Microsoft · Product Design
Analyze Data in Excel
Excel Ideas
Overview
Excel is intimidating to many people. It is used by millions daily, but only a few consider themselves experts in all of its features. In 2015, I joined Microsoft's Excel design team and embarked on a project aimed at making data less daunting. My objective was to design a solution where AI could work alongside users, empowering them to engage more deeply with their data without needing to learn all the intricacies of Excel.
At the time of my joining, there was no clear vision or roadmap to address the data literacy problem. Starting with research and developing high-level design concepts, I pitched the project successfully, securing funding. Subsequently, I took charge of leading and owning all aspects of UX decisions, collaborating with over 25 PMs, engineers, data scientists, and design researchers.
The project was shipped to millions of users worldwide and reached a monthly active user base of 1 million in 2020. Over time, Excel Ideas has evolved and influenced the recent announcement of Microsoft Copilot.
Phase 1
Creating a Product Vision
Phase 2
Design Iteration and Shipping to Production
Phase 3
Expanding the Scope & Leading Coherence in Office
Note: "Insights" and "Ideas" are used interchangeably as the feature was rebranded mid-project.
Phase 1 - Creating a Product Vision
The Problem
As the sole product designer and founding member of the project, I collaborated with an engineering manager, a PM lead, and a researcher in the early phase. For the initial 4 months, we conducted extensive user research and visited numerous users at their workplaces to see how they work with data.
Our findings revealed that thousands and millions of people rely on Excel for making business decisions, with 80% of their time dedicated to three key steps: data organization, analysis, and presentation. In practice, there is an abundance of data but a scarcity of data skills. Excel's existing design expected users to learn and adapt to our software in order to answer simple data-related questions.
USER JOURNEY MAP — 80% OF TIME SPENT IN 3 KEY STEPS
How Might We:“How might we help users find automated, personalized answers to their data without them having to learn everything in Excel?”
The Vision Story
Once I identified the problem and the opportunity, I led a one-week design sprint with all project members and other designers on the Excel team to explore various solutions. There were numerous ideas surrounding the utilization of charts, visualization, and an artificial intelligence project that the Microsoft Research lab was working on.
I quickly put together low-fidelity mockups and collaborated with an in-house video production team to create a video illustrating the project vision — automatically analyzing users' data and highlighting the key trends and insights for them. The narrative, exemplified by inspiring users like Mindy, successfully secured funding for the project. Intelligent data analysis emerged as one of the 4 core initiatives for Excel and Office for that fiscal year, extending into the present day.
Phase 2 - Design iteration and SHIPping TO PRODUCTION
Design Process
Based on the design sprint work and user research, I discovered that people tend to comprehend data better when presented in chart form. Collaborating with data scientists during Phase 1, we focused on the insights that users valued the most: correlations, outliers, trends, majorities, and more.
Before generating insights for any data, Excel needed to understand the data's meaning — for example, "Ratings" = numerical, "Brand" = categorical, "Year" = number used as a category name. I studied data visualization best practices extensively, engaging with experts, reading Edward Tufte's works and Storytelling with Data by Cole Knaflic, and interviewing a Bloomberg columnist. My goal was to create charts that were both simple and allowed users to swiftly grasp key insights, whether related to correlations, trends, or identifying outliers. I opted for basic chart types (line, bar, scatter) native to Excel, with color highlighting to draw attention to vital information.
INSIGHT TYPES — CORRELATION, OUTLIERS, TREND, MAJORITY, AND MORE
"Mobile first" principle adaptation: Given that the majority of Excel users operated on Windows machines, we prioritized development on Windows initially as a web app add-in using React. We then embedded the same web add-in into Excel for Mac and Excel Online. The visual design challenge was to seamlessly blend with surrounding UI across 4 Windows color themes, 2 Mac themes, and any web browser.
From left to right: White, grey, black and colorful themes for Excel on Windows, with background Excel UI on colorful theme.
Even though we did not have plans to ship on iOS and Android at that time, I designed 5 different entry points and 4 interaction models for mobile Excel app leveraging existing mobile design language and interaction model.
Exploration on the entry button for ideas
Exploration on how ideas shows up in Excel on iOS
Iterate after Production
We defined success with the "seen, tried, kept" framework: the number of people who saw the feature, who tried it, and who kept the resulting charts in their spreadsheet.
Example 1 — Empty sheet error message: From telemetry data, 38% of error messages occurred when users clicked "Ideas" on a blank sheet. The previous error simply said "blank sheet, try a different dataset." The improved error included a CTA button offering sample data and a visual example, resulting in a significant increase in "seen, tried, kept" metrics.
BEFORE / AFTER — BLANK SHEET ERROR MESSAGE
Example 2 — Feedback mechanism for ML personalization: Users opened Insights but did not frequently engage with the "insert chart" button, creating a gap between "seen" and "tried." To understand which insights users found useful, I created a feedback mechanism into the ML model.
Drawing inspiration from Facebook Feed and Instagram Ads design patterns, I developed 3 different options within Insights for users to provide feedback. Conducted A/B testing and selected Option 3 due to its improved visibility, resulting in 10 times more feedback compared to the other two options. This accelerated the ML model training process.
Instagram (left) and Facebook (right)'s design pattern on providing feedback to suggested content
Provide feedback to insights — option 1, 2, 3
Phase 3- shipped worldwide and extend beyond Excel
Expand Scope and Lead Coherence in Office
Excel Ideas successfully shipped to all Office users worldwide and hit 1 million MAU very shortly after the official launch.
Looking back at the user journey from the early research, it was evident that users always start from "asking a question." This inspired me to initiate a new project: natural language query — helping users proactively search for answers. I designed an experience inside Ideas that allows users to use natural language to ask Excel questions. Ideas finds the answer and explains how it was calculated via a formula, a chart, or a table. This feature has also shipped to the public.
NATURAL LANGUAGE PROCESSING (NLP) ALLOWS USERS TO ASK QUESTIONS TO EXCEL
Through the years I got to know many designers and PMs working in similar spaces across Microsoft — PowerPoint "Designer," Word "Editor," Outlook, PowerBI. Gathering this group of talented designers, we met weekly to share and discuss design patterns, visual language, information architecture, and more. Together we built the same card visual framework, navigation model, feedback mechanism, and visual elements for a cohesive experience across Office.
Intelligent system model where we defined the design principle for different surfaces in Office suite
As we continue to learn and evolve, surfacing suggestions on the side pane isn't always the best experience — especially when the user is in the middle of their workflow on the canvas, and our suggestion relies on the content the user is currently working on. I had an amazing team of PMs and engineers who were always supportive of me reaching out to users and exploring new ideas based on feedback. Here are some early concepts across different Excel surfaces to help users become more productive with the power of artificial intelligence.
Peer Feedback
In 2018, the product manager lead, with whom I partnered for over 3 years, provided unsolicited feedback to my design manager and summarized her evaluation of my contribution to the project.
Final Thoughts
Looking back, this was undoubtedly my favorite project to work on. I collaborated, researched, iterated, and was able to make a huge impact on all Office users around the world, in over dozens of languages. The product manager lead, engineering manager and I worked in a triad model where we made every decision that impacts our users together, from feature prioritization, release plan, project roadmap, and many more. I was awarded 6 patents and 3 promotions in 3 years while on this project, and had the great honor to work with an amazing product team of 25+ people across 2 countries, who supported and trusted me to make the UX decisions for our users.
Excel Ideas has been rebranded to "Analyze Data" in 2022, and became the 1st feature added to Excel's Home tab toolbar in over 20 years. In more recent years, it now lives on as Copilot, continuing to help Excel users turn raw data into instant insight. Check out the video below see how people talk about this feature.