Sessionization and User Journey Reconstruction from Raw Web Events,

Write SQL queries to sessionize and analyze user journeys from raw web events for [COMPANY NAME].

Table: `events`
- event_id, user_id (or anonymous_id), event_type, page_url, page_title, referrer, timestamp, device, browser, session_id (NULL — needs to be calculated)

**Query 1 — Session Assignment:**
- Define session: 30-minute gap between events = new session
- Use LAG() to detect gaps > 30 minutes
- Assign session_id using cumulative SUM of session breaks
- Output: session_id, user_id, start_time, end_time, duration, page_count

**Query 2 — Session Metrics:**
- Average session duration
- Pages per session
- Bounce rate (single-page sessions)
- Sessions by device, browser, hour of day
- Returning vs. new visitor sessions

**Query 3 — User Journey Paths:**
- Most common page sequences (first 5 pages of session)
- Entry pages (most common first page)
- Exit pages (most common last page)
- Internal search: what do users search for and where do they go after?

**Query 4 — Conversion Attribution:**
- For users who converted, what was their most common path?
- Number of sessions before conversion
- Time from first visit to conversion
- Multi-touch: all pages visited before conversion

**Query 5 — Engagement Scoring:**
- Session engagement score based on: duration, pages, scroll depth (if available), interactions
- User-level engagement: aggregate across all sessions
- Identify power users vs. casual visitors

SQL dialect: [PostgreSQL/BigQuery/ClickHouse]. Optimize for large datasets (use partitioning).