Startup Business Models

Quick Start (Inputs)

• 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

Workflow

1. Classify the model

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

1. 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.

1. 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.

1. 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.

1. Define measurement and roll-out

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

1. 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-competitive-analysis
• startup-fundraising
• startup-go-to-market

Pricing Change Measurement & Experiment Design

1) Define success and guardrails (before launch)

2) Pick an evaluation design

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)

references/saas-metrics-playbook.md

Resources

Templates

Data

Do / Avoid (Jan 2026)

Do

• Define your value metric (seat/usage/outcome) and validate willingness-to-pay early.
• Include COGS drivers in pricing decisions (especially usage-based).
• Use discount guardrails and renewal logic (avoid ad-hoc deals).

Avoid

• Pricing as an afterthought (“we’ll figure it out later”).
• Margin blindness (shipping usage growth that destroys gross margin).
• Misleading LTV calculations from immature cohorts.

What Good Looks Like

• Packaging: a clear value metric, tier logic, and discount policy (with enforcement rules).
• Unit economics: CAC, gross margin, churn, payback, and retention defined and tied to cohorts.
• Assumptions: one inputs sheet, ranges/sensitivities, and scenarios (base/best/worst).
• Experiments: pricing changes tested with decision rules (not “gut feel” rollouts).
• Risks: margin compression, adverse selection, channel conflict, and support cost modeled.

Optional: AI / Automation

• Summarize pricing research and competitor snapshots; verify manually before acting.
• Draft pricing page copy; humans verify claims and consistency with contracts.