PaperclipOrg
Claude Agent Skill · by Deanpeters

Feature Investment Advisor

Install Feature Investment Advisor skill for Claude Code from deanpeters/product-manager-skills.

Works with Paperclip

How Feature Investment Advisor fits into a Paperclip company.

Feature Investment Advisor drops into any Paperclip agent that handles this kind of work. Assign it to a specialist inside a pre-configured PaperclipOrg company and the skill becomes available on every heartbeat — no prompt engineering, no tool wiring.

S
SaaS FactoryPaired

Pre-configured AI company — 18 agents, 18 skills, one-time purchase.

$27$59
Explore pack
Source file
SKILL.md639 lines
Expand
---name: feature-investment-advisordescription: Evaluate feature investments using revenue impact, cost structure, ROI, and strategy. Use when deciding whether a feature deserves investment.intent: >-  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.type: interactivebest_for:  - "Assessing whether a feature should be built now"  - "Comparing ROI and strategic value of feature ideas"  - "Pressure-testing roadmap requests with financial logic"scenarios:  - "Should we build SSO for mid-market customers this quarter?"  - "Evaluate whether an AI assistant feature is worth the investment"  - "Help me decide if this roadmap request has enough ROI to build"---  ## 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: 1. **Revenue Connection** — How does this feature impact revenue?   - Direct monetization (new tier, add-on, usage charges)   - Indirect monetization (retention, conversion, expansion enablement) 2. **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) 3. **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 4. **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`](../workshop-facilitation/SKILL.md) 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: 1. **Direct monetization (new revenue stream)** — We'll charge for this (new pricing tier, paid add-on, usage-based fee)2. **Retention improvement (reduce churn)** — Addresses key churn reason; keeps customers from leaving3. **Conversion improvement (trial-to-paid)** — Helps convert free/trial users to paid customers4. **Expansion enabler (upsell/cross-sell)** — Creates upsell path or drives usage-based expansion5. **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? 1. **Time-sensitive competitive threat** — Competitor launched this; we're losing deals2. **Limited budget/team capacity** — We can only build one major feature this quarter3. **Dependencies on other work** — Requires platform improvements or other features first4. **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:**1. Validate pricing/adoption assumptions with customer research2. Build MVP to test core value prop3. 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:1. Track adoption vs. projections2. Measure churn impact (target: reduce churn by ___%)3. 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:**1. **Reduce scope** — Can we build a simpler version at 50% cost?2. **Change monetization** — Can we charge more or differently?3. **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:**1. Run feature demand survey with 50+ customers2. Prototype and test willingness-to-pay3. 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:1. See the detailed math breakdown2. Compare this to another feature3. Adjust assumptions (pricing, adoption, cost)4. 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:**1. Survey customers: who would pay $10/user for time tracking?2. Build MVP with core time tracking only3. Beta with 50 customers before broad launch4. 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:**1. Interview churned customers to validate export was primary reason2. Build core export (CSV, API) first3. Measure churn rate change after launch (target: 5% → 4.5%)4. 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:**1. **Survey churned customers** — Is dark mode in top 5 churn reasons?2. **Test demand** — Ask trial users: "Would dark mode affect your decision to convert?"3. **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 ### Related Skills- `saas-revenue-growth-metrics` — Revenue, ARPU, churn, NRR metrics used in impact calculations- `saas-economics-efficiency-metrics` — ROI, payback, contribution margin calculations- `finance-metrics-quickref` — Quick lookup for formulas and benchmarks- `acquisition-channel-advisor` — Similar ROI framework for channel decisions- `finance-based-pricing-advisor` — Pricing impact analysis for monetization features ### External Frameworks- **RICE Prioritization** — Combines Reach, Impact, Confidence, Effort (this skill adds financial lens)- **Value vs. Effort Matrix** — This skill quantifies "value" financially- **Jobs-to-be-Done** — Understand customer problems before evaluating financial impact- **Opportunity Solution Tree (Teresa Torres)** — Map opportunities before calculating ROI ### Provenance- Adapted from `research/finance/Finance_For_PMs.Putting_It_Together_Synthesis.md` (Decision Framework #1)- Quiz scenarios from `research/finance/Finance for Product Managers.md`