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:

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 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 leaving
3. Conversion improvement (trial-to-paid) — Helps convert free/trial users to paid customers
4. Expansion enabler (upsell/cross-sell) — Creates upsell path or drives usage-based expansion
5. 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 deals
2. Limited budget/team capacity — We can only build one major feature this quarter
3. Dependencies on other work — Requires platform improvements or other features first
4. 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 research
2. Build MVP to test core value prop
3. 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. projections
2. 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+ customers
2. Prototype and test willingness-to-pay
3. 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 breakdown
2. Compare this to another feature
3. 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 only
3. Beta with 50 customers before broad launch
4. 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 reason
2. Build core export (CSV, API) first
3. 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