Create a metric tree for repeat group orders in food-delivery under scale, incentive, and regulatory constraints

Problem Statement Description

Swiggy is exploring how to measure and improve repeat group orders for office lunch use cases, where one coordinator may initiate an order for multiple colleagues, collect preferences, manage payments or reimbursements, and depend on predictable restaurant preparation and delivery timing. The metric challenge is to define a clear metric tree that captures whether the group-ordering experience is creating durable repeat behavior, not just one-time incentive-driven spikes.

Assume the product operates at urban Indian food-delivery scale, with constraints around restaurant capacity, delivery partner batching, data privacy, accessibility, and promotional spend. The metric tree should help product, growth, operations, and restaurant teams understand what drives repeat group ordering, where drop-offs occur, and whether interventions are improving customer trust and operational reliability.

Focus on the office lunch coordinator as the primary user, while also accounting for participants, restaurants, and delivery partners as stakeholders in the experience. Your framing should distinguish between user intent, order creation, participant completion, successful fulfillment, satisfaction, and subsequent repeat behavior.

The experience should consider:

- A precise definition of “repeat group order,” including the user/entity being measured, denominator, qualifying time window, and whether repeat is tied to the coordinator, office, participant group, restaurant, or delivery location.

- How to separate organic repeat behavior from incentive-led repeat behavior, including coupon dependency, loyalty mechanics, and subsidy efficiency.

- Instrumentation across the group-order funnel, such as group creation, invite sharing, participant joins, cart completion, payment, restaurant acceptance, preparation, dispatch, delivery, and post-order feedback.

- Cohorts such as new vs existing coordinators, office size, city tier, weekday lunch windows, order value bands, cuisine categories, restaurant reliability, and delivery distance.

- Guardrail metrics for customer experience, restaurant operations, delivery reliability, cancellations, refunds, support contacts, late deliveries, food quality complaints, and accessibility of the flow.

- Privacy and data-quality considerations when identifying offices, groups, coordinators, repeat participants, shared addresses, and workplace ordering patterns.

- Decision usefulness: how the metric tree would guide prioritization across product UX improvements, restaurant selection, batching policies, coordinator tools, and incentive programs.

Your goal is to present a structured metric tree that a Swiggy product team could use to diagnose repeat group-order performance, evaluate trade-offs under operational and regulatory constraints, and make reliable product decisions without over-optimizing for short-term order volume alone.