Ymatou – E-commerce App

Ymatou is a cross-border e-commerce platform in China. In this project, I used heatmaps to identify issues in the purchase conversion process, collaborated with product managers to define requirements, and redesigned the information architecture and flow of the purchase page based on user research. As a result, the conversion rate increased by 1.32%.

My Role

My Team

Time period

May. 2020 to June.2020

UI Designer, Developer, Product Manager

Background

Heatmap, Funnel Model, User Persona, Scenario Description, User Journey, Design Analysis, Information Architecture, Prototype Design, Flowchart, and Wireframe.

UX

Skills

Process

Explore

01

Reframe

02

Create

03

Verify

04

Heatmap

User Persona

User Flow

User Experience Map

Information Architecture

Goal Breakdown

Flow Design

Prototype Design

Information Coupling

Conversion Funnel

Data Analysis

Heatmap

Explore

Explore user purchasing behavior and the factors that influence their buying decisions.

For e-commerce products, users make purchasing decisions based on the product and its price. Their needs drive their attention and browsing behavior. In the subsequent transaction process, factors such as trust, security, and the efficiency of offline product delivery also influence their decisions.

Combine product data analysis to validate the authenticity of user pain points.

Findings and Summary

Users value images, videos, and reviews in product information.

The display contains content that users are not interested in, which takes up high exposure and occupies a large area.

Based on the above information, how can we help users view product information more efficiently?

Identify and analyze the key information that attracts customers to purchase, and reconstruct the users’ mental models.

1.Challenge

How to identify and analyze the key information that attracts customers to purchase, and reconstruct users’ mental models.

3.Experience Goals

Help users improve browsing efficiency, make purchase decisions, and reduce cognitive load.

2.Business Objectives

Increase the purchase conversion rate of new users and the repurchase rate of existing users.

Understand Users: Simulate User Segmentation, Personas, and Journeys

Users are segmented based on two factors: needs and economic capacity. The reason for using these two factors is that needs represent the intensity of users’ demand for purchasing products, which is a key determinant of buying behavior. Economic capacity reflects users’ income and purchasing power, which also plays a crucial role in their decision-making.

User A: Yun Li

Strong demand, average economic ability, and a tendency to shop with a purpose

User B: Benben Wang

Moderate demand, strong financial ability, and a tendency to shop without a purpose

User C: Yan Zhang

Moderate demand, average financial ability, tend to shop without purpose

User A: Li Yun

High demand, average economic capacity, and purposeful shopping.

User B: Wang Benben


User C: Zhang Yan

Moderate demand, strong economic capacity, and a tendency toward casual shopping.

Moderate demand, average economic capacity, and a tendency toward casual shopping.

Summary of Pain Points and Problem Discovery in the User Journey

Review and Redefine Goals

Reframe

Deduce User Interaction Path

Reanalyze the information architecture, then perform information decoupling and aggregation.

Create

Reorganize the Information Framework

Prototype Design Comparison

Verify

Post-launch result validation and comparison of click-through rate and conversion rate.

Summary: Through the conversion funnel based on right-side click UV, we can see that the click-to-purchase conversion rate has improved, accelerating users’ purchase decisions.

Compared to before the launch, the purchase rate and order conversion rate have slightly improved.

Note: 

1. August is the platform discount period, and the data for this period is no longer counted. Due to the impact of the early notice of the discount, the early conversion rate will be low. 

2. The value is the average of the 20 days before the revision and the 20 days after one month of launch.

Reflection

Since this study mainly focused on data analysis, simulated users were used instead of real ones, and no actual user research was conducted. This results in a lack of authentic validation and a limited deep exploration of the reasons behind users’ click-through rates.↳

Actual observation is needed to understand users’ real purchasing behaviors.