How Quantitative Design Verification Transformed Suning’s “慧眼” Data Portal

1. Origin

Every project follows a demand‑design‑development‑launch‑repeat cycle, and any problem in a link can cause serious consequences. Adding design verification is an effective way for designers to optimise the process.

1.1 What is design verification

Design combines art and technology. After a design is delivered, the work is not finished; designers must check whether the launched product meets the initial metrics such as PV, UV, IP. If it meets expectations, they summarise experience to improve future accuracy; if not, they analyse flaws and optimise the next version. This loop is design verification.

1.2 What is quantitative analysis

Design verification includes qualitative and quantitative analysis. Qualitative analysis asks “what”, “how”, “why” and relies on intuition and experience. Quantitative analysis originates from mathematics, asks “what was done”, “how many times”, “how long”, and combines observation with logical reasoning.

Qualitative and quantitative analysis complement each other; quantitative analysis makes qualitative conclusions more scientific.

2. Huiyan Project Interface Evolution

2.1 Project background and user needs

Suning had multiple data‑analysis products that required users to open many pages. Huiyan was built as a unified data portal to integrate all resources and improve efficiency. After launch, the data‑display area was too small because navigation occupied most space, so the redesign focused on expanding the data‑board area.

2.2 Interface evolution

In the initial version, the “My Must‑See” page allocated only 46.3% of the area to data boards. The first solution hid secondary navigation and added hide/show for the report list, raising the board area to 58.4%–69.4%.

The second solution restructured navigation, merging secondary navigation into the top level, removing breadcrumbs, and adding positioning info. It also prioritised functions by importance, frequency and user count, moving secondary‑priority items to a floating pane, achieving a 90.9% board area.

2.3 Potential issues

While the second solution meets the area requirement, hiding the report list may cause users to wonder how to open new reports, which should be measured through quantitative metrics such as task success, time, errors, efficiency, and learnability.

3. Application of Design Verification

3.1 User‑experience goals

Huiyan aims to be Suning’s sole internal data portal, providing a unified view and improving analysts’ efficiency. The explicit goal of the redesign is to enlarge the report display area; the implicit goal is to preserve the experience of other areas.

3.2 Deriving quantitative metrics with GSM

The GSM model (Goal‑Signal‑Metric) translates abstract goals into concrete metrics. For Huiyan, goals lead to signals such as “user opens a new report” and metrics like task time, click count, error rate, etc.

3.3 Metric usage example

Using the “open new report” task, the GSM model yields signals and metrics. Short task time after page entry may indicate an inaccurate default report; long dwell time on certain steps may signal difficulty locating reports. Click count is more cost‑effective for error analysis.

3.4 Data reliability

Outliers caused by interruptions can bias results; applying confidence intervals helps assess the trustworthiness of metric data.

4. Summary

The article demonstrates how design verification and quantitative analysis can guide UI redesign, improve metric‑driven decision making, and provide a solid basis for evaluating and optimising design solutions.