13 KiB
Session: 2026-04-15 03:28:48 UTC
- Session Key: agent:main:feishu:direct:ou_e63ce6b760ad39382852472f28fbe2a2
- Session ID: 1e739eff-af7b-4269-b5ae-f6844bb12402
- Source: feishu
Conversation Summary
user: System: [2026-04-15 10:30:27 GMT+8] Feishu[xiaoxi] DM | 李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2) [msg:om_x100b52dbcae950bcb2cf481a8a167ec]
Conversation info (untrusted metadata):
{
"message_id": "om_x100b52dbcae950bcb2cf481a8a167ec",
"sender_id": "ou_e63ce6b760ad39382852472f28fbe2a2",
"sender": "李承龙",
"timestamp": "Wed 2026-04-15 10:30 GMT+8"
}
Sender (untrusted metadata):
{
"label": "李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2)",
"id": "ou_e63ce6b760ad39382852472f28fbe2a2",
"name": "李承龙"
}
给我看看你查询退费订单的sql assistant: 这是查询4月退费情况的核心SQL:
WITH april_orders AS (
-- 筛选4月成交的有效订单(剔除测试账号)
SELECT
o.id AS order_id,
o.out_trade_no,
o.pay_amount_int,
o.order_status,
o.created_at::date AS order_date
FROM bi_vala_order o
JOIN bi_vala_app_account a ON o.account_id = a.id
WHERE a.status = 1 -- 仅保留正式账号
AND o.created_at >= '2026-04-01'
AND o.created_at < '2026-04-16'
AND o.pay_amount_int > 0 -- 排除0元订单
),
refunded AS (
-- 从4月订单中筛选已退费的订单
SELECT DISTINCT ON (ao.order_id)
ao.order_id,
ao.order_date,
ao.pay_amount_int,
r.refund_amount_int
FROM april_orders ao
JOIN bi_refund_order r
ON ao.out_trade_no = r.out_trade_no -- 通过交易单号关联退费表
WHERE r.status = 3 -- 退费成功
AND ao.order_status = 4 -- 订单状态为已退款
)
SELECT
(SELECT COUNT(*) FROM april_orders) AS 总订单数,
(SELECT ROUND(SUM(pay_amount_int)/100.0, 2) FROM april_orders) AS GMV_元,
(SELECT COUNT(DISTINCT order_id) FROM refunded) AS 退费订单数,
(SELECT ROUND(SUM(refund_amount_int)/100.0, 2) FROM refunded) AS 退费金额_元,
-- GSV = GMV - 退费金额
(SELECT ROUND(SUM(pay_amount_int)/100.0, 2) FROM april_orders)
- COALESCE((SELECT ROUND(SUM(refund_amount_int)/100.0, 2) FROM refunded), 0) AS GSV_元,
-- 退费率 = 退费订单数 / 总订单数
ROUND(
(SELECT COUNT(DISTINCT order_id) FROM refunded)::numeric
/ (SELECT COUNT(*) FROM april_orders)::numeric * 100, 1
) AS 退费率;
关键逻辑说明:
- 测试账号剔除:
bi_vala_app_account.status = 1只保留正式用户 - 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 - 关联方式:两表通过
out_trade_no(交易单号)关联 - 金额换算:
pay_amount_int和refund_amount_int都是分为单位,除以100转元 user: System: [2026-04-15 10:33:19 GMT+8] Feishu[xiaoxi] DM | 李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2) [msg:om_x100b52dbc02b18a0b2d41d5df629fee]
Conversation info (untrusted metadata):
{
"message_id": "om_x100b52dbc02b18a0b2d41d5df629fee",
"sender_id": "ou_e63ce6b760ad39382852472f28fbe2a2",
"sender": "李承龙",
"timestamp": "Wed 2026-04-15 10:33 GMT+8"
}
Sender (untrusted metadata):
{
"label": "李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2)",
"id": "ou_e63ce6b760ad39382852472f28fbe2a2",
"name": "李承龙"
}
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 assistant: 440位退费用户中:
- 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)
- 这54人共涉及 60 笔退费订单
- 剩余 386 位用户是4月成交的订单已全部退费的
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? user: System: [2026-04-15 10:35:13 GMT+8] Feishu[default] DM | 李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2) [msg:om_x100b52dbd8cfe490b4a9110028622e9]
Conversation info (untrusted metadata):
{
"message_id": "om_x100b52dbd8cfe490b4a9110028622e9",
"sender_id": "ou_e63ce6b760ad39382852472f28fbe2a2",
"sender": "李承龙",
"timestamp": "Wed 2026-04-15 10:35 GMT+8"
}
Sender (untrusted metadata):
{
"label": "李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2)",
"id": "ou_e63ce6b760ad39382852472f28fbe2a2",
"name": "李承龙"
}
给我看看这一次的sql assistant:
WITH april_orders AS (
SELECT
o.id AS order_id,
o.out_trade_no,
o.account_id,
o.pay_amount_int,
o.order_status
FROM bi_vala_order o
JOIN bi_vala_app_account a ON o.account_id = a.id
WHERE a.status = 1
AND o.created_at >= '2026-04-01'
AND o.created_at < '2026-04-16'
AND o.pay_amount_int > 0
),
-- 第一步:找出4月所有退费用户(440人)
refund_users AS (
SELECT DISTINCT ao.account_id
FROM april_orders ao
JOIN bi_refund_order r ON ao.out_trade_no = r.out_trade_no
WHERE r.status = 3 AND ao.order_status = 4
),
-- 第二步:从440人中筛选仍有未退费订单的用户
-- 条件:该用户在4月订单中存在 order_status != 4 的订单
users_with_active_orders AS (
SELECT DISTINCT ao.account_id
FROM april_orders ao
WHERE ao.account_id IN (SELECT account_id FROM refund_users)
AND ao.order_status != 4 -- 非已退款 = 未退费
),
-- 第三步:统计这些用户对应的退费订单数
refund_orders_of_active_users AS (
SELECT DISTINCT ao.order_id, ao.account_id
FROM april_orders ao
JOIN bi_refund_order r ON ao.out_trade_no = r.out_trade_no
WHERE r.status = 3
AND ao.order_status = 4
AND ao.account_id IN (SELECT account_id FROM users_with_active_orders)
)
SELECT
(SELECT COUNT(*) FROM refund_users) AS 退费总用户数,
(SELECT COUNT(*) FROM users_with_active_orders) AS 仍有未退费订单的用户数,
(SELECT COUNT(*) FROM refund_orders_of_active_users) AS 这些用户的退费订单数;
逻辑说明:
april_orders:和之前一样,4月全部有效订单(剔除测试账号)refund_users:从中找出退费成功的用户(440人)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refund_orders_of_active_users:统计这批用户对应的退费订单数量 user: System: [2026-04-15 10:59:05 GMT+8] Feishu[default] DM | 李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2) [msg:om_x100b52dba1492c98b215297a08c23f8]
Conversation info (untrusted metadata):
{
"message_id": "om_x100b52dba1492c98b215297a08c23f8",
"sender_id": "ou_e63ce6b760ad39382852472f28fbe2a2",
"sender": "李承龙",
"timestamp": "Wed 2026-04-15 10:59 GMT+8"
}
Sender (untrusted metadata):
{
"label": "李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2)",
"id": "ou_e63ce6b760ad39382852472f28fbe2a2",
"name": "李承龙"
}
剩余的386个用户中,有多少是key_from中带有daren字样的
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%。
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? user: System: [2026-04-15 11:00:40 GMT+8] Feishu[default] DM | 李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2) [msg:om_x100b52dbbbba04a4b22566e2b3443ab]
Conversation info (untrusted metadata):
{
"message_id": "om_x100b52dbbbba04a4b22566e2b3443ab",
"sender_id": "ou_e63ce6b760ad39382852472f28fbe2a2",
"sender": "李承龙",
"timestamp": "Wed 2026-04-15 11:00 GMT+8"
}
Sender (untrusted metadata):
{
"label": "李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2)",
"id": "ou_e63ce6b760ad39382852472f28fbe2a2",
"name": "李承龙"
}
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 assistant:
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。
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。
assistant: 379位达人渠道全退用户的购课与完课先后关系如下:
| 类别 | 用户数 | 占比 |
|---|---|---|
| 先购课后完课 | 192 | 50.7% |
| 先完课后购课 | 65 | 17.2% |
| 从未完成过任何课时 | 122 | 32.2% |
关键发现:
- **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
- **约一半(192人)**是先购课再开始学习,之后退费
- 65人是体验过课程后再购买的,但最终也退了
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? user: System: [2026-04-15 11:06:10 GMT+8] Feishu[default] DM | 李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2) [msg:om_x100b52c444d6ccd4b2d7bed0c346f61]
Conversation info (untrusted metadata):
{
"message_id": "om_x100b52c444d6ccd4b2d7bed0c346f61",
"sender_id": "ou_e63ce6b760ad39382852472f28fbe2a2",
"sender": "李承龙",
"timestamp": "Wed 2026-04-15 11:06 GMT+8"
}
Sender (untrusted metadata):
{
"label": "李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2)",
"id": "ou_e63ce6b760ad39382852472f28fbe2a2",
"name": "李承龙"
}
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。 assistant: 379位达人渠道全退用户按渠道分类统计如下:
| 渠道 | 用户总数 | 先购课后完课 | 先完课后购课 | 从未完成课时 |
|---|---|---|---|---|
| 学霸渠道 | 337 | 172(51.0%) | 62(18.4%) | 103(30.6%) |
| 念妈渠道 | 32 | 14(43.8%) | 2(6.3%) | 16(50.0%) |
| 四个娃渠道 | 8 | 6(75.0%) | 0 | 2(25.0%) |
| 小暖阿姨渠道 | 1 | 0 | 0 | 1 |
| 晚柠渠道 | 1 | 0 | 1 | 0 |
几个要点:
- 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
- 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
- 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
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? user: System: [2026-04-15 11:13:51 GMT+8] Feishu[default] DM | 李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2) [msg:om_x100b52c4683c4884b3734406543f3ce]
Conversation info (untrusted metadata):
{
"message_id": "om_x100b52c4683c4884b3734406543f3ce",
"sender_id": "ou_e63ce6b760ad39382852472f28fbe2a2",
"sender": "李承龙",
"timestamp": "Wed 2026-04-15 11:13 GMT+8"
}
Sender (untrusted metadata):
{
"label": "李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2)",
"id": "ou_e63ce6b760ad39382852472f28fbe2a2",
"name": "李承龙"
}
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 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:
| 渠道 | 用户总数 | 先购课后完课 | 先完课后购课 | 从未完成课时 |
|---|---|---|---|---|
| 学霸渠道 | 337 | 173(51.3%) | 61(18.1%) | 103(30.6%) |
| 念妈渠道 | 240 | 128(53.3%) | 18(7.5%) | 94(39.2%) |
| 晚柠渠道 | 132 | 57(43.2%) | 27(20.5%) | 48(36.4%) |
对比分析:
| 指标 | 学霸 | 念妈 | 晚柠 |
|---|---|---|---|
| 全退用户总数 | 337 | 240 | 132 |
| 从未完成课时占比 | 30.6% | 39.2% | 36.4% |
| 先完课后购课占比 | 18.1% | 7.5% | 20.5% |
- 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
- 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
- 学霸渠道体量最大,各项比例处于中间水平
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?