finance-based-pricing-advisor

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:

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