pricing-teardown

name	pricing-teardown
description	Run a competitive pricing teardown and produce a pricing recommendation. Pulls 5-10 competitor pricing pages, normalizes to a common axis, identifies packaging anti-patterns, and runs a Van Westendorp sanity check on the user's own price.
tier	core
category	strategy
rr_companion	none
needs_mcp	[]
license	MIT
version	1.0.0
maintainer	jarektkaczyk
created-by	jarektkaczyk

Pricing Teardown

Pricing is the highest-leverage growth lever -- a 1% price increase typically delivers more profit than a 1% volume increase or 1% cost decrease (Marn & Rosiello, HBR 2003). But pricing is also the most under-instrumented decision in early-stage SaaS. This skill replaces gut with structured analysis.

When to use

• New plan launch or repackaging
• Conversion is high in trial but low at paywall
• High discount-request rate from sales
• Competitor moved (raised, lowered, repackaged) and team wants a response
• Annual pricing review
• Pre-Series A: founders priced based on what felt comfortable, not value

When NOT to use

• You haven't defined your ICP yet -- pricing without an ICP produces a number for no one; run icp-definer first
• You want to design a landing page -- pricing design is output, not input; lock the numbers first
• The problem is positioning, not price -- if customers don't understand the value, changing the number won't fix conversion; run positioning-canvas first

Use this instead

• positioning-canvas -- if the pricing problem is actually a category/value-framing problem
• icp-definer -- if you don't know which segment to price for
• gtm-motion-picker -- if the question is "should we have a sales tier" rather than "what should our sales tier cost"

Required inputs

1. Current pricing -- every plan, tier, add-on. Public + non-public/enterprise rates.
2. Top 5 competitors -- direct, indirect, and "do nothing" alternative
3. Customer mix -- % revenue per plan, ARPU per plan, plan-level churn if available
4. Sales pricing data -- quote-to-close ratio, average discount %, top objections
5. Value metric candidates -- what scales with customer value? (seats, API calls, contacts, revenue processed, GB stored, campaigns sent...)

Process

Step 1: Pull competitor pricing (from official sources only)

Hit the vendor's own pricing page -- not aggregator sites or blog comparisons. Capture:

• Tier names + prices + billing cadence (monthly/annual + discount)
• Value metric (per seat, per X, flat)
• Tier limits (the gates that force upgrades)
• Add-ons and overage charges
• Free tier shape (forever-free vs trial vs no free)
• "Contact us" tier (signal of enterprise motion)

If a competitor hides pricing entirely, note it -- strategic signal (sales-led + custom pricing).

Step 2: Normalize to a common axis

Build a comparison table where every competitor is normalized to the same value metric. If the market mostly prices per seat, convert your per-API-call pricing to "implied per seat" using customer averages. Pricing isn't comparable if units differ.

Columns: vendor, tier, price/[unit], what's included, upgrade gate, free-tier shape.

Step 3: Identify anti-patterns

Anti-pattern	Symptom	Fix
Value metric does not equal value	Per-seat pricing for a product where value scales with usage	Switch metric to what scales with value
Too many tiers (>4)	Decision paralysis, low-tier defaulting	Collapse to 3 tiers (Good / Better / Best)
No anchor tier	Mid-tier feels expensive	Add a deliberately-overpriced top tier to anchor mid-tier as "the reasonable choice"
Feature gates on table-stakes	Frustration, support tickets, churn	Move table-stakes to the lowest paid tier
Free tier with no upgrade path	Free users never convert	Add usage-based gate (volume, seats, time) that forces decision
Round numbers ($99, $999)	Leaves money on the table	Test $97, $129
Annual >= 20% off without commitment	High refund/churn risk	Cap annual discount at 15-20% OR require non-refundable commitment
No mid-market tier	Drop-off between SMB and enterprise	Add mid-tier with sales-assisted onboarding

Step 4: Value-metric audit

Ask: what does the customer get more of, the more they pay you? That should be the value metric. Common ones:

• Per seat -- value scales with team size (Slack, Notion, GitHub)
• Per usage -- value scales with volume processed (Stripe, Twilio, OpenAI)
• Per outcome -- value scales with results delivered (Intercom resolutions, lead-gen tools paid per qualified lead)
• Per asset under management -- value scales with what's protected/stored/served (Auth0 MAU, S3 GB)
• Flat -- only when value is binary (either you have access or you don't)

A misaligned value metric is the #1 fixable pricing error in SaaS.

Step 5: Van Westendorp Price Sensitivity Meter

If the user has access to customers, run this 4-question survey (n=50+ for signal):

1. At what price would you consider [product] too expensive and not buy?
2. At what price is it expensive but you'd still consider it?
3. At what price is it a bargain?
4. At what price is it so cheap you'd doubt the quality?

Plot cumulative curves. Intersection of "too expensive" and "too cheap" = Optimal Price Point. Intersection of "expensive" and "bargain" = Indifference Price Point (median customer's expected price).

If running the survey isn't feasible, use proxies:

• Discount-request frequency -- high = priced over indifference point
• Conversion rate at price wall -- low = priced over too-expensive point
• Feature-request patterns ("I'd pay more if you added X") = headroom signal

Step 6: Recommendation

Output one of these decisions:

• Hold -- price is right, fix something else (usually packaging or positioning)
• Raise -- gap below indifference point, anti-patterns absent -- typically 10-30% on new customers, grandfather existing
• Restructure -- current price fine, tiers/value-metric wrong
• Reposition -- pricing reflects wrong category -- see positioning-canvas first, then come back

Never recommend "lower" without strong evidence -- lowering price almost always destroys margin without buying volume in B2B SaaS.

Output format

PRICING TEARDOWN: [Product]
Date: [YYYY-MM-DD]

1. CURRENT STATE
   - Plans: [list]
   - Value metric: [current]
   - ARPU: $[X]
   - Plan mix: [%/plan]

2. COMPETITOR LANDSCAPE (normalized table)
   | Vendor | Tier | Price/[unit] | Free tier | Notable gate |

3. ANTI-PATTERNS DETECTED
   - [Pattern]: [evidence] -> [fix]

4. VALUE METRIC AUDIT
   - Current: [X]
   - Should be: [Y] (because [reason]) OR: current is correct

5. PRICE POINT ANALYSIS
   - Estimated indifference price: $[X] (basis: [survey/proxy])
   - Estimated optimal price: $[X]
   - Gap vs current: [+/- %]

6. RECOMMENDATION
   - Action: [Hold / Raise / Restructure / Reposition]
   - Specifics: [exact change]
   - Expected revenue impact: [+ X% on new MRR within Y months, basis: ...]
   - Risks: [what could go wrong]
   - Rollout: [grandfather existing? A/B test? sales script changes?]

7. WHAT WE DON'T KNOW
   [Honest list of data gaps that would sharpen the recommendation]

Common failure modes

• Cost-plus pricing -- pricing based on what it costs to serve. In SaaS, marginal cost is near zero; price on value, not cost.
• Competitor mirror -- copying a competitor's price without copying their cost structure or positioning is suicide.
• Discounting as default -- every discount is a permanent price cut for that segment. Use sparingly with rationale (annual commitment, multi-year, volume).
• Hidden pricing as cleverness -- works for true enterprise sales-led, fails everywhere else. Forces every prospect into a sales call; if you can't close on a call, the price is wrong.
• Pricing in isolation from packaging -- the bundle matters as much as the number. Don't change one without the other.

Handoffs

• If pricing reveals positioning is wrong -> positioning-canvas
• If pricing reveals ICP is wrong -> icp-definer
• If pricing change requires GTM motion change (e.g., adding sales-led tier) -> gtm-motion-picker