Business Context

QuickCart is a same-day grocery delivery app competing with Instacart, DoorDash, and local chains in 20 U.S. metro areas. The GM wants to use market share trends to decide whether to invest in pricing, selection, or retention in the next quarter.

Metric Scenario

In Chicago, QuickCart completed 420,000 monthly orders in March, up from 390,000 in February. Third-party market data estimates total online grocery delivery orders in Chicago were 2.8M in March, up from 2.6M in February. QuickCart's share therefore moved from 15.0% to 15.0% despite order growth. In Dallas, QuickCart grew from 210,000 to 260,000 monthly orders while the market grew from 1.9M to 2.0M, so share increased from 11.1% to 13.0%. Leadership wants to know whether Chicago's flat share is a demand problem, a conversion problem, or a retention problem, and how market share should influence product roadmap decisions.

Requirements

1. Define market share precisely for this product and explain when you would use order share vs customer share vs GMV share.
2. Show how you would calculate and interpret the metric for Chicago and Dallas.
3. Decompose market share into the product funnel components that could explain flat or rising share.
4. Identify which cuts of the data you would analyze first to separate acquisition, conversion, and retention effects.
5. Recommend 3 product decisions QuickCart should consider if share is flat in a growing market.
6. Name guardrail metrics you would monitor so market share gains do not come from unprofitable growth.

Data Available

• orders: order_id, customer_id, city, order_date, basket_value, fulfilled_flag, delivery_time_minutes, promo_amount
• app_events: user_id, city, session_id, event_name, timestamp, store_view_id, checkout_started_flag
• customer_monthly_activity: customer_id, city, month, active_flag, orders_count, churn_risk_score
• market_estimates: city, month, estimated_total_orders, estimated_total_customers, estimated_total_gmv
• pricing_competitor_panel: city, week, competitor, avg_delivery_fee, avg_item_price_index, assortment_index