Support pricing packaging

Support Pricing & Packaging

Purpose

Help teams structure and analyze pricing and packaging decisions aligned with customer value and business objectives.

Skill type

Conceptual skill with analytical components

Use this skill when

• A new product or feature needs a pricing decision
• Current pricing is creating customer friction or revenue leakage
• The team is designing packaging tiers or bundles
• Pricing needs to be revised as the product evolves

Do not use this skill when

• Pricing research data needs deep statistical analysis (use data-analytics skills)
• The goal is positioning or messaging (use develop-positioning-messaging)

Required inputs

• Product or feature being priced
• Target customer segment
• Business model (subscription, usage-based, one-time, freemium)

Optional inputs

• Current pricing structure
• Customer feedback on pricing
• Competitor pricing
• Unit economics data
• Value metrics (what customers pay for)

Upstream context

Works best when:

• Target segment and ICP are defined
• Product value is understood
• Competitive landscape is known

If upstream context is missing

Provide a pricing framework with options, but flag that the right choice depends on value metric and WTP data.

Downstream handoff

Output can feed:

• plan-product-launch (pricing communication for launch)
• build-business-case (pricing feeds revenue projections)

Instructions

1. Identify the value metric (what customers get more/less of as they pay more).
2. Review current pricing model and identify friction points.
3. Map packaging options to customer segments.
4. Evaluate 2–3 pricing structures with trade-offs.
5. Recommend packaging tiers with rationale.
6. Identify validation steps (pricing research, customer interviews).

Output

Provide:

• Value metric identification
• Pricing model options with trade-offs
• Recommended packaging structure and tiers
• Rationale per tier
• Validation steps before finalizing
• Risks and open questions

Risks / caveats

• Pricing changes are high-stakes and hard to reverse — always validate before shipping
• Willingness-to-pay data must come from customers, not internal assumptions
• Freemium only works when conversion logic is clear