Pricing Contrast Set

Pricing Contrast-Set — sub-skill

The three-tier architecture engine. The structure that outperforms two-tier and four-tier in 80%+ of B2B SaaS / services tests.

Activation

Routed here when operator says:

• "Design my pricing tiers"
• "Redesign my pricing page"
• "Apply the decoy effect"
• "Should I add a tier / remove a tier?"
• "Three-tier pricing"

The architecture

Tier 1 (Decoy)         Tier 2 (Target)       Tier 3 (Anchor)
═══════════════        ═══════════════       ═══════════════
Job: Make T2 look      Job: Where 70-85%     Job: Set the high
obviously better.      of buyers should      reference frame.
                       land.

Same value metric      Same value metric     Same value metric
as T2, strictly less.  but at the dominant   at a strict premium.
                       feature set.

Pricing: 25-35% of     Pricing: target ACV   Pricing: ≥3x T2,
T2 price.                                    sometimes 5-10x.

Design rules

1. Same value metric across all tiers

If T2 is per-seat, T1 and T3 must be per-seat. Don't mix per-seat with per-API-call across tiers — the buyer can't grok the comparison in <30 seconds.

2. Decoy is asymmetrically dominated

T1 has strictly less of the same value-metric quantity (fewer seats / fewer calls / fewer records). And/or strictly fewer features from the same feature set. The buyer should recognize T1 < T2 instantly.

Bad decoy example: T1 has 5 seats + integration A; T2 has 10 seats

• integration B. (Different integration breaks the symmetry.)

Good decoy example: T1 has 5 seats + integrations A; T2 has 25 seats

• integrations A + B. (T2 has everything T1 has, plus more.)

3. Anchor tier sets the high frame

T3 is priced ≥3x T2. It sets the reference frame so T2 reads as "reasonable" not "expensive." T3 should be a real product (one the top 10% of customers actually buy), not a fictional max tier.

If you can't articulate a real T3 buyer, you don't have a true anchor — you have an inflated decoy on the upper end.

4. Feature differentiation is grokable in <30 seconds

Test it. Show the page to someone who's never seen it. Ask: "Which tier would you pick if you ran a 50-person team?" If they need

30 seconds, the differentiation is muddy.

Clean differentiation:

• Quantity (seats, calls, records, GB)
• One or two binary features per tier (e.g., "SSO," "Audit log")
• One or two service levels (e.g., "Standard support," "Priority," "Named CSM")

Avoid:

• Long feature checklists (>6 lines per tier)
• "Up to" qualifiers everywhere (creates fuzz)
• Mid-tier features that exist nowhere else (creates parsing tax)

5. Value-metric scales continuously but with decreasing per-unit price

Decreasing per-unit price across tiers reads as "even better deal IF you need more" — natural quantity discount, no artificial cliff.

6. No "Enterprise — call us" below $30K ACV

Below that threshold, "contact us" adds friction without lift. Below mid-market, buyers need a number to feel safe. Above ~$30K ACV, a real enterprise tier (with custom procurement) is fine.

7. Annual prepay is meaningful or absent

Annual prepay should:

• Be ≥25% off monthly equivalent (so the buyer's CFO sees the math), AND
• Be tied to commercial value (churn-lock, cash flow, multi-year SLA)

Otherwise: don't offer it. ~16% off reads as "we want your cash" without giving a real concession.

Workflow

1. Load inputs

From brand-config.json:

• pricing.currentTiers — present state
• pricing.valueMetric.unit — the metric across tiers
• customer.psps[] — who's buying which tier today
• Last 90 days of tier landing distribution (if tracked)

2. Diagnose current tier health

Run the tier landing check:

3. Design the new tier set

For each tier, output:

Tier {name}: ${price}/{cadence}
  Value-metric: {unit + volume}
  Features: 
    - {feature 1}
    - {feature 2}
    - {feature 3 — max 6 features}
  Target buyer: {PSP description}
  Buyer count (predicted % landing): {N}%

4. Self-check before output

• Three tiers, no more (unless real Enterprise tier above $30K ACV)
• Same value metric across tiers
• Decoy is asymmetrically dominated
• Anchor is ≥3x target and has a real buyer
• Predicted landing distribution is ~75% on target
• Per-unit cost decreases across tiers
• Feature differentiation is <30 seconds to grok (validate this)
• No "contact us" below $30K ACV
• Annual prepay either ≥25% or absent

5. Generate the pricing-page tier cards

Output ready-to-ship copy:

[Tier 1 card]
[Tier 2 card — visually emphasized with "Most Popular" or similar]
[Tier 3 card]

With the anchor block from pricing-reference-frames above the cards.

6. Schedule the A/B test

Don't ship a new tier set blind. Route to pricing-tribunal for the hypothesis + test design. Tier rebuilds are Material-tier changes under the tribunal taxonomy.

Output format

Three-Tier Contrast Set — {company}
─────────────────────────────────────────────────────────────────

Tier 1 — Decoy: ${price}
  {value-metric quantity}
  Features: {short list}
  Designed to make T2 obvious. Predicted landing: ~5-10%.

Tier 2 — Target: ${price}    ★ Most Popular
  {value-metric quantity}
  Features: {short list}
  Where 70-85% of buyers should land.

Tier 3 — Anchor: ${price}
  {value-metric quantity}
  Features: {full list}
  Sets the high reference frame. Predicted landing: 10-25%.

PREDICTED TIER LANDING: {distribution}
ANCHOR (above tier cards): {use output from pricing-reference-frames}
A/B TEST RECOMMENDED: yes (route to pricing-tribunal)

References

• ../../pricing/references/pricing-framework.md — full contrast-set framework
• ../../pricing/references/decoy-effect-patterns.md — asymmetric dominance patterns
• ../pricing-reference-frames/SKILL.md — anchor copy ABOVE the cards
• ../pricing-tribunal/SKILL.md — for ship-it test design