القائمة
Evaluate pricing changes using ARPU, conversion, churn risk, NRR, and payback. Use when deciding whether a pricing move should ship.
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npx skills add https://github.com/TuYv/ccpm --skill finance-based-pricing-advisorيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
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Use when auditing a paid ad account for incremental contribution, wasted spend, or measurement integrity before scaling; runs a typed 20-item ROAS profile with verified vetoes and a SHIP/FIX/BLOCK/UNDECIDED gate on own exported data. Not for campaign structure design — use campaign-architect; not for creative production — use ad-creative-builder. 付费广告账户审计/ROAS评分
Use when the user asks to "write ad copy", "generate RSA headlines", or "build ad creative at volume"; produces ad units — RSA headlines/descriptions, hooks, and an angle matrix — message-matched to the destination landing page. Not for scoring an ad account — use ad-account-auditor; not for the post-click page — use landing-optimizer; not for organic articles — use content-writer. 广告创意/广告文案/RSA标题
Use when the user asks to "design an A/B test", "set up a creative/landing test", "run an incrementality test", or "is this result statistically and practically material?"; produces a hypothesis, variant matrix, sample-size/duration/power plan, and a documented effect/uncertainty read from own exported results. It applies only a precommitted owner-approved action rule; the statistical helper never chooses a business action. Not for producing variants — use ad-creative-builder; not for reading back one shipped change — use paid-measurement-loop. 广告AB测试设计/实验设计/显著性判定/增效测试
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عرض SKILL.md
| 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 | ["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 |
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.
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%.
A systematic approach to evaluate pricing changes financially:
Revenue Impact — How does this change ARPU/ARPA?
Conversion Impact — How does this affect trial-to-paid or sales conversion?
Churn Risk — Will existing customers leave due to price change?
Expansion Impact — Does this create or block expansion opportunities?
CAC Payback Impact — Does pricing change affect unit economics?
Direct monetization changes:
Discount strategies:
Packaging changes:
Use this when:
Don't use this when:
Use workshop-facilitation as the default interaction protocol for this skill.
It defines:
Other (specify) when useful)This file defines the domain-specific assessment content. If there is a conflict, follow this file's domain logic.
This interactive skill asks up to 4 adaptive questions, offering 3-5 enumerated options at decision points.
Agent asks:
"Let's evaluate the financial impact of your pricing change. Please provide:
Current pricing:
Proposed pricing change:
Business context:
You can provide estimates if you don't have exact numbers."
Agent asks:
"What type of pricing change are you considering?
Choose a number, or describe your specific pricing change."
Based on selection, agent adapts questions:
Agent asks:
"Price increase details:
Who is affected?
When would this take effect?
Agent asks:
"Premium tier details:
Expected adoption:
Cannibalization risk:
Agent asks:
"Add-on details:
Expected adoption:
Agent asks:
"Usage pricing details:
Expected impact:
Expansion potential:
Agent asks:
"Discount details:
Trade-off:
Agent asks:
"Packaging change details:
Expected impact:
Agent asks:
"Now let's quantify the impact. Based on your pricing change, estimate:
Revenue impact:
Conversion impact:
Churn risk:
Expansion impact:
You can provide estimates. We'll model scenarios (conservative, base, optimistic)."
Agent asks:
"To assess whether this pricing change makes sense, I need your current baseline:
Current metrics:
Growth context:
Competitive context:
Agent synthesizes:
Agent offers 3-4 recommendations:
When:
Recommendation:
"Implement this pricing change — Strong financial case
Revenue Impact:
Churn Risk: Low
Conversion Impact:
CAC Payback Impact:
Why this works: [Specific reasoning based on numbers]
How to implement:
Expected timeline:
Success criteria:
When:
Recommendation:
"Test with a segment before broad rollout — Impact is uncertain
Why test:
Test design:
Cohort A (Control):
Cohort B (Test):
Duration: 60-90 days (need statistical significance)
Metrics to track:
Decision criteria:
Roll out broadly if:
Don't roll out if:
Expected timeline:
Risk: Medium. Test mitigates risk before broad rollout."
When:
Recommendation:
"Modify your approach — Original proposal has risks
Original Proposal:
Problem: [Specific issue: e.g., "20% price increase will likely cause 10% churn, wiping out revenue gains"]
Alternative Approach:
Option 1: Smaller price increase
Option 2: Grandfather existing, raise for new only
Option 3: Value-based pricing (charge more for high-value segments)
Recommended: [Specific option with reasoning]
Why this is better:
How to implement: [Specific steps for alternative approach]"
When:
Recommendation:
"Don't change pricing — Risks outweigh benefits
Why:
Problem: [Specific issue: e.g., "Churn-driven revenue loss exceeds price increase gains"]
What would need to change:
For price increase to work:
Alternative strategies:
Instead of raising prices:
When to revisit pricing:
Decision: Hold pricing for now, focus on [retention / expansion / acquisition efficiency]."
Agent offers:
"Want to see what-if scenarios?
Or ask any follow-up questions."
Agent can provide:
See examples/ folder for sample conversation flows. Mini examples below:
Scenario: 20% price increase for new customers only
Current state:
Proposed change:
Impact:
Recommendation: Implement. Net revenue impact +$12K/year with low risk.
Scenario: 30% price increase for all customers
Current state:
Proposed change:
Impact:
Net impact: +$75K - $9.75K = +$65K MRR (but accelerating churn problem)
Recommendation: Don't change. Fix retention first (reduce 5% churn), then raise prices.
Scenario: Add $500/month premium tier
Current state:
Proposed change:
Impact:
Recommendation: Implement. Creates expansion path, minimal cannibalization risk.
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.
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.
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.
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).
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.
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.
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.
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.
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.
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.
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 decisionsThese are OUTSIDE the scope of this skill but relevant for broader pricing work:
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 pricingresearch/finance/Finance_For_PMs.Putting_It_Together_Synthesis.md (Decision Framework #3)research/finance/Finance for Product Managers.md