Case 03658
客户清单分析工具。基于Shop API客户清单数据,快速查询不同类型客户的数量、试用情况汇总、导购匹配情况。 核心能力: 1. 客户类型分布统计(普通/潜在/意向/成交客户数量及占比) 2. 客户试用情况汇总(感兴趣商品数、试用商品数、试用后成交转化) 3. 导购匹配分析(各导购关联客户数、匹配失败数量及原因) 4. 客户明细列表查询(支持分页、筛选、导出) 数据源:POST /api/v1/customer/list 使用场景: - 晨会/周会快速查询客户情况 - 实时监控客户类型分布 - 导购客户分配检查 - 匹配失败客户处理 触发条件: - 用户查询客户清单(如"今天有多少意向客户") - 用户统计客户类型(如"各类型客户占比多少") - 用户检查导购匹配(如"有多少客户匹配失败") - 用户查看客户试用情况(如"客户试用情况如何")From its SKILL.md
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客户清单分析 Skill
技能名称
customer-list-analysis
版本
v1.0
功能描述
基于 Shop API 客户清单数据,快速查询不同类型客户的数量、试用情况汇总、导购匹配情况。
数据源
API 端点
POST /api/v1/customer/list
请求参数
| 参数 | 类型 | 必填 | 说明 |
|---|---|---|---|
| storeId | string | 是 | 门店ID |
| fromDate | string | 是 | 开始时间 (YYYY-MM-DD HH:mm:ss) |
| toDate | string | 是 | 结束时间 (YYYY-MM-DD HH:mm:ss) |
| customerType | array | 否 | 客户类型 [1,2,3,4],空数组=全部 |
| badgeName | string | 否 | 导购姓名,为空查询全部 |
| pageNo | int | 否 | 页码,默认1 |
| pageSize | int | 否 | 每页数量,默认20 |
客户类型
| 类型值 | 名称 | 说明 |
|---|---|---|
| 1 | 普通客户 | 进店浏览但未产生试用行为 |
| 2 | 潜在客户 | 有普通试用行为 |
| 3 | 意向客户 | 有深度试用但未成交 |
| 4 | 成交客户 | 已完成购买 |
返回字段
| 字段 | 说明 |
|---|---|
| customerId | 客户ID |
| visitId | 访问ID |
| customerType | 客户类型 (1-4) |
| fromTime | 进店时间 |
| toTime | 离店时间 |
| duration | 停留时长(秒) |
| interestedItems | 感兴趣商品数 |
| trialItems | 试用商品数 |
| trialTransTotal | 试用后成交件数 |
| transMoney | 成交金额 |
| transTotal | 成交件数 |
| badgeName | 关联导购姓名(为空表示匹配失败) |
核心能力
1. 客户类型分布统计
总客户数: XXX人
├── 普通客户: XX人 (XX%) - 进店未试用
├── 潜在客户: XX人 (XX%) - 有普通试用
├── 意向客户: XX人 (XX%) - 深度试用未成交
└── 成交客户: XX人 (XX%) - 已完成购买
2. 客户试用情况汇总
按客户类型汇总:
- 平均感兴趣商品数
- 平均试用商品数
- 试用后成交转化率
- 平均成交金额
3. 导购匹配分析
总客户数: XXX人
├── 已匹配导购: XX人 (XX%)
│ ├── 导购A: XX人
│ ├── 导购B: XX人
│ └── ...
└── 匹配失败: XX人 (XX%) - 需处理
4. 客户明细列表
支持分页查询,可导出客户明细。
使用示例
from analyze import analyze_customer_list
# 查询今日客户清单
result = analyze_customer_list(
store_id="416759_1714379448487",
from_date="2026-03-25 00:00:00",
to_date="2026-03-25 23:59:59",
customer_type=[], # 全部客户
badge_name=None # 全部导购
)
# 查询特定导购的客户
result = analyze_customer_list(
store_id="416759_1714379448487",
from_date="2026-03-25 00:00:00",
to_date="2026-03-25 23:59:59",
customer_type=[3, 4], # 意向+成交
badge_name="杨丽"
)
输出格式
{
"status": "ok",
"store_id": "416759_1714379448487",
"query_period": {
"from": "2026-03-25 00:00:00",
"to": "2026-03-25 23:59:59"
},
"summary": {
"total_customers": 50,
"by_type": {
"普通客户": {"count": 10, "percentage": 20.0},
"潜在客户": {"count": 15, "percentage": 30.0},
"意向客户": {"count": 15, "percentage": 30.0},
"成交客户": {"count": 10, "percentage": 20.0}
},
"by_badge": {
"matched": {"count": 45, "percentage": 90.0},
"unmatched": {"count": 5, "percentage": 10.0},
"clerks": {
"杨丽": {"count": 20, "types": {"意向": 10, "成交": 10}},
"李翠": {"count": 15, "types": {"潜在": 8, "意向": 7}},
"未匹配": {"count": 5, "types": {"普通": 3, "潜在": 2}}
}
}
},
"trial_summary": {
"平均感兴趣商品数": 3.5,
"平均试用商品数": 2.1,
"试用后成交转化率": 35.0
},
"customer_list": [...],
"page": {
"total": 50,
"size": 20,
"pages": 3,
"current": 1
}
}
依赖
api_client.get_shop_data()- Shop API 调用~/.openclaw/workspace-front-door/- API 客户端路径
版本
v1.0.0 - 客户清单查询、类型分布、试用汇总、导购匹配
What ships with it: 5 files
15.1 KB alongside SKILL.md, 3 of them executable
scripts/
- _bootstrap.part1.txt947 B
- _bootstrap.part2.txt947 B
- _bootstrap.pyruns504 B
- analyze.pyruns12.2 KB
- __init__.pyruns549 B