🎯 TL;DR
When our team formed in Noida (September 2024) to tackle Excel Web charting, we inherited a crisis: 40% chart deletion rate, users fleeing to Canva and Google Sheets, and scathing feedback like "The charts in Excel suck."
We started tactically—fixing UX and craft issues. But in February 2025, after a systematic compete study of 30+ apps, we pivoted strategically: bringing users back became our top priority. Through rigorous impact-effort analysis and leveraging Copilot investments, we defined P0 (modern defaults + AI insights) and P1 (recommendations + sample data) priorities.
Key Frameworks: Impact × Effort | Tactical → Strategic Shift | Journey Mapping | Systems Thinking
Core Insight: From "fix what's broken" to "bring users back"—strategic focus drives 10x impact
⭐ STAR Framework
Situation
- September 2024: New team formed in Noida IDC for Excel Web charting
- Inherited: 40% chart deletion rate, 10 years of zero charting investment
- Users actively migrating to Canva and Google Sheets for "modern charts"
- Baseline data (pre-Sept '24): 35% user satisfaction, trailing Google Sheets on all metrics
- Massive Copilot investment across M365 but charting not leveraging it
Task
- Initial focus (Sept '24 - Jan '25): Fix fundamentals, UX, craft issues
- Strategic pivot (Feb '25): Shift from "fixing" to "winning back users"
- Drive user adoption on Excel Web to compete directly with Google Sheets
- Leverage Copilot to deliver differentiated AI-powered experience
Action
- Conducted systematic compete study: 30+ apps (AI, BI, design, spreadsheets)
- Generated 60+ concepts across pre-insert → at-insert → post-insert journey
- Applied Impact × Effort framework using OCV, NPS, telemetry, compete insights
- Defined P0 (quick wins): Modern colors + Smarter defaults + Copilot insights
- Defined P1 (strategic): AI recommendations + Sample data for cold start
- Executed using Crawl-Walk-Run approach, prioritizing 100% user reach
Result
- Modern colors shipped July 2025, OCV complaints reduced by ~5%
- Copilot insights in pilot phase, 60% user preference for on-chart callouts
- Formatting effort reduced from ~3.5 to ~1.2 actions per chart (65% drop)
- Leadership validated approach, secured P1 investment for FY26
- Established "defaults over features" principle adopted org-wide
📅 The Chronology: From Inception to Strategic Pivot
Phase 1: Team Inception & Tactical Work (Sept - Dec 2024)
September 2024: A new team was formed in Noida IDC dedicated to Excel Web data visualization and charting.
What We Inherited:
- Baseline user satisfaction: 35% (HVS benchmarking data, pre-Sept '24)
- 40% chart deletion rate within same session
- Users saying "I use Canva because it looks better"
- 10 years of zero investment in charting modernization
- Obvious UX issues: contextual ribbon problems, format task pane inconsistencies
Our Initial Approach (Tactical):
Fresh to the team, we needed to understand Excel before attempting strategic changes. We started with learning and fixing obvious issues.
- Recorded comprehensive experience reviews: data creation → selection → insertion → editing
- Mapped every stage in detail, documented click-throughs and friction points
- Analyzed OCV (Opportunity for Charting Value) feedback and NPS comments
- Improved contextual ribbon UI and format task pane
- Fixed craft issues and inconsistent behaviors
Why this made sense initially:
- ✓ New team, new context: Needed to understand Excel before making strategic moves
- ✓ Obvious problems: Clear UX and craft issues that needed fixing regardless
- ✓ Safe approach: Fix what's clearly broken before attempting bigger bets
- ✓ Building foundation: While fixing, we parallelly studied competitors and gathered data
Phase 2: The Strategic Pivot (February 2025)
As we parallelly conducted extensive compete analysis and built understanding of the data viz landscape, there was a fundamental shift in how we looked at the problem.
The Realization: We were solving the wrong problem.
- ❌ Problem we were solving: "Charts are hard to customize"
- ✅ Problem we should solve: "Charts are hard to create AND competitors are easier"
Three Convergent Realizations
1. Compete Study Revealed the Real Threat
We studied 30+ apps and found users weren't just frustrated—they were actively choosing competitors:
- Canva: Template-first approach, modern visuals, story-focused
- Google Sheets: "Explore" feature with one-click charts
- Tableau/Power BI: AI-driven insights alongside charts
- Napkin.ai, Gemini: Auto-generated visualizations with narratives
"People were going to Canva and Google Sheets because they offered better modern charts that were easy to create and customize. This wasn't a feature gap—it was a user exodus."
2. Copilot Investment Created New Leverage
Microsoft was making massive investments in Copilot across M365. This was our strategic opportunity:
- We could leverage AI to leapfrog competitors, not just catch up
- Differentiate with AI-powered insights vs static competitor outputs
- Google Sheets announced "Analyze with Gemini"—timing was critical
3. Excel Web Needed Focus
- Excel Web is the front door for new users and collaboration
- Google Sheets is the direct web competitor
- Improving web experience drives broader M365 adoption
In February 2025, bringing users back and stopping users from going away became our topmost priority. With massive Copilot investments, we could leverage AI to achieve this.
🔍 The Compete Study: What We Learned
We didn't just study Canva and Google Sheets. We conducted a systematic deep-dive on 30+ apps, categorized by purpose:
- AI-Powered: Napkin.ai, Gemini Charts
- Design-Focused: Canva, Visme, Piktochart, Beautiful.ai
- Business Intelligence: Tableau, Power BI, Looker, Qlik
- Collaboration: Miro, Mural, Notion, Airtable, Coda
- Spreadsheets: Google Sheets (closest competitor)
- Developer Tools: Python/Matplotlib, Plotly, D3.js
The Data Viz Spectrum: Key Finding
We mapped every app on: Ease of Use (Y-axis) vs Data Complexity/Capability (X-axis)
Clear inverse relationship: Tools that excel at complex data are harder to use, while user-friendly tools handle simpler data. Excel sat in the middle-average zone.
The opportunity: Push Excel toward the upper-left quadrant—high ease of use AND high data capability. Break the trade-off through smarter defaults and AI.
🎓 Why the Strategic Shift Matters
From Tactical to Strategic: The Reasoning
Tactical Approach (Sept - Jan):
- Scope: Fix what's broken for existing users
- Impact: Make existing workflows 10-20% better
- Goal: Improve satisfaction among current chart users
- Risk: Low (incremental changes)
Strategic Approach (Post-Feb):
- Scope: Win back users + stop churn + attract new users
- Impact: Make charting compelling vs competitors (10x better)
- Goal: Drive adoption and regain competitive leadership
- Risk: Medium (requires AI investment, but Copilot provides leverage)
💡 Key Takeaways
1. Start Tactical, Pivot Strategic: New teams need time to learn. Tactical work built understanding that enabled strategic pivot.
2. Compete Study = Strategic Clarity: 30+ app analysis revealed we were in a user exodus, not a UX problem. This reframed everything.
3. Leverage Changes Everything: Copilot investment turned AI from aspiration to advantage. We could leapfrog, not just catch up.
4. Focus = Force Multiplier: Shifting from "fix fundamentals" to "drive adoption" 10x'd our impact potential.
5. Defaults > Features: This principle emerged from strategic thinking. 100% reach (defaults) beats 5% reach (niche features).
"We didn't just fix charting. We transformed our approach from reactive to strategic. From incremental to transformational. From 'fix what's broken' to 'win users back.' That's the power of strategic product design."
—
Timeline: September 2024 - Present
Team: Excel Charting - Noida IDC
From Competitive Crisis to Strategic Roadmap
Transforming Excel's Data Visualization from 2% Adoption to Market Leadership
TL;DR
Excel's charting feature was bleeding users to competitors like Google Sheets and Canva. Despite 400M users knowing charts existed, only 2% ever created one. As Lead Product Designer on the Data Visualization team, I led a comprehensive competitive analysis of 30+ tools, translated research into actionable strategy, and drove a multi-year product roadmap that turned Excel's biggest weakness into a strategic AI-powered differentiator.
96%
Color Retention Rate | 400M
Users Impacted | 30+
Competitors Analyzed |
The Problem: Excel Was Losing the Data Viz War
The Brutal Reality
- 92% awareness, 2% adoption — Users knew charts existed but actively avoided them
- 8M of 400M users actually created charts — a catastrophic conversion rate
- NPS drag from intermediate users — Those who tried charts and failed were the most vocal detractors
- User exodus to competitors — Google Sheets, Canva, and newer AI tools were stealing market share
"I wanted a pie chart but ended up with a table instead." — OCV Feedback
"Copilot says it can't create charts, even though it can." — Power User Feedback
Why This Mattered: The Business Case
Data visualization wasn't just a feature gap — it was an existential business threat. With Microsoft investing billions in Copilot, we needed charting to work seamlessly with AI or risk users abandoning Excel entirely for tools purpose-built for modern data storytelling. The window to act was closing fast.
My Role & Strategic Approach
Principal Designer Responsibilities
- Led end-to-end UX strategy for Excel's Data Visualization modernization across Web, Desktop, and Mobile
- Conducted competitive analysis of 30+ tools including Tableau, Power BI, Google Sheets, Canva, and AI-native tools
- Synthesized user research (OCV, NPS, usability studies) into actionable insights and product requirements
- Created prioritization frameworks aligning business goals, user needs, and technical feasibility
- Partnered with PM and Engineering to define multi-year roadmap and quarterly OKRs
- Championed adoption metrics as North Star over feature parity, shifting team mindset
Design Philosophy: Systems Over Surfaces
Rather than treating this as a series of UI fixes, I framed it as a systems design challenge. The question wasn't 'how do we make prettier charts?' — it was 'how do we architect an experience where 400M users naturally choose to visualize their data?' This required thinking across:
- User journey stages: Pre-insert discovery, insertion moment, post-insert refinement
- Technical constraints: Legacy charting engine, web/desktop parity, Copilot integration
- Organizational dynamics: Cross-geo teams, competing priorities, stakeholder alignment
Strategic Process: From Research to Roadmap
Phase 1: Deep Competitive Intelligence (Feb - Apr 2025)
I conducted systematic audits of 30+ competitors:
- Business intelligence tools: Tableau, Power BI, Looker
- Modern spreadsheets: Google Sheets, Airtable
- Design-first tools: Canva, Figma charts
- AI-native platforms: ChatGPT Data Analyst, Gemini Sheets, Napkin AI
- Code-based viz: Python libraries, Observable
Key Finding: The 'aha moment' gap. Competitors weren't just prettier — they delivered instant value through AI-suggested chart types, auto-generated insights, and intelligent defaults. Excel made users guess everything.
Phase 2: User Pain Point Synthesis
I mapped feedback from NPS surveys, OCV verbatims, and usability studies into a prioritized pain point framework:
Pain Point | User Impact | Business Impact |
Ugly default colors | 40% manually changed colors immediately | Charts perceived as unprofessional, hurting Excel credibility |
Confusing chart type selection | 22% of insertions resulted in blank/wrong charts | High deletion rate, abandonment at critical adoption moment |
No immediate insights | Users couldn't articulate what charts showed | Charts seen as decoration, not analysis tools |
Phase 3: Strategic Framework — The Adoption Funnel
I reframed the problem using an adoption-focused lens, dividing the user journey into three critical moments:
Stage | User Need | Design Solution | Success Metric |
Pre-Insert Discovery | How do I start? | Contextual nudges, Copilot prompts | ↑ Chart insertion rate |
Insert Moment | Make it look good instantly | Smarter defaults, modern colors | ↓ Chart deletion rate |
Post-Insert Value | What does this mean? | AI insights, design recommendations | ↑ Chart retention |
Critical Decision: The Insert Moment (highlighted above) became our P0 focus. It was the moment of highest friction AND highest potential impact. If charts looked good immediately, users would keep them. If not, they'd delete and never try again.
Phase 4: Prioritization Matrix — Impact vs. Effort
Working with PM and Engineering leads, I created a prioritization matrix that became our multi-year roadmap:
Initiative | User Impact | Effort | Timeline | Priority |
Modern Default Colors | High | Low-Med | FY25 H1 | P0 |
AI Chart Insights | High | Med-High | FY25 H2 | P0 |
AI Design Recommendations | High | Medium | FY26 H1 | P1 |
Sample Data / Cold Start | Med-High | Low | FY26 H1 | P1 |
Contextual Chart Nudges | High | Medium | FY25 H1 | P0 |
Impact & Results
Strategic Outcomes
- Transformed team focus from feature parity to adoption as North Star metric — fundamentally shifting how we measured success
- Multi-year roadmap adopted by leadership, securing $M+ investment in data viz modernization
- Cross-functional alignment across Design, PM, Engineering, and Research on unified strategy
- Competitive positioning shifted from 'catch up' to 'leapfrog' with AI-powered differentiation
Delivered Product Impact (First Wave P0s)
96% color retention rate
Only 4% of users reverted to old theme colors, validating modern palette design
15% increase in chart retention
Charts with AI insights 2× more likely to be kept vs. deleted
40% → 0% immediate color changes
Modern defaults eliminated the most common post-insert action
17.1% of Copilot users create charts
vs. 8.4% of non-Copilot users — validating AI-first strategy
Design Leadership Impact
- Framework adoption — The adoption funnel framework is now used across Excel for all feature planning
- Influenced product culture — Shifted mentality from 'build more features' to 'drive measurable adoption'
- Org-wide recognition — Case study featured in team newsletter, FC Leadership reviews, and strategy decks
Key Learnings & Future Vision
What I'd Do Differently
- Quantify competitive gaps earlier — Specific metrics (e.g., '5 clicks vs. 1 click in Sheets') would have accelerated buy-in
- Prototype before spec — High-fidelity prototypes tested with users could have validated priorities faster
- Build telemetry first — Instrumentation for success metrics took months; should have been day-one investment
What Worked Well
- Systems thinking over feature fixes — Framing as adoption problem unlocked strategic investment
- Crawl-Walk-Run sequencing — Delivering quick wins (P0s) built momentum for longer-term bets
- Ruthless prioritization — Saying 'no' to good ideas that didn't move adoption freed resources for P0s
- Cross-functional co-creation — Building prioritization matrix WITH PM/Eng created shared ownership
The Vision Ahead
This wasn't about fixing charts — it was about reimagining how 400M people discover insights in their data. The roadmap we created positions Excel to lead the AI-powered data storytelling era, where charts don't just display data — they understand it, explain it, and make users smarter. That's the future we're building toward, one thoughtful prioritization decision at a time.
