Snowball Money
Stop-loss Order
Snowball Money
Stop-loss Order
Snowball Money
Stop-loss Order
Snowball Money
Stop-loss Order
Snowball Money
Stop-loss Order
2024
Protecting Investors Without Hiding the Risk.
A stop-loss feature designed to help retail crypto investors limit losses, through real-time chart visualization, clear trigger prices, and instant loss previews.
Protecting Investors Without Hiding the Risk. A stop-loss feature designed to help retail crypto investors limit losses, through real-time chart visualization, clear trigger prices, and instant loss previews.
Protecting Investors Without Hiding the Risk.
A stop-loss feature designed to help retail crypto investors limit losses, through real-time chart visualization, clear trigger prices, and instant loss previews.
Protecting Investors Without Hiding the Risk. A stop-loss feature designed to help retail crypto investors limit losses, through real-time chart visualization, clear trigger prices, and instant loss previews.
Protecting Investors Without Hiding the Risk. A stop-loss feature designed to help retail crypto investors limit losses, through real-time chart visualization, clear trigger prices, and instant loss previews.
Role
Senior Product Designer & Product Manager
Project Type
DeFi crypto Investment App
DeFi crypto Investment App
Platform
iOS & Android
My Contribution
UX Audit, Usability Testing, Interaction Design, Data Visualization, End-to-End Design, Design System.
Role
Senior Product Designer & Product Manager
Project Type
DeFi crypto Investment App
Platform
iOS & Android
My Contribution
UX Audit, Usability Testing, Interaction Design, Data Visualization, End-to-End Design, Design System.
Discovery.
Defining the Problem
Retail investors had no way to protect themselves from sudden price drops. Crypto trades 24/7, and most users only noticed a crash after it happened. Stop-loss existed on professional exchanges, but its form based interface and trading jargon made it feel risky to the very people it was meant to protect.
Retail investors had no way to protect themselves from sudden price drops. Crypto trades 24/7, and most users only noticed a crash after it happened. Stop-loss existed on professional exchanges, but its form based interface and trading jargon made it feel risky to the very people it was meant to protect.
Retail investors had no way to protect themselves from sudden price drops. Crypto trades 24/7, and most users only noticed a crash after it happened. Stop-loss existed on professional exchanges, but its form based interface and trading jargon made it feel risky to the very people it was meant to protect.
Signal 1
Low Comprehension
Low Comprehension
Only 50% of users in testing understood what their stop-loss order would actually do. The concept made sense, the numbers didn't.
Only 50% of users in testing understood what their stop-loss order would actually do. The concept made sense, the numbers didn't.
Signal 2
Protection Came Too Late
Protection Came Too Late
Crypto trades 24/7. Users discovered sharp drops after they happened, with no tool to act on them in advance.
Crypto trades 24/7. Users discovered sharp drops after they happened, with no tool to act on them in advance.
Crypto trades 24/7. Users discovered sharp drops after they happened, with no tool to act on them in advance.
Signal 3
Competitive Gap
Competitive Gap
Stop-loss was standard on professional exchanges, but retail crypto apps either buried it or skipped it entirely.
Stop-loss was standard on professional exchanges, but retail crypto apps either buried it or skipped it entirely.
Stop-loss was standard on professional exchanges, but retail crypto apps either buried it or skipped it entirely.
Research.
What We Learned
Research.
What We Learned
Before redesigning anything, I ran three research streams and a team workshop to understand why users struggled: usability testing of V1, behavioral data analysis, and competitive benchmarking.
Before redesigning anything, I ran three research streams and a team workshop to understand why users struggled: usability testing of V1, behavioral data analysis, and competitive benchmarking.
Before redesigning anything, I ran three research streams and a team workshop to understand why users struggled: usability testing of V1, behavioral data analysis, and competitive benchmarking.
50%
Misread their own order
Misread their own order
In V1 testing, half of users described their stop-loss incorrectly after setting it. They completed the form, but couldn't say what would happen next.
In V1 testing, half of users described their stop-loss incorrectly after setting it. They completed the form, but couldn't say what would happen next.
3
Questions every user asked
Questions every user asked
When will it sell? At what price? How much will I lose? V1 answered none of them visually. The redesign had to answer all three at a glance.
When will it sell? At what price? How much will I lose? V1 answered none of them visually. The redesign had to answer all three at a glance.
2
Mental models in conflict
Mental models in conflict
Users thought in prices on a chart, while the form asked for numbers and percentages. The interface needed to speak the user's language, not the exchange's.
Users thought in prices on a chart, while the form asked for numbers and percentages. The interface needed to speak the user's language, not the exchange's.
🎤
6 usability sessions with retail crypto investors, testing whether users could explain their order after setting it
6 usability sessions with retail crypto investors, testing whether users could explain their order after setting it
📊
Behavioral data analysis in Amplitude: where users dropped off in the order flow and how often orders were edited or cancelled
Behavioral data analysis in Amplitude: where users dropped off in the order flow and how often orders were edited or cancelled
🔍
Competitive teardown of 7 exchanges and trading apps: how professional tools visualize stop levels and risk
Competitive teardown of 7 exchanges and trading apps: how professional tools visualize stop levels and risk
🔀
Workshop with product and engineering to map edge cases: invalid prices, slippage, and market gaps
Workshop with product and engineering to map edge cases: invalid prices, slippage, and market gaps
Key Research Insight
Key Research Insight
Users weren't avoiding stop-loss because it was too complex, they were avoiding it because they couldn't see it. Numbers in a form meant nothing until they were placed on the chart where users already looked. The opportunity wasn't to simplify the tool, but to make its consequences visible before the order was placed.
Crypto wallets traditionally have very low daily engagement. Users open the app when prices move or when they want to
make a transaction — not as a daily habit. We had strong acquisition but weak retention.
Crypto wallets traditionally have very low daily engagement. Users open the app when prices move or when they want to make a transaction — not as a daily habit. We had strong acquisition but weak retention.
Crypto wallets traditionally have very low daily engagement. Users open the app when prices move or when they want to make a transaction — not as a daily habit. We had strong acquisition but weak retention.
Design Process.
How I Approached This
The first version shipped and technically worked. Testing it revealed the real problem, so the process started from evidence, not assumptions.
The first version shipped and technically worked. Testing it revealed the real problem, so the process started from evidence, not assumptions.
The first version shipped and technically worked. Testing it revealed the real problem, so the process started from evidence, not assumptions.
Core Design Decision.
The Chart Insight
As the user types their stop price, a dashed horizontal line appears on the real price chart at exactly that level. They can immediately see: "this is where my order fires."
As the user types their stop price, a dashed horizontal line appears on the real price chart at exactly that level. They can immediately see: "this is where my order fires."
As the user types their stop price, a dashed horizontal line appears on the real price chart at exactly that level. They can immediately see: "this is where my order fires."
V1 COMPREHENSION
Text-only form: 50% of users could correctly explain their own order after setting it.
V1 COMPREHENSION
Text-only form: 50% of users could correctly explain their own order after setting it.
V1 COMPREHENSION
Text-only form: 50% of users could correctly explain their own order after setting it.
V2 WITH CHART LINES
Real-time price visualization: 88% comprehension. The chart is not decoration, it is the primary cognitive tool.
V2 WITH CHART LINES
Real-time price visualization: 88% comprehension. The chart is not decoration, it is the primary cognitive tool.
V2 WITH CHART LINES
Real-time price visualization: 88% comprehension. The chart is not decoration, it is the primary cognitive tool.
Design Decision 02.
Dual input mode
Users can enter either dollar amounts or percentages with instant bidirectional recalculation. Serves two very different mental models.
Users can enter either dollar amounts or percentages with instant bidirectional recalculation. Serves two very different mental models.
Users can enter either dollar amounts or percentages with instant bidirectional recalculation. Serves two very different mental models.
Trader mental model
"I want to set my stop 12% below current price." → Types: 12%
Trader mental model
"I want to set my stop 12% below current price." → Types: 12%
Trader mental model
"I want to set my stop 12% below current price." → Types: 12%
Casual user mental model
"I want to sell if ETH hits $1,532." → Types: $1,532
Casual user mental model
"I want to sell if ETH hits $1,532." → Types: $1,532
Casual user mental model
"I want to sell if ETH hits $1,532." → Types: $1,532
Trust Design.
The Confirmation Screen
FEE TRANSPARENCY
Max total fees shown collapsed. Expandable to show Transaction fee + Network fee breakdown. Users who care can see the detail.
FEE TRANSPARENCY
Max total fees shown collapsed. Expandable to show Transaction fee + Network fee breakdown. Users who care can see the detail.
FEE TRANSPARENCY
Max total fees shown collapsed. Expandable to show Transaction fee + Network fee breakdown. Users who care can see the detail.
EXPLICIT CONSENT
Agreement required before Confirm activates. Informed consent matters more than zero friction for a financial feature.
EXPLICIT CONSENT
Agreement required before Confirm activates. Informed consent matters more than zero friction for a financial feature.
EXPLICIT CONSENT
Agreement required before Confirm activates. Informed consent matters more than zero friction for a financial feature.
PLAIN-LANGUAGE SUMMARY
A generated sentence restates the whole order before confirmation: what triggers it, what sells, at what price.
PLAIN-LANGUAGE SUMMARY
A generated sentence restates the whole order before confirmation: what triggers it, what sells, at what price.
PLAIN-LANGUAGE SUMMARY
A generated sentence restates the whole order before confirmation: what triggers it, what sells, at what price.
Edge Case.
Deleting an active order
Active orders appear in the Orders list with stop price, limit price, and a mini sparkline. Tapping opens full chart view with both trigger lines visible.
Active orders appear in the Orders list with stop price, limit price, and a mini sparkline. Tapping opens full chart view with both trigger lines visible.
Active orders appear in the Orders list with stop price, limit price, and a mini sparkline. Tapping opens full chart view with both trigger lines visible.
WHY DELETION HAS FRICTION
Accidentally deleting a risk management order on a volatile day could cause real financial harm. Two-step confirmation is not bad UX here, it's responsible UX.
WHY DELETION HAS FRICTION
Accidentally deleting a risk management order on a volatile day could cause real financial harm. Two-step confirmation is not bad UX here, it's responsible UX.
WHY DELETION HAS FRICTION
Accidentally deleting a risk management order on a volatile day could cause real financial harm. Two-step confirmation is not bad UX here, it's responsible UX.
MULTIPLE ORDERS
The orders list scales gracefully. Each card shows asset, amount, date, both price levels, and mini sparkline, scannable at a glance without opening each order.
MULTIPLE ORDERS
The orders list scales gracefully. Each card shows asset, amount, date, both price levels, and mini sparkline, scannable at a glance without opening each order.
MULTIPLE ORDERS
The orders list scales gracefully. Each card shows asset, amount, date, both price levels, and mini sparkline, scannable at a glance without opening each order.
Iteration.
How the design evolved
Impact.
Outcomes & Learnings
Impact.
Outcomes & Learnings
Measured against the metrics defined at the start of the project, three months post-launch.
Task completion is not comprehension. Test whether users understand, not just whether they finish.
Measured against the metrics defined at the start of the project, three months post-launch.
Task completion is not comprehension. Test whether users understand, not just whether they finish.
Measured against the metrics defined at the start of the project, three months post-launch.
Task completion is not comprehension. Test whether users understand, not just whether they finish.
FEATURE ADOPTION
FEATURE ADOPTION
23%
Of eligible users in month 1
Of eligible users in month 1
Of eligible users in month 1
✓ Target was 10%
✓ Target was 10%
COMPREHENSION
COMPREHENSION
88%
Correctly described their
own order post-creation
Correctly described their own order post-creation
Correctly described their
own order post-creation
Correctly described their
own order post-creation
Correctly described their own order post-creation
Up from 50% in V1
Up from 50% in V1
ORDER COMPLETION
ORDER COMPLETION
71%
Started → confirmed
order rate
Started → confirmed
order rate
Started → confirmed
order rate
Started → confirmed
order rate
Strong signal
Strong signal
CHURN
CHURN
−34%
Among users with active
orders during volatile events
Among users with active
orders during volatile events
Among users with active
orders during volatile events
Among users with active
orders during volatile events
