contact-research
用 Common Room 資料查出特定聯絡人的檔案、活動和對話切入點
中文翻譯
--- name: contact-research description: "用 Common Room 的資料研究特定的人。當使用者說『who is [name]』『look up [email]』『research [contact]』『is [name] a warm lead』，或任何聯絡人層級的問題時觸發。" --- # 聯絡人研究（Contact Research） 從 Common Room 取得完整的聯絡人檔案。支援用 email、社群帳號，或姓名加公司查詢。回傳的是豐富化後的資料，包括活動歷史、Spark、分數、網站造訪紀錄和 CRM 欄位。 ## 步驟 1：找到聯絡人 Common Room 支援多種查詢方式，使用者給什麼就用什麼： | 使用者提供的資訊 | 查詢方式 | |---------------------|--------------| | Email 位址 | 用 email 查詢（最可靠） | | LinkedIn、Twitter/X 或 GitHub 帳號 | 用社群帳號查詢，要明確指定帳號類型 | | 姓名 + 公司 | 用姓名加組織網域做身分比對；如果有歧義，列出符合的對象 | | 只有姓名 | 用姓名搜尋；如果有多個符合，顯示簡短清單，請使用者確認 | 如果找不到符合的對象，就回覆：「Common Room doesn't have a record for this person.」不要猜測，也不要捏造檔案資料。 ## 步驟 2：取得聯絡人欄位 用 Common Room 的物件目錄查看有哪些欄位群組以及裡面的內容。要完整檔案就請求所有群組；針對性的問題就只請求相關的部分。 **要知道的關鍵欄位群組：** - **分數（Scores）**：一律回傳原始值或百分位，絕對不要用標籤 - **近期活動**：用 `Contact Initiated` 篩選（近 60 天），看的是對方自己的行動，不是你們團隊的 - **網站造訪**：總次數加上具體頁面（近 12 週） - **Spark**：要追蹤互動隨時間的演變時，取回所有的 Spark ## 步驟 3：執行 Spark enrichment（如果有的話） 如果有 Spark 就用。Spark 提供： - 專業背景和工作經歷 - 社群存在感和影響力訊號 - 人物類型分類：Champion、Economic Buyer、Technical Evaluator、End User 或 Gatekeeper - 推測在採購流程中的角色 如果沒有 Spark，但有真實的活動資料（近期行動、網站造訪、社群互動），就從這些訊號推測人物類型。如果 Spark 和活動資料都沒有，就歸類為 Unknown，不要只憑職稱去猜人物類型。 當使用者想了解這位聯絡人的互動如何隨時間演變時，要取回**所有 Spark**（不只最新的）。 ## 步驟 4：評估帳號背景 針對這位聯絡人所屬的公司，拉一份精簡的帳號快照。要記下： - 帳號層級的未結商機、擴展訊號或流失風險 - 這家公司其他聯絡人是否也很活躍 - 這個人的互動程度和同事相比如何 ## 步驟 5：找出對話切入角度 根據活動和訊號，挑出最強的 2 到 3 個切入點： - 最近一次 `Contact Initiated` 的活動（社群貼文、產品事件、支援單） - 他們最近造訪的特定網頁，特別是帶有評估意圖的 - 換工作、升職或公司新聞 - 他們的 Spark 人物類型，以及這代表的溝通風格 - 他們在已知進行中 deal 裡的角色 ## 輸出格式 只放實際有回傳資料的段落。沒有資料的段落直接省略，不要用猜測去填。 **資料豐富時：** ``` ## [Contact Name] — Profile **Overview** [2 sentences: who they are, their role, and relationship status] **Details** - Title: [title] - Company: [company] - Email: [email] - LinkedIn: [URL] - Other profiles: [Twitter/X, GitHub, CRM link if available] **Scores** [If scores returned] [All scores as raw values or percentiles] **Recent Activity** (last 60 days) [If activity returned] [3–5 bullets with dates] **Website Visits** (last 12 weeks) [If visit data exists] [Total visit count + list of pages visited] **Spark Profile** [If Spark data is non-null] [Persona type, background summary, influence signals] **Segments** [If segments returned] [List of segment names this contact belongs to] **Account Context** [1–2 sentences on their company's status] **Conversation Starters** [2–3 specific, signal-backed openers] ``` **資料稀少時（例如只回傳姓名、職稱、email、標籤；sparkSummary 是 null）：** ``` ## [Contact Name] — Profile (Limited Data) **Data available:** [List exactly what Common Room returned] [Present only the returned fields] **Web Search** [Any findings from searching their name + company] **Note:** Common Room has limited data on this contact. No activity history, scores, or Spark profile available. I can run deeper web searches or look up their company for additional context. ``` 不要用稀少的資料去產生對話切入點、人物類型推論或互動評估。這些都需要真實的訊號。 ## 品質標準 - 查詢必須針對輸入類型使用正確的方法，不要在 email 和帳號之間亂猜 - 分數只用原始值／百分位，絕對不要用標籤 - `Contact Initiated` 的活動（近 60 天）是主要的互動訊號，要放在最前面 - 如果沒有 Spark，就直接說沒有，不要只憑職稱捏造人物類型 - 只要聯絡人最近一次活動超過 30 天，就要標註出來 ## 參考檔案 - **`references/contact-signals-guide.md`**：完整的欄位說明、Spark 人物類型指南，以及對話切入點的原則
英文原文
--- name: contact-research description: "Research a specific person using Common Room data. Triggers on 'who is [name]', 'look up [email]', 'research [contact]', 'is [name] a warm lead', or any contact-level question." --- # Contact Research Retrieve a comprehensive contact profile from Common Room. Supports lookup by email, social handle, or name + company. Returns enriched data including activity history, Spark, scores, website visits, and CRM fields. ## Step 1: Locate the Contact Common Room supports multiple lookup methods — use whichever the user has provided: | What the user gives | Lookup method | |---------------------|--------------| | Email address | Look up by email (most reliable) | | LinkedIn, Twitter/X, or GitHub handle | Look up by social handle — specify handle type explicitly | | Name + company | Identity resolution by name + org domain; present matches if ambiguous | | Name only | Search by name; if multiple matches, show a brief list and ask the user to confirm | If no match is found, respond: "Common Room doesn't have a record for this person." Do not speculate or fabricate profile data. ## Step 2: Fetch Contact Fields Use the Common Room object catalog to see available field groups and their contents. For full profiles, request all groups. For targeted questions, request only what's relevant. **Key field groups to know about:** - **Scores** — always return as raw values or percentiles, never labels - **Recent activity** — use `Contact Initiated` filter (last 60 days) for their actions, not your team's - **Website visits** — total count + specific pages (last 12 weeks) - **Spark** — retrieve all Sparks when tracking engagement evolution over time ## Step 3: Run Spark Enrichment (If Available) If Spark is available, use it. Spark provides: - Professional background and job history - Social presence and influence signals - Persona classification: Champion, Economic Buyer, Technical Evaluator, End User, or Gatekeeper - Inferred role in the buying process If Spark is unavailable but real activity data exists (recent actions, website visits, community engagement), infer a persona from those signals. If neither Spark nor activity data is available, classify as Unknown — do not guess a persona from title alone. Retrieve **all Sparks** (not just the most recent) when the user wants to understand how this contact's engagement has evolved over time. ## Step 4: Assess Account Context Pull an abbreviated account snapshot for this contact's parent company. Note: - Open opportunities, expansion signals, or churn risk at the account level - Whether other contacts at this company are also active - How this person's engagement compares to their colleagues ## Step 5: Identify Conversation Angles Based on activity and signals, surface the strongest 2–3 hooks: - A recent `Contact Initiated` activity (community post, product event, support ticket) - A specific web page they visited recently — especially if it signals evaluation intent - A job change, promotion, or company news - Their Spark persona and what that suggests about communication style - Their role in a known active deal ## Output Format Only include sections where data was actually returned. Omit sections with no data rather than filling them with guesses. **When data is rich:** ``` ## [Contact Name] — Profile **Overview** [2 sentences: who they are, their role, and relationship status] **Details** - Title: [title] - Company: [company] - Email: [email] - LinkedIn: [URL] - Other profiles: [Twitter/X, GitHub, CRM link if available] **Scores** [If scores returned] [All scores as raw values or percentiles] **Recent Activity** (last 60 days) [If activity returned] [3–5 bullets with dates] **Website Visits** (last 12 weeks) [If visit data exists] [Total visit count + list of pages visited] **Spark Profile** [If Spark data is non-null] [Persona type, background summary, influence signals] **Segments** [If segments returned] [List of segment names this contact belongs to] **Account Context** [1–2 sentences on their company's status] **Conversation Starters** [2–3 specific, signal-backed openers] ``` **When data is sparse (e.g., only name, title, email, tags returned; sparkSummary is null):** ``` ## [Contact Name] — Profile (Limited Data) **Data available:** [List exactly what Common Room returned] [Present only the returned fields] **Web Search** [Any findings from searching their name + company] **Note:** Common Room has limited data on this contact. No activity history, scores, or Spark profile available. I can run deeper web searches or look up their company for additional context. ``` Do not generate conversation starters, persona inferences, or engagement assessments from sparse data. These require real signals. ## Quality Standards - Lookup must use the correct method for the input type — don't guess on email vs. handle - Scores as raw/percentile only — never labels - `Contact Initiated` activity (last 60 days) is the primary engagement signal — lead with it - If Spark is unavailable, say so — don't fabricate a persona from title alone - Flag any contact where the most recent activity is older than 30 days ## Reference Files - **`references/contact-signals-guide.md`** — full field descriptions, Spark persona guide, and conversation starter principles