Lightdash SaaS Demo

Overview

This repository contains customer relationship data that tracks the complete journey from company acquisition through individual user engagement. The data follows a hierarchical structure designed to provide insights into sales performance, user adoption, and customer success patterns.

Data Structure

Entity Relationship Diagram

ACCOUNTS (Companies) 
    ↓ account_id
    ├─► DEALS (Sales Pipeline)
    └─► USERS (Individual Contacts)
            ↓ user_id  
            └─► TRACKS (Product Usage)

Dataset Descriptions

accounts_raw.csv

Master company data - Contains information about organizations in the sales pipeline.

Column	Type	Description
account_id	UUID	Unique company identifier (Primary Key)
account_name	String	Company/organization name
industry	String	Business sector (e.g., Financial Services, Technology, Healthcare)
segment	String	Company size category (SMB, Midmarket, Enterprise)

deals_raw.csv

Sales pipeline data - Tracks revenue opportunities and deal outcomes.

Column	Type	Description
deal_id	UUID	Unique deal identifier (Primary Key)
account_id	UUID	Links to accounts table (Foreign Key)
stage	String	Sales stage (Qualified, Won, Lost, PoC)
plan	String	Service plan type
seats	Integer	Number of licensed seats
amount	Integer	Deal value in dollars
created_date	Timestamp	When the deal was created

users_raw.csv

Individual contact data - People within organizations who use the platform.

Column	Type	Description
user_id	UUID	Unique user identifier (Primary Key)
account_id	UUID	Links to accounts table (Foreign Key)
email	String	User email address
job_title	String	Role within organization
is_marketing_opted_in	Boolean	Marketing communication preference (0/1)
created_at	Timestamp	When user account was created
first_logged_in_at	Timestamp	Initial platform access
latest_logged_in_at	Timestamp	Most recent login

tracks_raw.csv

User activity data - Product usage and engagement events.

Column	Type	Description
user_id	UUID	Links to users table (Foreign Key)
event_id	UUID	Unique event identifier
event_name	String	Type of action performed
event_timestamp	Timestamp	When the event occurred

Common Event Types

• login_successful - User authentication
• report_generated - Report creation
• file_downloaded - File access
• workspace_created - New workspace setup
• api_call_made - API usage
• integration_failed - System integration errors

Key Relationships

• One-to-Many: Each account can have multiple deals and users
• One-to-Many: Each user can have multiple activity tracks
• Many-to-One: Multiple users belong to the same account
• Many-to-One: Multiple deals can exist for the same account

Analysis Capabilities

This data structure enables analysis across multiple dimensions:

Sales Performance

• Win rates by industry and company segment
• Average deal size by company characteristics
• Sales cycle length and conversion patterns

User Adoption

• User engagement by job role and company type
• Feature adoption rates
• Time to first value metrics

Customer Success

• Account health scoring based on user activity
• Expansion opportunity identification
• Churn risk prediction

Marketing Intelligence

• Lead qualification based on company characteristics
• User role targeting for campaigns
• Product usage patterns by segment

Sample Queries

Account Overview with Deal Summary

SELECT 
    a.account_name,
    a.industry,
    a.segment,
    COUNT(d.deal_id) as total_deals,
    SUM(d.amount) as total_pipeline_value,
    COUNT(u.user_id) as total_users
FROM accounts a
LEFT JOIN deals d ON a.account_id = d.account_id
LEFT JOIN users u ON a.account_id = u.account_id
GROUP BY a.account_id;

User Engagement Analysis

SELECT 
    u.job_title,
    COUNT(DISTINCT u.user_id) as user_count,
    COUNT(t.event_id) as total_events,
    COUNT(t.event_id) / COUNT(DISTINCT u.user_id) as avg_events_per_user
FROM users u
LEFT JOIN tracks t ON u.user_id = t.user_id
GROUP BY u.job_title
ORDER BY avg_events_per_user DESC;