finance-based-pricing-advisor skill | PlugMCP
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How do I install finance-based-pricing-advisor? Download skills/finance-based-pricing-advisor/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does finance-based-pricing-advisor live? The publisher keeps it at https://github.com/deanpeters/Product-Manager-Skills.
Which agents can use finance-based-pricing-advisor? Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository. SKILL.md About FAQ
Evaluate pricing changes using ARPU, conversion, churn risk, NRR, and payback. Use when deciding whether a pricing move should ship.
name: finance-based-pricing-advisor
argument-hint: "[pricing change to evaluate]"
description: Evaluate pricing changes using ARPU, conversion, churn risk, NRR, and payback. Use when deciding whether a pricing move should ship.
intent: >-
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.
type: interactive
best_for:
"Evaluating price increases, discounts, or new packaging"
"Estimating churn and conversion risk before a pricing change"
"Making a go/no-go call on monetization changes"
scenarios:
plugmcp Plug any tool into any agent. MCP servers for people who do not write servers. Gateway for volume, n8n for apps.
PlugMCP, 2026
"Should we raise prices 15% for new customers next quarter?"
"Evaluate a new premium tier for our SaaS product"
"Help me assess whether an annual discount will improve revenue"
theme: finance-metrics
estimated_time: "20-30 min"
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.
Input Works best with: The pricing change on the table — increase, new tier, add-on, or discount — and current pricing.
Also useful: Current ARPU/ARPA, conversion and churn baselines, NRR, and who's pushing for the change.
Anything supplied with the invocation itself — text after the skill name, a pasted context dump, or an appended ARGUMENTS: line — counts as answers already given. Use it and skip whatever it covers; don't re-ask.
Arriving empty-handed? That works too. The advisor opens by asking what change is proposed and what today's pricing looks like.
Example invocation: Evaluate raising our Pro plan from $49 to $59/seat; ARPU $52, monthly logo churn 1.8%, NRR 108%.
Key Concepts
The Pricing Impact Framework A systematic approach to evaluate pricing changes financially:
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
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
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?)
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
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)
Annual prepay discount (improve cash flow)
Volume discounts (larger deals)
Promotional pricing (temporary price reduction)
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 positioningNot packaging architecture: This evaluates one change, not redesigning all tiers
When to Use This Framework
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
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
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 "Let's evaluate the financial impact of your pricing change. Please provide:
Current ARPU or ARPA
Current pricing tiers (if applicable)
Current monthly churn rate
Current trial-to-paid conversion rate (if relevant)
What change are you considering? (price increase, new tier, add-on, etc.)
New pricing (if known)
Affected customer segment (all, new only, specific tier)
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 "What type of pricing change are you considering?
Price increase — Raise prices for new customers, existing customers, or bothNew premium tier — Add higher-priced tier with additional featuresPaid add-on — Monetize a new or existing feature separatelyUsage-based pricing — Charge for consumption (seats, API calls, storage, etc.)Discount strategy — Annual prepay discount, volume pricing, or promotional pricingPackaging 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):
Current price: $___
New price: $___
Increase: ___%
New customers only (grandfather existing)
All customers (existing + new)
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):
Current top tier price: $___
New premium tier price: $___
Key features in premium tier: [list]
What % of current customers might upgrade? ___%
What % of new customers might choose premium? ___%
Will premium tier cannibalize current top tier?"
If Option 3 (Paid Add-On):
Add-on name: ___
Price: $___ /month or /user
Currently free or new feature?
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):
Usage metric: (seats, API calls, storage, transactions, etc.)
Pricing: $___ per [unit]
Free tier or minimum? (e.g., first 1,000 API calls free)
Average customer usage: ___ units/month
Expected ARPU change: $current → $new
As customers grow usage, will ARPU increase?"
If Option 5 (Discount Strategy):
Discount type: (annual prepay, volume, promotional)
Discount amount: ___% off
Duration: (ongoing, limited time)
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): "Packaging change details:
What are you changing? (bundling, unbundling, pricing metric)
Current packaging: [describe]
New packaging: [describe]
ARPU change: $current → $new
Conversion change: ___% → ___%
Churn risk: (low, medium, high)"
Step 2: Assess Expected Impact "Now let's quantify the impact. Based on your pricing change, estimate:
Current ARPU: $___
Expected new ARPU: $___
ARPU lift: ___%
Current conversion rate: ___%
Expected new conversion rate: ___%
Conversion change: [increase / decrease / no change]
Current monthly churn: ___%
Expected churn after change: ___%
Churn risk: [low / medium / high]
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 "To assess whether this pricing change makes sense, I need your current baseline:
MRR or ARR: $___
Number of customers: ___
ARPU/ARPA: $___
Monthly churn rate: ___%
NRR: ___%
CAC: $___
LTV: $___
Current growth rate: ___% MoM or YoY
Target growth rate: ___%
Are you priced below, at, or above market?
Competitive pressure: (low, medium, high)"
Step 4: Deliver Recommendations
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
Net revenue impact clearly positive (>10% ARPU lift, <5% churn risk)
Minimal conversion impact
Strong value justification
"Implement this pricing change — Strong financial case
Current MRR: $___
ARPU lift: ___% ($current → $new)
Expected MRR increase: +$/month (+
Expected churn increase: ___% → % (+
Churn-driven MRR loss: -$___/month
Net MRR impact: +$___/month ✅
Current conversion: ___%
Expected conversion: % (
Impact on new customer acquisition: [minimal / manageable]
Current payback: ___ months
New payback: ___ months (faster due to higher ARPU)
Why this works:
[Specific reasoning based on numbers]
Grandfather existing customers (if raising prices)
Protect current base from churn
New pricing for new customers only
Communicate value
Emphasize features, outcomes, ROI
Justify price with value delivered
Monitor metrics (first 30-60 days)
Conversion rate (should stay within ___%)
Churn rate (should stay <___%)
Customer feedback
Month 1: +$___ MRR from new customers
Month 3: +$___ MRR (cumulative)
Month 6: +$___ MRR
Year 1: +$___ ARR
Conversion rate stays >___%
Churn rate stays <___%
NRR improves to >___%"
Recommendation Pattern 2: Test First (A/B Test)
Uncertain impact (wide range between conservative and optimistic)
Moderate churn or conversion risk
Large customer base (can test with subset)
"Test with a segment before broad rollout — Impact is uncertain
ARPU lift estimate: ___% (wide confidence interval)
Churn risk: Medium (___% → ___%)
Conversion impact: Uncertain (___% → ___% estimated)
Current pricing: $___
Size: ___% of new customers (or ___ customers)
New pricing: $___
Size: ___% of new customers (or ___ customers)
Duration: 60-90 days (need statistical significance)
Conversion rate (A vs. B)
ARPU (A vs. B)
30-day retention (A vs. B)
90-day churn (A vs. B)
NRR (A vs. B)
Conversion rate (B) >___% of control (A)
Churn rate (B) <___% higher than control
Net revenue (B) >___% higher than control
Conversion drops >___%
Churn increases >___%
Net revenue impact negative
Week 1-2: Launch test
Week 8-12: Enough data for statistical significance
Month 3: Decision to roll out or kill
Risk: Medium. Test mitigates risk before broad rollout."
Recommendation Pattern 3: Modify Approach
Original proposal has significant risk
Better alternative exists
Need to adjust pricing change to improve outcomes
"Modify your approach — Original proposal has risks
[Price increase / New tier / Add-on / etc.]
Expected ARPU lift: ___%
Churn risk: High (___% → ___%)
Net revenue impact: Uncertain or negative
Problem:
[Specific issue: e.g., "20% price increase will likely cause 10% churn, wiping out revenue gains"]
Option 1: Smaller price increase
Instead of ___% increase, try ___%
Lower churn risk (___% vs. ___%)
Still positive net revenue: +$___/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]
Lower churn risk
Comparable revenue upside
Easier to communicate
How to implement:
[Specific steps for alternative approach]"
Recommendation Pattern 4: Don't Change Pricing
Net revenue impact negative or marginal
High churn risk without offsetting gains
Competitive or strategic reasons to hold pricing
"Don't change pricing — Risks outweigh benefits
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
Instead of raising prices:
Improve retention — Reduce churn from ___% to ___% (same revenue impact as price increase, lower risk)Expand within base — Increase NRR from ___% to ___% via upsellsReduce CAC — More efficient acquisition (better than 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) "Want to see what-if scenarios?
Optimistic case — Higher ARPU lift, lower churnPessimistic case — Lower ARPU lift, higher churnBreakeven analysis — What churn rate makes this neutral? Or ask any follow-up questions."
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
ARPU: $100/month
Customers: 1,000
MRR: $100K
Churn: 3%/month
New customers/month: 50
New customer pricing: $120/month (+20%)
Existing customers: Grandfathered at $100
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
ARPU: $50/month
Customers: 5,000
MRR: $250K
Churn: 5%/month (already high)
All customers: $65/month (+30%)
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
Top tier: $200/month (500 customers)
ARPA: $200
New tier: $500/month with advanced features
Expected adoption: 10% of current top tier (50 customers)
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 analysissaas-economics-efficiency-metrics — CAC payback impact of pricing changesfinance-metrics-quickref — Quick lookup for pricing-related formulasfeature-investment-advisor — Evaluates whether to build features that enable pricing changesbusiness-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 costVan Westendorp Price Sensitivity — WTP research methodologyConjoint Analysis — Feature-to-price trade-off researchGood-Better-Best Packaging — Tier architecture designPrice Anchoring & Decoy Pricing — Psychological pricing tacticsPatrick 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 valuewillingness-to-pay-research — WTP research methodspackaging-architecture-advisor — Tier and bundle designpricing-psychology-guide — Anchoring, decoys, framingmonetization-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
How do I install finance-based-pricing-advisor? Download skills/finance-based-pricing-advisor/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does finance-based-pricing-advisor live? The publisher keeps it at https://github.com/deanpeters/Product-Manager-Skills.
Which agents can use finance-based-pricing-advisor? Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.