Verified against ChatGPT · 2026-08-05
Design pricing tiers around what each segment values, not round numbers
Turn a feature list and target segments into value-based pricing tiers, with a deliberately chosen value metric and an explicit anchor tier — instead of copying a competitor’s price points.
The prompt
Ready to copy — highlighted parts are example details you can swap.
You are a SaaS pricing strategist using value-based, tiered pricing — pricing tied to what each segment actually values, not round numbers copied from a competitor. Context: - Product: A scheduling and invoicing tool for independent tutors - Customer segments: Solo tutors, small tutoring agencies (3-15 tutors), franchise tutoring centers - Features/capabilities available to package: Scheduling, automated invoicing, parent portal, multi-tutor calendar, payroll export, API access - Candidate value metrics, if you have ideas: Per-tutor seat, or per-student billed Task: 1. From Per-tutor seat, or per-student billed or by inferring from the product, propose the value metric each tier should scale on (per-seat, per-usage-unit, per-outcome, flat). State why it scales with the value the customer receives, rather than with your delivery cost — those are not the same thing. 2. Design 2-4 tiers, each named for and mapped to one segment in Solo tutors, small tutoring agencies (3-15 tutors), franchise tutoring centers. State which segment's willingness-to-pay and must-have needs each tier is built around. 3. Assign each item in Scheduling, automated invoicing, parent portal, multi-tutor calendar, payroll export, API access to a tier. For any feature that would be a must-have reason to buy for a lower-priced segment, flag it explicitly if you're gating it behind a higher tier — that gate usually suppresses adoption of the entry tier rather than lifting revenue, and should be a deliberate choice, not an accident. 4. Name which tier is the intended default/anchor choice for most buyers, and explain the anchoring reasoning — that the presence of a deliberately less attractive higher or lower option makes the anchor tier look reasonably priced by comparison. Format: a tier table (name, target segment, price basis, included features), followed by the anchor-tier reasoning as its own short paragraph.
Customize the highlighted detailsoptional — the prompt above already works
Why this works
The value-metric step is the actual mechanic that separates value-based pricing from arbitrary tiering: a metric like per-tutor-seat or per-student-billed should track the value the customer receives as they grow, not your cost to serve them, which is why picking the wrong metric (e.g. flat pricing for a product whose value clearly scales with usage) quietly caps revenue as customers grow without anyone noticing why. The anchor-tier instruction is the documented pricing-psychology decoy effect — a deliberately less attractive option on either side of the middle tier makes that middle tier look like the obviously reasonable choice by comparison, which is why most SaaS pricing pages are built around a highlighted 'most popular' tier rather than three neutral options. Flagging must-have features gated behind a higher tier forces a real trade-off decision instead of an accidental one, since gating something a lower segment truly needs to buy at all usually loses more entry-tier customers than it gains in upgrade revenue.
What you get back
Solo — per-seat, $19/mo/tutor: scheduling, automated invoicing, parent portal. Agency (anchor tier) — per-seat, $15/mo/tutor (5+ seats): everything in Solo, plus multi-tutor calendar and payroll export. Franchise — custom, per-location: everything in Agency, plus API access and multi-location reporting. Anchor reasoning: Agency is the intended default — its per-seat price is lower than Solo's, which makes it look like the "smart" choice for anyone with 3+ tutors, while Franchise's higher, undefined price makes Agency look concretely priced and easy to commit to by comparison.
Verified against
ChatGPT GPT-5.1 · 2026-08-05
Claude Sonnet 5 · 2026-07-31
Changelog
- 2026-08-05 — Initial publish.
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