contact-research

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Contact Research

联系人调研

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.

从Common Room获取全面的联系人档案。支持通过邮箱、社交账号、姓名+公司进行查询。返回的丰富数据包括活动历史、Spark、评分、网站访问记录以及CRM字段。

Step 1: Locate the Contact

步骤1：定位联系人

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.

Common Room支持多种查询方式——使用用户提供的任意信息进行查询：

用户提供的信息	查询方式
邮箱地址	通过邮箱查询（最可靠）
LinkedIn、Twitter/X或GitHub账号	通过社交账号查询——需明确指定账号类型
姓名+公司	通过姓名+企业域名进行身份匹配；若结果模糊，需展示匹配项供用户确认
仅姓名	通过姓名搜索；若存在多个匹配结果，展示简短列表并请用户确认

如果未找到匹配结果，请回复：“Common Room中没有该联系人的记录。”请勿猜测或编造档案数据。

Step 2: Fetch Contact Fields

步骤2：获取联系人大字段

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

使用Common Room对象目录查看可用的字段组及其内容。如需完整档案，请求所有字段组；如需针对性问题的答案，仅请求相关字段组。

需重点了解的字段组：

评分 —— 始终返回原始数值或百分位数，绝不返回标签
近期活动 —— 使用「联系人发起」筛选条件（最近60天）获取联系人的行为记录，而非团队的行为记录
网站访问记录 —— 总访问次数+具体访问页面（最近12周）
Spark —— 当需要追踪互动随时间的演变时，获取所有Spark数据

Step 3: Run Spark Enrichment (If Available)

步骤3：运行Spark增强（若可用）

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.

若Spark功能可用，请使用它。Spark提供以下信息：

职业背景与工作经历
社交存在感与影响力信号
角色分类：倡导者、经济决策者、技术评估者、终端用户或 Gatekeeper
购买流程中的推断角色

若Spark不可用但存在真实活动数据（近期行为、网站访问、社区互动），可从这些信号中推断角色。若Spark和活动数据均不可用，则分类为“未知”——请勿仅通过职位头衔猜测角色。

当用户希望了解该联系人的互动随时间的演变情况时，需获取所有Spark数据（而非仅最新数据）。

Step 4: Assess Account Context

步骤4：评估客户账户背景

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

获取该联系人所属母公司的简要账户快照。需注意：

账户层面的未结机会、扩张信号或流失风险
该公司的其他联系人是否也活跃
该联系人的互动情况与同事相比如何

Step 5: Identify Conversation Angles

步骤5：确定沟通切入点

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

基于活动和信号，提炼出最有力的2-3个沟通钩子：

近期的「联系人发起」活动（社区帖子、产品活动、支持工单）
近期访问的特定网页——尤其是那些显示出评估意向的页面
职位变动、晋升或公司新闻
其Spark角色分类以及该分类对沟通风格的提示
其在已知活跃交易中的角色

Output Format

输出格式

Only include sections where data was actually returned. Omit sections with no data rather than filling them with guesses.

When data is rich:

undefined

仅包含实际返回数据的部分。省略无数据的部分，请勿用猜测内容填充。

当数据丰富时：

undefined

[Contact Name] — Profile

[联系人姓名] —— 档案

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):**

概述 [2句话：介绍联系人身份、职位以及与企业的关系状态]

详细信息

职位：[职位头衔]
公司：[公司名称]
邮箱：[邮箱地址]
LinkedIn：[URL链接]
其他档案：[Twitter/X、GitHub、CRM链接（若有）]

评分 [若返回评分数据] [所有评分以原始数值或百分位数呈现]

近期活动（最近60天）[若返回活动数据] [3-5条带日期的项目符号]

网站访问记录（最近12周）[若存在访问数据] [总访问次数+访问页面列表]

Spark档案 [若Spark数据非空] [角色类型、背景概述、影响力信号]

细分群体 [若返回细分群体数据] [该联系人所属的细分群体名称列表]

账户背景 [1-2句话介绍其公司的状态]

沟通开场白 [2-3个基于真实信号的具体开场白]


**当数据稀疏时（例如：仅返回姓名、职位、邮箱、标签；sparkSummary为空）：**

[Contact Name] — Profile (Limited Data)

[联系人姓名] —— 档案（数据有限）

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.

可用数据： [准确列出Common Room返回的内容]

[仅展示返回的字段]

网络搜索 [搜索其姓名+公司的任何发现]

注意： Common Room关于该联系人的数据有限。无活动历史、评分或Spark档案可用。我可以进行更深入的网络搜索或查询其公司以获取更多背景信息。


请勿从稀疏数据中生成沟通开场白、角色推断或互动评估。这些内容需要真实信号支持。

Quality Standards

质量标准

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

查询必须使用与输入类型匹配的正确方法——请勿混淆邮箱与社交账号
评分仅以原始数值/百分位数呈现——绝不使用标签
「联系人发起」活动（最近60天）是主要的互动信号——优先展示
若Spark不可用，请明确说明——请勿仅通过职位头衔编造角色
标记任何最近活动超过30天的联系人

Reference Files

参考文件

references/contact-signals-guide.md
— full field descriptions, Spark persona guide, and conversation starter principles

references/contact-signals-guide.md
—— 完整的字段说明、Spark角色指南以及沟通开场白原则