332 lines
13 KiB
Markdown
332 lines
13 KiB
Markdown
# Session: 2026-04-15 03:28:48 UTC
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- **Session Key**: agent:main:feishu:direct:ou_e63ce6b760ad39382852472f28fbe2a2
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- **Session ID**: 1e739eff-af7b-4269-b5ae-f6844bb12402
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- **Source**: feishu
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## Conversation Summary
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user: System: [2026-04-15 10:30:27 GMT+8] Feishu[xiaoxi] DM | 李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2) [msg:om_x100b52dbcae950bcb2cf481a8a167ec]
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Conversation info (untrusted metadata):
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```json
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{
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"message_id": "om_x100b52dbcae950bcb2cf481a8a167ec",
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"sender_id": "ou_e63ce6b760ad39382852472f28fbe2a2",
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"sender": "李承龙",
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"timestamp": "Wed 2026-04-15 10:30 GMT+8"
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}
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```
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Sender (untrusted metadata):
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```json
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{
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"label": "李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2)",
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"id": "ou_e63ce6b760ad39382852472f28fbe2a2",
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"name": "李承龙"
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}
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```
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给我看看你查询退费订单的sql
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assistant: 这是查询4月退费情况的核心SQL：
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```sql
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WITH april_orders AS (
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-- 筛选4月成交的有效订单（剔除测试账号）
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SELECT
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o.id AS order_id,
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o.out_trade_no,
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o.pay_amount_int,
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o.order_status,
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o.created_at::date AS order_date
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FROM bi_vala_order o
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JOIN bi_vala_app_account a ON o.account_id = a.id
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WHERE a.status = 1 -- 仅保留正式账号
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AND o.created_at >= '2026-04-01'
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AND o.created_at < '2026-04-16'
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AND o.pay_amount_int > 0 -- 排除0元订单
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),
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refunded AS (
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-- 从4月订单中筛选已退费的订单
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SELECT DISTINCT ON (ao.order_id)
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ao.order_id,
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ao.order_date,
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ao.pay_amount_int,
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r.refund_amount_int
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FROM april_orders ao
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JOIN bi_refund_order r
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ON ao.out_trade_no = r.out_trade_no -- 通过交易单号关联退费表
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WHERE r.status = 3 -- 退费成功
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AND ao.order_status = 4 -- 订单状态为已退款
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)
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SELECT
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(SELECT COUNT(*) FROM april_orders) AS 总订单数,
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(SELECT ROUND(SUM(pay_amount_int)/100.0, 2) FROM april_orders) AS GMV_元,
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(SELECT COUNT(DISTINCT order_id) FROM refunded) AS 退费订单数,
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(SELECT ROUND(SUM(refund_amount_int)/100.0, 2) FROM refunded) AS 退费金额_元,
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-- GSV = GMV - 退费金额
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(SELECT ROUND(SUM(pay_amount_int)/100.0, 2) FROM april_orders)
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- COALESCE((SELECT ROUND(SUM(refund_amount_int)/100.0, 2) FROM refunded), 0) AS GSV_元,
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-- 退费率 = 退费订单数 / 总订单数
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ROUND(
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(SELECT COUNT(DISTINCT order_id) FROM refunded)::numeric
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/ (SELECT COUNT(*) FROM april_orders)::numeric * 100, 1
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) AS 退费率;
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```
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**关键逻辑说明：**
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1. **测试账号剔除**：`bi_vala_app_account.status = 1` 只保留正式用户
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2. **退费双重校验**：退费表 `status = 3`（退费成功）且 订单表 `order_status = 4`（已退款），缺一不可
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3. **关联方式**：两表通过 `out_trade_no`（交易单号）关联
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4. **金额换算**：`pay_amount_int` 和 `refund_amount_int` 都是分为单位，除以100转元
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user: System: [2026-04-15 10:33:19 GMT+8] Feishu[xiaoxi] DM | 李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2) [msg:om_x100b52dbc02b18a0b2d41d5df629fee]
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Conversation info (untrusted metadata):
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```json
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{
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"message_id": "om_x100b52dbc02b18a0b2d41d5df629fee",
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"sender_id": "ou_e63ce6b760ad39382852472f28fbe2a2",
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"sender": "李承龙",
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"timestamp": "Wed 2026-04-15 10:33 GMT+8"
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}
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```
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Sender (untrusted metadata):
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```json
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{
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"label": "李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2)",
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"id": "ou_e63ce6b760ad39382852472f28fbe2a2",
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"name": "李承龙"
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}
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```
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这440人中有多少人是仍然有未退费的订单的用户，共涉及到多少笔退费订单
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assistant: 440位退费用户中：
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- **54 位用户**仍持有未退费的订单（即部分退费，还保留了其他订单）
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- 这54人共涉及 **60 笔**退费订单
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- 剩余 **386 位用户**是4月成交的订单已全部退费的
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也就是说大部分退费用户（87.7%）是全部退完了，只有12.3%的用户是退了部分、还留着其他订单的情况。需要进一步分析这些用户的订单明细吗？
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user: System: [2026-04-15 10:35:13 GMT+8] Feishu[default] DM | 李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2) [msg:om_x100b52dbd8cfe490b4a9110028622e9]
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Conversation info (untrusted metadata):
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```json
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{
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"message_id": "om_x100b52dbd8cfe490b4a9110028622e9",
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"sender_id": "ou_e63ce6b760ad39382852472f28fbe2a2",
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"sender": "李承龙",
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"timestamp": "Wed 2026-04-15 10:35 GMT+8"
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}
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```
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Sender (untrusted metadata):
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```json
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{
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"label": "李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2)",
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"id": "ou_e63ce6b760ad39382852472f28fbe2a2",
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"name": "李承龙"
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}
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```
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给我看看这一次的sql
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assistant:
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```sql
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WITH april_orders AS (
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SELECT
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o.id AS order_id,
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o.out_trade_no,
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o.account_id,
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o.pay_amount_int,
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o.order_status
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FROM bi_vala_order o
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JOIN bi_vala_app_account a ON o.account_id = a.id
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WHERE a.status = 1
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AND o.created_at >= '2026-04-01'
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AND o.created_at < '2026-04-16'
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AND o.pay_amount_int > 0
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),
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-- 第一步：找出4月所有退费用户（440人）
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refund_users AS (
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SELECT DISTINCT ao.account_id
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FROM april_orders ao
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JOIN bi_refund_order r ON ao.out_trade_no = r.out_trade_no
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WHERE r.status = 3 AND ao.order_status = 4
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),
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-- 第二步：从440人中筛选仍有未退费订单的用户
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-- 条件：该用户在4月订单中存在 order_status != 4 的订单
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users_with_active_orders AS (
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SELECT DISTINCT ao.account_id
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FROM april_orders ao
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WHERE ao.account_id IN (SELECT account_id FROM refund_users)
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AND ao.order_status != 4 -- 非已退款 = 未退费
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),
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-- 第三步：统计这些用户对应的退费订单数
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refund_orders_of_active_users AS (
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SELECT DISTINCT ao.order_id, ao.account_id
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FROM april_orders ao
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JOIN bi_refund_order r ON ao.out_trade_no = r.out_trade_no
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WHERE r.status = 3
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AND ao.order_status = 4
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AND ao.account_id IN (SELECT account_id FROM users_with_active_orders)
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)
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SELECT
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(SELECT COUNT(*) FROM refund_users) AS 退费总用户数,
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(SELECT COUNT(*) FROM users_with_active_orders) AS 仍有未退费订单的用户数,
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(SELECT COUNT(*) FROM refund_orders_of_active_users) AS 这些用户的退费订单数;
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```
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**逻辑说明：**
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1. `april_orders`：和之前一样，4月全部有效订单（剔除测试账号）
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2. `refund_users`：从中找出退费成功的用户（440人）
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3. `users_with_active_orders`：在这440人中，筛选出在4月订单里还存在 `order_status != 4`（未退款）订单的用户 → 即"退了一部分但还保留了其他订单"的用户
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4. `refund_orders_of_active_users`：统计这批用户对应的退费订单数量
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user: System: [2026-04-15 10:59:05 GMT+8] Feishu[default] DM | 李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2) [msg:om_x100b52dba1492c98b215297a08c23f8]
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Conversation info (untrusted metadata):
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```json
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{
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"message_id": "om_x100b52dba1492c98b215297a08c23f8",
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"sender_id": "ou_e63ce6b760ad39382852472f28fbe2a2",
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"sender": "李承龙",
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"timestamp": "Wed 2026-04-15 10:59 GMT+8"
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}
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```
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Sender (untrusted metadata):
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```json
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{
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"label": "李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2)",
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"id": "ou_e63ce6b760ad39382852472f28fbe2a2",
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"name": "李承龙"
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}
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```
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剩余的386个用户中，有多少是key_from中带有daren字样的
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assistant: 386位全部退费的用户中，**379位**的订单 `key_from` 带有 `daren` 字样（达人直播渠道），占比高达 **98.2%**。
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也就是说4月全部退完的用户几乎都来自达人直播渠道，剩下仅7位来自其他渠道。需要进一步看达人渠道的细分来源吗？
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user: System: [2026-04-15 11:00:40 GMT+8] Feishu[default] DM | 李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2) [msg:om_x100b52dbbbba04a4b22566e2b3443ab]
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Conversation info (untrusted metadata):
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```json
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{
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"message_id": "om_x100b52dbbbba04a4b22566e2b3443ab",
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"sender_id": "ou_e63ce6b760ad39382852472f28fbe2a2",
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"sender": "李承龙",
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"timestamp": "Wed 2026-04-15 11:00 GMT+8"
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}
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```
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Sender (untrusted metadata):
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```json
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{
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"label": "李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2)",
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"id": "ou_e63ce6b760ad39382852472f28fbe2a2",
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"name": "李承龙"
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}
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```
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这379位用户有多少先购课后完成一节课还是先完成一节课后购
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assistant:
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好问题，我需要对比这379位用户的**首次购课时间**和**首次完成课时时间**的先后关系。
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根据MEMORY.md中的学习数据逻辑，课时完成记录在分表 `bi_user_chapter_play_record_{分表号}` 中，需要通过 `bi_vala_app_character` 关联用户。先确认下分表情况。
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assistant: 379位达人渠道全退用户的购课与完课先后关系如下：
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| 类别 | 用户数 | 占比 |
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|---|---|---|
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| **先购课后完课** | 192 | 50.7% |
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| **先完课后购课** | 65 | 17.2% |
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| **从未完成过任何课时** | 122 | 32.2% |
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**关键发现：**
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- **超过三成（122人）**购课后一节课都没学就退了，说明这批达人渠道用户的学习意愿本身就偏低
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- **约一半（192人）**是先购课再开始学习，之后退费
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- **65人**是体验过课程后再购买的，但最终也退了
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需要进一步分析这些用户的学习深度（比如完成了几节课后退费）或者按达人来源细分吗？
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user: System: [2026-04-15 11:06:10 GMT+8] Feishu[default] DM | 李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2) [msg:om_x100b52c444d6ccd4b2d7bed0c346f61]
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Conversation info (untrusted metadata):
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```json
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{
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"message_id": "om_x100b52c444d6ccd4b2d7bed0c346f61",
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"sender_id": "ou_e63ce6b760ad39382852472f28fbe2a2",
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"sender": "李承龙",
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"timestamp": "Wed 2026-04-15 11:06 GMT+8"
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}
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```
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Sender (untrusted metadata):
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```json
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{
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"label": "李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2)",
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"id": "ou_e63ce6b760ad39382852472f28fbe2a2",
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"name": "李承龙"
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}
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```
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我想将这379人按照渠道做分类并且统计先购课还是先完课还是没有完课记录。将带有学霸和Henry字样的订单归为学霸渠道、带有念妈字样的订单归为念妈渠道、带有晚柠字样的订单归为晚柠渠道、带有四个娃字样的订单归为四个娃渠道、带有小暖阿姨字样的订单归为小暖阿姨渠道。
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assistant: 379位达人渠道全退用户按渠道分类统计如下：
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| 渠道 | 用户总数 | 先购课后完课 | 先完课后购课 | 从未完成课时 |
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|---|---|---|---|---|
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| **学霸渠道** | 337 | 172（51.0%） | 62（18.4%） | 103（30.6%） |
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| **念妈渠道** | 32 | 14（43.8%） | 2（6.3%） | 16（50.0%） |
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| **四个娃渠道** | 8 | 6（75.0%） | 0 | 2（25.0%） |
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| **小暖阿姨渠道** | 1 | 0 | 0 | 1 |
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| **晚柠渠道** | 1 | 0 | 1 | 0 |
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**几个要点：**
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- **学霸渠道是绝对主力**，337人占全退用户的88.9%，其中103人（30.6%）买了一节课都没上就退了
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- **念妈渠道**退费用户中一半（16人）从未完成过课时，学习转化比学霸渠道更差
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- 四个娃、小暖阿姨、晚柠三个渠道体量很小，样本不足以做趋势判断
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需要进一步分析学霸渠道这337人的学习深度或退费时间分布吗？
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user: System: [2026-04-15 11:13:51 GMT+8] Feishu[default] DM | 李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2) [msg:om_x100b52c4683c4884b3734406543f3ce]
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Conversation info (untrusted metadata):
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```json
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{
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"message_id": "om_x100b52c4683c4884b3734406543f3ce",
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"sender_id": "ou_e63ce6b760ad39382852472f28fbe2a2",
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"sender": "李承龙",
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"timestamp": "Wed 2026-04-15 11:13 GMT+8"
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}
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```
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Sender (untrusted metadata):
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```json
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{
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"label": "李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2)",
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"id": "ou_e63ce6b760ad39382852472f28fbe2a2",
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"name": "李承龙"
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}
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```
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将用户群体从4月份成交并退费的用户扩展到全部购课并退费的用户，然后只看学霸、念妈和晚柠三个渠道
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assistant: 扩展到全量购课并全部退费的用户后，三个渠道的统计如下：
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| 渠道 | 用户总数 | 先购课后完课 | 先完课后购课 | 从未完成课时 |
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|---|---|---|---|---|
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| **学霸渠道** | 337 | 173（51.3%） | 61（18.1%） | 103（30.6%） |
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| **念妈渠道** | 240 | 128（53.3%） | 18（7.5%） | 94（39.2%） |
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| **晚柠渠道** | 132 | 57（43.2%） | 27（20.5%） | 48（36.4%） |
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**对比分析：**
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| 指标 | 学霸 | 念妈 | 晚柠 |
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|---|---|---|---|
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| 全退用户总数 | 337 | 240 | 132 |
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| 从未完成课时占比 | 30.6% | **39.2%** | 36.4% |
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| 先完课后购课占比 | 18.1% | **7.5%** | 20.5% |
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- **念妈渠道**的"从未完成课时"占比最高（39.2%），且"先完课后购课"比例最低（7.5%），说明这个渠道的用户体验产品的意愿最弱，冲动购买后直接退费的比例偏高
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- **晚柠渠道**的"先完课后购课"比例最高（20.5%），说明用户有一定体验意愿，但体验后购买再退费
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- **学霸渠道**体量最大，各项比例处于中间水平
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需要进一步看这些用户完成了多少节课后退费，或者按购买时间段拆分吗？
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