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电商用户行为分析
by
smallKeyboy
· GitHub ↗
· v1.0.0
· MIT-0
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Install in OpenClaw
/install user-behavior-analytics
Description
电商用户行为分析 - 活跃度趋势追踪与可视化图表生成
README (SKILL.md)
电商用户行为分析
分析电商平台的用户购物行为数据，追踪用户活跃度趋势，生成可视化图表和洞察报告。
功能特性
1. 活跃度趋势分析
- DAU/MAU 趋势追踪
- 活跃用户增长率
- 用户活跃周期识别
- 回流用户分析
2. 可视化图表生成
- 折线图：活跃度趋势
- 柱状图：每日/周/月活跃对比
- 热力图：用户活跃时段分布
- 漏斗图：活跃度层级转化
3. 行为洞察
- 活跃度异常波动预警
- 高活跃用户特征识别
- 流失风险用户标记
- 活跃度提升建议
使用方法
数据输入格式
支持以下数据格式：
- CSV 文件（推荐）
- Excel 文件
- JSON 格式
必需字段
| 字段名 | 说明 | 示例 |
|---|---|---|
| user_id | 用户唯一标识 | U001, U002 |
| date | 行为日期 | 2024-01-15 |
| action | 行为类型 | view, add_cart, purchase |
| platform | 平台来源 | app, web, mini_program |
可选字段
| 字段名 | 说明 |
|---|---|
| product_id | 商品ID |
| category | 商品类目 |
| amount | 交易金额 |
| session_duration | 会话时长 |
使用示例
示例 1：分析月度活跃度趋势
请分析这份数据的用户活跃度趋势，按周汇总，生成趋势图
输出：
- 周活跃用户趋势折线图
- 峰值/谷值标注
- 异常波动说明
示例 2：识别高价值活跃用户
找出活跃度前20%的用户，分析他们的行为特征
输出：
- 高活跃用户行为模式
- 活跃时段偏好
- 购买转化率对比
示例 3：活跃度预警
最近一周活跃度下降明显，帮我分析原因
输出：
- 下降幅度量化
- 可能原因分析
- 改进建议
分析维度
时间维度
- 日/周/月活跃度
- 同比/环比分析
- 季节性规律
用户维度
- 新老用户活跃对比
- 用户分层活跃度
- 渠道来源活跃度
行为维度
- 浏览活跃度
- 加购活跃度
- 购买活跃度
- 复购活跃度
输出报告
分析完成后，将生成包含以下内容的报告：
- 执行摘要 - 核心发现和关键指标
- 趋势图表 - 可视化活跃度变化
- 数据洞察 - 深度分析和原因解读
- 行动建议 - 可执行的优化策略
适用场景
- 电商运营团队日常监控
- 用户增长策略制定
- 活动效果评估
- 用户流失预警
- 产品迭代效果验证
注意事项
- 数据量建议：1万-100万条记录效果最佳
- 时间跨度：建议至少7天，最佳为30天以上
- 数据隐私：不存储用户原始数据，仅输出分析结果
Usage Guidance
Before installing, confirm you are comfortable providing e-commerce behavior datasets to the agent. Prefer anonymized user IDs, remove unnecessary personal data, and treat generated reports as analytics guidance rather than automatic business decisions.
Capability Analysis
Type: OpenClaw Skill
Name: user-behavior-analytics
Version: 1.0.0
The skill bundle consists solely of metadata and documentation (SKILL.md) for an e-commerce user behavior analysis tool. It contains no executable code, installation scripts, or external dependencies. The instructions provided in SKILL.md are aligned with the stated purpose of data visualization and trend analysis, with no evidence of prompt injection, data exfiltration, or malicious intent.
Capability Tags
Capability Assessment
Purpose & Capability
The stated purpose, inputs, and outputs all align with e-commerce user behavior analytics and chart/report generation.
Instruction Scope
The instructions focus on analysis, visualization, and recommendations; there is no evidence of autonomous purchases, account mutation, hidden tool use, or goal redirection.
Install Mechanism
There is no install spec and no code files, so there is no artifact-backed package, dependency, or execution-chain risk.
Credentials
The skill expects user-provided CSV/Excel/JSON datasets containing user IDs, behavior events, and optional transaction fields. That is proportionate to analytics, but users should minimize or anonymize sensitive customer data.
Persistence & Privilege
No credentials, privileged config paths, background persistence, or local storage mechanisms are declared; the SKILL.md also says raw data is not stored.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install user-behavior-analytics - After installation, invoke the skill by name or use
/user-behavior-analytics - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
初始版本
Metadata
Frequently Asked Questions
What is 电商用户行为分析?
电商用户行为分析 - 活跃度趋势追踪与可视化图表生成. It is an AI Agent Skill for Claude Code / OpenClaw, with 58 downloads so far.
How do I install 电商用户行为分析?
Run "/install user-behavior-analytics" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is 电商用户行为分析 free?
Yes, 电商用户行为分析 is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does 电商用户行为分析 support?
电商用户行为分析 is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created 电商用户行为分析?
It is built and maintained by smallKeyboy (@smallkeyboy); the current version is v1.0.0.
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