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

Introduction

This article shares Kuashou e‑commerce's exploration and practice in constructing 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 Kuaishou e‑commerce business context, building a metric system is necessary to provide data support for operational decisions. Precise analysis of sales profit, user‑behavior conversion, and other key indicators helps optimize product strategy, market positioning, and overall operational efficiency.

1. Kuaishou E‑Commerce Business Introduction

The business follows the "people‑goods‑place" model. The content scene consists of live streams and short videos; live streams drive most transactions, while short videos focus on "seeding" users. Content connects users to value through product‑driven live streams, influencer collaborations, and traffic strategies. The user lifecycle progresses from potential user to content consumption, seeding, first purchase, and repeat purchase, abstracted into audience exploration, cultivation, and conversion stages.

The operation includes five major scenarios: (1) identifying potential consumers, (2) integrating content and commerce, (3) IP‑based marketing interaction, (4) merchant self‑sale and influencer distribution, and (5) membership operations and retention strategies.

2. Business Problems Encountered

Across data production, querying, and usage, various roles face challenges. Data product managers evaluate metric reuse for new business needs, but low efficiency hampers project speed and resource allocation. Data engineers often lack clear metric definitions, leading to uncertainty during implementation. After development, product managers must decide appropriate visualization forms. When data is delivered to business users (e.g., e‑commerce operators), inconsistent metric names or vague definitions increase difficulty in understanding and using data, reducing decision efficiency. Additionally, business personnel and managers frequently lack a holistic view of data performance, limiting accurate assessment of current status and future strategy formulation.

3. Metric System Management

Effective management requires clear metric definitions, standardized naming, consistent data pipelines, and governance processes to ensure data quality and accessibility for all stakeholders.

4. Summary

The Kuaishou e‑commerce metric system demonstrates how a well‑designed data framework can support decision‑making, improve operational efficiency, and address cross‑functional challenges in data production, analysis, and consumption.

5. Q&A

Addressed common questions about metric definition, reuse, visualization, and governance.