Using Data to Supercharge Design: Discover Problems, Validate Solutions

1. Why Designers Need Data

Design serves users, and understanding user goals, behaviors, and attitudes—quantified as data—helps uncover needs and create better experiences. Data is a crucial pathway to grasping users and informing design decisions.

2. Roles of Data in Projects

Data supports two main scenarios: (1) project‑driven justification, providing evidence for design choices; and (2) ongoing monitoring to gauge product usage and whether design goals are met.

The design process can be split into three phases where data plays a role:

Design‑pre Data: Discover Problems

Before design begins, data helps clarify user needs, business value, and design objectives. Balancing business and user demands often requires trade‑offs; designers must find a pragmatic equilibrium based on data insights.

Example: analysis of 1688.com’s transaction‑relationship buyers revealed that searches leading to orders had twice the conversion rate of other paths, prompting the addition of an “old seller” tag in search results.

Further findings highlighted ordinary and 1‑2 star members as key drivers for transaction growth.

Design‑mid Data: Guide Thinking

When multiple design concepts exist, data offers reference points to judge which direction is most suitable.

Case study: 1688.com’s channel homepage suffered low conversion because users had to navigate through a generic homepage to reach the women’s‑apparel category. Data‑driven solutions included adding direct category links and personalized recommendations based on industry preferences.

Analysis showed about two‑thirds of users had clear industry preferences, supporting personalized recommendations, while roughly 30% favored a customization option.

Design‑post Data: Validate Solutions

Design success is measured against goals using metrics (GSM model: Goal, Signal, Metric). For example, a feature intended to help users find production locations was evaluated through heat‑maps, revealing that a small map hindered efficiency, leading to a revised multi‑option approach.

3. How to Use Data for Daily Monitoring

After launch, designers need to know who uses the product, how they behave, and whether further optimization is possible. Common monitoring methods include:

Horizontal comparison with similar products.

Vertical comparison with the product’s own historical data.

User segmentation to spot divergent behaviors.

Supplementary qualitative research and statistical tools (e.g., SPSS factor or cluster analysis) can deepen insights.

4. Data Isn't the Core Value—You Are

Data provides context, but designers must focus on user goals, behaviors, and attitudes—the micro‑data directly tied to design outcomes—to maximize their own value.