Smart Pricing — Strategy increase your revenue

"# Pricing Strategy
（This agent execute with GPT-5.5)

The right price isn't the one that feels fair — it's the one that reflects delivered
value, fits your go-to-market motion, and holds up against what customers actually
consider as alternatives. This skill works through that from first principles.

When to Use / Best For

• You're setting prices for the first time: pre-launch or post-MVP, before a pricing
  page goes live — work through the value metric, tier count, and price points grounded
  in customer value, not cost-plus guesswork or competitor copy-paste.
• Something about your pricing is wrong and you can't name it: low conversion,
  low ARPU, high churn at a specific tier, or stuck trying to move upmarket. The skill
  diagnoses the actual problem first — ""my pricing is wrong"" can mean five different
  things, and each one calls for different analysis.
• You're adding a tier, changing a metric, or raising prices: restructuring existing
  pricing is higher-stakes than setting first prices. This skill maps the tradeoffs,
  designs the migration path for existing customers, and surfaces the assumptions that
  would flip the recommendation.
• You need to decide between freemium, free trial, and paid-only: not a preference
  call — a decision logic based on infrastructure cost, time-to-value, market size,
  and network effects. Works through it in order, not as a framework tour.

How It Works

Step 1 — Diagnose before advising: pricing advice fails when it goes generic. Before
reaching for frameworks, the skill names the specific question: is this a packaging
problem, a metric problem, or a price point problem? Each calls for different analysis.
""Help me with pricing"" gets a diagnosis, not a template.

The three decisions inside every pricing question:

The skill identifies which of these is actually on the table and focuses there — not a
walk through all three when only one is the question.

Value framing — ceiling and floor, not cost-plus:
A defensible price sits between what the customer believes the product is worth (ceiling)
and the cost of their next realistic alternative (floor). Cost-to-serve is a sanity
check, not an input. The right question is ""what's this worth compared to the
alternative,"" not ""what does it cost me plus margin.""

Value metric selection: the metric is often more consequential than the price point.
A strong metric scales with the value the customer receives, is predictable enough to
budget, and aligns customer success with your revenue growth. Diagnostic test: ""As this
customer uses more of [metric], do they get proportionally more value?"" If no, pricing
and value drift apart over time.

Business-model adaptation: the same frameworks don't apply to a $20/month note-taking
app and a $2,000/seat compliance platform. The skill adapts: marketplaces get take-rate
and liquidity analysis; usage-based products get unit-price and volume-curve design;
services get hourly-vs-project-vs-outcome-based framing; consumer subscriptions get
free-tier economics.

GTM motion alignment: self-serve pricing must be understandable in 30 seconds —
complex metered pricing kills conversion without a sales team to explain it. Sales-led
pricing uses list price as a negotiation anchor. Hybrid has to design the handoff
boundary deliberately or sales and self-serve poach each other's customers.

Research methods when data is needed (effort-to-value order):

• Competitive scan — pull 5–10 comparable products' pricing pages, normalize to per-unit
• Customer interviews with price anchoring — 8–12 calls with real target customers
• Van Westendorp survey — 100+ respondents, maps the acceptable price range
• Gabor-Granger — randomized price points with yes/no purchase intent, produces a
  demand curve directly
• MaxDiff / conjoint — feature importance ranking and combined pricing/feature tradeoffs
• Live A/B test — real pricing page, only viable at meaningful traffic volume

Output format: every recommendation includes the specific question being answered,
a concrete recommendation with numbers (not ranges as a hedge), the reasoning that
drove it, the 2–3 assumptions whose wrongness would flip it, and the next validation
step before committing.

Why This Skill / What Makes It Different

vs. ChatGPT or generic pricing advice: ChatGPT applies the same SaaS tier template
regardless of business model, GTM motion, or what customers actually consider as
alternatives. This skill diagnoses before advising — the specific question matters,
and the analysis is specific to it.

vs. reading pricing framework articles: frameworks are useful; applying them to
your actual numbers, with your specific customer and competitive context, is what
produces an actionable recommendation. The skill commits to concrete output: specific
tiers, a specific metric, and specific price points — not a tour of frameworks with
""it depends"" at the end.

vs. pricing consultants at early stage: the same questions a pricing consultant
would ask (GTM motion, value metric, persona count, freemium logic) with the same
structured output (recommendation + reasoning + assumptions + next step), available
without the engagement cost or timeline.

Freemium decision logic that isn't a preference call: whether to offer a free tier
is worked through in four questions — resource cost per free user, time-to-value,
addressable market size, and whether free users generate network effects. The output
is a defensible decision, not a gut call dressed up as strategy.

Price increase management: raising prices on existing customers is one of the
highest-leverage and highest-risk pricing moves. The skill covers the four migration
approaches (grandfather, delayed, tied to a release, full restructure), the
communication patterns that reduce churn, and how to model whether the revenue gain
net of churn clears the bar — before committing to the change."