigation:**
+- Negotiate multi-year contract with price protection
+- Maintain data export capability (ensure vendor supports data portability)
+- Budget includes 20% annual growth buffer
+- Switching cost is manageable ($65k) if needed
+
+### Risk 3: Usage growth exceeds estimates
+
+**Probability:** Low (current trajectory is 15%/year, estimated 20%)
+
+**Impact:** Higher subscription costs
+
+**Mitigation:**
+- Monitor usage monthly against plan
+- Optimize event instrumentation to reduce unnecessary events
+- Renegotiate tier if growth is faster than expected
+- Even at 2x usage growth, still cheaper than Build
+
+### Risk 4: Security or compliance issues
+
+**Probability:** Very Low (vendor is SOC 2 Type II certified)
+
+**Impact:** Cannot use vendor, forced to build
+
+**Mitigation:**
+- Verify vendor security certifications before contract
+- Review data handling and privacy policies
+- Include compliance requirements in vendor evaluation
+- This risk applies to any vendor; not specific to this decision
+
+---
+
+## 6. Next Steps
+
+**If approved:**
+
+1. **Vendor evaluation** (2 weeks) - VP Product + Data Lead
+   - Demo top 3 vendors (Amplitude, Mixpanel, Heap)
+   - Map requirements to capabilities
+   - Validate pricing and terms
+   - Decision by: Feb 1
+
+2. **Pilot implementation** (2 weeks) - Engineering Lead
+   - 2-week pilot with selected vendor
+   - Instrument 3 key product flows
+   - Validate data accuracy and latency
+   - Go/no-go decision by: Feb 15
+
+3. **Full rollout** (4 weeks) - Data Team + Engineering
+   - Instrument all product events
+   - Migrate existing dashboards
+   - Train team on new platform
+   - Launch by: March 15
+
+**Success metrics:**
+- **Time to value**: Analytics operational within 2 months (by March 15)
+- **Cost**: Stay within $20k implementation + $120k annual budget
+- **Adoption**: 50+ team members using platform within 30 days of launch
+- **Value delivery**: Reduce manual analytics time from 20 hours/week to <5 hours/week
+
+**Decision review:**
+- **6-month review** (Sept 2024): Validate cost and value delivered
+  - Key question: Are we getting value proportional to cost?
+  - Metrics: Usage stats, time savings, decisions influenced by data
+
+- **Annual review** (Jan 2025): Assess whether to continue, renegotiate, or reconsider build
+  - Key indicators: Usage growth trend, missing features impact, pricing changes
+
+**What would change our mind:**
+- If vendor quality degrades significantly (downtime, bugs, poor support)
+- If pricing increases >30% beyond projections
+- If we identify analytics as core competitive differentiator (requires custom innovation)
+- If regulatory requirements force on-premise solution
+
+---
+
+## 7. Appendix: Assumptions Log
+
+**Development estimates:**
+- Based on: 3 similar internal projects (API platform, reporting tool, data pipeline)
+- Historical variance: ±30% from initial estimate
+- Team composition: 2-3 senior engineers for 3-4 months
+- Scope: Event ingestion, storage, query engine, dashboarding UI
+
+**SaaS pricing:**
+- Based on: Vendor quotes for 100M events/month, 50 users
+- Confirmed with: 2 reference customers at similar scale
+- Growth assumption: 20% annual event growth (aligned with product roadmap)
+- User assumption: 20 power users (product, sales, exec) need full access
+
+**Opportunity cost:**
+- Based on: Engineering team would otherwise work on product features
+- Estimated value: Product features could drive $150k additional revenue
+- Source: Product roadmap prioritization (deferred features)
+
+**Time-to-value benefit:**
+- Based on: 6-month head start with SaaS (2 months vs. 8 months)
+- Estimated value: Better decisions sooner = avoided mistakes + seized opportunities
+- Conservative estimate: 50% of opportunity cost = $75k
+
+**Discount rate:**
+- Company cost of capital: 10%
+- Used to calculate present value of multi-year costs
+
+---
+
+## Self-Assessment (Rubric Scores)
+
+**Estimation Quality:** 4/5
+- Comprehensive estimation with ranges and probabilities
+- Justification provided for estimates with sources
+- Could improve: More rigorous data collection from reference customers
+
+**Probability Calibration:** 4/5
+- Probabilities justified with base rates (historical project performance)
+- Well-calibrated ranges
+- Could improve: External validation of probability estimates
+
+**Decision Analysis Rigor:** 5/5
+- Sound expected value calculation with NPV
+- Appropriate decision criteria
+- Multiple scenarios tested
+
+**Sensitivity Analysis:** 5/5
+- Comprehensive one-way sensitivity on key variables
+- Break-even analysis performed
+- Conditions that change conclusion clearly stated
+
+**Alternative Comparison:** 4/5
+- Three alternatives analyzed fairly
+- Could improve: Consider more creative alternatives (e.g., open-source + custom)
+
+**Assumption Transparency:** 5/5
+- All key assumptions stated explicitly with justification
+- Alternative assumptions tested in sensitivity analysis
+
+**Narrative Clarity:** 4/5
+- Clear structure and logical flow
+- Could improve: More compelling framing for exec audience
+
+**Audience Tailoring:** 4/5
+- Appropriate detail for executive audience
+- Could improve: Add one-page executive summary
+
+**Risk Acknowledgment:** 5/5
+- Comprehensive risk analysis with probabilities and mitigations
+- Downside scenarios quantified
+- "What would change our mind" conditions stated
+
+**Actionability:** 5/5
+- Clear recommendation with specific next steps
+- Owners and timeline defined
+- Success metrics and review cadence specified
+
+**Average Score:** 4.5/5 (Exceeds standard for medium-stakes decision)
+
+---
+
+**Analysis completed:** January 15, 2024
+**Analyst:** [Name]
+**Reviewed by:** CTO
+**Status:** Ready for executive decision