Guidance for interpreting aggregated sent email patterns to build a comprehensive user profile. This framework helps extract meaningful insights from statistical email data.

Personal Fact Extraction

Sent emails reveal facts about who someone is through their communication patterns, topics, and relationships.

Identity signals from recipients: Look at the top recipients and domains. Heavy communication with specific organizations reveals affiliations. Frequent emails to academic domains suggest research or education focus. Regular contact with specific companies indicates business relationships or partnerships. The diversity of domains shows breadth of professional network.

Interests from topic clusters: Subject line keywords and frequent topics reveal what someone cares about. Recurring technical terms indicate expertise areas. Project names and product mentions show current focus. Topic consistency over time reveals sustained interests versus temporary concerns.

Role indicators from communication patterns: High email volume with project keywords suggests project leadership or coordination roles. Frequent thread initiations indicate proactive communication style. Many multi-person emails (Cc lists) suggest coordinator or manager roles. One-on-one correspondence patterns suggest individual contributor or consultant work.

Background context: Domain patterns reveal industry (tech, education, nonprofit, government). Communication with international domains shows global work. Academic email addresses mixed with commercial ones suggest cross-sector involvement. Personal domain usage indicates entrepreneurial or independent work.

Communication Style Analysis

Style emerges from how someone writes, when they write, and how they structure communication.

Formality assessment: Analyze top openers and common words. Formal style uses phrases like "please find", "thank you for", "following up". Casual style uses "hey", "just wanted", "quick question". Mixed formal/casual suggests context-switching between audiences.

Conciseness indicators: Average snippet length reveals typical message length. Heavy "brief" distribution indicates concise communicator. High "detailed" or "long" counts suggest thorough, explanatory style. Compare length distribution to recipient types - shorter with executives, longer with team members suggests adaptability.

Responsiveness patterns: Thread depth analysis shows conversation engagement. High single-email threads indicate initiator personality. Deep multi-email threads show sustained engagement and follow-through. Weekend and night email ratios reveal work-life integration patterns and time zone considerations.

Tone characteristics: Word frequency analysis reveals emotional tone. High use of "thanks", "appreciate", "great" suggests positive, grateful tone. Frequent "urgent", "asap", "critical" indicates high-pressure environment or crisis management. Balanced word usage suggests measured, professional tone.

Time preferences: Hour distribution shows natural working patterns. Morning emails (6-9am) indicate early starter. Late night emails (10pm+) show night owl or timezone misalignment. Consistent 9-5 pattern suggests structured schedule. Scattered patterns suggest flexible or reactive work style.

Active Project Identification

Projects emerge from clusters of related recipients, recurring topics, and temporal patterns.

Project clustering method: Identify topic keywords that co-occur with specific recipient groups. A project is likely when 3+ emails to same recipient cluster share common subject keywords. Recurring topics with different recipients suggest cross-functional projects. New topics appearing suddenly with high frequency indicate recently launched initiatives.

Deliverable patterns: Keywords like "draft", "review", "feedback", "final" indicate production work. "Presentation", "deck", "slides" suggest communication deliverables. "Report", "analysis", "data" indicate analytical work. "Meeting", "sync", "discussion" show coordination-heavy projects.

Status assessment: High recent frequency (5+ emails in last 7 days) indicates active project. Declining frequency suggests winding down. Sustained moderate frequency shows ongoing maintenance. Temporal gaps followed by resurgence indicate phased or cyclical work.

Participant identification: Core project participants appear in multiple related emails. Primary stakeholders receive most project emails. Supporting cast appears occasionally. External participants (outside primary domain) suggest client work or partnerships.

Project themes: Extract project names from repeated proper nouns in subjects. Identify initiatives from action words ("launch", "build", "implement"). Recognize maintenance work from recurring administrative keywords. Distinguish strategic work (planning keywords) from execution work (action keywords).

Evolution Tracking

Compare current profile to previous snapshots to identify changes and trends.

Identity evolution: New domains in top recipients indicate expanded network or new affiliations. Shifted topic clusters show changed focus areas. Reduced diversity suggests increased specialization. Increased diversity suggests broader scope or new responsibilities.

Style evolution: Length pattern shifts show changing communication norms. Time pattern changes reveal lifestyle or work arrangement changes. Formality shifts indicate audience changes or organizational culture adaptation. Tone shifts may reflect personal growth or environmental changes.

Project lifecycle: New projects appear in topics and recipient clusters. Completed projects fade from recent activity but remain in historical data. Ongoing projects show consistent presence across snapshots. Project velocity (rate of new project emergence) indicates capacity and workload.

Comparison methodology: Preserve exact previous profile snapshots with timestamps. Note which metrics changed significantly (>20% shift). Identify new elements (topics, recipients, patterns) not in previous snapshot. Flag disappeared elements that were previously prominent. Synthesize changes into narrative: "Since last analysis, shifted from X focus to Y focus" or "Communication style became more concise over time".

Profile Synthesis

Combine all dimensions into coherent narrative profile.

Facts about the person: Start with "Based on N sent emails over [period]..." Describe primary affiliations from domain analysis. State main interests from topic clusters. Mention distinctive patterns (night owl, concise writer, frequent collaborator). Include cross-references that reinforce conclusions (e.g., technical topics + tech company domains + technical jargon = technical professional).

Communication style summary: Characterize overall style in 2-3 sentences. Note distinctive habits or patterns. Mention adaptability if style varies by recipient. Include supporting metrics (e.g., "75% of emails under 200 chars indicates very concise style").

Active projects: List 3-5 most active current projects with evidence. Include key participants for each. Note project stage or status. Mention any high-priority or time-sensitive work.

Profile confidence: Note data limitations. Snippet analysis is proxy for full content. 30-day window captures current state, not long-term patterns. More emails = higher confidence in patterns. Flag areas where more data would improve insights.