feature-investment-advisor

SKILL.md

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

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