https://github.com/lightdash/lightdash-demo-saas
https://github.com/lightdash/lightdash-demo-saas
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/lightdash/lightdash-demo-saas
- Owner: lightdash
- Created: 2024-07-08T21:33:31.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2026-02-17T19:08:42.000Z (4 months ago)
- Last Synced: 2026-02-17T22:19:54.593Z (4 months ago)
- Size: 16.6 MB
- Stars: 3
- Watchers: 10
- Forks: 3
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
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README
# 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
```sql
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
```sql
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;
```