How Kuaishou E‑Commerce Built a Data Metric System to Boost Decision‑Making

Introduction

This article shares Kuaishou e‑commerce's exploration and practice in building a data metric system.

Outline

Necessity of metric system construction

How to build a good metric system

Kuaishou e‑commerce metric system product practice

Metric system management

Summary

Q&A

1. Necessity of Metric System Construction

In the context of Kuaishou e‑commerce, a metric system is essential to provide data support for business decisions, enabling precise analysis of sales profit, user‑behavior conversion, and helping optimize product strategy and market positioning to improve overall operational efficiency.

1.1 Kuaishou E‑Commerce Business Overview

The business follows the "people‑goods‑scene" model. The content scene consists of live streams and short videos, where live streams drive most transactions through content planning, product attraction, and marketing tools. Short videos focus on "planting grass" to attract users. The shelf scene uses product cards that connect with the content scene, fulfilling the "people find goods" need via shop search, mall pages, and recommendations.

A complete user lifecycle moves from potential users to content consumption, then to planting grass, first purchase, and repeat purchase, abstracted into three stages: audience discovery, audience cultivation, and audience conversion.

Based on these scenarios, five major operation scenes are identified: potential consumer positioning, content‑merchant integration, IP‑based marketing interaction, merchant self‑sale and influencer distribution, and membership/retention strategies.

2. Business Problems Encountered

During data production, query, and usage, different roles face various issues:

Data product managers evaluate metric reuse for new business needs, but low efficiency hampers project speed and resource allocation.

Data engineers may lack clear definitions and scopes for technical metrics, increasing uncertainty during implementation.

After delivery, operators (e.g., e‑commerce staff) may encounter inconsistent metric names or vague definitions, making data interpretation difficult.

Business managers often lack a holistic view of data performance, limiting accurate assessment of current status and future strategy formulation.

3. Metric System Product Practice

The practice involves aligning business scenarios with metric definitions, establishing clear metric ownership, and creating visualizations that suit each stakeholder. Continuous feedback loops ensure metrics stay relevant as business evolves.

4. Metric System Management

Management includes governance of metric definitions, regular reviews, and a centralized catalog to avoid duplication and ensure consistency across teams.

5. Summary

Building a robust metric system requires understanding business context, addressing cross‑functional pain points, defining clear metrics, and maintaining governance to support data‑driven decision making.

6. Q&A

Answers to common questions about metric definition, implementation challenges, and best practices are provided.