/startup-business-models

name: startup-business-models
description: Use when choosing or evaluating a startup revenue model, pricing/value metric, packaging/tier design, or calculating unit economics (LTV, CAC, payback, gross margin, NRR), including usage-based/credit/AI pricing and variable compute/COGS constraints.

Startup Business Models

Systematic workflow for choosing revenue models, pricing, and unit economics.

Quick Start (Inputs)

Ask for the smallest set of inputs that makes the decision meaningful:

• Business type: SaaS, usage-based/API, marketplace, services, hardware + service
• ICP/segment(s): SMB / mid-market / enterprise (and ACV/ARPA bands)
• Current pricing and packaging: value metric, tiers, limits, discount policy, billing cadence
• Unit economics drivers: fully-loaded CAC, gross margin/COGS (include LLM/infra/third-party), churn/retention, expansion (NRR)
• Constraints: sales motion (PLG vs sales-led), implementation constraints (billing metering, proration), gross margin floor, payback target

If numbers are missing, proceed with ranges + explicit assumptions and highlight what to measure next.

Workflow

1) Classify the model

• Subscription, usage-based, freemium, marketplace take-rate, transaction fee, ads, outcome-based, credit-based, hybrid.

2) Build a segment-level unit economics snapshot

• Use `references/unit-economics-calculator.md` for formulas, benchmarks, and common pitfalls.
• Prefer cohort/segment views over blended averages.

3) Evaluate model fit and risks

• Align price metric with value delivered and cost incurred (especially usage + AI compute).
• Identify failure modes: margin compression, adverse selection, channel conflict, support cost explosions, metering/overage friction.

4) Propose pricing + packaging changes

• Use `references/pricing-research-guide.md` for WTP methods and pricing interview scripts.
• Use `assets/pricing-tier-design.md` to draft tiers, limits, upgrade triggers, and enforcement rules.

5) Define measurement and roll-out

• Define success metric + guardrails, evaluation design, and explicit lag windows (conversion now, retention later).

6) Deliver a decision-ready output

• Recommendation, rationale, assumptions, scenarios (base/best/worst), and next experiments.

2026 Heuristics (Context-Dependent)

• Prioritize payback and gross margin over a single ratio; LTV:CAC is easiest to game.
• Typical SaaS targets (directional, by segment/stage): LTV:CAC 3-5x, payback 6-12 months (PLG) or 12-18 months (sales-led early), NRR >100% (mid-market/enterprise) and gross margin >70% (software-only).
• For usage-based / AI products: model contribution margin per unit (token/job/workflow) and set pricing guardrails (rate limits, minimums, commit tiers, credit expiries).

Related Skills (Routing)

• [startup-idea-validation](../startup-idea-validation/)
• [startup-competitive-analysis](../startup-competitive-analysis/)
• [startup-fundraising](../startup-fundraising/)
• [startup-go-to-market](../startup-go-to-market/)

Pricing Change Measurement & Experiment Design

Use this when you are changing pricing, packaging, value metric, limits, discounts, or billing cadence.

1) Define success and guardrails (before launch)

| Type | Examples | |------|----------| | Primary success metric | Net revenue retention (NRR), ARPA/ARPU, gross margin %, payback period, upgrade rate, expansion MRR | | Guardrails | New logo conversion, activation rate, refund rate, support load, churn (logo + revenue), sales cycle length |

2) Pick an evaluation design

| Design | Best when | How to read results | |--------|-----------|---------------------| | A/B (randomized) | Self-serve / PLG flows | Compare conversion, ARPA, refunds, and downstream retention by assignment | | Holdout/control cohort | Pricing is hard to randomize | Compare treated vs. holdout cohorts matched on segment, channel, and start month | | Step rollout (time-based) | Enterprise contracts, invoicing cycles | Compare pre/post with a parallel cohort (not exposed yet) to reduce seasonality bias | | Geo/account rollout | Regions/segments are separable | Compare regions/segments; watch for channel mix shifts |

3) Use explicit lag windows (avoid premature conclusions)

• Short lag (days to 2 weeks): checkout conversion, activation, sales cycle friction, refund/support spikes.
• Medium lag (4 to 8 weeks): upgrades, expansion MRR, usage growth, discounting behavior, proration effects.
• Long lag (90 to 180+ days, B2B): churn, net revenue retention, renewal outcomes, contraction risk.

4) Report an "all-in" view (not just conversion)

• Revenue quality: net revenue after refunds, discounts, and credits; gross margin impact (including variable compute/COGS).
• Segments: break down by plan, seat band, channel, ACV/ARR band, and customer age (new vs. renewal).
• Decision rule: write a go/no-go threshold (example: "NRR +2pts with no >0.5pt drop in activation and no >10% increase in support load").

SaaS Metrics (Read When Needed)

Use `references/saas-metrics-playbook.md` for definitions and templates (MRR/ARR, churn, NRR, Quick Ratio, Magic Number, burn multiple, stage focus).

Resources

| Resource | Purpose | |----------|---------| | [unit-economics-calculator.md](references/unit-economics-calculator.md) | LTV, CAC, payback calculations | | [pricing-research-guide.md](references/pricing-research-guide.md) | WTP research methodology | | [saas-metrics-playbook.md](references/saas-metrics-playbook.md) | SaaS-specific metrics deep dive |

Templates

| Template | Purpose | |----------|---------| | [business-model-canvas.md](assets/business-model-canvas.md) | Full model design | | [unit-economics-worksheet.md](assets/unit-economics-worksheet.md) | Calculate and track metrics | | [pricing-tier-design.md](assets/pricing-tier-design.md) | Pricing & packaging worksheet |

Data

| File | Purpose | |------|---------| | [sources.json](data/sources.json) | Business model resources |

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