Business Context

ShopLink is a two-sided e-commerce marketplace that recently completed a major checkout and search infrastructure upgrade. Leadership says the system is now "faster," but the Head of Product wants a clear framework for evaluating overall business performance, not just technical uptime.

Metric Scenario

In the last 30 days, ShopLink had 12.4M sessions, 3.1M monthly active users, 428K orders, and $34.2M in gross merchandise value (GMV). Compared with the prior month, sessions increased 8%, MAU increased 5%, conversion rate fell from 3.8% to 3.45%, Day-30 buyer retention declined from 29% to 25%, and average order value rose from $76 to $80. Search latency improved from 620 ms to 410 ms, while checkout error rate dropped from 1.9% to 0.8%.

The VP asks: What are the key metrics you would use to evaluate whether the system is actually performing better for the business and for users? They want a structured answer that distinguishes primary KPIs from supporting and diagnostic metrics.

Requirements

1. Define the most important top-line metric or North Star Metric for ShopLink and explain why.
2. Identify the key performance metrics across acquisition, engagement, conversion, retention, and monetization.
3. Explain how you would prioritize leading indicators vs lagging indicators.
4. Show how you would interpret the mixed signals in the current data.
5. Recommend which metrics should be reviewed daily, weekly, and monthly.

Data Available

• user_sessions: session_id, user_id, session_start, device_type, traffic_source, search_count
• search_events: query_id, user_id, query_time, latency_ms, result_count, click_flag
• checkout_events: cart_id, user_id, checkout_start_time, payment_success_flag, error_code
• orders: order_id, user_id, order_time, order_value, refund_flag
• user_retention_daily: user_id, signup_date, active_date, placed_order_flag