Business Context

NimbusOps is a B2B workflow SaaS company with 45,000 monthly active accounts. The COO wants a weekly KPI dashboard built in Excel, SQL, and Tableau or Power BI to monitor operational health after noticing that revenue grew 6% last quarter while trial-to-paid conversion fell from 18% to 15% and logo churn rose from 2.8% to 3.6%.

Metric Scenario

Leadership currently receives inconsistent numbers from Finance, Sales Ops, and Product Analytics because teams define active accounts, qualified trials, and churn differently. You are asked to design a reliable KPI tracking approach that starts with SQL as the source of truth, uses Excel for ad hoc validation and variance checks, and publishes executive dashboards in Tableau or Power BI.

Current weekly metrics include: 12,000 new trial accounts, 7,800 product-qualified accounts, 1,800 paid conversions, 1,620 retained paying accounts from the prior month cohort, 540 churned accounts, $4.2M MRR, median first-response support time of 3.4 hours, and average weekly active users per paying account of 8.6. The CEO wants to know which KPIs should be leading vs lagging, how they should be defined, and how to diagnose movement when one metric changes but another does not.

Requirements

1. Define the core KPI set and specify exact business logic for each metric.
2. Explain how you would use SQL, Excel, and Tableau/Power BI together to calculate, validate, and monitor the KPIs.
3. Show how you would decompose a drop in conversion or rise in churn into operational drivers.
4. Identify data quality checks and dashboard design choices needed for executive reporting.
5. Recommend actions if conversion remains below 16% for 3 weeks while churn stays above 3.5%.

Data Available

• accounts table with account_id, signup_date, plan_type, industry, company_size, region
• product_events table with account_id, user_id, event_name, event_time, feature_name
• subscriptions table with account_id, start_date, end_date, mrr, billing_status, cancellation_reason
• support_tickets table with ticket_id, account_id, created_at, first_response_at, priority
• crm_pipeline table with account_id, lead_source, sales_owner, demo_completed, opportunity_stage