WebConf Taiwan
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    # AI 時代下, 如何運用數據幫助產品發揮影響力 - 姜乃文 {%hackmd o8oUosN0QnqMSkF1_6kH7g %} #### 📚 議程介紹 {%preview https://webconf.tw/agenda/5 %} ###### ▼▼▼ 開始筆記 ▼▼▼ ## Opening ### 定義問題 ![IMG_8968](https://hackmd.io/_uploads/SkJYhZtGbe.jpg) - 快速定義追蹤Define - 明確定義問題,聚焦核心指標 - 快速驗證 Verify - 用最小數據集完成第一次驗證循環 - 快速學習前進 Iterate - 將數位洞察轉化為下一輪決策輸入 市面上的 AI 產品已經實踐的增益產品開發: - 自動化部署 - 協作平台整合 - 開發效率提升 ### 產品數據追蹤的本質:不是裝追蹤碼,而是資料的復現能力 ![IMG_8970](https://hackmd.io/_uploads/r1_EhZtG-l.jpg) 追蹤即決策語言 :::success 關鍵心法:追蹤=問題結構化,忌諱多頭多工 ::: ### 快速掌握產品數據追蹤的方法 ![IMG_8971](https://hackmd.io/_uploads/Hk0kFZtz-g.jpg) - 定義問題(同上) ⬇️ 作法 - 定義「最小可用問題MUQ(Mimimum Usable Question)」 - 快速建立事件追蹤 - Tracking Tool/後台/Server - 數據先行文化 - 功能與追蹤埋設一樣重要 ### 定義「最小可用問題MUQ」 在開始任何追蹤之前,先問自己 「我真正需要回答的問題是甚麼?」會驅動整個數據策略 >[!Note] MUQ 公式: 我要回答的問題是__,所以最小需要的數據是:A__, B__, C__ 這個產品功能上線了,真的有為公司創造出價值嗎? - 最小需要的數據: A.實體價值 - Revenue / CLTV / Profit B. 隱形價值 - NPS / Brand Impression / RS(推薦率) ::: warning 再多事後猜測、推測,都比不過先埋下追蹤碼(一去不回頭) ::: ### 快速建立事件追蹤 ![IMG_8973](https://hackmd.io/_uploads/ry9PaZKfWe.jpg) - Page level event: - 追蹤使用者基本頁面瀏覽行為與導航路徑 - 目的是複現事件位置 - 常見錯誤:只埋點按鈕,但忽略掉按鈕可能發生在不同位置 - 命名直覺,讓大家宜看知到是件發生位置 - Funnel Event 套件化 - 建立可重複使用的轉換漏斗追蹤模組 - 疊加事件做更深入的分析 - 深度行為追蹤 - 補捉 scroll cta 移動軌跡熱點等互動細節 - 重點在推測出使用者的意圖 - 追蹤一致性 - 比「追蹤全面性」更重要的是命名與結構的一致,低使用門檻(直覺) - 猜測使用者的意圖 ### 數據先行文化|功能與追蹤埋設一樣重要 ![IMG_8974](https://hackmd.io/_uploads/Sy35aZtM-l.jpg) - User story 作為追蹤起點 - 讓每個人都能理解追蹤的價值 - 例如:同類型頁面的瀏覽,當使用者在 Pricing 頁不斷切換,可能表示使用者對其敏感 - 提供Ready-to-use Spec: POC時代,降低開發時往復的溝通過程,在一開始就拉齊名詞解釋與定義,讓團隊在同一個picture內說話。eg:提供完整追蹤規格文件,包含清晰的事件命名規則 - 務必要直覺且好記 - 最重要的目的:復現使用者做這件事當下的狀態 - 在已經建構好的追蹤中,知道使用者從哪個來源來?什麼身份? - 讓團隊成員知道分析師這邊能提供的答案類型 - 建立命名規範與範例:統一的命名規範能大幅降低溝通成本。建議採用「物件_動作」格式,並提供豐富的範例庫供團隊參考,確保一致性。 - 中央控管機制(白名單) - 例如 APP 加入 Web 因為是兩組人開發,沒有統一而造成兩方對齊成本增加 - 降低新手加入的門檻、拉齊團隊的共同語言 ## AI 時代的 POC 如何加速 - 快速設計 - 快速輸入、輸出很重要 - 明確化:決策->輸入->輸出 - 先確定要問的問題,做出決策,訂出成功指標 1. 決策 用戶會因此完成目標嗎? 繼續投入或轉向 目標達成率 2. 輸入 我最少需要甚麼資料? 資料蒐集範圍 資料完整度 3. 輸出 成功的指標是甚麼 是否達到預期 關鍵指標 - 快速做 ![IMG_8975](https://hackmd.io/_uploads/r1_72ZKM-l.jpg) - 使用可複用模板與框架 - 要能複現,前提是有個重複運用的框架 - RAG Pipeline - 不斷優化底層知識與架構 - prompt library - evaluation sheet - 快速學 - Data-> insight -> Desicion ## 實戰案例來哩 ### 案例一:時間管理產品 - APP 核心: - 首頁 - Todo - Table - - 初步構想:個人時間管理需求連結既有 Dashboard,想把這個模板化的成果做成 Product - AI 協作:把所有資料丟入,約五分鐘就產生出「嚇死人」的結果 - 快速上線:申請網域與伺服器上線(lovable 現在應該也有類似服務,zeabur 也可以申請部署,適合新手上路) - 社群分享與用戶 - 分享給第一層圈圈的朋友 - 分享給自已經營的社群 - 追蹤碼埋設挑戰 - 起因看到人數一直在流失,所以想要埋追蹤碼 - Web 和 APP 埋碼差異大 - App iOS 跟 Android 各需要一套 ![IMG_8976](https://hackmd.io/_uploads/H10bCbYfbx.jpg) 功能優先決策框架:頻繁場景(可以召回使用者) > 重要功能 (可以之後再補做) ### 用戶行為動機分析 ![IMG_8977](https://hackmd.io/_uploads/ryLkkGYzZl.jpg) ### Intension Score 意向分數 Metrics: * 使用者是主動(Ex: View, Click)還是被動觸發(Ex: Scroll) * 功能分數 * 預測準確度:我們認為重要的功能,使用者是否頻繁使用? * 關鍵行為覆蓋率:在關鍵流程中,使用者分別是走哪些流程,回推 Persona,分群使用者 * 行為一致性 分辨使用者是 - 高分:已經習慣這個產品的使用者 - 低分:需要去經營的對像 ### 不可或缺數據指標 ![IMG_8979](https://hackmd.io/_uploads/Bk1rlGKM-l.jpg) - 用戶活躍度 - 留存 - 轉換率 - ROI :::info AI 協助即時監控異常波動,透過多元數據整合打破資訊孤島,讓決策更加精準高效。 ::: ### 處理利害關係人的策略 ![IMG_8981](https://hackmd.io/_uploads/B10WgzYGbl.jpg) ### AI時代下的數據分析師角色 - 傳統角色 - 報表製作 - 數據清理 - 統計分析 - 新時代角色(有人可以補這塊嗎) - AI 工具整合 - 產品策略夥伴 - 影響力創造者 工具只是手段,影響力才是目的 ## 重點回顧 ![IMG_8982](https://hackmd.io/_uploads/HJp3eGFzbe.jpg) ## 結語 **影響力** > 戰場從來不在模型或是分析,而是誰能讓數字發揮影響力 感謝各位大大做筆記QQ --- ###### ( ੭ ̇ᗜ ̇ )੭ 聊天室 ( ੭ ̇ᗜ ̇ )੭ 肚子好餓 午餐吃甚麼哩? 好餓 偷業配自己 side project https://zwoo.gwp4.com 沒想到來 web conf 會遇到雞排的傷害 !?有雞排 >> 剛剛有講者放雞排的照片當例子 中午要去對面買飲料 >>對面有什麼好喝的嗎? >>讓我Google一下, >>同事推薦不夜侯 好像有豆花(我剛也看到這家XD) >>不夜侯柳橙綠好喝 >> 不夜侯很讚! >> 列入清單!!感謝推薦 >> 有要一起買嗎XD 上面打字太快了吧 甚麼都要快XD 不是只有雞排照片,是炸的很好吃的雞排的照片。還有他會好吃的三大元素。 > 這就是 product sense,看到照片沒吃到就知道雞排好吃 簡報字好多 😅 剛剛例子裡的這樣的個人化推薦會不會造成爭議啊(雖然只是例子) > [name=DanSnow] 偷打個廣告,小弟做的事件管理工具 for web 的 https://github.com/DanSnow/typespec-events > [name=Eileen] 偷打個廣告2,小妹之前分享的事件命名的[簡報](https://www.canva.com/design/DAG7Q1abcNg/lrjidxEId_sDxC_scxXC-Q/view?utm_content=DAG7Q1abcNg&utm_campaign=designshare&utm_medium=link2&utm_source=uniquelinks&utlId=hcb0d718087),歡迎交流 > 這個簡報太精美了吧 > [name=DanSnow] 再打個廣告,用 AI 幫你分析資料的工具 https://github.com/DanSnow/data-visualize

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