pricing-strategy

Pricing Strategy Expert

Strategic expertise in SaaS pricing, monetization models, and value-based pricing.

Pricing Models

pricing_models:
  per_user:
    description: "Charge per active user/seat"
    pros: ["Predictable revenue", "Easy to understand"]
    cons: ["Can limit adoption", "Encourages seat sharing"]
    best_for: "Collaboration tools, CRM"
    examples: "Slack, Salesforce"

usage_based: description: "Charge based on consumption" pros: ["Low barrier to entry", "Fair value exchange"] cons: ["Revenue unpredictability", "Complex billing"] best_for: "API services, infrastructure" examples: "AWS, Twilio, Stripe"

tiered: description: "Feature-based plan tiers" pros: ["Clear upgrade path", "Natural segmentation"] cons: ["Complexity risk", "Feature arbitrage"] best_for: "Most B2B SaaS" examples: "HubSpot, Zoom"

freemium: description: "Free tier with paid upgrades" pros: ["Viral growth potential", "Product-led growth"] cons: ["Conversion challenges", "Free user costs"] best_for: "PLG companies" examples: "Dropbox, Notion"

## Value-Based Pricing

### Research Methods

```yaml
pricing_research:
  van_westendorp:
    questions:
      - "At what price would this be too cheap?"
      - "At what price would this be a bargain?"
      - "At what price would this be expensive?"
      - "At what price would this be too expensive?"

  conjoint_analysis:
    purpose: "Understand feature value trade-offs"
    output: "Willingness to pay per feature"

  customer_interviews:
    questions:
      - "How do you currently solve this problem?"
      - "What would you pay for [benefit]?"
      - "What budget do you have for this?"

Tier Design

tier_structure:
  free:
    purpose: "Acquisition, product trial"
    includes: "Core functionality, limited usage"

  starter:
    target: "Individual users, small teams"
    features: "Core + basic analytics"

  professional:
    target: "Growing teams"
    features: "Advanced features, integrations"

  enterprise:
    target: "Large organizations"
    features: "Custom, security, SLA, dedicated support"

Psychological Pricing

pricing_psychology:
  anchoring: "Show highest price first"
  charm_pricing: "$99 instead of $100"
  price_framing: "$3/day vs $90/month"
  bundling: "Bundle price < sum of parts"
  annual_discount: "15-20% for annual payment"

Metrics

pricing_metrics:
  arpu: "MRR / Active Users"
  arpa: "MRR / Active Accounts"
  price_realization: "Actual Price / List Price (target >90%)"
  discount_rate: "Average discount given (target <15%)"

Best practices

1. Value first — value-based pricing for the customer
2. Segment pricing — different segments = different willingness to pay
3. Test everything — data matters more than intuition
4. Annual contracts — encourage annual contracts
5. Simple packaging — complexity kills conversion
6. Regular reviews — review prices annually