什么是 留存策略与留存分析？

留存技能旨在为 AI 智能体提供管理用户生命周期和产品增长的全套工具。它专注于将复杂的产品数据合成为可操作的策略，涵盖从第 30 日留存率等核心指标到高级净收入留存率 (NRR) 计算的所有内容。通过实施此技能，开发人员可以利用 Openclaw Skills 构建更具韧性的产品，从而准确了解用户流失的时间和原因。

该技能的核心是使智能体能够设计养成习惯的参与循环和特定生命周期的策略。无论您是处于激活阶段，还是试图赢回流失用户，此技能都能提供改善功能粘性和长期客户价值所需的基准和逻辑工作流。

下载入口:https://github.com/openclaw/skills/tree/main/skills/ivangdavila/retention

安装与下载

1. ClawHub CLI

从源直接安装技能的最快方式。

2. 手动安装

将技能文件夹复制到以下位置之一

优先级：工作区 > 本地 > 内置

3. 提示词安装

将此提示词复制到 OpenClaw 即可自动安装。

留存策略与留存分析 应用场景

• 跟踪第 1、7 和 30 日留存指标，以评估产品与市场的匹配度。
• 执行留存分析，以识别季节性模式和特定产品更新的影响。
• 检测早期流失信号，例如登录频率下降或团队成员移除。
• 创建具有时效性消息和激励措施的自动召回活动。
• 针对 B2B 和 B2C 模式，根据行业标准进行产品性能基准测试。

1. 分析用户注册群组，为横向和纵向留存模式建立基准。
2. 通过识别驱动习惯形成的触发因素、行动和奖励来监控参与循环。
3. 通过标记偏离基准行为的情况（例如登录频率下降 50%）来识别流失信号。
4. 将用户旅程细分为生命周期阶段，从初始激活到扩展和召回。
5. 通过根据用户离开的具体原因提供暂停或激励措施来优化取消流程。

留存策略与留存分析 配置指南

要在您的智能体环境中部署此策略，请确保您的数据管道支持基于群组的导出。使用以下命令初始化留存框架：

# 初始化留存分析模块
openclaw skill:add retention

# 为您的业务模式配置基准指标
openclaw config:set retention_type=B2B_SaaS

留存策略与留存分析 数据架构与分类体系

该技能将产品数据组织成以下模式进行分析：

name: Retention
description: User retention strategy, cohort analysis, churn prevention, and reactivation campaigns
metadata:
  category: product
  skills: ["retention", "churn", "cohorts", "engagement", "lifecycle"]

Core Metrics

Cohort Analysis

Track by signup week, not calendar week:

• Horizontal axis: weeks since signup (0, 1, 2, 3...)
• Vertical axis: signup cohort (Jan W1, Jan W2...)
• Cell value: % of cohort still active

Identify:

• Which cohorts retain better (product changes, marketing source)
• At which week users drop off (week 2 cliff = aha moment too late)
• Seasonal patterns (holiday signups retain worse)

Churn Signals

Early warning indicators (flag before churn):

• Login frequency drops 50%+ from baseline
• Core feature usage stops
• Support tickets spike then go silent
• Billing page visits without upgrade
• Team member removals
• Data export requests

Engagement Loops

Retention requires habit formation:

Design for variable rewards - predictable = boring.

Lifecycle Stages

Reactivation Campaigns

Timing matters:

• 7 days inactive: Soft nudge ("We miss you")
• 14 days inactive: Value reminder + what's new
• 30 days inactive: Incentive offer (discount, extended trial)
• 90 days inactive: Last chance + feedback ask

Message formula:

[Acknowledge absence] + [New value added] + [Easy re-entry CTA]
"Your dashboard is waiting. We added [feature]. One click to resume →"

Feature Stickiness

Measure which features predict retention:

• Usage correlation: Users of feature X retain 2x better
• Time to feature: Users who reach feature X in day 1 retain 3x
• Feature breadth: Users of 3+ features retain 5x vs 1 feature

Double down on sticky features in onboarding.

Churn Prevention

When churn signal detected:

1. Immediate: In-app message acknowledging drop ("Need help?")
2. Day 3: Email from founder (personal, not marketing)
3. Day 7: Offer call or live support
4. Before renewal: Proactive outreach with usage summary

Cancel flow optimization:

• Ask reason (required, 4-5 options)
• Offer pause instead of cancel
• Show what they'll lose (data, history, price lock)
• Easy return policy ("reactivate anytime, data saved 90 days")

Retention Benchmarks by Model

Common Mistakes

• Measuring retention from signup, not activation
• Treating all churned users the same (voluntary vs involuntary)
• Reactivation emails without new value proposition
• Ignoring payment failures as churn (30-40% of churn is involuntary)
• No segmentation in cohort analysis (power users mask problems)