How I Simplified Complex Data with a Pivot Table Redesign?
This case study outlines how I redesigned the Pivot Table report, making it more user-friendly and accessible for business users to analyze data effortlessly.
Overview
Team
Product Designer
Product Manager
Developer
Timeline
Feb - Mar 2023
Shipped
Cateogry
Web
Line of business
B2B SaaS
Setting the Context
The Pivot Table Redesign was initiated after a drop in usage was identified through our data. Users found the existing Pivot Table difficult to navigate and felt it lacked essential features. Feedback indicated that the tool was no longer meeting their expectations, falling short of modern standards and missing basic functionalities typically expected in a pivot table.
I led the design process end-to-end, focusing on simplifying the interface, enhancing customization, and integrating the solution with the Syncfusion library. The goal was to create a more intuitive, user-friendly experience for Kissflow's users, ranging from data analysts to managers.
Wait! Wondering what a Pivot Table is?
A pivot table is a tool that helps you organize, summarize, and analyze large sets of data, allowing you to view the data from different perspectives.
Here in the example you can quickly learn how the Instead of manually counting the number of red or black cards, this table groups and sums them up for you.
You can easily compare between the suits (e.g., how many red cards vs black cards) and spot patterns (e.g., 13 cards per suit, two red suits, and two black suits)
The Final Reveal ✨
Let’s break the surprise! Take a look at the final design before diving into the process and problems I solved. This will give you a better context for the case study.
Identifying the Problem
Now, let’s take a closer look at the challenges our users encountered with the existing design. To make it easier to understand, I've listed some of the key problems based on user feedback and my own design intuition:
Usability Issues
Lack of Basic Features
Outdated Design
I hope this gives you an idea of the challenges we were addressing. Let’s dive into the background now.
Project Background
How the project begin
After releasing the Analytics module to our Kissflow users, we began collecting data and gathering user feedback. The data revealed that Analytics was underutilized compared to other modules. This is when I decided to take action and work on the Analytics user experience. I presented the issues to the stakeholders, and with their support, I was entrusted to lead the project.
The Importance of Data
Once the need to work on this was clear, we collected data from our customers and found that, compared to other reports in Kissflow Analytics, the Pivot Table report had 200% lower usage.
We identified key insights from the data:
Increased drop-off rate
Low usage
The data shows the drop in pivot report usage compared to other reports in analytics.
In conclusion, the data provided a clear motive to work on the problem.
My Initial Approach
The PM and I initially planned to list all the bugs and UI/UX issues that needed fixing based on previous user feedback and public forum requests. However, as we compiled the list, we unexpectedly found many issues.
That's when I realized that fixing bugs alone would be like putting a band-aid on a leaky pipe—the Pivot Table needed a complete redesign rather than just minor improvements.
Understanding the Pain Points
Identifying Key Users
Before diving into their pain points, it's essential to understand who the real users are.
Business owners / End users
These users typically have limited knowledge of data creation. They mainly consume data rather than create it. Common roles include CEOs, Managers, or general users.
Data analyst / IT Developers
These are the users who create and manage data. They assist the business users in analyzing the data they have generated. This group mainly consists of data analysts and low-code developers.
Now that we understand our users, we began with user interviews, usability testing, and listening to customer calls to understand their pain points.
User Feedback
"The pivot was too complex for me to understand; I felt like I needed to learn it before using it."
"It lacks some necessary features to perform basic functions like sorting and filtering."
"Many UX issues and bugs were seen, making the tool frustrating to use."
Stakeholder Feedback
"The design must align with industry standards."
"We have a low number of pivot report users; we need to increase the adoption rate."
"Resolve the UX issues, bugs, and visual problems that users are complaining about."
To conclude, we gained a clear understanding of what the users wanted and the areas we needed to focus on. With this knowledge, we proceeded to conduct a competitor analysis.
Competitor Analysis
Since pivot tables are common in Business Intelligence tools, I analyzed tools such as Domo, Qlik, Zoho Analytics, Tableau Public, SAP Analytics Cloud, and Power BI.
The analysis focused on key features such as user interface design, data interaction capabilities, customization options, and advanced functionalities like sorting, filtering, and drill-down features.
Key Insights
User Interface:
Zoho and Tableau excel in offering customizable and modern UIs, while tools like SAP and Power BI emphasize simplicity and clarity.
Essential Features:
Common features like expand/collapse, full-screen viewing, filtering, and sorting are standard but differ in execution, leaving room for innovation.
Data Interaction:
Tableau Public and Power BI stand out with interactive features like focus modes, tooltips, and drill-down capabilities, enhancing user engagement.
Considerations
Based on our analysis,
We decided not to overload the pivot table with excessive customization. Instead, we focused on providing key features like subtotal control, column sorting, and expand/collapse functionalities.
Interactive elements like tooltips and hover effects were identified as potential enhancements, with plans for further validation through user testing.
We emphasized simplicity in the UI to ensure it was both accessible and easy to use while remaining powerful for data analysis.
Facing New Challenges
Midway through the project, we had to use the Syncfusion library instead of building the pivot table from scratch due to time and cost constraints. This required quickly adapting the design to fit pre-built components while ensuring a seamless user experience within the technical limits.
Iterations
With all the research and the goal set, I began designing the pivot table. I went through multiple iterations and learned valuable lessons throughout the process.
Initial Disappointments
After designing multiple layouts, I expected users to love the design, thinking I had done a great job. However, while users liked the design, they felt it still wasn’t quite right. Their feedback was vague.
What We Tried:
We aimed to modernize the design with a clean, minimalistic look, hoping to address the clutter and outdated feel.
We focused on creating a more spacious layout and simplifying the visual presentation.
User Response:
Despite the cleaner look, users still struggled with usability. The design lacked the functionality they needed, and the spacious layout made it harder to navigate quickly.
Midway Adjustments
Up to this point, the feedback was still unclear, leaving us to piece together what users were truly looking for.
What We Tried:
We tightened the layout to make it more compact and data-dense, similar to traditional pivot tables. We used bold fonts and shaded backgrounds to make totals and subtotals stand out more.
User Response:
This approach improved readability and visual hierarchy, making it easier to spot key information, but something was still missing.
The Final Breakthrough
After numerous iterations and re-examining feedback, I realized the problem was all about familiarity. Users were instinctively comparing our design to the Excel pivot tables they were accustomed to. We finally achieved a design that users loved.
What We Tried:
We reduced cell sizes to mimic the dense, grid-like structure of Excel.
We added the missing features like sorting, filtering, and resizing, while fixing the sticky header issue.
The design was kept clean and modern, but also functional and easy to use.
User Response:
Users felt that the design finally provided the functionality they needed while looking and feeling like a pivot table should.
Final Design
The final design not only met user expectations but also improved the overall experience, leading to a higher adoption rate and better usability.
Advanced features
Customizable Themes
Drilldown Data
Conditional Formatting
Basic features
Custom Sorting
Filter
User Widget
Pivot Table Interactions
Fixed Header
Resize
Expandable Rows & Column
Accessability
Right to Left (RTL)
