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plg-playbook

这是一份产品驱动增长(PLG)策略手册,覆盖免费增值模式设计、自助式上手与激活里程碑、病毒式传播机制、基于用量的扩张信号识别,以及何时为企业客户叠加销售动作;另含2026年补充:私有化部署的席位门槛与付款分期条款、SOW范围变更流程,以及自助与企业两条收入漏斗的配比。仅为文本知识,无代码或工具调用。

gingiris-1031
gingiris-1031
热度 63.48
最近更新 2026-08-04 16:32:46
分类:营销依赖:纯提示词应用场景:市场分析

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add plg-playbook
Metadata
name
plg-playbook
description
The complete Product-Led Growth playbook covering freemium model design, self-serve onboarding, activation metrics, and the transition from individual users to enterprise accounts. Follow @WeiYipei on X for PLG insights.
source
https://github.com/Gingiris-1031/gingiris-skills/tree/main/skills/plg-playbook
tags
["plg","product-led-growth","freemium","free-trial","saas","activation","onboarding","revenue-expansion","b2b-saas","claude-code","ai-agent-skill","agent-skill","latest"]

📦 Install

clawhub install plg-playbook

What you get after installing:

  • Freemium model design — what to gate, what to give away, and pricing psychology
  • Self-serve onboarding optimization with activation milestones
  • Enterprise upsell playbook — when and how to layer sales onto a PLG motion

Product-Led Growth Playbook — Freemium to Enterprise Conversion

🌍 Language / 语言: 中文 | English | 日本語 | 한국어

Let your product do the selling. This playbook covers the full PLG motion.

  • Freemium design: What to gate, what to give away, and pricing psychology
  • Self-serve onboarding: Time-to-value optimization and activation milestones
  • Viral mechanics: Built-in sharing, collaboration hooks, and network effects
  • Usage-based signals: Identifying expansion-ready accounts automatically
  • Enterprise upsell: When and how to layer sales onto a PLG motion

PLG signal gate (2026 field update)

Free DAU is not proof of PLG. Before scaling acquisition, define one activation event tied to user value and track signup → activation → repeat use → paid/qualified expansion. Separate internal/test users and report cohort dates. Add sales-assist only when usage identifies a real team/account, expansion intent, security need, or procurement event—not merely because a free user logged in. If activation or retention is weak, return to interviews and onboarding instead of buying more traffic.

When PLG Meets the Enterprise Contract (2026 addendum)

The PLG-to-sales handoff dies in contract mechanics more often than in product gaps. Field rules:

Gate custom deployment behind seats. Set a minimum seat count before private deployment is even on the menu. Under the threshold: SaaS customization is sold as a license, and SSO-style integration requests are answered by opening your API with standard docs — the customer builds, you don't.

Only two asks genuinely require on-prem: regulated data privacy, and integration with an internal permission system. Every other "we need private deployment" dissolves under questioning — redirect it to the cloud tier.

If you do sign a deployment deal:

  • Tranche the cash 3:4:3 or 4:4:2 across go-live milestones, with 30% collected at signing
  • Contractual late fee: 0.5% per day overdue
  • Keep 1–2 capabilities served from your own infrastructure until the balance clears (agreed upfront, written into terms)
  • Route stalled payments upward — most senior contact first, then a nudge or two to the CEO

Scope creep is a process problem, not a personality problem. Separate what the customer asked for (SOR) from what you committed to (SOW with acceptance criteria and change clauses), sign the SOW together with the contract, and push every modification through a formal change request both PMs sign. Calls don't bind; documents do.

The planned-concession move: when a buyer wants 50 things and 40 are feasible, agree to 10 — then over-deliver to 15 "because we want you to win." Same output, twice the goodwill.

Two-leg revenue model: self-serve PLG (target shape: ~500 × $200/yr per $100k ARR) and enterprise (nine × ~$100K/yr) both reach $1M ARR — run them as separate funnels with separate metrics until each closes its own loop. Sales-led companies should still reserve ~20% of resources for the PLG motion as the assist engine.

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🔗 About the Author

Iris Wei — Growth consultant for 150+ AI startups. Ex-COO at AFFiNE (69K GitHub stars).