Quick Glance 👉
During my 1-year full-time work experience at Ant Group, an Alibaba subsidiary, I owned the design of AntInsights — a SaaS data analytics & collaboration platform tailored for 20,000+ internal users. The product goal is to help users efficiently explore data, uncover insights, and make informed, data-driven decisions.
One of my major projects involved the redesign of a SaSS self-analytics platform with the objective of reducing the learning curve for non-technical, new users and enhancing user adoption.
My redesign led to the increase in Task Success Rate, rising from 52% to an ideal 100% in usability tests. Besides, it successfully contributed to increase of user adoption from 5K+ to 9K+ (an 80% increase) within two quarters after the launch 🎉.
Want to learn more about my work experience at Ant Group? Check it out!
Context.
6 self-analytics SaaS tools are designed with the goal to enable 1w+ internal employees to efficiently and flexibly access and explore data. All these tools were in the Minimum Viable Product (MVP) stage, and still need wider adoption by internal users to enhance operational problem-solving productivity and promote enterprise-level data-driven decision-making.
Problem.
Steep learning curve of self-analytics impedes early adoption among frontline employees
A large portion of internal, frontline employees in Ant Group, e.g., maketers, and business development, lack a data analysis background. Since launching the MVP of these self-analytics tools, many complaints were collected from non-technical users about the steep learning curve and from the BI team regarding the repetitive and resource-intensive technical support.
We need to expand the user adoption from 5K+ to 9K+ within 2 quarters, but how might we ease the learning curve of self-analytics to encourage early adoption?
Short-term Solution.
👩🦯 User Guide: Provide a user guide with a typical analysis use case for users to follow
🤔️ Pain points: Non-technical users don't understand data terminology, making it difficult for them to grasp self-analytics tool features. Lengthy product documentation is also hard to consume.
💊 Solution: To quickly onboard new users, I built a user guide for each tool, providing a typical analysis scenario for the user to follow, with each steps explained in the context for easy comprehension.
Gain an overview of the tutorial and the use case
Learn each feature to finish the given use case step by step
Hover the feature to easily find the relevant UI
Encouraging words upon completion
Hints for revisit the guide
Long-term Solution1.
💬 Query Panel Redesign: Reduce new users’ cognitive load with more intuitive UI
🤔️ Pain points: Cluttered information display leads to cognitive overload, making the learning curve steeper for new users.
💊 Solution: Optimize the information presentation in the query panel to improve the intuitiveness of the query process and alleviate cognitive load for new users.
Long-term Solution2.
📊 Results Panel Redesign: Improving data insights “accessibility”
🤔️ Pain points: Users with low data literacy often find if challenging to find valuable insights from the visualizations or conduct further data analysis.
💊 Solution: Improve the accessibility of data insights with 1. Improved information clarity and result readability. 2. Integrated Auto-Insights offering key insights and actionable suggestions in plain text; 3. Abnormal data highlighted on the graph and a Smart Diagnosis feature integrated for efficient cause identification.
Cross-team Collab.
🫶 Collaborate across teams to align goals and win-win
As the lead designer for this project, I not only led the design efforts but also took charge of project management. I actively collaborated with stakeholders to align the design efforts with their goals, ensuring that the redesign project was in sync with our team's objectives and could be executed seamlessly.
Initial Research.
Oversee 6 tools: Inconsistent design patterns and shared pain points for new users
To ensure the redesign's extensibility and holistic approach, I did initial research on 6 tools, by talking to different product managers and collecting prior user feedback.
Through information synthesis, I found that inconsistent design patterns and common pain points particularly concerning new users, call for a unified design solution.
UX Strategy.
🎯 Focus on 1 most commonly used tool: “Event Analysis” > Scale to 6 tools
🎯 Focus on 1st-time, non-technical users’ experience > Satisfy all users
User Tests.
Task-based usability tests to uncover root causes for the steep learning curve
To better understand the challenges faced by first-time users, I conducted usability tests on Event Analysis, involving 8 new, non-technical users. As data analytics tasks are inherently linked to specific problem-solving needs in users' daily work, I customized the testing tasks based on typical use cases gathered from various roles, ensuring relevance to their daily work.
Key Findings.
According to the usability tests, none of the non-technical users successfully completed the given analysis task using Events Analysis, and the Avg. Task Success Rate is only 52%. I categorized usability issues from users’ think-aloud process during the usability tests and their feedback in the follow-up user interviews.
Key Insights.
Diving deep into the usability test results, I clarified and prioritized the focuses for the redesign.
Idea Comparisons.
Idea evaluation based on user experience benefits, technical costs, and timelines
I proposed some key ideas to solve the pain points identified in user interviews and usability tests. By discussing with product managers, we further evaluated these ideas based on their impact and feasibility to determine their priority.
🌟 Considering the user guide's high priority and the intricate research&design efforts required, my major focus in this case study will be on the design process of the user guide.
Explore a suitable user guide design pattern for self-analytics tools
I started with three common design patterns for the user guide and analyzed their effectiveness in the scenario of self-analytics for new, non-technical users. While these design patterns are low-cost, they couldn’t ease the learning curve effectively.
Understanding the importance of well connecting the tutorial content with the actual UI, I proposed other design solutions and solicited user feedback for idea evaluation.
Given the positive feedback from users on the guidebook idea, I decided to move forward to refining the interactive details.
User Guide - Start.
Introduce a real-world use case to reduce the learning curve
- New users love stories, not dry feature explanations
In the concept testing, user feedback indicated that such a lengthy guide imposed a substantial cognitive burden, leading to reduced motivation to learn. Users also highlighted that a real-world use case could be very useful for them to understand the core features, gain hands-on experience and thus enhance the learning outcome.
User Guide - During.
Guide the user “in-context” by linking the tutorial with the real UI
- Let users know where to start
I refined the design so that the user knows clearly where the UI components are to finish each sub-task.
User Guide - After.
Support flexible learning paths
- Consider different scenarios that users want to close the user guide
While we focus on new, non-technical users’ learning experience, it’s important to make the guide flexible to follow. I dig deeper into a variety of possible learning processes to ensure that users can always review the guide after they close it.
Improve the accessibility of visual analytics
- Show insights and recommendations for further analysis, not just data or visualizations.
To enhance the accessibility of visual analytics, I leveraged technology insights and worked closely with algorithm engineers who had already developed AI techniques for uncovering data insights and diagnosing abnormal data.
Query Panel.
Show abstract data terminology with intuitive visuals
Final Design.
For more details of the design and prototype, check the Final Solution above!
Product Impact.
💪 Product Goal: High Task Success Rate for new users
To enhance the accessibility of visual analytics, I leveraged technology insights and worked closely with algorithm engineers who had already developed AI techniques for uncovering data insights and diagnosing abnormal data.
🙋🏻♀️ Business Goal: Increased adoption by internal users
We made it! Our redesign successfully achieved an 80% increase in user adoption, growing from 5,000+ to 9,000+ users within 3 months of launching!
Team Impact.
🫶 Warm recognition by team members
What I learned.
🎯 Create design strategy to breakdown the “wicked problem”
While the initial scope - redesign of the 6 tools, is broad, I learned to refine the product focus and develop my design strategy by prioritizing user needs while keeping business goals and the timeline in mind. Going wide to find common issues across different tools and then digging deeper into the most severe user pain points, I was able to navigate through ambiguity and identify my design focus. It's essential to aim for a holistic solution, but it's equally crucial to breakdown the wicked problem.
👫 Think beyond a designer’s role and proactively seek advice from stakeholders
Collaborating in an interdisciplinary environment, I learned that a well-operated product is more than the design of interfaces or interactions, but a collaborative work of different roles. To facilitate cross-team collaboration, designers should actively seek input from different stakeholders, understanding their needs and incorporating diverse perspectives. In this project, engaging with BI professionals provided insights into the tutorial content design, working alongside engineers allowed me to incorporate technical perspectives for non-technical users, and partnering with product managers deepened my understanding of business objectives. I am excited about wearing different hats in the future, by proactively seeking input from different stakeholders.
👀️ Design from a human’s perspective, not an “object” perspective for tools
When dealing with abstract terminology and complex domains in the tool design, it's common for us designers to design from a technical, 'object-oriented' perspective. However, I believe that the language of elegant design should mirror human communication, being intuitive to learn and adopt. The lesson I learned was to first digest technical knowledge, and then revisit it from a non-technical, new user's perspective. By finding metaphors, we can better construct a conceptual model that aligns with users' mental model.
Want to learn more about my work experience at Ant Group? Check it out!