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finance-based-pricing-advisor

📁 deanpeters/product-manager-skills 📅 1 day ago
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npx skills add https://github.com/deanpeters/product-manager-skills --skill finance-based-pricing-advisor

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Skill 文档

Purpose

Evaluate the financial impact of pricing changes (price increases, new tiers, add-ons, discounts) using ARPU/ARPA analysis, conversion impact, churn risk, NRR effects, and CAC payback implications. Use this to make data-driven go/no-go decisions on proposed pricing changes with supporting math and risk assessment.

What this is: Financial impact evaluation for pricing decisions you’re already considering.

What this is NOT: Comprehensive pricing strategy design, value-based pricing frameworks, willingness-to-pay research, competitive positioning, psychological pricing, packaging architecture, or monetization model selection. For those topics, see the future pricing-strategy-suite skills.

This skill assumes you have a specific pricing change in mind and need to evaluate its financial viability.

Key Concepts

The Pricing Impact Framework

A systematic approach to evaluate pricing changes financially:

  1. Revenue Impact — How does this change ARPU/ARPA?

    • Direct revenue lift from price increase
    • Revenue loss from reduced conversion or increased churn
    • Net revenue impact

  2. Conversion Impact — How does this affect trial-to-paid or sales conversion?

    • Higher prices may reduce conversion rate
    • Better packaging may improve conversion
    • Test assumptions

  3. Churn Risk — Will existing customers leave due to price change?

    • Grandfathering strategy (protect existing customers)
    • Churn risk by segment (SMB vs. enterprise)
    • Churn elasticity (how sensitive are customers to price?)

  4. Expansion Impact — Does this create or block expansion opportunities?

    • New premium tier = upsell path
    • Usage-based pricing = expansion as customers grow
    • Add-ons = cross-sell opportunities

  5. CAC Payback Impact — Does pricing change affect unit economics?

    • Higher ARPU = faster payback
    • Lower conversion = higher effective CAC
    • Net effect on LTV:CAC ratio

Pricing Change Types

Direct monetization changes:

  • Price increase (raise prices for all customers or new customers only)
  • New premium tier (create upsell path)
  • Paid add-on (monetize previously free feature)
  • Usage-based pricing (charge for consumption)

Discount strategies:

  • Annual prepay discount (improve cash flow)
  • Volume discounts (larger deals)
  • Promotional pricing (temporary price reduction)

Packaging changes:

  • Feature bundling (combine features into tiers)
  • Unbundling (separate features into add-ons)
  • Pricing metric change (seats → usage, or vice versa)

Anti-Patterns (What This Is NOT)

  • Not value-based pricing: This evaluates a proposed change, not “what should we charge?”
  • Not WTP research: This analyzes impact, not “what will customers pay?”
  • Not competitive positioning: This is financial analysis, not market positioning
  • Not packaging architecture: This evaluates one change, not redesigning all tiers

When to Use This Framework

Use this when:

  • You have a specific pricing change to evaluate (e.g., “Should we raise prices 20%?”)
  • You need to quantify revenue, churn, and conversion trade-offs
  • You’re deciding between pricing change options (test A vs. B)
  • You need to present pricing change impact to leadership or board

Don’t use this when:

  • You’re designing pricing strategy from scratch (use value-based pricing frameworks)
  • You haven’t validated willingness-to-pay (do customer research first)
  • You don’t have baseline metrics (ARPU, churn, conversion rates)
  • Change is too small to matter (<5% price change, <10% of customers affected)

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 your pricing change. Please provide:

Current pricing:

  • Current ARPU or ARPA
  • Current pricing tiers (if applicable)
  • Current monthly churn rate
  • Current trial-to-paid conversion rate (if relevant)

Proposed pricing change:

  • What change are you considering? (price increase, new tier, add-on, etc.)
  • New pricing (if known)
  • Affected customer segment (all, new only, specific tier)

Business context:

  • Total customers (or MRR/ARR)
  • CAC (to assess payback impact)
  • NRR (to assess expansion context)

You can provide estimates if you don’t have exact numbers.”

Step 1: Identify Pricing Change Type

Agent asks:

“What type of pricing change are you considering?

  1. Price increase — Raise prices for new customers, existing customers, or both
  2. New premium tier — Add higher-priced tier with additional features
  3. Paid add-on — Monetize a new or existing feature separately
  4. Usage-based pricing — Charge for consumption (seats, API calls, storage, etc.)
  5. Discount strategy — Annual prepay discount, volume pricing, or promotional pricing
  6. Packaging change — Rebundle features, change pricing metric, or tier restructure

Choose a number, or describe your specific pricing change.”

Based on selection, agent adapts questions:

If Option 1 (Price Increase):

Agent asks:

“Price increase details:

  • Current price: $___
  • New price: $___
  • Increase: ___%

Who is affected?

  1. New customers only (grandfather existing)
  2. All customers (existing + new)
  3. Specific segment (e.g., SMB only, new plan only)

When would this take effect?

  • Immediately
  • Next billing cycle
  • Gradual rollout (test first)”

If Option 2 (New Premium Tier):

Agent asks:

“Premium tier details:

  • Current top tier price: $___
  • New premium tier price: $___
  • Key features in premium tier: [list]

Expected adoption:

  • What % of current customers might upgrade? ___%
  • What % of new customers might choose premium? ___%

Cannibalization risk:

  • Will premium tier cannibalize current top tier?”

If Option 3 (Paid Add-On):

Agent asks:

“Add-on details:

  • Add-on name: ___
  • Price: $___ /month or /user
  • Currently free or new feature?

Expected adoption:

  • What % of customers would pay for this? ___%
  • Is this feature currently used (if free)?
  • Will making it paid hurt retention?”

If Option 4 (Usage-Based Pricing):

Agent asks:

“Usage pricing details:

  • Usage metric: (seats, API calls, storage, transactions, etc.)
  • Pricing: $___ per [unit]
  • Free tier or minimum? (e.g., first 1,000 API calls free)

Expected impact:

  • Average customer usage: ___ units/month
  • Expected ARPU change: $current → $new

Expansion potential:

  • As customers grow usage, will ARPU increase?”

If Option 5 (Discount Strategy):

Agent asks:

“Discount details:

  • Discount type: (annual prepay, volume, promotional)
  • Discount amount: ___% off
  • Duration: (ongoing, limited time)

Trade-off:

  • Lower price vs. improved cash flow (annual prepay)
  • Lower price vs. larger deal size (volume)
  • Lower price vs. urgency (promotional)”

If Option 6 (Packaging Change):

Agent asks:

“Packaging change details:

  • What are you changing? (bundling, unbundling, pricing metric)
  • Current packaging: [describe]
  • New packaging: [describe]

Expected impact:

  • ARPU change: $current → $new
  • Conversion change: ___% → ___%
  • Churn risk: (low, medium, high)”

Step 2: Assess Expected Impact

Agent asks:

“Now let’s quantify the impact. Based on your pricing change, estimate:

Revenue impact:

  • Current ARPU: $___
  • Expected new ARPU: $___
  • ARPU lift: ___%

Conversion impact:

  • Current conversion rate: ___%
  • Expected new conversion rate: ___%
  • Conversion change: [increase / decrease / no change]

Churn risk:

  • Current monthly churn: ___%
  • Expected churn after change: ___%
  • Churn risk: [low / medium / high]

Expansion impact:

  • Does this create expansion opportunities? (new tier to upgrade to, usage growth)
  • Expected NRR change: ___% → ___%

You can provide estimates. We’ll model scenarios (conservative, base, optimistic).”

Step 3: Evaluate Current State

Agent asks:

“To assess whether this pricing change makes sense, I need your current baseline:

Current metrics:

  • MRR or ARR: $___
  • Number of customers: ___
  • ARPU/ARPA: $___
  • Monthly churn rate: ___%
  • NRR: ___%
  • CAC: $___
  • LTV: $___

Growth context:

  • Current growth rate: ___% MoM or YoY
  • Target growth rate: ___%

Competitive context:

  • Are you priced below, at, or above market?
  • Competitive pressure: (low, medium, high)”

Step 4: Deliver Recommendations

Agent synthesizes:

  • Revenue impact (ARPU lift × customer base)
  • Conversion impact (new customers affected)
  • Churn impact (existing customers affected)
  • Net revenue impact
  • CAC payback impact
  • Risk assessment

Agent offers 3-4 recommendations:

Recommendation Pattern 1: Implement Broadly

When:

  • Net revenue impact clearly positive (>10% ARPU lift, <5% churn risk)
  • Minimal conversion impact
  • Strong value justification

Recommendation:

“Implement this pricing change — Strong financial case

Revenue Impact:

  • Current MRR: $___
  • ARPU lift: ___% ($current → $new)
  • Expected MRR increase: +$/month (+%)

Churn Risk: Low

  • Expected churn increase: ___% → % (+% points)
  • Churn-driven MRR loss: -$___/month
  • Net MRR impact: +$___/month ✅

Conversion Impact:

  • Current conversion: ___%
  • Expected conversion: % (% change)
  • Impact on new customer acquisition: [minimal / manageable]

CAC Payback Impact:

  • Current payback: ___ months
  • New payback: ___ months (faster due to higher ARPU)

Why this works: [Specific reasoning based on numbers]

How to implement:

  1. Grandfather existing customers (if raising prices)
    • Protect current base from churn
    • New pricing for new customers only
  2. Communicate value
    • Emphasize features, outcomes, ROI
    • Justify price with value delivered
  3. Monitor metrics (first 30-60 days)
    • Conversion rate (should stay within ___%)
    • Churn rate (should stay <___%)
    • Customer feedback

Expected timeline:

  • Month 1: +$___ MRR from new customers
  • Month 3: +$___ MRR (cumulative)
  • Month 6: +$___ MRR
  • Year 1: +$___ ARR

Success criteria:

  • Conversion rate stays >___%
  • Churn rate stays <___%
  • NRR improves to >___%”

Recommendation Pattern 2: Test First (A/B Test)

When:

  • Uncertain impact (wide range between conservative and optimistic)
  • Moderate churn or conversion risk
  • Large customer base (can test with subset)

Recommendation:

“Test with a segment before broad rollout — Impact is uncertain

Why test:

  • ARPU lift estimate: ___% (wide confidence interval)
  • Churn risk: Medium (___% → ___%)
  • Conversion impact: Uncertain (___% → ___% estimated)

Test design:

Cohort A (Control):

  • Current pricing: $___
  • Size: ___% of new customers (or ___ customers)

Cohort B (Test):

  • New pricing: $___
  • Size: ___% of new customers (or ___ customers)

Duration: 60-90 days (need statistical significance)

Metrics to track:

  • Conversion rate (A vs. B)
  • ARPU (A vs. B)
  • 30-day retention (A vs. B)
  • 90-day churn (A vs. B)
  • NRR (A vs. B)

Decision criteria:

Roll out broadly if:

  • Conversion rate (B) >___% of control (A)
  • Churn rate (B) <___% higher than control
  • Net revenue (B) >___% higher than control

Don’t roll out if:

  • Conversion drops >___%
  • Churn increases >___%
  • Net revenue impact negative

Expected timeline:

  • Week 1-2: Launch test
  • Week 8-12: Enough data for statistical significance
  • Month 3: Decision to roll out or kill

Risk: Medium. Test mitigates risk before broad rollout.”

Recommendation Pattern 3: Modify Approach

When:

  • Original proposal has significant risk
  • Better alternative exists
  • Need to adjust pricing change to improve outcomes

Recommendation:

“Modify your approach — Original proposal has risks

Original Proposal:

  • [Price increase / New tier / Add-on / etc.]
  • Expected ARPU lift: ___%
  • Churn risk: High (___% → ___%)
  • Net revenue impact: Uncertain or negative

Problem: [Specific issue: e.g., “20% price increase will likely cause 10% churn, wiping out revenue gains”]

Alternative Approach:

Option 1: Smaller price increase

  • Instead of ___% increase, try ___%
  • Lower churn risk (___% vs. ___%)
  • Still positive net revenue: +$___/month

Option 2: Grandfather existing, raise for new only

  • Protect current base (zero churn risk)
  • Higher prices for new customers only
  • Gradual ARPU improvement over time

Option 3: Value-based pricing (charge more for high-value segments)

  • Keep SMB pricing flat
  • Raise enterprise pricing ___%
  • Lower churn risk (enterprise is stickier)

Recommended: [Specific option with reasoning]

Why this is better:

  • Lower churn risk
  • Comparable revenue upside
  • Easier to communicate

How to implement: [Specific steps for alternative approach]”

Recommendation Pattern 4: Don’t Change Pricing

When:

  • Net revenue impact negative or marginal
  • High churn risk without offsetting gains
  • Competitive or strategic reasons to hold pricing

Recommendation:

“Don’t change pricing — Risks outweigh benefits

Why:

  • Expected revenue lift: +$/month (%)
  • Expected churn impact: -$/month (%)
  • Net revenue impact: -$___/month 🚨 or marginal

Problem: [Specific issue: e.g., “Churn-driven revenue loss exceeds price increase gains”]

What would need to change:

For price increase to work:

  • Churn rate must stay below ___% (currently ___%)
  • OR conversion rate must stay above ___% (currently ___%)
  • OR you need to reduce CAC to offset lower conversion

Alternative strategies:

Instead of raising prices:

  1. Improve retention — Reduce churn from ___% to ___% (same revenue impact as price increase, lower risk)
  2. Expand within base — Increase NRR from ___% to ___% via upsells
  3. Reduce CAC — More efficient acquisition (better than pricing)

When to revisit pricing:

  • After improving retention (churn <___%)
  • After validating willingness-to-pay (WTP research)
  • After competitive landscape changes

Decision: Hold pricing for now, focus on [retention / expansion / acquisition efficiency].”

Step 5: Sensitivity Analysis (Optional)

Agent offers:

“Want to see what-if scenarios?

  1. Optimistic case — Higher ARPU lift, lower churn
  2. Pessimistic case — Lower ARPU lift, higher churn
  3. Breakeven analysis — What churn rate makes this neutral?

Or ask any follow-up questions.”

Agent can provide:

  • Scenario modeling (optimistic/pessimistic/breakeven)
  • Sensitivity tables (if churn is X%, revenue impact is Y)
  • Comparison to alternative pricing strategies

Examples

See examples/ folder for sample conversation flows. Mini examples below:

Example 1: Price Increase (Good Case)

Scenario: 20% price increase for new customers only

Current state:

  • ARPU: $100/month
  • Customers: 1,000
  • MRR: $100K
  • Churn: 3%/month
  • New customers/month: 50

Proposed change:

  • New customer pricing: $120/month (+20%)
  • Existing customers: Grandfathered at $100

Impact:

  • New customer ARPU: $120 (+20%)
  • Churn risk: Low (existing protected)
  • Conversion impact: Minimal (<5% drop estimated)

Recommendation: Implement. Net revenue impact +$12K/year with low risk.

Example 2: Price Increase (Risky)

Scenario: 30% price increase for all customers

Current state:

  • ARPU: $50/month
  • Customers: 5,000
  • MRR: $250K
  • Churn: 5%/month (already high)

Proposed change:

  • All customers: $65/month (+30%)

Impact:

  • ARPU lift: +30% = +$75K MRR
  • Churn risk: High (5% → 8% estimated)
  • Churn-driven loss: 3% × 5,000 × $65 = -$9.75K MRR/month

Net impact: +$75K – $9.75K = +$65K MRR (but accelerating churn problem)

Recommendation: Don’t change. Fix retention first (reduce 5% churn), then raise prices.

Example 3: New Premium Tier

Scenario: Add $500/month premium tier

Current state:

  • Top tier: $200/month (500 customers)
  • ARPA: $200

Proposed change:

  • New tier: $500/month with advanced features
  • Expected adoption: 10% of current top tier (50 customers)

Impact:

  • Upsell revenue: 50 × ($500 – $200) = +$15K MRR
  • Cannibalization risk: Low (features justify premium)
  • NRR impact: Increases from 105% to 110%

Recommendation: Implement. Creates expansion path, minimal cannibalization risk.

Common Pitfalls

Pitfall 1: Ignoring Churn Impact

Symptom: “We’ll raise prices 30% and make $X more!” (no churn modeling)

Consequence: Churn wipes out revenue gains. Net impact negative.

Fix: Model churn scenarios (conservative, base, optimistic). Factor churn-driven revenue loss into net impact.

Pitfall 2: Not Grandfathering Existing Customers

Symptom: “We’re raising prices for everyone effective immediately”

Consequence: Massive churn spike from existing customers who feel betrayed.

Fix: Grandfather existing customers. Raise prices for new customers only.

Pitfall 3: Testing Without Statistical Power

Symptom: “We tested on 10 customers and it worked!”

Consequence: 10 customers isn’t statistically significant. Results are noise.

Fix: Test with large enough sample (100+ customers per cohort) for 60-90 days.

Pitfall 4: Pricing Changes Without Value Justification

Symptom: “We’re raising prices because we need more revenue”

Consequence: Customers see price increase without corresponding value increase. Churn.

Fix: Tie price increases to value improvements (new features, better support, outcomes delivered).

Pitfall 5: Ignoring CAC Payback Impact

Symptom: “Higher ARPU is always better!”

Consequence: If conversion drops 30%, effective CAC increases dramatically. Payback period explodes.

Fix: Calculate CAC payback impact. Higher ARPU with lower conversion might make payback worse, not better.

Pitfall 6: Annual Discounts That Hurt Margin

Symptom: “30% discount for annual prepay!” (improves cash but destroys LTV)

Consequence: Customers lock in low prices for a year. Revenue per customer decreases.

Fix: Limit annual discounts to 10-15%. Balance cash flow improvement with LTV protection.

Pitfall 7: Copycat Pricing (Competitor-Based)

Symptom: “Competitor raised prices, so should we”

Consequence: Your customers, value prop, and cost structure are different. What works for them may not work for you.

Fix: Use competitors as data points, not decisions. Make pricing decisions based on your unit economics.

Pitfall 8: Premature Optimization

Symptom: “Let’s A/B test 47 different price points!”

Consequence: Analysis paralysis. Spending months on 5% pricing optimizations while missing 50% growth opportunities elsewhere.

Fix: Big pricing changes (tiers, packaging, add-ons) matter more than micro-optimizations. Start there.

Pitfall 9: Forgetting Expansion Revenue

Symptom: “We’re maximizing ARPU at acquisition”

Consequence: High upfront pricing prevents landing customers. Miss expansion opportunities.

Fix: Consider “land and expand” strategy. Lower entry price, higher expansion revenue via upsells.

Pitfall 10: No Pricing Change Communication Plan

Symptom: “We’re raising prices next month” (no customer communication)

Consequence: Surprised customers churn. Poor reviews. Reputation damage.

Fix: Communicate pricing changes 30-60 days in advance. Emphasize value, not just price.

References

Related Skills

  • saas-revenue-growth-metrics — ARPU, ARPA, churn, NRR metrics used in pricing analysis
  • saas-economics-efficiency-metrics — CAC payback impact of pricing changes
  • finance-metrics-quickref — Quick lookup for pricing-related formulas
  • feature-investment-advisor — Evaluates whether to build features that enable pricing changes
  • business-health-diagnostic — Broader business context for pricing decisions

External Frameworks (Comprehensive Pricing Strategy)

These are OUTSIDE the scope of this skill but relevant for broader pricing work:

  • Value-Based Pricing — Price based on value delivered, not cost
  • Van Westendorp Price Sensitivity — WTP research methodology
  • Conjoint Analysis — Feature-to-price trade-off research
  • Good-Better-Best Packaging — Tier architecture design
  • Price Anchoring & Decoy Pricing — Psychological pricing tactics
  • Patrick Campbell (ProfitWell): Pricing research and benchmarks

Future Skills (Comprehensive Pricing)

For topics NOT covered here, see future pricing-strategy-suite:

  • value-based-pricing-framework — How to price based on value
  • willingness-to-pay-research — WTP research methods
  • packaging-architecture-advisor — Tier and bundle design
  • pricing-psychology-guide — Anchoring, decoys, framing
  • monetization-model-advisor — Seat-based vs. usage vs. outcome pricing

Provenance

  • Adapted from research/finance/Finance_For_PMs.Putting_It_Together_Synthesis.md (Decision Framework #3)
  • Pricing scenarios from research/finance/Finance for Product Managers.md
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