skills.rest
amplitude
amplitudeOFFICIAL
Explore all skills by this author →
WebsiteGitHub

diagnose-monetization

Diagnoses revenue leaks and prioritizes monetization opportunities by risk-adjusted impact.

142|16|Updated Mar 20, 2026
Search Tags:#pricing#monetization#packaging#conversion#revenue#ltv#cac
One-click install
npx skills add https://github.com/amplitude/builder-skills --skill diagnose-monetization
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: diagnose-monetization
Source: https://github.com/amplitude/builder-skills/tree/main/growth-skills/skills/diagnose-monetization
Command: npx skills add https://github.com/amplitude/builder-skills --skill diagnose-monetization

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Diagnose revenue leaks, analyze willingness-to-pay signals, evaluate packaging and pricing, and identify opportunities to capture more value. Use when a PM needs to improve conversion to paid, optimize pricing, reduce revenue churn, or find upsell and expansion opportunities.

Core Features & Use Cases

  • Map revenue architecture, size each component (Revenue = Users x Conversion Rate x ARPU x (1 - Revenue Churn) + Expansion Revenue), identify pricing tiers, determine value metric, and compute LTV:CAC by segment.
  • Diagnose the Free-to-Paid funnel: map steps, time-to-conversion, triggers, and segment performance to prioritize fixes.
  • Evaluate packaging and pricing alignment with value metrics and customer segments; identify under-gating, over-gating, and misalignment opportunities.
  • Analyze expansion and contraction: decompose NRR, identify expansion triggers, and surface expansion-ready cohorts.
  • Produce a prioritized monetization opportunity matrix with risk-adjusted revenue impact and actionable next steps.
  • Anti-plays: avoid price hikes without data, gate critical features, or neglect retention when monetization changes.
  • Open questions: data needed to validate pricing sensitivity and segmentation hypotheses.
  • Guidance for experimentation: pair with craft-experiment-design before shipping pricing changes.

Quick Start

Run a monetization diagnosis across your product’s pricing, packaging, and usage data to surface the top revenue leaks and improvement opportunities.

Frequently Asked Questions about diagnose-monetization

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I identify revenue leaks in my SaaS pricing and packaging?▼

To identify revenue leaks in your SaaS pricing and packaging, map your revenue architecture and evaluate free-to-paid funnel performance against value metrics. This process surfaces under-gating, over-gating, and segment misalignment opportunities to capture more value.

What is the best way to diagnose free-to-paid conversion issues?▼

The best way to diagnose free-to-paid conversion issues is to map each funnel step, measure time-to-conversion, and analyze segment performance. This identifies exact drop-off points and triggers to prioritize fixes for your monetization funnel.

How do I evaluate if my packaging aligns with customer value metrics?▼

To evaluate if your packaging aligns with customer value metrics, analyze tier structures against usage data and willingness-to-pay signals. This reveals under-gated and over-gated features, highlighting opportunities to realign packaging with customer segments.

How can I find upsell and expansion opportunities to improve net revenue retention?▼

To find upsell and expansion opportunities for improving net revenue retention, decompose your NRR and identify expansion triggers across usage cohorts. This surfaces expansion-ready segments and quantifies the risk-adjusted revenue impact of targeting them.

What data do I need to optimize pricing and compute LTV:CAC by segment?▼

To optimize pricing and compute LTV:CAC by segment, you need user counts, conversion rates, ARPU, and revenue churn data. These inputs map revenue architecture and validate pricing sensitivity hypotheses for targeted monetization changes.

Why should I avoid raising prices without analyzing usage and revenue data?▼

You should avoid raising prices without analyzing usage and revenue data because doing so is an anti-play that risks severe churn. Validating pricing sensitivity and segmentation hypotheses first ensures changes are risk-adjusted and data-driven.

Related Skills

Explore More Product & Management Skills→
tabooestabooes
1

finance-based-pricing-advisor

Quantify pricing change impacts on revenue, churn, and CAC payback for SaaS products.

Community
Advanced
sefllessseflless

ab-test-setup

Plan A/B tests with hypotheses, sample size calculations, and variant design.

Community
Intermediate
sandrasocialsandrasocial
1

user-journey-scanner

Map SSELFIE's paid user journey from entry to monetization across canonical tiers.

Community
Intermediate
gaos6egaos6e

pricing-strategy

Design SaaS pricing tiers using value metrics and research methods.

Community
Intermediate