prospect

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Prospecting

潜在客户开发

Build targeted account and contact lists using Common Room's Prospector. Supports iterative refinement through natural conversation, intent-based discovery, and both net-new prospecting and signal-based queries against existing accounts.

使用Common Room的Prospector构建目标客户账户和联系人列表。支持通过自然对话进行迭代优化、基于意图的发现，以及针对新潜在客户的开发和针对现有客户账户的基于信号的查询。

Critical Distinction: Two Object Types

关键区别：两种对象类型

Common Room's Prospector operates against two fundamentally different object types. Always clarify which one is in play before running a query:

ProspectorOrganization
— Companies not yet in Common Room

Net-new companies that match specified criteria
Available fields are firmographic only: name, domain, size, industry, capital raised, annual revenue, location
Fewer filter options — no signal-based filters, no scores, no activity history
Use when: building a brand-new target list, territory planning, top-of-funnel expansion

Organization
(in Common Room) — Companies already in your CR workspace

Full signal data available: product usage, community activity, CRM fields, scores, custom fields
Much richer filter set — includes signal-based, score-based, segment-based, and firmographic filters
Use when: finding warm accounts to prioritize, identifying expansion candidates, surfacing intent signals within existing pipeline

When a user's request could apply to both (e.g., "Show companies hiring AI engineers this month"), clarify:

"Are you looking for net-new companies not yet in Common Room, or filtering accounts already in your workspace?"

The catalog should make this distinction explicit so the LLM can select the right Prospector endpoint.

Common Room的Prospector针对两种完全不同的对象类型运行。在运行查询前，请务必明确当前使用的是哪一种：

ProspectorOrganization
—— 尚未加入Common Room的公司

符合指定条件的全新公司
仅提供企业统计类字段：名称、域名、规模、行业、融资额、年收入、所在地
过滤选项较少——无基于信号的过滤器、无评分、无活动历史
使用场景：构建全新的目标列表、区域规划、漏斗顶部拓展

Organization
（Common Room内）—— 已在你的CR工作区中的公司

可获取完整的信号数据：产品使用情况、社区活动、CRM字段、评分、自定义字段
过滤器集更丰富——包括基于信号、评分、细分群体和企业统计的过滤器
使用场景：寻找需要优先跟进的高意向客户账户、识别拓展候选客户、在现有销售线索中挖掘意向信号

当用户的请求可能适用于两种类型时（例如：“展示本月正在招聘AI工程师的公司”），请确认：

“你是要查找尚未加入Common Room的全新公司，还是筛选已在你工作区中的客户账户？”

目录应明确区分这两种类型，以便LLM选择正确的Prospector端点。

Step 0: Load User Context (Me)

步骤0：加载用户上下文（Me对象）

Fetch the

Me

object to get the user's segments. When prospecting against

Organization

records (accounts already in CR), default to filtering within "My Segments" unless the user asks for a broader search.

获取

Me

对象以获取用户的细分群体。当针对

Organization

记录（已在CR中的客户账户）进行潜在客户开发时，默认在“我的细分群体”内进行过滤，除非用户要求更广泛的搜索。

Step 1: Gather Targeting Criteria

步骤1：收集目标条件

If criteria are already provided, proceed. Otherwise ask:

"What kind of accounts or contacts are you looking for? For example: company size, industry, job titles, signals like recent product activity or community engagement, geographic region, or specific intent signals like recent funding or job postings."

Use the Common Room object catalog to see available filters for each object type. The key distinction:

ProspectorOrganization — firmographic and technographic filters only (industry, size, geography, funding, tech stack)
Organization — all firmographic filters plus signal-based, score-based, segment-based, and CRM filters

Lookalike search: If the user asks to "find companies like [X]", first look up the reference company in Common Room (or via web search if not in CR). Extract its key attributes — industry, employee range, tech stack, funding stage, geography — and propose those as filter criteria. Present the derived criteria to the user for confirmation before running the search, since lookalike targeting works best when the user can refine which attributes matter most.

如果已提供条件，直接进行下一步。否则询问：

“你正在寻找哪种类型的客户账户或联系人？例如：公司规模、行业、职位头衔、近期产品活动或社区参与等信号、地理区域，或近期融资、招聘信息等特定意向信号。”

请查看Common Room对象目录，了解每种对象类型可用的过滤器。核心区别：

ProspectorOrganization —— 仅支持企业统计和技术栈类过滤器（行业、规模、地理位置、融资情况、技术栈）
Organization —— 支持所有企业统计过滤器，以及基于信号、评分、细分群体和CRM的过滤器

相似公司搜索：如果用户要求“查找类似[X]的公司”，首先在Common Room中查找参考公司（如果不在CR中则通过网络搜索）。提取其关键属性——行业、员工规模、技术栈、融资阶段、地理位置——并将这些作为过滤条件提出。在运行搜索前，请将推导的条件提交给用户确认，因为相似目标定位在用户能够优化关键属性时效果最佳。

Step 2: Support Iterative Refinement

步骤2：支持迭代优化

Prospecting is conversational. Support multi-turn refinement naturally:

Run initial query with provided criteria
If results are large (50+), summarize and offer: "I found [N] results. Want to narrow by [suggested filter]?"
If results are too few (< 5), suggest: "Only [N] results with those filters — I can broaden by relaxing [specific criterion]."
Apply each refinement as a follow-up query, not a new search from scratch

Example flow:

Rep: "Find cybersecurity companies in California." → 500 results
Rep: "Only show ones over 300 employees using AWS." → 47 results
Rep: "Focus on the ones with recent hiring activity." → 12 results ✓

潜在客户开发是一个对话式的过程。自然支持多轮优化：

使用提供的条件运行初始查询
如果结果较多（50+条），进行汇总并询问：“我找到了[N]条结果。是否需要通过[建议的过滤器]缩小范围？”
如果结果过少（<5条），建议：“使用这些过滤器仅找到[N]条结果——我可以放宽[特定条件]来扩大范围。”
将每一次优化作为后续查询应用，而非重新开始新的搜索

示例流程：

销售代表：“查找加利福尼亚州的网络安全公司。” → 500条结果
销售代表：“仅展示员工超过300人且使用AWS的公司。” → 47条结果
销售代表：“重点关注近期有招聘活动的公司。” → 12条结果 ✓

Step 3: Run the Query and Present Results

步骤3：运行查询并展示结果

Execute the Prospector query with confirmed criteria. Sort by signal strength or fit score where available (not alphabetically).

For
ProspectorOrganization
(net-new) results:

Company	Domain	Industry	Size	Capital Raised	Revenue	Location

For
Organization
(in CR) results:

Company	Industry	Size	Top Signal	Signal Date	Score	CRM Stage

Flag any results where data is thin or the most recent signal is older than 90 days.

使用确认后的条件执行Prospector查询。在可用情况下，按信号强度或匹配度评分排序（而非按字母顺序）。

针对
ProspectorOrganization
（全新客户）的结果：

公司	域名	行业	规模	融资额	收入	所在地

针对
Organization
（CR内）的结果：

公司	行业	规模	主要信号	信号日期	评分	CRM阶段

标记任何数据不足或最新信号超过90天的结果。

Step 3.5: Enrich Net-New Results with Web Search

步骤3.5：通过网络搜索丰富全新客户结果

For

ProspectorOrganization

results (net-new companies not in CR), run a quick web search on the top 3–5 companies to add context beyond firmographics. CR has no behavioral signals for these companies, so web search fills the gap — look for recent funding, product launches, leadership changes, or news coverage. Include findings as brief annotations next to each company in the results.

对于

ProspectorOrganization

的结果（未加入CR的全新公司），对排名前3-5的公司进行快速网络搜索，以补充企业统计信息之外的背景。CR没有这些公司的行为信号，因此网络搜索可以填补空白——查找近期融资、产品发布、领导层变动或新闻报道。在结果中每个公司旁边添加简短注释，包含这些发现。

Step 4: Offer Next Steps

步骤4：提供后续操作选项

"Want me to draft outreach for the top 3–5 prospects?"
"Should I run a full account brief on any of these?"
"Want to refine the criteria or add another filter?"
"I can format this as a CSV if you'd like to export it."
"For any net-new companies here, I can add them to Common Room for enrichment." (future capability)

“是否需要我为排名前3-5的潜在客户撰写开发信？”
“是否需要我对其中任何客户进行完整的账户简报？”
“是否需要优化条件或添加其他过滤器？”
“如果需要，我可以将此格式化为CSV以便导出。”
“对于这里的任何全新公司，我可以将它们添加到Common Room进行数据丰富。”（未来功能）

Quality Standards

质量标准

Always confirm which object type (ProspectorOrg vs Organization) before running the query
Default to "My Segments" when querying Organization records, unless user specifies otherwise
Support iterative refinement — treat each follow-up as a filter adjustment, not a fresh start
Never mix result fields from ProspectorOrganization and Organization in the same list
Fewer high-quality results beat a long unqualified list
Only show data the query returned — leave blank or "—" for missing fields, don't invent values

在运行查询前，务必确认对象类型（ProspectorOrg vs Organization）
查询Organization记录时，默认使用“我的细分群体”，除非用户另有指定
支持迭代优化——将每一次后续请求视为过滤器调整，而非重新开始
切勿在同一列表中混合ProspectorOrganization和Organization的结果字段
少量高质量结果优于大量不合格的列表
仅展示查询返回的数据——缺失字段留空或填“—”，不得编造内容

Reference Files

参考文件

references/prospect-guide.md
— filter types, signal-based sorting, object type distinctions, and list-building strategies

references/prospect-guide.md
—— 过滤器类型、基于信号的排序、对象类型区别和列表构建策略