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Excel Data Visualisation: From Competitive Crisis to Strategic Roadmap

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Introduction

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.
 
Core Insight: From "fix what's broken" to "bring users back"—strategic focus drives 10x impact
 

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.
 
 
 

Strategic Process & Chronology: 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
 
 
 

Three Convergent Realizations

 

1. Compete Study Results Came In

After studying 30+ apps systematically, patterns emerged:
What we found:
  • Ease vs Complexity Trade-off: Tools good at complex data are hard to use; easy tools handle simple data
  • Excel's position: Middle-average—not great at ease, not great at complexity
  • User behavior: Actively choosing Canva (templates), Google Sheets (ease), Tableau (insights)
The key insight:
Users weren't complaining about Excel's features—they were leaving for competitors' experiences.
User quotes that shifted our thinking:
  • "I use Canva because it looks better" → Not a feature gap, an aesthetic gap
  • "Charts in Excel suck" → Not about power, about first impression
  • "I wish Excel could tell me what's interesting" → Not about customization, about intelligence
Data that mattered:
  • 40% chart deletion rate → Charts weren't adding value
  • Businesses selling "how-to" courses → Market signal of poor UX
  • 92% of users never insert charts → Discovery/adoption problem, not a power user problem
 

2. Copilot Investment Changed the Game

Microsoft's massive Copilot investment across M365 created new strategic leverage:
The opportunity:
  • Not just catch up: We could leapfrog with AI-powered insights
  • Native advantage: Our charts are editable objects, competitors return static PNGs
  • Integration advantage: Copilot + Excel data + Charts = unique value prop
  • Timing: Google announced "Analyze with Gemini" for Sheets—competitive urgency
The realization:
We had strategic ammunition (Copilot) but weren't deploying it effectively. Fixing ribbons wouldn't leverage our AI advantage.
 

3. Excel Web = Competitive Battleground

Platform dynamics:
  • Excel Web is the entry point for new users
  • Google Sheets is the direct competitor (both are web-first)
  • Web experience drives collaboration scenarios
  • Modern users expect web parity with desktop
The strategic question:
If we don't win on web, do we win at all?
The answer:
No. Web is where the competitive war is happening. Desktop users are already committed to Excel. Web users are choosing every day.
 
 

🔍 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."
 
 
 
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