unit-economics
Establishes whether the business makes money on each customer or unit — contribution margin, acquisition cost, payback period, lifetime value, and the cohort behavior underneath. Use this to assess whether growth is profitable, evaluate a channel or segment, support a pricing decision, judge how fast the business can afford to grow, or diagnose why revenue growth is not producing profit.
git clone --depth 1 https://github.com/cbrock84/headcount /tmp/unit-economics && cp -r /tmp/unit-economics/plugins/finance/skills/unit-economics ~/.claude/skills/unit-economicsSKILL.md
# Unit economics The question is simple and usually unanswered: does one more customer make the business better off, and how long does that take? ## Build it in this order 1. **Define the unit.** A customer, an account, a seat, an order. State it, because most disagreements about unit economics are disagreements about the unit. 2. **Contribution margin** — revenue per unit minus the costs that vary with it. Include everything that actually varies: payment processing, hosting attributable to usage, support load, delivery, third-party fees. Understating variable cost is the single most common error and it flatters everything downstream. 3. **Acquisition cost** — fully loaded. All sales and marketing spend, including salaries, divided by customers acquired in the same period. Excluding people costs is the second most common error and typically understates by half or more. 4. **Payback period** — acquisition cost divided by monthly contribution. This is the number that governs how fast you can grow without financing it. 5. **Lifetime value** — contribution × expected lifetime, from observed retention. Not from a churn-rate assumption chosen because it produces a good ratio. ## Read it honestly - **Ratios hide the constraint.** A healthy lifetime-value ratio with a long payback still means growth consumes cash faster than it produces it. Payback governs the growth rate; the ratio governs whether it is worth doing at all. - **Segment before concluding.** Blended economics almost always conceal one segment subsidizing another. The average is the least useful number. - **Use cohorts, not averages.** Retention improves or decays over time, and blended figures mask which. If early cohorts retain better than recent ones, the business is deteriorating while the average looks stable. - **Do not extrapolate lifetime beyond your data.** A twelve-month-old company cannot observe a three-year lifetime, and assuming one is how unprofitable businesses appear profitable. ## What it should change Good unit economics by segment tell you where to spend. Bad ones tell you to fix the model before scaling — no acquisition efficiency rescues a negative contribution margin, it only reaches the loss faster. ## Never - Compare acquisition cost against revenue rather than contribution. - Report lifetime value without stating the retention data behind it and its observation window. - Treat improving ratios as progress without checking whether the mix simply shifted.
Concentrates marketing and sales effort on a named set of accounts rather than on volume — qualifying whether the model fits your economics at all, building the account list and the buying group inside each, tiering effort against account value, coordinating so the account experiences one campaign rather than several, and measuring account progression instead of leads. Use this to decide whether to run an account-based program, build one, or work out why an existing one produces activity and no pipeline.
Designs orchestrator-and-subagent hierarchies for a repository — splitting agents by exclusive write surface, pairing every producer with an independent auditor, and enforcing the split with a script that runs in CI. Use this whenever the user wants to set up, expand, audit, or fix a multi-agent or subagent structure for a codebase; asks how to divide work between agents; wants agent charters, roles, or a surface map written; or is hitting agents that collide on the same files, review their own work, or drift from their remit. Also use when sizing a roster or deciding whether a new agent is justified.
Governs models and AI systems in production — intended use, evaluation, monitoring, human oversight, documentation, and the decision to deploy or retire. Use this before deploying a model or AI feature, when defining evaluation criteria, when a model's behavior has drifted, when assessing AI risk or regulatory exposure, or when deciding whether an AI system is fit for a consequential decision.
Produces executive-level research — market sizing, competitor mapping, trend analysis, and strategic intelligence — grounded in cited sources with the confidence in each claim made explicit. Use this to analyze a market or industry, map competitors, evaluate a market-entry or build-versus-buy decision, produce a research brief, or assemble evidence for a decision. Also use when comparing options that need a structured, evidence-based verdict rather than an opinion.
Optimizes for AI assistants and AI-generated answers — being retrievable, being cited, and being represented accurately when a model answers on your behalf. Use this when traffic is shifting from links to AI answers, when a brand is misrepresented or absent in AI responses, when planning content for retrieval rather than ranking, or when deciding how AI search changes an existing SEO program.
Improves visibility and conversion in the App Store and Google Play — metadata, keywords, screenshots, ratings, and the listing experience that turns an impression into an install. Use this to audit or optimize an app listing, plan a launch listing, diagnose poor install conversion, or improve store search visibility.
Protects and restores data — backup coverage and scope, retention, immutability against ransomware, and proving restores actually work. Use this to design a backup regime, verify restores, plan retention, protect backups from ransomware, or recover from data loss.
Runs the planning cycle — annual budget, rolling forecast, consolidation of business unit inputs, and the variance analysis that explains actuals against plan. Use this to build or challenge a budget, run a forecast cycle, consolidate departmental submissions, explain why results diverged from plan, or improve forecast accuracy.