How Kuaishou E‑Commerce Built a Powerful Data Metric System

Introduction

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

Key Sections

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, constructing a metric system is necessary to provide data support for business decisions, precisely analyze key indicators such as sales profit and user behavior conversion, help optimize product strategy and market positioning, and improve overall operational efficiency.

2. Business Background

Kuaishou e‑commerce follows the "people‑goods‑place" model. The content scene consists of live streaming and short videos; live streaming drives most transactions through content planning, product attraction, and marketing tools, while short videos focus on "planting grass" to attract users. The goods scene uses product cards that connect with the content scene, enabling traffic sharing through shop search, mall pages, and "you may also like" features.

3. User Lifecycle

A complete user lifecycle progresses from potential user to content consumption, then to seeding, first purchase, and repeat purchase, which can be abstracted into three stages: audience exploration, audience cultivation, and audience conversion.

4. Five Operational Scenarios

Potential consumer positioning and excavation

Content‑commerce integration for recommendation

IP‑based marketing interaction to expand interest

Merchant self‑selling and influencer distribution to drive conversion

Membership operation and old‑customer recall to increase retention

5. Problems in Data Production, Query, and Usage

Different roles encounter 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 of technical metrics, leading to uncertainty during implementation.

After development, product managers must decide appropriate visualization styles for metrics.

Business users (e.g., e‑commerce operators) may face inconsistent metric names or vague definitions, making data interpretation difficult.

Managers at various levels often lack a holistic view of business data, limiting accurate assessment and strategic planning.

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Conclusion

The article concludes with a summary of the metric system practice and a Q&A session, and invites readers to scan the QR code to download the full e‑book.