Tools · Marketing
Pricing Strategy
Describe an offering — get a pricing model, tiers, and the assumptions to test.
How it works
Corpus-grounded (value-based pricing via the marketing cluster). Recommends a pricing model + the price metric, designs good-better-best tiers, gives psychological-pricing tactics (anchoring, charm, decoy), and surfaces monetization risks + the riskiest pricing assumptions (willingness-to-pay, value-metric alignment).
You bring
{ offering, market?, cluster? }
You get
{ offering_summary, recommended_model, rationale, price_metric, tiers[]{name, who, price_guidance, included[]}, psychological_pricing[], monetization_risks[], riskiest_assumptions[], grounded_in, provenance }
Use it for
- →Marketing-guide reader: 'what should I charge?' → a defensible model + tiers + what to test
- →Move a product from cost-plus to value-based pricing
- →Design a good-better-best tier ladder with a sensible price metric
See it work
example outputOffering: a project-management web app for freelance graphic designers managing client revisions and approvals.
Pricing Strategy — ProofLoop (PM for freelance designers)
Offering: A web app that organizes client revision rounds, approvals, and file versions for solo and small-studio designers.
Recommended model
Value-based, tiered subscription — priced on the unit that scales with the value (active clients), not on seats. Freelancers have few seats but many client relationships, so seat-pricing would under-monetize and feel arbitrary.
Why
The felt value is "never lose a revision thread and get paid faster on approval." That value rises with the number of active client projects, which makes active clients the honest price metric and creates natural expansion as the designer's book grows.
Price metric
Active clients per month.
That’s the first part. This is a one-shot deliverable — run it on your data to get the whole thing →
Run it now
Design your pricing
Get a recommended pricing model and price metric, a tier structure, psychological-pricing tactics, and the riskiest pricing assumptions to validate.
Prefer code? Call it over the API or hand it to your AI agent via MCP — POST /api/bicycle/pricing-strategy · design_pricing. API & agent access →