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Measure Incremental Growth for Uber Eats

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Problem

Business Context

Uber Eats launched a new Quick Reorder shelf on the Home feed in the rider app for 30% of monthly active eaters in 12 large U.S. metros. The feature lets users reorder from previously purchased restaurants in one tap. Two weeks after launch, leadership sees a 6.5% increase in Quick Reorder-originated orders and a 2.1% increase in total orders per exposed user, but restaurant search sessions are down 8.4% and store page views are down 11.2%.

Metric Scenario

The Head of Growth wants to know whether Quick Reorder is generating incremental order growth or simply cannibalizing existing demand that would have happened through Search, Storefront, or personalized carousels anyway. Baseline weekly metrics before launch: exposed-user cohort averaged 1.42 orders/user/week, 18.6% weekly orderer rate, and $31.80 gross bookings per active eater. Post-launch, these moved to 1.45, 18.9%, and $32.10 respectively. Among users who used Quick Reorder at least once, 27% of weekly orders came from the new shelf, but their total orders only increased 3.0% versus pre-period. Repeat orders from previously ordered restaurants increased 14%, while first-time restaurant orders fell 9%.

Requirements

  1. Define the primary metric you would use to measure incrementality vs cannibalization for Quick Reorder.
  2. Show how you would distinguish channel shift from true net-new demand.
  3. Decompose the metric by user, restaurant, and funnel dimensions to identify where cannibalization is occurring.
  4. Specify guardrails so the feature does not improve reorder volume while harming marketplace health.
  5. Explain what conclusion you would draw from the data above and what additional evidence you would need.

Data Available

  • eats_order_events: order_id, eater_id, restaurant_id, order_ts, gross_bookings, basket_size, fulfillment_type, order_surface
  • home_feed_impressions: eater_id, session_id, impression_ts, module_name, module_position, clicked_store_id
  • search_sessions: eater_id, session_id, query, search_ts, result_click_store_id
  • store_page_events: eater_id, restaurant_id, event_ts, add_to_cart, checkout_start
  • eater_cohorts: eater_id, signup_date, city_id, Eats_frequency_segment, Uber One status
  • restaurant_attributes: restaurant_id, cuisine, price_tier, historical_repeat_rate, availability_hours