Product Management 12 min read

Why Designers Must Master Data: From Metrics to Emoji Search Success

This article explains why designers need to understand data, outlines key quantitative and qualitative metrics, and demonstrates how data-driven decisions improved an emoji‑search feature, while emphasizing that intuition remains essential alongside analytics.

We-Design
We-Design
We-Design
Why Designers Must Master Data: From Metrics to Emoji Search Success

1. Why Designers Need to Understand Data

In internet products, data comes from user‑behavior statistics; designers can use it to gain insights and support design proposals. Companies increasingly involve designers early in the product process, encouraging them to start from the problem source, use data to understand users, and create better design solutions.

Before design, data helps improve observation of users and identify pain points. During design, data provides decision support, such as A/B testing, to choose the most suitable solution. After design, data validates the impact of changes and guides further optimization, creating a virtuous cycle.

2. Which Data Metrics Should Be Observed?

Data can be divided into quantitative and qualitative types.

Quantitative Data

Statistical data that describes what has happened, often expressed as numbers or indicators (e.g., PV, UV). It is abundant in internet products.

Qualitative Data

Textual descriptions that explain the nature of phenomena, derived from interviews or observations.

Common metrics include:

Traffic metrics : PV (Page View) and UV (Unique Visitor) to gauge product vitality and prioritize features.

Quality metrics : e.g., first‑click position in search or average dwell time, which vary by business scenario.

Conversion metrics : CTR, purchase rate, element‑level conversion, and flow conversion to assess user actions and identify optimization points.

3. How to Use Data to Assist Design? – A Case Study of Emoji‑Search Optimization

The process is split into three stages.

Before Design

Analyze existing data to discover user behaviors:

One‑third of users click the emoji‑search entry, but overall it accounts for only 10% of search traffic, indicating high drop‑off after opening the panel.

Users who understand the feature repeatedly switch emojis during a single search.

Identify two user groups:

New users: unaware of the feature and how to use it.

Experienced users: know the feature and switch emojis frequently.

Set design goals:

New users – improve feature communication and add clear instructions.

Experienced users – increase emoji‑switching efficiency.

During Design

For new users, replace the ambiguous add‑button icon with a clearer “emoji collides to create a new emoji” illustration and update the label to “Tap an emoji to search related emojis,” adding on‑screen guidance.

For experienced users, fix the switch panel to the top of the page so they can change emojis without scrolling back, and reduce panel height to avoid covering content.

After Launch

Three months of monitoring showed a 51% increase in conversion rate for emoji‑search and an 80% rise in panel click‑through, confirming that the redesign met its goals.

4. Data and Intuition

Data is a valuable ally for understanding, growing, and optimizing products, but it does not replace clear‑headed decision‑making. As a Facebook product design director said, “Don’t become dependent on their allure; sometimes a little instinct goes a long way.” Designers should balance data with intuition, avoiding over‑reliance on endless A/B tests.

product designA/B testingdesignUser ResearchUXdata-driven design
We-Design
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We-Design

Tencent WeChat Design Center, handling design and UX research for WeChat products.

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