# Pricing And Packaging > Use when the user wants to design or sanity-check pricing and packaging — the tiers, what's in each, and the price points. Also use when the user mentions pricing, packaging, tiers, plans, price points, good-better-best, pricing model, value metric, ARPU, or "how should we price this." Produces a packaging structure (tiers and what's in each), price points with rationale, and a modeled blended ARPU and revenue mix. - Skill: `sarojkjha/pricing-and-packaging` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add sarojkjha/pricing-and-packaging` - Raw SKILL.md: https://api.skillmd.com/api/skills/sarojkjha/pricing-and-packaging/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: MIT - Author: sarojkjha (https://skillmd.com/u/sarojkjha) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/sarojkjha/pricing-and-packaging --- # Pricing & Packaging Design tiers that price to value and make the upgrade path obvious — then model the ARPU so the structure is something you can defend. ## When to use When setting prices for a new product or revisiting tiers that grew by accident, when packaging is unclear and buyers can't tell which plan is for them, or when the team is discounting constantly or leaving money on the table. ## Before you start 1. **Read the brand/product context first.** Pull the ICP/segments and what each values, the differentiator, and proof from `.agents/product-marketing.md`. If positioning/value isn't settled, run `positioning-statement` and `value-proposition` first — price follows value. 2. **Gather inputs:** a rough sense of willingness to pay and what competitors charge (mark these `(assumption — confirm)` if estimated). 3. **Confirm the objective:** a defensible tier structure. (If the question is really "can we afford our CAC?", that's `unit-economics`.) ## Method Price to value, not cost — cost sets a floor, not the price. Pick the value metric that scales with the customer's value (seats, usage, outcomes), and let packaging follow it. Build good-better-best tiers where the most-wanted features drive upgrades and the top tier anchors the others, then model the blended ARPU and cross-check it against unit economics. ## Workflow 1. **Anchor on value, not cost.** 2. **Pick the value metric** — what scales with the customer's value. 3. **Design good-better-best tiers;** put upgrade-driving features behind the right tiers and give the top tier a reason to exist. 4. **Set price points** with a sensible ladder, then **model with the engine** (see Run the tool). 5. **Cross-check the ARPU** against `unit-economics` — does it support CAC and payback? 6. **Pressure-test** the upgrade path and present. ## The framework ``` blended ARPU = Σ(tier price × tier customers) / Σ(tier customers) revenue mix = each tier's (price × customers) as a share of the total price ladder = each tier's price relative to the tier below it anchor = top tier ÷ the SECOND-highest tier (a real anchor needs a meaningful jump — flagged weak below ~1.5x) healthy: top tier anchors; most revenue from the middle/top, not the entry tier ``` ## Run the tool > Paths assume you installed with `npx skills add`. From a clone of this repo, use `skills/pricing-and-packaging/resources/…` instead. ```bash node .agents/skills/pricing-and-packaging/resources/price-packaging.js --input=tiers.json node .agents/skills/pricing-and-packaging/resources/price-packaging.js # built-in demo ``` Input JSON: `{ "tiers": [ { "name": "Starter", "price": 29, "customers": 600 }, ... ] }`. It prints each tier's price, customers, revenue, revenue share, and price-ladder step, plus the blended ARPU, the **anchor check** (top tier vs the second-highest — flagged weak below ~1.5x), and where revenue concentrates. Use these exact numbers. Designing pricing for a product with **no customers yet**? Enter your estimated mix as counts per 100 (e.g. `60` / `30` / `10`) and mark it as an assumption — the mix percentages come out identical either way. > This is the same model that powers the public **Pricing & ARPU Modeler** (link below), so the agent and the web tool produce identical numbers. ## Reference The packaging model and its input shape live in `resources/price-packaging.js`. Value, differentiator, and segment willingness-to-pay come from `.agents/product-marketing.md`. ## Present the result - The **value metric** the pricing scales on. - The **tiers** — name, who each is for, what's in it, and price. - **Blended ARPU, revenue mix, the price ladder, and the anchor check** (from the engine), with the mix flagged as an estimate when it's based on projections. - The **upgrade logic** — why a buyer moves from one tier to the next. - **Flags** — anything the model or the pressure-test surfaced (a weak anchor, revenue stuck in the entry tier, etc.). ## Guardrails & common mistakes - **Price to value, not cost.** Cost-plus pricing leaves money on the table and ignores what the buyer actually pays for. - **The value metric must scale with worth.** If it doesn't, customers feel nickel-and-dimed or under-charged. - **Cross-check ARPU against unit economics.** An ARPU that doesn't cover CAC and payback won't survive the market. - **Mark the customer mix as an assumption.** When it's a projection, it's an estimate, not a forecast — don't present modeled revenue as fact. ## Related AAJ resources - **Pricing & ARPU Modeler** (free interactive tool) — https://aajconsult.com/tools/pricing-arpu-modeler - **SaaS Pricing & Packaging playbook** — https://aajconsult.com/playbooks/saas-pricing-packaging ## Related skills `value-proposition` · `unit-economics` · `marketing-budget-planning` · `brand-product-context` ## Credits Original AAJ skill. The Agent Skills format and the marketing-skills catalog by Corey Haines (`coreyhaines31/marketingskills`, MIT) were references for structure and coverage; this skill is independently written. See the repository README for the full reference list.