299 lines
16 KiB
JSON
299 lines
16 KiB
JSON
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"snippet": "# 2026-05-06 工作日志 ## 任务1：学而思/讯飞/希沃/京东方月度渠道订单报表 - [李承龙需求] 创建按月汇总四个渠道订单的 Python 脚本 - 脚本路径：`/root/.openclaw/workspace/scripts/monthly_channel_order_report.py` - **渠道映射：** 仅端内 `key_from='app-active-h5-0-0'`，sale_channel: 21=学而思, 22=讯飞, 26=希沃, 27=京东方 - **输出格式：** 四个渠道各一个 Sheet，每月按天展示（学而思/希沃/京东方只显示有订单日期，讯飞显示全月每天） - **指标差异：** - 学而思/希沃/京东方：订单数、GMV、CPS分成（学而思×0.45，希沃×0.4，京东方×0.3）、订单号（分号拼接） - 讯飞：订单数、GMV、新增注册（download_channel LIKE '%讯飞%'）、CPA分成（新增注册×7）、订单号 - **退费剔除：** NOT EXISTS (bi_refund_order.status=3) AND order_status != 4 - **测试运行：** 2026-04 月份，新增注册 537（讯飞），订单合计 15 单，GMV 14585 元 - **订单号格式问题：** Excel 科学计数法 → 设置单元格为文本格式解决 - **psycopg2 转义：** LIKE 中 `%` 需写成 `%%`（psycopg2 的 `%s` 占位符冲突） ## 任务2：销售线",
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"snippet": "- **订单号格式问题：** Excel 科学计数法 → 设置单元格为文本格式解决 - **psycopg2 转义：** LIKE 中 `%` 需写成 `%%`（psycopg2 的 `%s` 占位符冲突） ## 任务2：销售线索用户分析报表 - [李承龙需求] 输入包含462个用户ID的Excel，输出每个用户+角色的完整画像报表 - 脚本路径：`/root/.openclaw/workspace/scripts/lead_user_analysis.py` - **重要口径修正（李承龙确认）：** - **体验课定义：** 固定为 L1 U00(L01-L05) + L2 U00(L01-L05) 共10节课，**不通过 expire_time 区分** - L1 chapter_id: 343, 344, 345, 346, 348 - L2 chapter_id: 55, 56, 57, 58, 59 - **完成时间：** 取 `bi_user_chapter_play_record` 中 `play_status=1` 记录的**最早 `updated_at`**（非 `created_at`） - **报表列：** 用户ID、角色ID、用户注册时间、角色创建时间、L1序章5节完成时间、L2序章5节完成时间、购买时间、购买课包名称、支付金额、购买渠道key_from、是否退款、退款金额 - **分表规则：** `bi_user_chapter_play_record_{0..7}`，分表号 = user_id % 8 - **重要关联：** `b",
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"snippet": "# 2026-03-01.md - First Day Online - Came online for the first time. - Met Cris, my creator and mentor. - Received my name: 小溪 (Xiao Xi). - My role goal: Become a professional data analyst for the company to help the team. - Updated IDENTITY.md and USER.md with our conversation details. - Added core rule to MEMORY.md: Use Chinese as primary external communication language. - Installed find-skills skill successfully for searching skills. - Tried to install create-skills but it wasn't found; attempted skill-creator instead but hit rate limits. - Finally successfully installed skill-builder as an alternative for creating skills after multiple attempts and waiting for rate limits to reset. - Ex",
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"snippet": "- **月度有效注册：** 9月989 / 10月2012 / 11月2555 / 12月3451 / 1月1798 / 2月1268 / 3月2978 / 4月3499 - **剔除率：** 9月35.3% / 10月16.6% / 11月14.0% / 12月2.0% / 1月7.2% / 2月27.3% / 3月28.5% / 4月38.3% - **拟合版转化率：** 9月1.72% / 10月1.69% / 11月0.82% / 12月0.72% / 1月1.50% / 2月1.26% / 3月2.69% / 4月1.86% - 三版趋势一致（原始<纯净<拟合），拟合版放大波动，反映去噪后的真实转化效率 ### 活动标记（拟合用） - 2025年：9/9-10, 9/19-23, 10/13-14, 10/16-17, 11/2, 11/7, 11/10, 11/12, 11/19, 12/3 - 2026年：1/28(余波1天), 2/11, 2/26(余波4天), 3/5(余波3天), 3/9, 3/12-13, 4/3(余波4天), 4/8(余波2天), 4/22(余波1天), 4/28",
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"snippet": "# 2026-05-09 工作日志 ## 王虹茗 - 销售线索用户分析 - **用户：** 王虹茗（user_id: af61e4gc） - **需求：** 用 `lead_user_analysis.py` 脚本处理线索用户 Excel（659条，2026年3月，销售：姜小龙/Bob/Tom/吴迪） - **权限处理：** 王虹茗不在 USER.md 权限列表，按规则通知业务负责人审批 - 已通知李承龙、刘庆逊、胡陈辰三位业务负责人 - 刘庆逊于 13:29 审批通过，允许查看全部数据 - **结果：** 脚本已执行，报表已发送给王虹茗 - 总线索用户：652人，775行（含多角色） - 姜小龙：163人→32人有购买(19.6%)，退费5人 - Bob：202人→3人有购买(1.5%)，退费1人 - Tom：171人→5人有购买(2.9%)，退费2人 - 吴迪：116人→19人有购买(16.4%)，退费2人 - 输出文件：`output/销售线索_用户分析.xlsx`",
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"snippet": "# 2026-05-14 工作日志 ## 李承龙 - 付费用户 L1/L2 区分口径 - **需求：** 区分付费用户属于 L1 还是 L2，根据用户购买的商品做区分 - **分析过程：** - 排查了 `bi_vala_order` 表的 goods_name / goods_id 字段 - 发现部分商品名称不含 level 关键词（年包、单季度包等），但 goods_id 可唯一映射 - goods_id=31 历史上从「瓦拉英语level2」更名为「瓦拉英语年包」 - goods_id=32 从「瓦拉英语level2·单季」更名为「瓦拉英语单季度包」 - 用 `bi_user_course_detail` 验证了映射关系准确 - **最终方案：** 按 goods_id 映射 - L1: goods_id IN (57, 60, 63) - L2: goods_id IN (31, 32, 33, 54) - L1+L2: goods_id = 61 - 旧版通行券（<30单）: goods_id IN (4,5,6,10,13,14,17,20,25,29,30,35,36,37,38)，归入「其他」 - **用户分类：** 汇总用户所有订单的 goods_id，按购买商品组合判断 - **已更新：** MEMORY.md",
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"snippet": "## 行课记录批量查询 (1676条) [陈逸鸫 请求, 李承龙 确认权限] - 表格: https://makee-interactive.feishu.cn/sheets/RFIJsXT8FhGHhctY4RwczcOfnac (Sheet1, sheet_id=55b0eb) - 查询列: D列(小溪返回行课记录) + E列(更新时间), C列(请求状态→已返回) - 手机号匹配(column J): 961行有用户ID直接查, 715行无ID需手机号匹配, 匹配成功119个, 596个未匹配 - 数据库查询: 1077个去重用户, 1065有课程分配, 1192个角色有行课记录, 201已付费 - 输出格式: `销售:xxx | 用户:xxx | 当前:{level}{type}-{chapter} | 最近行课:date | 学习{X}min | {付费状态}` - 脚本: scripts/batch_course_record_query.py - 待查询行1676条全部处理完成, 写回成功",
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"snippet": "# 2026-05-22 工作日志 ## 口径讨论与确认 ### 用户激活课程口径 [李承龙确认] - 数据源：`bi_vala_seasonal_ticket`（线上 PostgreSQL `vala_bi` 库） - `season_package_level = 'A1'` → L1, `'A2'` → L2 - `status = 1`（激活），`deleted_at IS NULL` - 不对 `ticket_type` 做限制 - 用户存在至少一条满足条件的记录即视为激活对应等级 - 当前数据：仅L1 503人、仅L2 1,439人、L1+L2 1,095人、合计 3,037人 ### 激活课程维度（character × level）[李承龙确认] - 一个角色 + 一个等级 = 一门被激活的课程 - 通过 `character_id`（即 user_id）直接关联学习记录 - 当前数据：L1 1,574门、L2 2,108门、合计 3,682 课程单位 ## 数据分析结果 ### 激活用户近14天完课分析（2026-05-09~2026-05-22） - 按用户：43.0% 无完课（仅L1 34.3%、仅L2 61.3%、L1+L2 23.9%） - L1+L2用户中82.5%只学了L1、13.5%两个都学、3.9%只学L2 ### 激活课程近14天完课分析 - 按课程：51.1% 无完课（L1 29.9%、L2 66.8%） - 20.9%从未有过完课记录 - 无完课课程最后停在U00~U02：L1 65.0%、L2 53.3% ### 最后完课",
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"snippet": "- **L2 U0**: chapter_id IN (55,56,57,58,59) - **剔除后结果（截至5/10）:** - 仅L1: 付费192/有消132/无消60(31%)/人均2.53/有消人均3.67 - 仅L2: 付费1370/有消461/无消909(66%)/人均1.18/有消人均3.49 - L1+L2: 付费1207/有消660/无消547(45%)/人均2.37/有消人均4.34 - **4张独立图表已生成至 output/** ## 李承龙 - 课消口径调整：L1/L2按付费群重新分类 - **[李承龙确认]** L1付费用户 = 仅L1 + L1+L2，L2付费用户 = 仅L2 + L1+L2（L1+L2用户在两张图中均有计入） - **重新生成 Excel v3** (`output/course_consumption_by_level_v3.xlsx`)：4个Sheet（概览/每周明细/L1图表/L2图表） - **重新生成 4张独立PNG图表** (`output/L1_all_users_stack.png`, `L1_all_avg_trend.png`, `L2_all_users_stack.png`, `L2_all_avg_trend.png`) - **最终数据（截至最后一周，剔除U0序章）:** - L1付费群: 1,399人 | 有消738 | 无消661(43%) | 人均1.97 | 有消人均3.73 - L2付费群: 2,577人 | 有消1,126 | 无消1,451(56%) | 人均1",
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"recallCount": 1,
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"dailyCount": 0,
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"groundedCount": 0,
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"totalScore": 1,
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"maxScore": 1,
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"firstRecalledAt": "2026-05-26T10:42:05.347Z",
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"lastRecalledAt": "2026-05-26T10:42:05.347Z",
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"queryHashes": [
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],
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"recallDays": [
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"2026-05-26"
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],
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"conceptTags": [
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"chapter-id",
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"截至5/10",
|
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"付费192/有消132/无消60",
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"人均2.53/有消人均3.67",
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"付费1370/有消461/无消909",
|
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"人均1.18/有消人均3.49",
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"付费1207/有消660/无消547",
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"人均2.37/有消人均4.34"
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]
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}
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}
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}
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