eSports / UMG
Reestablish UMG as the home for competitive gaming
RESULT - Dedicated event tournament page increased engagement which led to a partnership with Microsoft’s Coalition Studio
Client
Lululemon
Role
Product Design Lead
Timeline
2018 - 9 months
Platform
AI Cloud Software
Client
Lululemon
Role
Product Design Lead
Timeline
2018 - 9 months
Platform
AI Cloud Software
Problem
Predictions arrived as data, not as decisions
The existing workbench put a wall of data in front of fashion allocators and left them to find what mattered. Planners drilled down through layers to reach a single high alert metric, then repeated the process for the next one. In interviews they described spending most of their day documenting where a plan needed attention rather than acting on it, and asked for something that would flag problems before those problems reached the supply chain.
01 - No Summary, 02 - Weak hierarchy, 03 - Important optics below the fold
Research & Discovery
Understanding the problem firsthand
Field Study at Lululemon HQ
I flew to Lululemon's corporate offices to observe how fashion planners used the software day-to-day. I conducted three rounds of research, moving from broad stakeholder context to granular user behavior:
1
Business Stakeholder Interviews
2
User Interviews with fashion planners
3
Contextual Inquiry: Watching planners work inside the live system
Responses & Feedback
"
"
"It takes too long to identify where my plan needs attention."
"It takes too long to identify where my plan needs attention."
"It takes too long to identify where my plan needs attention."
"
"
"I don't want to drill down from a product to see how it's performing."
"
"
"I need an alert that tells me about problems before they halt my supply chain."
Design Thinking Workshop
Once field data was collected, I conducted an OOUX exercise to orient objects to actions, mapping user pain points directly to interface behaviors.
This exercise helped align design, engineering, and business stakeholders around six shared design principles that would govern every decision moving forward.
▤
▤
Summarize at the right level
↑
↑
Show the impact
◈
◈
Suggest actions via ML
◉
◉
Personalization per role
→
→
Guide users' next steps
⊞
⊞
Establish threshold criteria
A/B Testing
Lo-fidelity wireframes THEN A/B TESTING
A
CONTROL — icon grid
B
TREATMENT — priority list
Hey Alexi!
Thu Nov 15, 2017
Performance
Day
Week
Month
Quarter
1Y
Sales
Gross Margin
Alerts
20
Below Inv.
11
Late Arrivals
4
DC Overstock
6
DC Imbalance
3
New Stores
11
Air Approval
3
View More
Sort by: 1st Selling Week ▾
Sculpt Tank II
⚠ Lost Sales: 1,200 units
Extra Mile Short Sleeve
⚠ Lost Sales: 1,200 units
Extra Mile Short Sleeve
⚠ Lost Sales: 1,200 units
Hey Alexi!
Thu Nov 15, 2017
Performance
Day
Week
Month
Quarter
1Y
Sales
Gross Margin
Alerts (31)
CRITICAL
Below Inventory
20
→
WARNING
Late Arrivals
11
→
WARNING
DC Overstock
4
→
WARNING
DC Imbalance
6
→
OK
New Stores
3
→
INFO
Air Approval
11
→
Sort by: 1st Selling Week ▾
7 equal-weight alert cards. Critical items share visual space with info-level notifications — scanning requires reading every card.
Severity-ranked list. CRITICAL surfaces immediately at top; INFO recedes to bottom. Count badges replace the icon + large-numeral column format.
TEST RESULTS · 3 WEEKS, 16 PLANNERS
Time to critical action
A
2.6s
B
6.4s
Alert acknowledgment rate
A
82%
B
64%
Task completion rate
A
89%
B
67%
Manager satisfaction
A
4.6 / 5
B
2.9 / 5
DECISION
Variant A - 7 equal-weight cards was adopted as the standard alert component.
WINNER
A
KEY LEARNINGS
01
Seeing all seven categories at once beat ranking them. Planners scanned the grid faster than a list.
02
Explicit severity labels (CRITICAL / WARNING / INFO) and tabular count badges scanned faster than icons under time pressure.
03
The card grid won on every metric, and the ranked list's labeling and badges were folded into what shipped.
Final Design
The model flags the miss. The planner picks the move.
Outcomes
WHAT SHIPPED, AND WHAT IT CHANGED
32%
Improvement in forecast accuracy after planners could act on exceptions early.
27%
Reduction in supply chain costs across the planning organization.
2.5×
Faster time to critical action with the exposed alert-card pattern (6.4s → 2.6s).
Explore more
eSports / UMG
Reestablish UMG as the home for competitive gaming
RESULT - Dedicated event tournament page increased engagement which led to a partnership with Microsoft’s Coalition Studio
Client
Lululemon
Role
Product Design Lead
Timeline
2018 - 9 months
Platform
AI Cloud Software
Client
Lululemon
Role
Product Design Lead
Timeline
2018 - 9 months
Platform
AI Cloud Software
Problem
Predictions arrived as data, not as decisions
The existing workbench put a wall of data in front of fashion allocators and left them to find what mattered. Planners drilled down through layers to reach a single high alert metric, then repeated the process for the next one. In interviews they described spending most of their day documenting where a plan needed attention rather than acting on it, and asked for something that would flag problems before those problems reached the supply chain.
01 - No Summary, 02 - Weak hierarchy, 03 - Important optics below the fold
Research & Discovery
Understanding the problem firsthand
Field Study at Lululemon HQ
I flew to Lululemon's corporate offices to observe how fashion planners used the software day-to-day. I conducted three rounds of research, moving from broad stakeholder context to granular user behavior:
1
Business Stakeholder Interviews
2
User Interviews with fashion planners
3
Contextual Inquiry: Watching planners work inside the live system
Responses & Feedback
"
"
"It takes too long to identify where my plan needs attention."
"It takes too long to identify where my plan needs attention."
"It takes too long to identify where my plan needs attention."
"
"
"I don't want to drill down from a product to see how it's performing."
"
"
"I need an alert that tells me about problems before they halt my supply chain."
Design Thinking Workshop
Once field data was collected, I conducted an OOUX exercise to orient objects to actions, mapping user pain points directly to interface behaviors.
This exercise helped align design, engineering, and business stakeholders around six shared design principles that would govern every decision moving forward.
▤
▤
Summarize at the right level
↑
↑
Show the impact
◈
◈
Suggest actions via ML
◉
◉
Personalization per role
→
→
Guide users' next steps
⊞
⊞
Establish threshold criteria
A/B Testing
Lo-fidelity wireframes THEN A/B TESTING
A
CONTROL — icon grid
B
TREATMENT — priority list
Hey Alexi!
Thu Nov 15, 2017
Performance
Day
Week
Month
Quarter
1Y
Sales
Gross Margin
Alerts
20
Below Inv.
11
Late Arrivals
4
DC Overstock
6
DC Imbalance
3
New Stores
11
Air Approval
3
View More
Sort by: 1st Selling Week ▾
Sculpt Tank II
⚠ Lost Sales: 1,200 units
Extra Mile Short Sleeve
⚠ Lost Sales: 1,200 units
Extra Mile Short Sleeve
⚠ Lost Sales: 1,200 units
Hey Alexi!
Thu Nov 15, 2017
Performance
Day
Week
Month
Quarter
1Y
Sales
Gross Margin
Alerts (31)
CRITICAL
Below Inventory
20
→
WARNING
Late Arrivals
11
→
WARNING
DC Overstock
4
→
WARNING
DC Imbalance
6
→
OK
New Stores
3
→
INFO
Air Approval
11
→
Sort by: 1st Selling Week ▾
7 equal-weight alert cards. Critical items share visual space with info-level notifications — scanning requires reading every card.
Severity-ranked list. CRITICAL surfaces immediately at top; INFO recedes to bottom. Count badges replace the icon + large-numeral column format.
TEST RESULTS · 3 WEEKS, 16 PLANNERS
Time to critical action
A
2.6s
B
6.4s
Alert acknowledgment rate
A
82%
B
64%
Task completion rate
A
89%
B
67%
Manager satisfaction
A
4.6 / 5
B
2.9 / 5
DECISION
Variant A - 7 equal-weight cards was adopted as the standard alert component.
WINNER
A
KEY LEARNINGS
01
Seeing all seven categories at once beat ranking them. Planners scanned the grid faster than a list.
02
Explicit severity labels (CRITICAL / WARNING / INFO) and tabular count badges scanned faster than icons under time pressure.
03
The card grid won on every metric, and the ranked list's labeling and badges were folded into what shipped.
Final Design
The model flags the miss. The planner picks the move.
Outcomes
WHAT SHIPPED, AND WHAT IT CHANGED
32%
Improvement in forecast accuracy after planners could act on exceptions early.
27%
Reduction in supply chain costs across the planning organization.
2.5×
Faster time to critical action with the exposed alert-card pattern (6.4s → 2.6s).
Explore more
eSports / UMG
Reestablish UMG as the home for competitive gaming
RESULT - Dedicated event tournament page increased engagement which led to a partnership with Microsoft’s Coalition Studio
Client
Lululemon
Role
Product Design Lead
Timeline
2018 - 9 months
Platform
AI Cloud Software
Client
Lululemon
Role
Product Design Lead
Timeline
2018 - 9 months
Platform
AI Cloud Software
Problem
Predictions arrived as data, not as decisions
The existing workbench put a wall of data in front of fashion allocators and left them to find what mattered. Planners drilled down through layers to reach a single high alert metric, then repeated the process for the next one. In interviews they described spending most of their day documenting where a plan needed attention rather than acting on it, and asked for something that would flag problems before those problems reached the supply chain.
01 - No Summary, 02 - Weak hierarchy, 03 - Important optics below the fold
Research & Discovery
Understanding the problem firsthand
Field Study at Lululemon HQ
I flew to Lululemon's corporate offices to observe how fashion planners used the software day-to-day. I conducted three rounds of research, moving from broad stakeholder context to granular user behavior:
1
Business Stakeholder Interviews
2
User Interviews with fashion planners
3
Contextual Inquiry: Watching planners work inside the live system
Responses & Feedback
"
"
"It takes too long to identify where my plan needs attention."
"It takes too long to identify where my plan needs attention."
"It takes too long to identify where my plan needs attention."
"
"
"I don't want to drill down from a product to see how it's performing."
"
"
"I need an alert that tells me about problems before they halt my supply chain."
Design Thinking Workshop
Once field data was collected, I conducted an OOUX exercise to orient objects to actions, mapping user pain points directly to interface behaviors.
This exercise helped align design, engineering, and business stakeholders around six shared design principles that would govern every decision moving forward.
▤
▤
Summarize at the right level
↑
↑
Show the impact
◈
◈
Suggest actions via ML
◉
◉
Personalization per role
→
→
Guide users' next steps
⊞
⊞
Establish threshold criteria
A/B Testing
Lo-fidelity wireframes THEN A/B TESTING
A
CONTROL — icon grid
B
TREATMENT — priority list
Hey Alexi!
Thu Nov 15, 2017
Performance
Day
Week
Month
Quarter
1Y
Sales
Gross Margin
Alerts
20
Below Inv.
11
Late Arrivals
4
DC Overstock
6
DC Imbalance
3
New Stores
11
Air Approval
3
View More
Sort by: 1st Selling Week ▾
Sculpt Tank II
⚠ Lost Sales: 1,200 units
Extra Mile Short Sleeve
⚠ Lost Sales: 1,200 units
Extra Mile Short Sleeve
⚠ Lost Sales: 1,200 units
Hey Alexi!
Thu Nov 15, 2017
Performance
Day
Week
Month
Quarter
1Y
Sales
Gross Margin
Alerts (31)
CRITICAL
Below Inventory
20
→
WARNING
Late Arrivals
11
→
WARNING
DC Overstock
4
→
WARNING
DC Imbalance
6
→
OK
New Stores
3
→
INFO
Air Approval
11
→
Sort by: 1st Selling Week ▾
7 equal-weight alert cards. Critical items share visual space with info-level notifications — scanning requires reading every card.
Severity-ranked list. CRITICAL surfaces immediately at top; INFO recedes to bottom. Count badges replace the icon + large-numeral column format.
TEST RESULTS · 3 WEEKS, 16 PLANNERS
Time to critical action
A
2.6s
B
6.4s
Alert acknowledgment rate
A
82%
B
64%
Task completion rate
A
89%
B
67%
Manager satisfaction
A
4.6 / 5
B
2.9 / 5
DECISION
Variant A - 7 equal-weight cards was adopted as the standard alert component.
WINNER
A
KEY LEARNINGS
01
Seeing all seven categories at once beat ranking them. Planners scanned the grid faster than a list.
02
Explicit severity labels (CRITICAL / WARNING / INFO) and tabular count badges scanned faster than icons under time pressure.
03
The card grid won on every metric, and the ranked list's labeling and badges were folded into what shipped.
Final Design
The model flags the miss. The planner picks the move.
Outcomes
WHAT SHIPPED, AND WHAT IT CHANGED
32%
Improvement in forecast accuracy after planners could act on exceptions early.
27%
Reduction in supply chain costs across the planning organization.
2.5×
Faster time to critical action with the exposed alert-card pattern (6.4s → 2.6s).