feature-investment-advisor

Purpose Guide product managers through evaluating whether to build a feature based on financial impact analysis. Use this to make data-driven prioritization decisions by assessing revenue connection (direct or indirect), cost structure (dev + COGS + OpEx), ROI calculation, and strategic value—then deliver actionable build/don't build recommendations with supporting math. This is not a generic prioritization framework—it's a financial lens for feature decisions that complements other prioritization methods (RICE, value vs. effort, user research). Use when financial impact is a key decision factor. Key Concepts The Feature Investment Framework A systematic approach to evaluate features financially: Revenue Connection — How does this feature impact revenue? Direct monetization (new tier, add-on, usage charges) Indirect monetization (retention, conversion, expansion enablement) Cost Structure — What does it cost to build and run? Development cost (one-time investment) COGS impact (ongoing infrastructure, processing) OpEx impact (ongoing support, maintenance) ROI Calculation — Is the return worth the investment? Direct monetization: Revenue impact / Development cost Retention features: LTV impact across customer base / Development cost Factor in gross margin, not just revenue Strategic Value — Non-financial value that might override pure ROI Competitive moat (prevents churn to competitor) Platform enabler (unlocks future features) Market positioning (needed for enterprise deals) Risk reduction (compliance, security) Anti-Patterns (What This Is NOT) Not feature scoring alone: Combines financial analysis with strategic judgment Not revenue-only thinking: Considers margins, costs, and ROI, not just top-line revenue Not ignoring retention: Indirect revenue impact (churn reduction) is equally valid Not building without validation: Assumes you've done discovery; this is the financial lens When to Use This Framework Use this when: Prioritizing between features with quantifiable revenue/retention impact Evaluating expensive features (>1 engineer-month of work) Making build/buy/partner decisions Defending feature prioritization to stakeholders or leadership Choosing between direct monetization (add-on) vs. indirect (retention) Don't use this when: Feature is table stakes (must-have for competitive parity) Impact is purely qualitative (brand, UX delight without measurable retention effect) You haven't validated the problem (do discovery first) Feature is < 1 week of work (just build it) Facilitation Source of Truth Use workshop-facilitation as the default interaction protocol for this skill. It defines: session heads-up + entry mode (Guided, Context dump, Best guess) one-question turns with plain-language prompts progress labels (for example, Context Qx/8 and Scoring Qx/5) interruption handling and pause/resume behavior numbered recommendations at decision points quick-select numbered response options for regular questions (include Other (specify) when useful) This file defines the domain-specific assessment content. If there is a conflict, follow this file's domain logic. Application This interactive skill asks up to 4 adaptive questions , offering 3-5 enumerated options at decision points. Step 0: Gather Context Agent asks: "Let's evaluate the financial impact of this feature investment. Please provide: Feature description: What's the feature? (1-2 sentences) Target customer segment (SMB, mid-market, enterprise, all) Current business context: Current MRR/ARR (or customer count if pre-revenue) Current ARPU/ARPA Current monthly churn rate Gross margin % Constraints: Development cost estimate (team size × time) Any ongoing COGS or OpEx implications? You can provide estimates if you don't have exact numbers." Step 1: Identify Revenue Connection Agent asks: "How does this feature impact revenue? Choose the option that best describes the revenue connection: Direct monetization (new revenue stream) — We'll charge for this (new pricing tier, paid add-on, usage-based fee) Retention improvement (reduce churn) — Addresses key churn reason; keeps customers from leaving Conversion improvement (trial-to-paid) — Helps convert free/trial users to paid customers Expansion enabler (upsell/cross-sell) — Creates upsell path or drives usage-based expansion No direct revenue impact — Table stakes, platform improvement, or strategic value only Choose a number, or describe a custom revenue connection." Based on selection, agent adapts: If 1 (Direct monetization): "What pricing are you considering?" "What % of customers do you expect to adopt this?" (conservative, base, optimistic) Calculate: Potential Monthly Revenue = Customer Base × Adoption Rate × Price If 2 (Retention improvement): "What % of churn does this feature address?" (e.g., "30% of churned customers cited this gap") "What churn reduction do you expect?" (e.g., "5% → 4% monthly churn") Calculate: LTV Impact = Increase in Customer Lifetime × Customer Base × ARPU × Margin If 3 (Conversion improvement): "Current trial-to-paid conversion rate?" "Expected conversion lift?" (e.g., "20% → 25% conversion") Calculate: Additional MRR = Trial Users × Conversion Lift × ARPU If 4 (Expansion enabler): "What expansion opportunity does this create?" (upsell tier, usage growth, add-on) "What % of customers will expand?" Calculate: Expansion MRR = Customer Base × Expansion Rate × ARPU Increase If 5 (No direct revenue impact): Skip to strategic value assessment Step 2: Assess Cost Structure Agent asks: "What's the cost structure for this feature? Development cost (one-time): Team size: ___ engineers Time estimate: ___ weeks/months Estimated dev cost: $ Ongoing costs (if any): COGS impact: $ /month (hosting, infrastructure, processing) OpEx impact: $ /month (support, maintenance) If no ongoing costs, enter $0." Agent calculates: One-time investment: Development cost Ongoing monthly cost: COGS + OpEx Contribution margin impact: (Revenue - COGS) / Revenue Agent flags: If COGS is >20% of projected revenue: "⚠️ This feature significantly dilutes margins" If ongoing costs are high relative to revenue: "⚠️ Consider if this is sustainable" Step 3: Evaluate Constraints and Timing Agent asks: "What constraints or timing considerations apply? Time-sensitive competitive threat — Competitor launched this; we're losing deals Limited budget/team capacity — We can only build one major feature this quarter Dependencies on other work — Requires platform improvements or other features first No major constraints — We have capacity and flexibility Choose a number, or describe your constraints." Based on selection: If 1 (Competitive threat): Strategic value increases (churn prevention) Urgency factor in recommendation If 2 (Limited capacity): Compare ROI against other features in backlog Recommend stack ranking If 3 (Dependencies): Flag dependency risk Suggest sequencing If 4 (No constraints): Proceed to recommendations Step 4: Deliver Recommendations Agent synthesizes: Revenue impact (from Step 1) Cost structure (from Step 2) Constraints (from Step 3) ROI calculation Strategic value assessment Agent offers 3-4 recommendations: Recommendation Pattern 1: Strong Financial Case When: ROI >3:1 (direct monetization) or LTV impact >10:1 (retention/expansion) Positive contribution margin No major red flags Recommendation: " Build now — Strong financial case Revenue Impact: [Direct/Indirect revenue impact calculation] Conservative estimate: $/month Optimistic estimate: $/month Cost: Development: $ Ongoing COGS/OpEx: $/month Net margin impact: % ROI: Year 1 ROI: :1 Payback period: ___ months Why this makes sense: [Specific reasoning based on numbers] Next steps: Validate pricing/adoption assumptions with customer research Build MVP to test core value prop Monitor [specific metric] to measure impact" Recommendation Pattern 2: Weak Financial Case, Build Anyway (Strategic) When: ROI <2:1 or marginal financial impact But high strategic value (competitive, platform, compliance) Recommendation: " Build for strategic reasons (financial case is marginal) Financial Reality: Revenue impact: $/month (modest) Development cost: $ ROI: :1 (below 3:1 threshold) Strategic Value: [Competitive moat / Platform enabler / Market requirement] Prevents churn to competitor X Required for enterprise segment (30% of pipeline) Recommendation: Build, but monitor closely: Track adoption vs. projections Measure churn impact (target: reduce churn by %) Re-evaluate after 6 months if adoption is low Risk: Opportunity cost—other features may have better ROI" Recommendation Pattern 3: Don't Build (Poor ROI) When: ROI <1:1 (direct monetization) or negative LTV impact Margin-diluting No compelling strategic value Recommendation: " Don't build — Financial case doesn't support investment Why: Revenue impact: $/month Development cost: $ ROI: :1 (below breakeven) Margin impact: Dilutes gross margin from % to % Alternative approaches: Reduce scope — Can we build a simpler version at 50% cost? Change monetization — Can we charge more or differently? Deprioritize — Focus on higher-ROI features like [alternative] What would need to change: If adoption rate increases from % to %, ROI becomes viable If we can reduce dev cost to $, payback becomes acceptable" Recommendation Pattern 4: Build Later / Need More Data When: Assumptions are highly uncertain Revenue impact depends on unvalidated hypotheses Medium strategic value Recommendation: " Build later — Validate assumptions first Current uncertainty: Adoption rate assumption: % (needs validation) Churn impact: ___% reduction (hypothesis, not proven) Pricing unknown What to validate: Run feature demand survey with 50+ customers Prototype and test willingness-to-pay Interview churned customers to confirm this addresses churn reason Decision criteria: If % of customers say they'd pay $ , build If churn interviews confirm this is top 3 reason, build Otherwise, deprioritize Timeline: Spend 2-4 weeks validating Re-evaluate with real data" Step 5: Allow Follow-Up Questions Agent offers: "Do you want to: See the detailed math breakdown Compare this to another feature Adjust assumptions (pricing, adoption, cost) Export recommendation summary Or, ask any follow-up questions." Agent can: Show detailed calculations Sensitivity analysis (what if adoption is 2x? 0.5x?) Compare multiple features side-by-side Export summary for stakeholders Examples See examples/ folder for sample conversation flows. Mini examples below: Example 1: Direct Monetization (Time Tracking Add-On) User: "We're considering building time tracking as a paid add-on." Agent: [Gathers context] User provides: Current: 1,000 customers, $500 ARPU, 80% gross margin Feature: Time tracking add-on, $10/user/month Dev cost: $100K (2 engineers × 3 months) Adoption estimate: 20% of customers Agent calculates: Customers with 10 users/account avg = 1,000 accounts × 10 users = 10,000 users Adoption: 20% of users = 2,000 users Revenue: 2,000 × $10 = $20K/month = $240K/year Margin: $240K × 80% = $192K gross profit/year ROI: $192K / $100K = 1.92:1 in year 1 Payback: $100K / $20K = 5 months Agent recommends: " Build now — Positive ROI with fast payback Revenue Impact: $240K/year (conservative 20% adoption) ROI: 1.92:1 in year 1, 3.8:1 in year 2 Payback: 5 months Why this works: Fast payback (5 months) means low risk 80% gross margin on add-on revenue 20% adoption is conservative (test willingness-to-pay) Next steps: Survey customers: who would pay $10/user for time tracking? Build MVP with core time tracking only Beta with 50 customers before broad launch Track adoption rate vs. 20% target" Example 2: Retention Feature (Data Export) User: "We're losing customers because they can't export their data easily. Should we build a comprehensive export feature?" Agent: [Gathers context] User provides: Current: $2M MRR, 500 customers, $4K ARPA, 5% monthly churn Feature: Full data export (CSV, API, scheduled exports) Dev cost: $150K (3 engineers × 2 months) Churn context: 30% of churned customers cited data export as a reason Agent calculates: Current annual churn: ~46% (5% monthly compounded) Customers churned annually: 500 × 46% = 230 customers Customers churned due to export issue: 230 × 30% = 69 customers MRR lost: 69 × $4K = $276K MRR lost/year = $3.3M ARR If feature reduces this by 50%: Churn prevented: 35 customers/year MRR saved: $140K/year LTV impact (assuming 24-month lifetime): $140K × 24 = $3.36M Agent calculates ROI: Investment: $150K LTV saved: $3.36M (conservative 50% reduction) ROI: $3.36M / $150K = 22.4:1 🚀 Agent recommends: " Build immediately — Exceptional ROI on retention Impact: Prevents ~35 customer churns/year Saves $140K MRR annually LTV impact: $3.36M over customer lifetime ROI: 22:1 (exceptional) Why this is a no-brainer: 30% of churn cites this gap (validated problem) Even if you only prevent 25% (not 50%), ROI is still 11:1 Retention improvements compound (reduces future churn too) Next steps: Interview churned customers to validate export was primary reason Build core export (CSV, API) first Measure churn rate change after launch (target: 5% → 4.5%) Add scheduled exports if adoption is high" Example 3: Poor ROI Feature (Dark Mode) User: "Users are requesting dark mode. Should we prioritize it?" Agent: [Gathers context] User provides: Current: $500K MRR, 2,000 customers, $250 ARPA, 3% monthly churn, 75% gross margin Feature: Full dark mode across product Dev cost: $80K (2 designers + 2 engineers × 1 month) Revenue connection: Indirect—may improve retention slightly Agent asks: "What evidence do you have that dark mode will reduce churn?" User: "Feature requests from 50 users. No data on churn impact." Agent calculates: 50 feature requests out of 2,000 customers = 2.5% of base No validated churn impact Dev cost: $80K Optimistic scenario: Prevents 5 churns/year (2.5% of base) MRR saved: 5 × $250 = $1,250/month = $15K/year LTV impact (24-month lifetime): $15K × 24 = $360K ROI: $360K / $80K = 4.5:1 (looks okay, but based on weak assumptions) Agent recommends: " Build later — Weak evidence for ROI Current case: Revenue impact: $15K/year (optimistic) Development cost: $80K ROI: 4.5:1 (assumes dark mode prevents churn) Problem: Only 50 users requested this (2.5% of base) No evidence dark mode reduces churn Churn interviews don't mention this as a top reason Alternative approach: Survey churned customers — Is dark mode in top 5 churn reasons? Test demand — Ask trial users: "Would dark mode affect your decision to convert?" Reduce scope — Build basic dark mode (20% cost) as experiment Better features to consider: If retention is priority: Interview churned customers, find top 3 churn drivers If revenue is priority: Focus on expansion features (upsell, add-ons) Decision criteria to build: If churn interviews show dark mode is top 3 reason → build If conversion research shows 10%+ impact → build Otherwise → deprioritize" Common Pitfalls Pitfall 1: Confusing Revenue with Profit Symptom: "This feature will generate $1M in revenue!" (ignoring $800K COGS) Consequence: $1M revenue at 20% margin is worth $200K profit, not $1M. Feature looks great until you factor in costs. Fix: Always calculate contribution margin. Use Revenue × Margin % , not just revenue. Pitfall 2: Ignoring Payback Period Symptom: "ROI is 5:1, let's build!" (but payback is 36 months and customers churn at 24 months) Consequence: You never recover the investment because customers leave before payback. Fix: Check payback period. Must be shorter than average customer lifetime. Pitfall 3: Overestimating Adoption Symptom: "100% of customers will use this paid add-on!" Consequence: Real adoption is 10-20%. Revenue projections are 5-10x too high. Fix: Use conservative adoption estimates (10-20% for add-ons). Validate with willingness-to-pay research. Pitfall 4: Building Without Validation Symptom: "We think this will reduce churn" (no customer interviews) Consequence: You build a feature that doesn't address real churn reasons. Churn stays flat. Fix: Interview churned customers first. Validate that this feature addresses top 3 churn reasons. Pitfall 5: Ignoring Opportunity Cost Symptom: "This feature has 2:1 ROI, let's build!" (other features have 10:1 ROI) Consequence: You build a mediocre feature while better options sit in the backlog. Fix: Compare ROI across features. Build highest-ROI features first (unless strategic value overrides). Pitfall 6: Strategic Value as Excuse Symptom: "ROI is terrible but it's strategic!" (no clear strategy) Consequence: "Strategic" becomes a catch-all for building low-value features. Fix: Define what "strategic" means (competitive moat, platform enabler, compliance). If it doesn't fit, it's not strategic. Pitfall 7: Margin Dilution Blindness Symptom: "This feature adds $500K revenue!" (but COGS is $400K) Consequence: Your gross margin drops from 80% to 60%. Feature destroys unit economics. Fix: Calculate contribution margin. If margin is <50%, reconsider or charge a premium. Pitfall 8: Celebrating Vanity Metrics Symptom: "This feature will increase engagement!" (but not revenue or retention) Consequence: You build features that feel good but don't impact business outcomes. Fix: Tie features to revenue or retention. Engagement is a leading indicator, not an outcome. Pitfall 9: Forgetting Time Value of Money Symptom: "This feature pays back in 5 years" Consequence: $1 in 5 years is worth ~$0.65 today (at 9% discount rate). ROI is overstated. Fix: For long payback periods (>24 months), use NPV (net present value) to discount future cash flows. Pitfall 10: Building Features for Loud Minorities Symptom: "50 customers requested this!" (out of 10,000) Consequence: You optimize for 0.5% of your base while ignoring the other 99.5%. Fix: Weight feature requests by revenue impact or customer segment. 10 enterprise customers > 100 SMB customers if enterprise is your strategy. References