what metrics would you use to evaluate meesho's product discovery algorithm?

Introduction

Evaluating Meesho's product discovery algorithm is crucial for optimizing the platform's performance and user experience. To approach this product success metrics problem effectively, I will follow a simple product success metric framework. I'll cover core metrics, supporting indicators, and risk factors while considering all key stakeholders.

Step 1

Product Context

Meesho is a social commerce platform that enables small businesses and individuals to start their online stores. The product discovery algorithm is a critical component of Meesho's ecosystem, responsible for connecting buyers with relevant products and sellers.

Key stakeholders include:

1. Buyers: Seeking relevant, affordable products
2. Sellers: Aiming to reach potential customers and increase sales
3. Meesho: Driving platform growth and revenue

User flow:

1. Buyer opens the app/website
2. Algorithm presents personalized product recommendations
3. Buyer browses, searches, or filters products
4. Algorithm refines recommendations based on user interactions
5. Buyer selects and purchases products

The discovery algorithm aligns with Meesho's strategy of empowering small businesses and providing a seamless shopping experience. Compared to competitors like Amazon or Flipkart, Meesho focuses more on social commerce and lower-tier cities.

Product Lifecycle Stage: Growth - The algorithm is continuously evolving to improve user experience and drive platform adoption.

Software-specific context:

• Platform: Mobile app and web-based
• Integration points: User profiles, product catalog, order history
• Deployment model: Continuous updates and A/B testing