Athenic AI Sample Data Pack
Created for testing plain-English analytics prompts.

Files included:
1. customers.csv
   Account-level attributes: customer_id, account_name, industry, region, country, segment, plan_type, acquisition_channel, signup_date, company_size, account_owner, nps_score.

2. users.csv
   User-level signup and activation data. Join to customers on customer_id.

3. subscriptions.csv
   Subscription lifecycle data: plan, MRR, status, churn_date, renewal_date, expansion_mrr, contraction_mrr. Join to customers on customer_id.

4. invoices.csv
   Revenue/order data by date, product, amount, region, and acquisition channel. Join to customers on customer_id.

5. product_usage.csv
   Monthly feature usage by customer and feature. Join to customers on customer_id.

6. support_tickets.csv
   Support volume, category, priority, satisfaction, and resolution time. Join to customers on customer_id.

7. marketing_campaigns.csv
   Campaign performance data by channel, spend, leads, MQLs, signups, pipeline, and revenue influenced.

8. sales_pipeline.csv
   Deal-stage data, sales reps, deal sizes, expected close dates, outcomes, and loss reasons. Some deals connect to customers through customer_id.

9. athenic_prompt_test_guide.csv
   The 35 sample prompts mapped to the best tables to use.

Recommended AI Analyst setup:
- Upload all CSV files as data sources.
- Add all tables to the AI Analyst / Knowledge Graph.
- Suggested joins:
  - customers.customer_id = subscriptions.customer_id
  - customers.customer_id = invoices.customer_id
  - customers.customer_id = product_usage.customer_id
  - customers.customer_id = support_tickets.customer_id
  - customers.customer_id = users.customer_id
  - customers.customer_id = sales_pipeline.customer_id where customer_id is not blank
- For marketing prompts, marketing_campaigns can stand alone, or can be compared with customers.acquisition_channel and invoices.acquisition_channel.

Suggested metric definitions:
- Revenue: SUM(invoices.amount) where invoices.status = 'Paid'
- MRR: SUM(subscriptions.mrr) for active subscriptions
- Churned Customers: COUNT(customers) where subscriptions.status = 'Churned'
- Active Customers: COUNT(customers) where subscriptions.status = 'Active'
- Expansion MRR: SUM(subscriptions.expansion_mrr)
- Feature Adoption: COUNT(DISTINCT customer_id) using a feature / total active customers
- Activation Within 7 Days: activation_date is within 7 days of users.signup_date
- Support Volume: COUNT(support_tickets.ticket_id)
- Win Rate: Closed Won deals / (Closed Won + Closed Lost deals)
- Sales Cycle Length: close_date - created_date for closed deals

Important:
This is synthetic demo data. Do not use it for real business decisions.
