Contact Research

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:

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

Contact Signals — Interpretation Guide

Contact Signal Types

Activity Signals

Relationship Signals

Job / Professional Signals

Spark Persona Classification

• Champion — daily user, advocates internally
• Economic Buyer — holds budget authority, may not use product directly
• Technical Evaluator — evaluates fit and integration, influences technical decisions
• End User — primary product user, influences renewal through NPS/feedback
• Gatekeeper — controls access, must be navigated carefully

Conversation Starters

A strong starter references something specific, shows homework, opens a door without forcing one, and connects to value.

• "I saw your question in the community about [topic] — we just released a feature that addresses exactly that. Would it be useful to walk through it?"
• "Your team's usage of [feature] jumped last month — curious if you're running into [common blocker] at that stage."