ecom-analytics

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Chinese

E-Commerce Analytics

电商分析

Overview

概述

E-commerce analytics measures online store performance across traffic, conversion, and revenue dimensions. This skill covers GA4 e-commerce tracking setup, funnel analysis, and key metric interpretation to diagnose why a store is or isn't performing.

电商分析从流量、转化和收入维度衡量在线商店的绩效。本技能涵盖GA4电商追踪设置、漏斗分析以及关键指标解读，以诊断商店表现良好或不佳的原因。

Framework

框架

IRON LAW: Diagnose by Funnel Stage, Not by Symptom

"Sales are down" is a symptom, not a diagnosis. Decompose into funnel stages:
Traffic × Conversion Rate × AOV = Revenue

If revenue drops 20%, is it because traffic dropped (acquisition problem),
conversion dropped (UX/pricing problem), or AOV dropped (product mix problem)?
Each requires a completely different fix.

IRON LAW: 按漏斗阶段诊断，而非症状

"销售额下降"是症状，而非诊断结果。需将其分解为漏斗阶段：
流量 × 转化率 × AOV = 收入

如果收入下降20%，是因为流量减少（获客问题）、转化率下降（用户体验/定价问题），还是AOV下降（产品组合问题）？
每个问题都需要完全不同的解决方案。

E-Commerce Funnel & Key Metrics

电商漏斗与关键指标

Stage	Metrics	What It Tells You
Acquisition	Sessions, Users, Traffic sources, CPC, CAC	Are you attracting enough visitors? From where? At what cost?
Engagement	Pages/session, Time on site, Bounce rate, Product views	Are visitors interested? Are they browsing?
Conversion	Add-to-cart rate, Checkout initiation rate, Purchase conversion rate	Where in the funnel are they dropping off?
Revenue	Revenue, AOV, Items per order, Revenue per session	How much are they spending? Is the mix healthy?
Retention	Repeat purchase rate, Purchase frequency, Customer lifetime value	Are they coming back?

阶段	指标	反映的问题
获客	Sessions, Users, Traffic sources, CPC, CAC	是否吸引了足够的访客？来自哪些渠道？成本如何？
互动	Pages/session, Time on site, Bounce rate, Product views	访客是否感兴趣？是否在浏览商品？
转化	Add-to-cart rate, Checkout initiation rate, Purchase conversion rate	访客在漏斗的哪个环节流失？
收入	Revenue, AOV, Items per order, Revenue per session	访客的消费金额是多少？产品组合是否健康？
留存	Repeat purchase rate, Purchase frequency, Customer lifetime value	访客是否会再次光顾？

GA4 E-Commerce Events

GA4电商事件

Event	Trigger	Key Parameters
`view_item`	Product page view	item_id, item_name, price, category
`add_to_cart`	Add to cart click	items array, value, currency
`begin_checkout`	Checkout started	items, value, coupon
`add_payment_info`	Payment entered	payment_type
`purchase`	Order completed	transaction_id, value, tax, shipping, items

事件	触发条件	关键参数
`view_item`	浏览商品页面	item_id, item_name, price, category
`add_to_cart`	点击加入购物车	items数组, value, currency
`begin_checkout`	启动结账流程	items, value, coupon
`add_payment_info`	输入支付信息	payment_type
`purchase`	完成订单	transaction_id, value, tax, shipping, items

Diagnosis Framework

诊断框架

Phase 1: Traffic Check

Is total traffic up/down/flat vs prior period?
Which channels changed? (organic, paid, social, direct, referral)
Is traffic quality declining? (bounce rate, pages/session by source)

Phase 2: Conversion Check

Where is the biggest funnel drop-off?
Compare: View → Add to cart → Checkout → Purchase
Industry benchmark conversion rates: 1-3% overall, 5-10% add-to-cart

Phase 3: Revenue Check

AOV trend: rising (upselling working) or falling (discounting eroding value)?
Product mix: is revenue shifting to lower-margin products?
Revenue per session: the master metric (traffic quality × conversion × AOV)

Phase 4: Retention Check

Repeat purchase rate by cohort
Time between first and second purchase
LTV trend by acquisition channel

第一阶段：流量检查

总流量较上期是上升/下降/持平？
哪些渠道的流量发生了变化？（自然搜索、付费广告、社交平台、直接访问、推荐）
流量质量是否下降？（按渠道查看跳出率、每次会话浏览页数）

第二阶段：转化检查

漏斗中流失最严重的环节是哪里？
对比：浏览商品 → 加入购物车 → 启动结账 → 完成购买
行业基准转化率：整体1-3%，加入购物车率5-10%

第三阶段：收入检查

AOV趋势：上升（交叉销售/向上销售有效）还是下降（折扣策略拉低价值）？
产品组合：收入是否向低利润率产品倾斜？
每次会话收入：核心指标（流量质量 × 转化率 × AOV）

第四阶段：留存检查

按用户群组查看复购率
首次购买与二次购买的间隔时间
按获客渠道查看LTV趋势

Output Format

输出格式

markdown

undefined

markdown

undefined

E-Commerce Performance Report: {Store}

电商绩效报告：{Store}

Summary Dashboard

摘要仪表板

Metric	Current	Prior Period	Change	Status
Sessions	{N}	{N}	{%}	🟢/🟡/🔴
Conversion Rate	{%}	{%}	{%}	🟢/🟡/🔴
AOV	${X}	${X}	{%}	🟢/🟡/🔴
Revenue	${X}	${X}	{%}	🟢/🟡/🔴

指标	当前值	上期值	变化率	状态
Sessions	{N}	{N}	{%}	🟢/🟡/🔴
Conversion Rate	{%}	{%}	{%}	🟢/🟡/🔴
AOV	${X}	${X}	{%}	🟢/🟡/🔴
Revenue	${X}	${X}	{%}	🟢/🟡/🔴

Funnel Analysis

漏斗分析

Stage	Volume	Rate	Drop-off	Benchmark
Sessions	{N}	100%	—	—
Product Views	{N}	{%}	{%}	—
Add to Cart	{N}	{%}	{%}	5-10%
Checkout	{N}	{%}	{%}	40-60% of ATC
Purchase	{N}	{%}	{%}	1-3% overall

阶段	数量	转化率	流失率	行业基准
Sessions	{N}	100%	—	—
Product Views	{N}	{%}	{%}	—
Add to Cart	{N}	{%}	{%}	5-10%
Checkout	{N}	{%}	{%}	加购用户的40-60%
Purchase	{N}	{%}	{%}	整体1-3%

Diagnosis

诊断结论

Primary issue: {funnel stage} — {specific problem}
Root cause: {analysis}

核心问题：{漏斗阶段} — {具体问题}
根本原因：{分析结果}

Recommendations

建议

{action targeting the diagnosed stage}

undefined

{针对诊断阶段的行动方案}

undefined

Gotchas

注意事项

Conversion rate is meaningless without traffic quality context: A 5% conversion rate from email (high-intent) and 0.5% from display ads (low-intent) are both normal. Don't compare across channels.
GA4 sessions ≠ Universal Analytics sessions: GA4 uses event-based model. Session timeout and attribution rules differ. Expect 5-15% discrepancy during migration.
Mobile conversion is always lower: Mobile: 1-2%, Desktop: 3-5% is typical. Don't mix them in one number — analyze separately.
Seasonality matters: Compare same period YoY, not just MoM. E-commerce has strong seasonal patterns (11.11, Christmas, Chinese New Year).
Revenue ≠ profit: A 20% revenue increase from aggressive discounting may reduce profit. Track margin alongside revenue.

脱离流量质量谈转化率毫无意义：邮件渠道（高意向）的5%转化率和展示广告渠道（低意向）的0.5%转化率都是正常水平，不要跨渠道对比。
GA4 Sessions ≠ 通用分析Sessions：GA4采用基于事件的模型，会话超时和归因规则不同。迁移期间可能出现5-15%的数据差异。
移动端转化率始终更低：移动端通常为1-2%，桌面端为3-5%，不要将两者合并分析——应分开统计。
季节性因素至关重要：应同比（YoY）对比同期数据，而非仅环比（MoM）。电商具有明显的季节性规律（如双11、圣诞节、春节）。
收入 ≠ 利润：通过大幅折扣实现的20%收入增长可能会降低利润，需同步追踪利润率与收入。

References

参考资料

For GA4 setup guide, see
```
references/ga4-setup.md
```
For e-commerce benchmark data by industry, see
```
references/ecom-benchmarks.md
```

GA4设置指南请查看
```
references/ga4-setup.md
```
各行业电商基准数据请查看
```
references/ecom-benchmarks.md
```