Experimentation Case Study
When 95% Completion Didn't Move the Needle: Diagnosing Experiment Flaws
A product tour experiment for Jira Software, a project management tool, increased signups by 10%—but 95% of users abandoned the demo. I designed a new interactive board experience and achieved 95% completion, yet saw no signup impact. Through systematic diagnosis, we identified four critical methodology flaws that would inform our next experiment and drive a 66% increase in conversions.
Experiment Lead
A/B Testing
Interactive Components
B2B SaaS
Project Management Software
Product
JSW Product Tour
What I Did
Design & Research
Role
Product Designer
Timeline
6 Weeks
Experimentation Case Study
When 95% Completion Didn't Move the Needle: Diagnosing Experiment Flaws
A product tour experiment for Jira Software, a project management tool, increased signups by 10%—but 95% of users abandoned the demo. I designed a new interactive board experience and achieved 95% completion, yet saw no signup impact. Through systematic diagnosis, we identified four critical methodology flaws that would inform our next experiment and drive a 66% increase in conversions.
Experiment Lead
A/B Testing
B2B SaaS
Interactive Components
Project Management Software
Product
JSW Product Tour
What I Did
Design & Research
Role
Product Designer
Timeline
6 Weeks
The Context
The Challenge
Our Hypothesis
We were converting users without educating them about the product's value—potentially impacting activation and retention. An interactive board component with better guidance would improve completion rates and lead to more qualified signups.
Goal: Design an interactive board experience that reduces drop-off and increases modal completion rates, ultimately driving more product signups.
The previous interactive timeline experiment showed promising results with a 10% signup increase. Leadership celebrated the win. However, deeper analysis revealed a critical issue: only 2.4% of 31,072 visitors completed the entire modal experience.
The data showed a massive 95.5% drop-off between the first interaction (updating an epic) and the second step (adding a story). This meant we were converting users without educating them about the product's value—potentially impacting long-term activation and retention.
"Overall positive results for interactive demo with high engagement towards the first action of the demo and 2.4% of visitors finishing entire modal experience."
— Previous Experiment Results
Previous Experiment Data
Total Visitors
31,072
Step 1: Update Epic
30,394 (97.8%)
Step 2: Add Story
1,379 (4.4%)
Completed Modal
740 (2.4%)
Drop-off Rate
95.5%
Digging Deeper
Design Process
01
Discover
Analyzed previous experiment data, identified 95.5% drop-off between steps 1-2
03
Design
Created interactive board with drag-and-drop, editable fields, step guidance
04
Test
Shipped to production, monitored completion and signup metrics
02
Research
Analyzed SaaS onboarding patterns: guided tours, progressive disclosure, interactive demos
Cross-Functional Collaboration
Led design direction while partnering closely with multiple teams to ensure technical feasibility, accurate messaging, and rigorous experiment methodology.
Product Manager
My Role: Presented data analysis showing 95% drop-off, proposed interactive board solution
Their Role: Defined success metrics, prioritized goals in alignment workshop, secured stakeholder
buy-in
Engineering
My Role: Created detailed interaction specs, design system documentation, collaborated on technical constraints
Their Role: Built interactive components, implemented A/B test infrastructure, set up exposure tracking
Content Design
My Role: Designed step marker UI, microcopy placement, and content hierarchy strategy
Their Role: Wrote action-driven copy for each step, ensured clarity and brand voice consistency
Data Science
My Role: Requested specific analytics on drop-off points and completion funnels
Their Role: Analyzed previous experiment data, calculated statistical significance, validated methodology
Testing Approach
The Experiment
Launched an A/B test targeting all desktop users visiting the English product tour page. The interactive board component replaced static
"Plan" tab content in the treatment group, with the goal of increasing modal completion rates and driving more signups.
Control Group (50%)
Standard product tour with static images and text describing the board view.
•
Product tour tabs with static screenshots
•
Text descriptions of features
•
No interactive elements
Treatment Group (50%)
Enhanced experience with interactive board, step guidance, and hands-on exploration.
•
Drag-and-drop cards between columns
•
Editable fields and pre-filled epics
•
Live Testing
Shipped directly to production as an A/B experiment. Worked closely with Engineering to productionize the interactive components, then monitored engagement data including clicks, completions, and drop-off rates to validate the design and surface insights.
The components built for this experiment were designed to be production-ready, allowing us to quickly roll out successful patterns to other pages if results were positive.
Outcome
Results
UX Success
95%
Business Metrics
+0.6%
No statistically significant increase in signups (±3.1%). But this wasn't a UX failure—it was an experiment design failure.
Four Methodology Flaws That Invalidated Our Results
01
Wrong Audience
30% of participants were logged-in users who already had Jira Software—diluting results.
Fix: Target only new-to-JSW users for accurate signup impact measurement
02
Below-the-
Fold Placement
Only 37% of users scrolled far enough to see the interactive board—limiting impact.
Fix: Position key interactions above fold or track exposure on scroll
03
Exposure Tracking Error
Fired on page load, not when users actually saw the demo—counting people who never engaged.
Fix: Trigger exposure only when treatment visible in viewport
04
Platform Bug
Board styling flashed between dark/light mode due to platform change—impacting user experience.
Fix: Rigorous QA and stable technical environment before launch
What This Taught Me
01
Completion ≠ Conversion
Users engaging with features doesn't automatically drive business metrics. Both matter, but they measure different aspects of success.
02
Methodology = Design Quality
Even well-designed experiences need proper targeting, exposure tracking, and technical stability to prove business impact.
03
Question "Winning" Metrics
A 10% signup increase masked 95% of users abandoning the experience. Always dig deeper than surface metrics.
04
Failed Experiments = Learning
This established best practices for targeting, exposure tracking, and methodology that drove a 66% win in the next experiment.
What Happened Next
These weren't theoretical recommendations—I immediately applied all four fixes to redesign Google Ads landing pages for paid traffic.
+66%
Signup
Increase
+69.7%
Account
Creations
Statsig
All
Metrics
This proved that experiment methodology is as critical as design quality for driving business results.
More Works More Works
Experimentation Case Study
When 95% Completion Didn't Move the Needle: Diagnosing Experiment Flaws
A product tour experiment for Jira Software, a project management tool, increased signups by 10%—but 95% of users abandoned the demo. I designed a new interactive board experience and achieved 95% completion, yet saw no signup impact. Through systematic diagnosis, we identified four critical methodology flaws that would inform our next experiment and drive a 66% increase in conversions.
Experiment Lead
A/B Testing
Interactive Components
B2B SaaS
Project Management Software
Product
JSW Product
Tour
What I Did
Design &
Research
Role
Product
Designer
Timeline
6 Weeks
Context
The Paradox
The previous interactive timeline experiment showed promising results with a 10% signup increase. Leadership celebrated the win. However, deeper analysis revealed a critical issue: only 2.4% of 31,072 visitors completed the entire modal experience.
The data showed a massive 95.5% drop-off between the first interaction (updating an epic) and the second step (adding a story). This meant we were converting users without educating them about the product's value—potentially impacting long-term activation and retention.
"Overall positive results for interactive demo with high engagement towards the first action of the demo and 2.4% of visitors finishing entire modal experience."
— Previous Experiment Results
Previous Experiment Data
Total Visitors
31,072
Step 1: Update Epic
30,394 (97.8%)
Step 2: Add Story
1,379 (4.4%)
Completed Modal
740 (2.4%)
Drop-off Rate
95.5%
Our Hypothesis
We were converting users without educating them about the product's value—potentially impacting activation and retention. An interactive board component with better guidance would improve completion rates and lead to more qualified signups.
Goal: Design an interactive board experience that reduces drop-off and increases modal completion rates, ultimately driving more product signups.
Digging Deeper
Design Process
01
Discover
Analyzed previous experiment data, identified 95.5% drop-off between steps 1-2
03
Design
Created interactive board with drag-and-drop, editable fields, step guidance
02
Research
Analyzed SaaS onboarding patterns: guided tours, progressive disclosure, interactive demos
04
Test
Shipped to production, monitored completion and signup metrics
What I Designed & Why
Step-by-step guidance
Visual markers + action-driven copy to reduce confusion about what to do next
Create
Restart demo
TO DO
Design AI shopping suggestions for homepage
Features
TASK
IN PROGRESS
Create AI-generated shopping suggestions for homepage
Features
STORY
Add advanced analytics tracking events
Analytics
STORY
DONE
Improve payment checkout time on mobile
Payments
BUG
Define requirements to use new AI integrations
Features
TASK
Interactive components
Drag-and-drop cards, editable fields—extended Atlassian design system
Realistic but simplified
Real Jira patterns + pre-filled content to reduce cognitive load
Cross-Functional Collaboration
Led design direction while partnering closely with multiple teams to ensure technical feasibility, accurate messaging, and rigorous experiment methodology.
Product Manager
My Role: Presented data analysis showing 95% drop-off, proposed interactive board solution
Their Role: Defined success metrics, prioritized goals in alignment workshop, secured stakeholder buy-in
Engineering
My Role: Created detailed interaction specs, design system documentation, collaborated on technical constraints
Their Role: Built interactive components, implemented A/B test infrastructure, set up exposure tracking
Content Design
My Role: Designed step marker UI, microcopy placement, and content hierarchy strategy
Their Role: Wrote action-driven copy for each step, ensured clarity and brand voice consistency
Data Science
My Role: Requested specific analytics on drop-off points and completion funnels
Their Role: Analyzed previous experiment data, calculated statistical significance, validated methodology
Testing Approach
The Experiment
Launched an A/B test targeting all desktop users visiting the English product tour page. The interactive board component replaced static "Plan" tab content in the treatment group, with the goal of increasing modal completion rates and driving more signups.
Control Group (50%)
Standard product tour with static images and text describing the board view.
•
Product tour tabs with static screenshots
•
Text descriptions of features
•
No interactive elements
Treatment Group (50%)
Enhanced experience with interactive board, step guidance, and hands-on exploration.
•
Drag-and-drop cards between columns
•
Editable fields and pre-filled epics
•
Live Testing
Shipped directly to production as an A/B experiment. Worked closely with Engineering to productionize the interactive components, then monitored engagement data including clicks, completions, and drop-off rates to validate the design and surface insights.
The components built for this experiment were designed to be production-ready, allowing us to quickly roll out successful patterns to other pages if results were positive.
Outcome
Results
UX Success
95%
Business Metrics
+0.6%
No statistically significant increase in signups (±3.1%). But this wasn't a UX failure—it was an experiment design failure.
Four Methodology Flaws That Invalidated Our Results
01
Wrong Audience
30% of participants were logged-in users who already had Jira Software—diluting results.
Fix: Target only new-to-JSW users for accurate signup impact measurement
02
Below-the-Fold Placement
Only 37% of users scrolled far enough to see the interactive board—limiting impact.
Fix: Position key interactions above fold or track exposure on scroll
03
Exposure Tracking Error
Fired on page load, not when users actually saw the demo—counting people who never engaged.
Fix: Trigger exposure only when treatment visible in viewport
04
Platform Bug
Board styling flashed between dark/light mode due to platform change—impacting user experience.
Fix: Rigorous QA and stable technical environment before launch
What This Taught Me
01
Completion ≠ Conversion
Users engaging with features doesn't automatically drive business metrics. Both matter, but they measure different aspects of success.
02
Methodology = Design Quality
Even well-designed experiences need proper targeting, exposure tracking, and technical stability to prove business impact.
03
Question "Winning" Metrics
A 10% signup increase masked 95% of users abandoning the experience. Always dig deeper than surface metrics.
04
Failed Experiments = Learning
This established best practices for targeting, exposure tracking, and methodology that drove a 66% win in the next experiment.
What Happened Next
These weren't theoretical recommendations—I immediately applied all four fixes to redesign Google Ads landing pages for paid traffic.
+66%
Signup Increase
+69.7%
Account Creations
Statsig
All Metrics
This proved that experiment methodology is as critical as design quality for driving business results.
Context
The Paradox
The previous interactive timeline experiment showed promising results with a 10% signup increase. Leadership celebrated the win. However, deeper analysis revealed a critical issue: only 2.4% of 31,072 visitors completed the entire modal experience.
The data showed a massive 95.5% drop-off between the first interaction (updating an epic) and the second step (adding a story). This meant we were converting users without educating them about the product's value—potentially impacting long-term activation and retention.
"Overall positive results for interactive demo with high engagement towards the first action of the demo and 2.4% of visitors finishing entire modal experience."
— Previous Experiment Results
Previous Experiment Data
Total Visitors
31,072
Step 1: Update Epic
30,394 (97.8%)
Step 2: Add Story
1,379 (4.4%)
Completed Modal
740 (2.4%)
Drop-off Rate
95.5%
Our Hypothesis
We were converting users without educating them about the product's value—potentially impacting activation and retention. An interactive board component with better guidance would improve completion rates and lead to more qualified signups.
Goal: Design an interactive board experience that reduces drop-off and increases modal completion rates, ultimately driving more product signups.
Digging Deeper
Design Process
01
Discover
Analyzed previous experiment data, identified 95.5% drop-off between steps 1-2
02
Research
Analyzed SaaS onboarding patterns: guided tours, progressive disclosure, interactive demos
Analyzed SaaS onboarding patterns: guided tours, proprogressive disclosure, interactive demos
03
Design
Created interactive board with drag-and-drop, editable fields, step guidance
04
Test
Shipped to production, monitored completion and signup metrics
What I Designed & Why
Step-by-step guidance
Visual markers + action-driven copy to reduce confusion about what to do next
Create
Restart demo
TO DO
Design AI shopping suggestions for homepage
Features
TASK
IN PROGRESS
Create AI-generated shopping suggestions for homepage
Features
STORY
Add advanced analytics tracking events
Analytics
STORY
DONE
Improve payment checkout time on mobile
Payments
BUG
Define requirements to use new AI integrations
Features
TASK
Interactive components
Drag-and-drop cards, editable fields—extended Atlassian design system
Drag-and-drop cards, editable fields—extended Atlassian design system
Realistic but simplified
Real Jira patterns + pre-filled content to reduce cognitive load
Real Jira patterns + pre-filled content to reduce cognitive load
Cross-Functional Collaboration
Led design direction while partnering closely with multiple teams to ensure technical feasibility, accurate messaging, and rigorous experiment methodology.
Product Manager
My Role: Presented data analysis showing 95% drop-off, proposed interactive board solution
Their Role: Defined success metrics, prioritized goals in alignment workshop, secured stakeholder buy-in
Engineering
My Role: Created detailed interaction specs, design system documentation, collaborated on technical constraints
Their Role: Built interactive components, implemented A/B test infrastructure, set up exposure tracking
Content Design
My Role: Designed step marker UI, microcopy placement, and content hierarchy strategy
Their Role: Wrote action-driven copy for each step, ensured clarity and brand
voice consistency
Data Science
My Role: Requested specific analytics on drop-off points and completion funnels
Their Role: Analyzed previous experiment data, calculated statistical significance, validated methodology
Testing Approach
The Experiment
Launched an A/B test targeting all desktop users visiting the English product tour page. The interactive board component replaced static "Plan" tab content in the treatment group, with the goal of increasing modal completion rates and driving more signups.
Control Group (50%)
Standard product tour with static images and text describing the board view.
•
Product tour tabs with static screenshots
•
Text descriptions of features
•
No interactive elements
Treatment Group (50%)
Enhanced experience with interactive board, step guidance, and hands-on exploration.
•
Drag-and-drop cards between columns
•
Editable fields and pre-filled epics
•
Live Testing
Shipped directly to production as an A/B experiment. Worked closely with Engineering to productionize the interactive components, then monitored engagement data including clicks, completions, and drop-off rates to validate the design and surface insights.
The components built for this experiment were designed to be production-ready, allowing us to quickly roll out successful patterns to other pages if results were positive.
Outcome
Results
UX Success
95%
Business Metrics
+0.6%
No statistically significant increase in signups (±3.1%). But this wasn't a UX failure—it was an experiment design failure.
Four Methodology Flaws That Invalidated Our Results
01
Wrong Audience
30% of participants were logged-in users who already had Jira Software—diluting results.
Fix: Target only new-to-JSW users for accurate signup impact measurement
02
Below-the-Fold Placement
Only 37% of users scrolled far enough to see the interactive board—limiting impact.
Fix: Position key interactions above fold or track exposure on scroll
03
Exposure Tracking Error
Fired on page load, not when users actually saw the demo—counting people who never engaged.
Fix: Trigger exposure only when treatment visible in viewport
04
Platform Bug
Board styling flashed between dark/light mode due to platform change—impacting user experience.
Fix: Rigorous QA and stable technical environment before launch
What This Taught Me
01
Completion ≠ Conversion
Users engaging with features doesn't automatically drive business metrics. Both matter, but they measure different aspects of success.
02
Methodology = Design Quality
Even well-designed experiences need proper targeting, exposure tracking, and technical stability to prove business impact.
03
Question "Winning" Metrics
The 10% signup increase masked a 95% engagement failure. Always dig deeper than primary metrics.
04
Failed Experiments = Learning
This established best practices for targeting, positioning, and tracking that would drive a 66% win in the next experiment.
What This Taught Me
01
Completion ≠ Conversion
Users engaging with features doesn't automatically drive business metrics. Both matter, but they measure different aspects of success.
02
Methodology = Design Quality
Even well-designed experiences need proper targeting, exposure tracking, and technical stability to prove business impact.
03
Question "Winning" Metrics
The 10% signup increase masked a 95% engagement failure. Always dig deeper than primary metrics.
04
Failed Experiments = Learning
This established best practices for targeting, positioning, and tracking that would drive a 66% win in the next experiment.
What Happened Next
These weren't theoretical recommendations—I immediately applied all four fixes to redesign Google Ads landing pages for paid traffic.
+66%
Signup Increase
+69.7%
Account Creations
Statsig
All Metrics
This proved that experiment methodology is as critical as design quality for driving business results.
What Happened Next
These weren't theoretical recommendations—I immediately applied all four fixes to redesign Google Ads landing pages for paid traffic.
+66%
Signup Increase
+69.7%
Account Creations
Statsig
All Metrics
This proved that experiment methodology is as critical as design quality for driving business results.
More Works More Works
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