🤖 每日自动备份 - 2026-06-23 08:00:01
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小溪
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"snippet": "- 口径 = MEMORY.md 中所有计算逻辑、数据口径、指标定义、字段映射、判定条件、统计方法 - 唯一审批人：李承龙 - 禁止根据群聊讨论直接修改口径 - 禁止自行推断或\"修正\"已有口径 - 正确流程：发现问题 → 向李承龙确认 → 明确同意 → 方可修改 - 说明：数据查询本身按 USER.md 权限规则执行，不需要审批；本规则仅约束 MEMORY.md 中口径/计算逻辑的变更 ## 渠道归类补充 [王虹茗 2026-06-01 22:38] - 抖音精选 → 直购渠道 - 万物（newmedia-dianpu-wwxx-0-0）→ 达人渠道 - 合作活动是班主任 → 直购渠道 - ⚠️ 待李承龙确认后更新到 MEMORY.md 渠道映射规则",
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"snippet": "- 口径 = MEMORY.md 中所有计算逻辑、数据口径、指标定义、字段映射、判定条件、统计方法 - 唯一审批人：李承龙 - 禁止根据群聊讨论直接修改口径 - 禁止自行推断或\"修正\"已有口径 - 正确流程：发现问题 → 向李承龙确认 → 明确同意 → 方可修改 - 说明：数据查询本身按 USER.md 权限规则执行，不需要审批；本规则仅约束 MEMORY.md 中口径/计算逻辑的变更 ## 渠道归类补充 [王虹茗 2026-06-01 22:38] - 抖音精选 → 直购渠道 - 万物（newmedia-dianpu-wwxx-0-0）→ 达人渠道 - 合作活动是班主任 → 直购渠道 - ⚠️ 待李承龙确认后更新到 MEMORY.md 渠道映射规则",
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"snippet": "# 2026-06-15 工作日志 ## 陈逸鸫 - 订单口径变更：停直购 **来源：** 陈逸鸫（群聊「数据更新V2」） **变更内容：** 1. 以后不管直购单，只看小红书销转，直购 GMV 不再进销转对账 2. full_refresh 不再 merge 直购进订单汇总 3. 直购表（1sosYE）可删除（需人工在飞书操作，API 不支持删 sheet） 4. 仍支持按手机查 keyfrom/GSV/退款（不定销售归属） **脚本修改：** - `scripts/refresh_order_summary.py`：去掉 Step 1.5 读取直购表逻辑，不再合并直购行 - `scripts/full_refresh_sales.py`：不涉及直购，无需改动 - `scripts/refresh_direct_sheet.py`：保留不动（如后续仍需独立查直购数据） ## 陈逸鸫 - full_refresh 执行 - 时间：2026-06-15 08:24-08:26 - 销售三表：吴迪 697 / 小龙 1198 / 成都 2203 - 有效 user_id：2009 - 订单汇总：383 条（不含直购） - 直购表：75 条已刷新 - 订单汇总合并后：461 条（含直购，这是口径变更前的最后一次含直购的合并） ## 陈逸鸫 - 线索表字段调整（2026-06-15） **来源：** 陈逸鸫（群聊「小红书数据需求」） **线索表（销售三表）新列结构（A~Z）：** | 列 | 字段名 | 脚本填写 | |----|--------|---------|",
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"snippet": "# 2026-06-15 工作日志 ## 陈逸鸫 - 订单口径变更：停直购 **来源：** 陈逸鸫（群聊「数据更新V2」） **变更内容：** 1. 以后不管直购单，只看小红书销转，直购 GMV 不再进销转对账 2. full_refresh 不再 merge 直购进订单汇总 3. 直购表（1sosYE）可删除（需人工在飞书操作，API 不支持删 sheet） 4. 仍支持按手机查 keyfrom/GSV/退款（不定销售归属） **脚本修改：** - `scripts/refresh_order_summary.py`：去掉 Step 1.5 读取直购表逻辑，不再合并直购行 - `scripts/full_refresh_sales.py`：不涉及直购，无需改动 - `scripts/refresh_direct_sheet.py`：保留不动（如后续仍需独立查直购数据） ## 陈逸鸫 - full_refresh 执行 - 时间：2026-06-15 08:24-08:26 - 销售三表：吴迪 697 / 小龙 1198 / 成都 2203 - 有效 user_id：2009 - 订单汇总：383 条（不含直购） - 直购表：75 条已刷新 - 订单汇总合并后：461 条（含直购，这是口径变更前的最后一次含直购的合并） ## 陈逸鸫 - 线索表字段调整（2026-06-15） **来源：** 陈逸鸫（群聊「小红书数据需求」） **线索表（销售三表）新列结构（A~Z）：** | 列 | 字段名 | 脚本填写 | |----|--------|---------|",
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"snippet": "# 2026-06-16 工作日志 ## 李承龙 - 强化审批规则（12:42） **背景：** 李承龙发现 `full_refresh_sales.py` 在今天上午被修改（08:55），且 `sales_leads_full_refresh.py` 在群聊中被陈逸鸫的上下文触发修改。 **李承龙要求：** 1. 所有 workspace 脚本、skill、定时任务、轮询、MEMORY.md 的创建和修改，仅李承龙、李若松、张昆鹏三人有权下达指令 2. 每次修改必须记录时间、内容、来源人 3. 不允许再在任何对话/群聊中看到在别人指令下修改或创建上述内容 **已执行：** 1. AGENTS.md 规则已强化：新增 MEMORY.md 纳入审批范围、新增\"禁止顺手修改\"条款、新增修改后必须登记的要求 2. MEMORY.md 规则已同步强化 3. SKILL_REGISTRY.md 已补登记： - `full_refresh_sales.py`（创建于 2026-06-02，李承龙） - `sales_leads_full_refresh.py`（创建于 2026-06-08，李承龙） - `refresh_order_summary.py`（创建于约 2026-06，李承龙） 4. 变更记录已追加到各条目 ## 李承龙 - 增加操作前强制拦截检查（14:06） **李承龙要求：** 在 AGENTS.md 和 MEMORY.md 的审批规则前增加操作前强制检查清单。 **已执行：**",
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"snippet": "# 2026-06-16 工作日志 ## 李承龙 - 强化审批规则（12:42） **背景：** 李承龙发现 `full_refresh_sales.py` 在今天上午被修改（08:55），且 `sales_leads_full_refresh.py` 在群聊中被陈逸鸫的上下文触发修改。 **李承龙要求：** 1. 所有 workspace 脚本、skill、定时任务、轮询、MEMORY.md 的创建和修改，仅李承龙、李若松、张昆鹏三人有权下达指令 2. 每次修改必须记录时间、内容、来源人 3. 不允许再在任何对话/群聊中看到在别人指令下修改或创建上述内容 **已执行：** 1. AGENTS.md 规则已强化：新增 MEMORY.md 纳入审批范围、新增\"禁止顺手修改\"条款、新增修改后必须登记的要求 2. MEMORY.md 规则已同步强化 3. SKILL_REGISTRY.md 已补登记： - `full_refresh_sales.py`（创建于 2026-06-02，李承龙） - `sales_leads_full_refresh.py`（创建于 2026-06-08，李承龙） - `refresh_order_summary.py`（创建于约 2026-06，李承龙） 4. 变更记录已追加到各条目 ## 李承龙 - 增加操作前强制拦截检查（14:06） **李承龙要求：** 在 AGENTS.md 和 MEMORY.md 的审批规则前增加操作前强制检查清单。 **已执行：**",
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"snippet": "| 2026-06-16 约11:00 | `sales_leads_full_refresh.py` | 修复日期解析 | 群聊「数据更新V2」陈逸鸫上下文 | 已登记，标记违规 | | 2026-06-15 | `refresh_order_summary.py` | 去掉直购表合并 | 群聊「数据更新V2」陈逸鸫 | 已登记 | ## 李承龙 - 销售转化渠道分类规则迁移（14:14） **操作：** 1. 将 MEMORY.md 中「销售转化渠道分类规则」（2026-06-15 王虹茗确认、李承龙确认）迁移到 `scripts/full_refresh_sales.py` 2. 新增 `classify_sales_channel(key_from)` 函数，将 key_from 归为四类：端内 / 销转 / 达人 / 直购 3. 从 MEMORY.md 中删除该规则段落 4. SKILL_REGISTRY.md 已追加变更记录",
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"snippet": "| 2026-06-16 约11:00 | `sales_leads_full_refresh.py` | 修复日期解析 | 群聊「数据更新V2」陈逸鸫上下文 | 已登记，标记违规 | | 2026-06-15 | `refresh_order_summary.py` | 去掉直购表合并 | 群聊「数据更新V2」陈逸鸫 | 已登记 | ## 李承龙 - 销售转化渠道分类规则迁移（14:14） **操作：** 1. 将 MEMORY.md 中「销售转化渠道分类规则」（2026-06-15 王虹茗确认、李承龙确认）迁移到 `scripts/full_refresh_sales.py` 2. 新增 `classify_sales_channel(key_from)` 函数，将 key_from 归为四类：端内 / 销转 / 达人 / 直购 3. 从 MEMORY.md 中删除该规则段落 4. SKILL_REGISTRY.md 已追加变更记录",
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"snippet": "# 2026-05-11 工作日志 ## 转化率口径确认 [李承龙确认] ### 转化率（端内注册转付费转化率） - **定义：** 注册用户中在端内完成付费的比例 - **分母：** 按注册日期分组，`bi_vala_app_account.status=1` 非测试账号 - **分子（含退费）：** 分母中在端内（`key_from IN ('app-active-h5-0-0', 'app-sales-bj-qhm-0')`）有支付成功订单的去重用户数 - **分子（剔除退费）：** 同上，但仅剔除端内订单**全部被退费**的用户——只要用户还有任何一笔未退费的端内订单就保留（退费判定：`bi_refund_order.status=3` 且 `bi_vala_order.order_status=4`） - **时间基准：** 按注册日期分组，不限制订单发生时间 - **订单时间字段：** `pay_success_date` ### 7日转化率 - 分子限制：`pay_success_date ≤ 注册日期 + 7天`（含注册当日） ### 14日转化率 - 分子限制：`pay_success_date ≤ 注册日期 + 14天`（含注册当日） ### 注意事项 - 需关联 `bi_vala_app_account` 剔除测试账号，`deleted_at IS NULL` 剔除已删除账号 - 按注册日期分组时，注册日期取 `created_at` 的日期部分 - 7日/14日/20日窗口包含注册当日 - 端内订单筛选 `order_status IN",
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||||||
|
"memory:memory/2026-06-01.md:16:31": {
|
||||||
|
"key": "memory:memory/2026-06-01.md:16:31",
|
||||||
|
"path": "memory/2026-06-01.md",
|
||||||
|
"startLine": 16,
|
||||||
|
"endLine": 31,
|
||||||
|
"source": "memory",
|
||||||
|
"snippet": "- 同一用户可能有多条记录，只要存在任意一条即视为已加微 - 参考数据：老狼履约明细 31 人中，已加微 28 人（90.3%），未加微 3 人（24105, 25485, 25945） - MEMORY.md 已更新 ## 🚫 口径变更审批规则建立 [李承龙确认 2026-06-01 15:14] - **严重问题：** 之前在群聊中未经李承龙确认就修改了加微判断口径（从 stride_contact_bindings 改为 student_info），存在数据口径被非授权变更的风险 - **根因：** 缺少口径变更的审批机制，小溪在群聊中接受了其他人的计算逻辑并直接写入了长期记忆 - **解决方案：** 1. MEMORY.md 新增「口径变更审批规则」章节，明确李承龙为数据口径唯一审批人 2. AGENTS.md 新增「MEMORY.md 口径变更审批」章节，作为强制执行规则 3. 规则要点： - 口径 = MEMORY.md 中所有计算逻辑、数据口径、指标定义、字段映射、判定条件、统计方法 - 唯一审批人：李承龙 - 禁止根据群聊讨论直接修改口径 - 禁止自行推断或\"修正\"已有口径 - 正确流程：发现问题 → 向李承龙确认 → 明确同意 → 方可修改",
|
||||||
|
"recallCount": 1,
|
||||||
|
"dailyCount": 0,
|
||||||
|
"groundedCount": 0,
|
||||||
|
"totalScore": 1,
|
||||||
|
"maxScore": 1,
|
||||||
|
"firstRecalledAt": "2026-06-22T07:01:26.682Z",
|
||||||
|
"lastRecalledAt": "2026-06-22T07:01:26.682Z",
|
||||||
|
"queryHashes": [
|
||||||
|
"0100fb44019e"
|
||||||
|
],
|
||||||
|
"recallDays": [
|
||||||
|
"2026-06-22"
|
||||||
|
],
|
||||||
|
"conceptTags": [
|
||||||
|
"90.3",
|
||||||
|
"memory.md",
|
||||||
|
"stride-contact-bindings",
|
||||||
|
"student-info",
|
||||||
|
"agents.md",
|
||||||
|
"同一",
|
||||||
|
"用户",
|
||||||
|
"可能"
|
||||||
|
]
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|||||||
235
scripts/wechat_refund_correlation.py
Normal file
235
scripts/wechat_refund_correlation.py
Normal file
@ -0,0 +1,235 @@
|
|||||||
|
#!/usr/bin/env python3
|
||||||
|
"""
|
||||||
|
分析「是否添加班主任微信」与「退款率」的相关性
|
||||||
|
数据源：
|
||||||
|
- 加微判断：vala_class.public.student_info（vala_account_id 存在即已加微）
|
||||||
|
- 订单数据：vala_bi.bi_vala_order + bi_vala_app_account + bi_refund_order
|
||||||
|
"""
|
||||||
|
import psycopg2
|
||||||
|
import os
|
||||||
|
from collections import defaultdict
|
||||||
|
|
||||||
|
PG_HOST = "bj-postgres-16pob4sg.sql.tencentcdb.com"
|
||||||
|
PG_PORT = 28591
|
||||||
|
PG_USER = "ai_member"
|
||||||
|
PG_PASS = os.environ.get("PG_ONLINE_PASSWORD", "")
|
||||||
|
|
||||||
|
def get_conn(db):
|
||||||
|
return psycopg2.connect(host=PG_HOST, port=PG_PORT, user=PG_USER, password=PG_PASS, dbname=db)
|
||||||
|
|
||||||
|
print("=" * 70)
|
||||||
|
print("📊 是否添加班主任微信 vs 退款率 相关性分析")
|
||||||
|
print("=" * 70)
|
||||||
|
|
||||||
|
# ============================================================
|
||||||
|
# Step 1: 从 vala_class 获取所有已加微的 account_id
|
||||||
|
# ============================================================
|
||||||
|
print("\n[1/5] 获取已加班主任微信的用户列表...")
|
||||||
|
conn_class = get_conn("vala_class")
|
||||||
|
cur = conn_class.cursor()
|
||||||
|
cur.execute("""
|
||||||
|
SELECT DISTINCT vala_account_id
|
||||||
|
FROM student_info
|
||||||
|
WHERE vala_account_id IS NOT NULL
|
||||||
|
""")
|
||||||
|
wechat_accounts = {row[0] for row in cur.fetchall()}
|
||||||
|
cur.close()
|
||||||
|
conn_class.close()
|
||||||
|
print(f" 已加微用户数: {len(wechat_accounts):,}")
|
||||||
|
|
||||||
|
# ============================================================
|
||||||
|
# Step 2: 获取所有购课用户（非测试账号）
|
||||||
|
# ============================================================
|
||||||
|
print("\n[2/5] 获取购课用户及订单数据...")
|
||||||
|
conn_bi = get_conn("vala_bi")
|
||||||
|
cur = conn_bi.cursor()
|
||||||
|
|
||||||
|
# 获取所有非测试账号的购课订单
|
||||||
|
cur.execute("""
|
||||||
|
SELECT
|
||||||
|
o.account_id,
|
||||||
|
o.id AS order_id,
|
||||||
|
o.trade_no,
|
||||||
|
o.out_trade_no,
|
||||||
|
o.order_status,
|
||||||
|
o.pay_amount_int,
|
||||||
|
o.pay_success_date,
|
||||||
|
o.key_from
|
||||||
|
FROM bi_vala_order o
|
||||||
|
INNER JOIN bi_vala_app_account a ON o.account_id = a.id AND a.status = 1 AND a.deleted_at IS NULL
|
||||||
|
WHERE o.pay_success_date IS NOT NULL
|
||||||
|
AND o.order_status IN (3, 4)
|
||||||
|
""")
|
||||||
|
orders = cur.fetchall()
|
||||||
|
print(f" 购课订单数: {len(orders):,}")
|
||||||
|
|
||||||
|
# 按 account_id 分组
|
||||||
|
user_orders = defaultdict(list)
|
||||||
|
for row in orders:
|
||||||
|
user_orders[row[0]].append({
|
||||||
|
'order_id': row[1],
|
||||||
|
'trade_no': row[2],
|
||||||
|
'out_trade_no': row[3],
|
||||||
|
'order_status': row[4],
|
||||||
|
'pay_amount_int': row[5],
|
||||||
|
'pay_success_date': row[6],
|
||||||
|
'key_from': row[7]
|
||||||
|
})
|
||||||
|
|
||||||
|
buying_users = set(user_orders.keys())
|
||||||
|
print(f" 购课用户数（去重）: {len(buying_users):,}")
|
||||||
|
|
||||||
|
# ============================================================
|
||||||
|
# Step 3: 获取所有退费记录
|
||||||
|
# ============================================================
|
||||||
|
print("\n[3/5] 获取退费记录...")
|
||||||
|
cur.execute("""
|
||||||
|
SELECT trade_no, out_trade_no, refund_amount, status
|
||||||
|
FROM bi_refund_order
|
||||||
|
WHERE status = 3
|
||||||
|
""")
|
||||||
|
refund_rows = cur.fetchall()
|
||||||
|
print(f" 退费记录数: {len(refund_rows):,}")
|
||||||
|
|
||||||
|
# 构建退费记录列表：每条记录 (trade_no, out_trade_no, amount_fen)
|
||||||
|
refund_records = []
|
||||||
|
for row in refund_rows:
|
||||||
|
trade_no, out_trade_no, amount, status = row
|
||||||
|
amount_fen = int(float(amount) * 100) if amount else 0
|
||||||
|
refund_records.append((trade_no, out_trade_no, amount_fen))
|
||||||
|
|
||||||
|
cur.close()
|
||||||
|
conn_bi.close()
|
||||||
|
|
||||||
|
# ============================================================
|
||||||
|
# Step 4: 判断每笔订单是否退费
|
||||||
|
# ============================================================
|
||||||
|
print("\n[4/5] 计算退费情况...")
|
||||||
|
|
||||||
|
def get_refund_info(order):
|
||||||
|
"""判断订单是否退费，返回 (is_refunded, total_refund_amount_fen)"""
|
||||||
|
# order_status=4 → 直接判定退费
|
||||||
|
if order['order_status'] == 4:
|
||||||
|
# 计算退费金额：匹配所有退费记录
|
||||||
|
total = 0
|
||||||
|
tn = order['trade_no']
|
||||||
|
otn = order['out_trade_no']
|
||||||
|
seen = set() # 避免同一笔退费记录重复计算
|
||||||
|
for i, (r_tn, r_otn, r_amt) in enumerate(refund_records):
|
||||||
|
if (r_tn and r_tn == tn) or (r_otn and r_otn == otn):
|
||||||
|
if i not in seen:
|
||||||
|
seen.add(i)
|
||||||
|
total += r_amt
|
||||||
|
return True, total
|
||||||
|
# order_status=3 → 需在退费表中存在 status=3 的记录
|
||||||
|
if order['order_status'] == 3:
|
||||||
|
total = 0
|
||||||
|
tn = order['trade_no']
|
||||||
|
otn = order['out_trade_no']
|
||||||
|
seen = set()
|
||||||
|
for i, (r_tn, r_otn, r_amt) in enumerate(refund_records):
|
||||||
|
if (r_tn and r_tn == tn) or (r_otn and r_otn == otn):
|
||||||
|
if i not in seen:
|
||||||
|
seen.add(i)
|
||||||
|
total += r_amt
|
||||||
|
if total > 0:
|
||||||
|
return True, total
|
||||||
|
return False, 0
|
||||||
|
|
||||||
|
# 按用户统计
|
||||||
|
wechat_stats = {
|
||||||
|
'users': set(),
|
||||||
|
'total_orders': 0,
|
||||||
|
'refunded_orders': 0,
|
||||||
|
'total_gmv': 0,
|
||||||
|
'total_refund_amount': 0,
|
||||||
|
}
|
||||||
|
no_wechat_stats = {
|
||||||
|
'users': set(),
|
||||||
|
'total_orders': 0,
|
||||||
|
'refunded_orders': 0,
|
||||||
|
'total_gmv': 0,
|
||||||
|
'total_refund_amount': 0,
|
||||||
|
}
|
||||||
|
|
||||||
|
for account_id, order_list in user_orders.items():
|
||||||
|
has_wechat = account_id in wechat_accounts
|
||||||
|
target = wechat_stats if has_wechat else no_wechat_stats
|
||||||
|
target['users'].add(account_id)
|
||||||
|
|
||||||
|
for order in order_list:
|
||||||
|
target['total_orders'] += 1
|
||||||
|
target['total_gmv'] += order['pay_amount_int']
|
||||||
|
|
||||||
|
is_ref, refund_amt = get_refund_info(order)
|
||||||
|
if is_ref:
|
||||||
|
target['refunded_orders'] += 1
|
||||||
|
target['total_refund_amount'] += refund_amt
|
||||||
|
|
||||||
|
# ============================================================
|
||||||
|
# Step 5: 输出结果
|
||||||
|
# ============================================================
|
||||||
|
print("\n[5/5] 输出分析结果...")
|
||||||
|
print("\n" + "=" * 70)
|
||||||
|
print("📊 分析结果")
|
||||||
|
print("=" * 70)
|
||||||
|
|
||||||
|
def print_stats(label, stats):
|
||||||
|
users = len(stats['users'])
|
||||||
|
orders = stats['total_orders']
|
||||||
|
refunded = stats['refunded_orders']
|
||||||
|
gmv = stats['total_gmv'] / 100
|
||||||
|
refund_amt = stats['total_refund_amount'] / 100
|
||||||
|
gsv = gmv - refund_amt
|
||||||
|
|
||||||
|
refund_rate = (refunded / orders * 100) if orders > 0 else 0
|
||||||
|
refund_amount_rate = (refund_amt / gmv * 100) if gmv > 0 else 0
|
||||||
|
avg_orders_per_user = orders / users if users > 0 else 0
|
||||||
|
|
||||||
|
print(f"\n{'─' * 50}")
|
||||||
|
print(f" {label}")
|
||||||
|
print(f"{'─' * 50}")
|
||||||
|
print(f" 购课用户数: {users:>8,} 人")
|
||||||
|
print(f" 订单总数: {orders:>8,} 单")
|
||||||
|
print(f" 人均订单数: {avg_orders_per_user:>8.1f} 单/人")
|
||||||
|
print(f" 退费订单数: {refunded:>8,} 单")
|
||||||
|
print(f" 退费率(按订单): {refund_rate:>8.1f}%")
|
||||||
|
print(f" GMV: {gmv:>10,.0f} 元")
|
||||||
|
print(f" 退费金额: {refund_amt:>10,.0f} 元")
|
||||||
|
print(f" GSV: {gsv:>10,.0f} 元")
|
||||||
|
print(f" 退费金额占比: {refund_amount_rate:>8.1f}%")
|
||||||
|
return {
|
||||||
|
'users': users, 'orders': orders, 'refunded': refunded,
|
||||||
|
'refund_rate': refund_rate, 'gmv': gmv, 'refund_amt': refund_amt,
|
||||||
|
'gsv': gsv, 'refund_amount_rate': refund_amount_rate
|
||||||
|
}
|
||||||
|
|
||||||
|
w = print_stats("✅ 已加班主任微信", wechat_stats)
|
||||||
|
n = print_stats("❌ 未加班主任微信", no_wechat_stats)
|
||||||
|
|
||||||
|
# 对比
|
||||||
|
print(f"\n{'=' * 70}")
|
||||||
|
print("📊 对比分析")
|
||||||
|
print(f"{'=' * 70}")
|
||||||
|
print(f" 退费率差异: 已加微 {w['refund_rate']:.1f}% vs 未加微 {n['refund_rate']:.1f}%")
|
||||||
|
diff = w['refund_rate'] - n['refund_rate']
|
||||||
|
print(f" 退费率差值: {diff:+.1f} 个百分点")
|
||||||
|
if n['refund_rate'] > 0:
|
||||||
|
ratio = w['refund_rate'] / n['refund_rate']
|
||||||
|
print(f" 退费率比值: 已加微是未加微的 {ratio:.2f} 倍")
|
||||||
|
|
||||||
|
print(f"\n 退费金额占比差异: 已加微 {w['refund_amount_rate']:.1f}% vs 未加微 {n['refund_amount_rate']:.1f}%")
|
||||||
|
|
||||||
|
# 用户维度退费率（有退费的用户 / 总用户）
|
||||||
|
wechat_refund_users = sum(1 for uid in wechat_stats['users']
|
||||||
|
if any(get_refund_info(o)[0] for o in user_orders[uid]))
|
||||||
|
no_wechat_refund_users = sum(1 for uid in no_wechat_stats['users']
|
||||||
|
if any(get_refund_info(o)[0] for o in user_orders[uid]))
|
||||||
|
|
||||||
|
print(f"\n 用户维度退费率:")
|
||||||
|
print(f" 已加微: {wechat_refund_users}/{len(wechat_stats['users'])} = {wechat_refund_users/len(wechat_stats['users'])*100:.1f}%")
|
||||||
|
if len(no_wechat_stats['users']) > 0:
|
||||||
|
print(f" 未加微: {no_wechat_refund_users}/{len(no_wechat_stats['users'])} = {no_wechat_refund_users/len(no_wechat_stats['users'])*100:.1f}%")
|
||||||
|
|
||||||
|
print(f"\n{'=' * 70}")
|
||||||
|
print("分析完成 ✅")
|
||||||
Loading…
Reference in New Issue
Block a user