Late deliveries lead to customer churn. What data we should look at to prove this hypothesis for a food delivery app?

Introduction

To approach this late delivery problem effectively, I will follow a simple product success metric framework. This structured approach covers core metrics, supporting indicators, and risk factors while considering all key stakeholders for a food delivery app.

Step 1

Product Context

The food delivery app in question is a platform connecting customers with local restaurants and delivery drivers. Key stakeholders include:

1. Customers: Seeking convenient, timely food delivery
2. Restaurants: Aiming to expand their customer base and increase sales
3. Delivery drivers: Looking for flexible work opportunities
4. The company: Focused on growth, profitability, and market share

User flow typically involves:

1. Browsing restaurants and menus
2. Placing an order and payment
3. Order preparation by the restaurant
4. Driver pickup and delivery
5. Customer receives and rates the experience

This app likely competes with other major food delivery platforms, differentiating through features like real-time tracking, exclusive restaurant partnerships, or subscription models.

In terms of product lifecycle, the food delivery market is mature but still evolving, with ongoing innovation in areas like autonomous delivery and ghost kitchens.

Software-specific considerations:

• Platform: Mobile-first, with web support
• Integration points: Restaurant POS systems, payment gateways, mapping services
• Deployment model: Cloud-based with frequent updates