Timeline
14 months
My Role
Lead Product Designer - Full stack
Team
Product, Revenue Management, Engineering
Scope
Research, UI Design, Prototyping, Rollout
13%
Reduction in rates-related support tickets
30%
Growth in weekly active users
Outcomes
Property Growth vs. Homeowner Churn Rate
Understanding The Problem
I analyzed 500+ support tickets and owner exit data. The signal about revenue performance was clear.
51%
Of churning owners cited revenue concerns as their reason for leaving
NPS detractor analysis · 18K+ ratings & comments · 2022–2023
51%
Of churning owners cited revenue concerns as their reason for leaving
NPS detractor analysis · 18K+ ratings & comments · 2022–2023
43%
Named Vacasa's rate-setting specifically
NPS detractor analysis · 18K+ ratings & comments · 2022–2023
Discovery
20+ owner interviews revealed they were filling a gap we had created with services like AirDNA.
Owners were paying for data services, referencing Airbnb, and calling competitors for data Vacasa already had. A follow-up survey of 219 owners confirmed it that every single one wanted access to market data.
But deeper interviews surfaced a harder problem — owners didn't just want market data. They wanted to know which specific properties Vacasa was comparing them to. Legal forbade it.
1
AirDNA dashboard
Design Direction
Legal constraints meant I couldn't show individual comps — so I designed around what I could.
The design problem became: How do you make a rate range legible, trustworthy, and actionable for owners across every level of data literacy?
Market rate range
Owner’s rate
Rolling 12-month forward view
Specific comparable properties
Shaded band - aggregate min/max of advertised rates from comparable propertyer
Bold overlaid line - their home’s advertised nightly rate plotted against the market band
Time axis from today forward - revealing seasonal rate patterns ahead
Legal constraint - individual data not permitted
Today
Case STUDY
Revenue Data Visualization for Vacasa Owner Account Portals
Research
I tested three visualization approaches before committing and an area chart was one clear winner.
Comprehension — not preference — was the deciding metric. An owner who prefers a chart they can't correctly read is worse than no chart at all.
Jan
Feb
Mar
Apr
May
Jun
Jul
Vacasa
MAx comps
Min comps
Variant A · Scatter Plot
Comprehension
40%
Preference
5%
Your Home
Market Comps
Jan
Feb
Mar
Apr
May
Jun
Jul
Max
$306.02
Booked Rate
$299.02
Min
$247.11
Mar 8
Variant C · Area Chart
Comprehension
90%
Preference
60%
✓ Selected
Variant B · Line Graph
Comprehension
60%
Preference
35%
2
Max Comps
Your Home
Min Comps
Jan
Feb
Mar
Apr
May
Jun
Jul
1
The Solution
The design gave owners their first clear view of where their rate sat in the market and where it was headed.
3
A shaded band showing the market range. The owner's rate as a distinct overlaid line. Scenario-specific hover states that explained what the data meant for that owner's specific situation — not just what it showed.
Contextual hover state
Second Release
Native Light and Dark views
4
Results
We made real gains, but realistically there were limits.
The tool moved the metrics it could move. Churn was driven by a combination of factors well beyond pricing visibility — operational issues, communication gaps, and service consistency problems no single feature was positioned to fix.
+30%
Growth in weekly active users
+13%
Reduction in rates-related support tickets
3
The Direction
The research pointed clearly to what a complete solution required, but the project was abandoned.
We were building to give owners real booked rates, visible comp set, and verifiable comparisons owners could trust. The design was done. The data partnership was in place. This is the version I'd build next.
5
In Reflection
Designing this taught me that visualization is as much a trust problem as a clarity problem. I can make data legible, but I can't make it trustworthy if the underlying data doesn't earn it. Next time: test with real data earlier, and ask users what they'd need to trust it.
View all work
© 2026 Dan Rattigan
Context of the Problem
By 2023, Vacasa (the largest vacation property manager in the US) was losing 1,000+ owners a month to competitors promising better returns.
Internally, we knew those promises were hollow — but owners had nothing from Vacasa to help them see it. Without visibility into how their nightly rate compared to the market, they were left to draw their own conclusions.
Timeline
14 months
My Role
Sole Lead Product Designer - Full stack
Team
Product, Revenue Management, Engineering
Scope
Research, UI Design, Prototyping, Rollout
Outcomes
13%
Reduction in rates-related support tickets
30%
Growth in weekly active users
Context
By 2023, Vacasa (the largest vacation property manager in the US) was losing 1,000+ owners a month to competitors promising better returns.
Internally, we knew those promises were hollow — but owners had nothing from Vacasa to help them see it. Without visibility into how their nightly rate compared to the market, they were left to draw their own conclusions.
Property Growth vs. Homeowner Churn Rate
The Problem
I analyzed 500+ support tickets and owner exit data. The signal about revenue performance was clear.
51%
Of churning owners cited revenue concerns as their reason for leaving
NPS detractor analysis · 18K+ ratings & comments · 2022–2023
43%
Named Vacasa's rate-setting specifically
NPS detractor analysis · 18K+ ratings & comments · 2022–2023
Discovery
20+ owner interviews revealed they were filling a gap we had created with services like AirDNA ( ).
1
Owners were paying for data services, referencing Airbnb, and calling competitors for data Vacasa already had. A follow-up survey of 219 owners confirmed it that every single one wanted access to market data.
But deeper interviews surfaced a harder problem — owners didn't just want market data. They wanted to know which specific properties Vacasa was comparing them to. Legal forbade it.
v
Screenshot of AirDNA dashboard
1
{
3
Design Direction
Legal constraints meant I couldn't show individual comps — so I designed around what I could ( ).
The design problem became: how do you make a rate range legible, trustworthy, and actionable for owners across every level of data literacy?
Market rate range
Owner’s rate
Rolling 12-month forward view
Specific comparable properties
Shaded band - aggregate min/max of advertised rates from comparable propertyer
Bold overlaid line - their home’s advertised nightly rate plotted against the market band
Time axis from today forward - revealing seasonal rate patterns ahead
Legal constraint - individual data not permitted
Today
3
Case STUDY
Revenue Data Visualization for Vacasa Owner Account Portals
Research
I tested three visualization approaches before committing and there was one clear winner ( ).
2
Comprehension — not preference — was the deciding metric. An owner who prefers a chart they can't correctly read is worse than no chart at all.
Jan
Feb
Mar
Apr
May
Jun
Jul
Vacasa
MAx comps
Min comps
Variant A · Scatter Plot
Comprehension
40%
Preference
5%
Your Home
Market Comps
Jan
Feb
Mar
Apr
May
Jun
Jul
Max
$306.02
Booked Rate
$299.02
Min
$247.11
Mar 8
Variant C · Area Chart
Comprehension
90%
Preference
60%
✓ Selected
Variant B · Line Graph
Comprehension
60%
Preference
35%
2
Max Comps
Your Home
Min Comps
Jan
Feb
Mar
Apr
May
Jun
Jul
The Solution
The design gave owners their first clear view of where their rate sat in the market ( ) and where it was headed.
4
A shaded band showing the market range. The owner's rate as a distinct overlaid line. Scenario-specific hover states that explained what the data meant for that owner's specific situation — not just what it showed.
Contextual hover state
Native Light and Dark views
4
Results
We made real gains, but realistically there were limits.
The tool moved the metrics it could move. Churn was driven by a combination of factors well beyond pricing visibility — operational issues, communication gaps, and service consistency problems no single feature was positioned to fix.
+13%
Reduction in rates-related support tickets
+30%
Growth in weekly active users
The Direction
The research pointed clearly to what a complete solution ( ) required, but the project was abandoned.
We were building to give owners real booked rates, visible comp set, and verifiable comparisons owners could trust. The design was done. The data partnership was in place. This is the version I'd build next.
5
5
In Reflection
Designing this taught me that visualization is as much a trust problem as a clarity problem. I can make data legible, but I can't make it trustworthy if the underlying data doesn't earn it. Next time: test with real data earlier, and ask users what they'd need to trust it.
View all work
© 2026 Dan Rattigan
Chris
Services
Taxes
Home Info
Statements
Support Hub
Performance
Calendar
Dashboard
Rate Comparison
Gross Rent
Net Rent
Market Rate Comparison
Today
Nov 2023
Feb 2024
May 2024
1 Year
$600
$450
$300
$150
$0
Founder’s Home Vi...
1W
1M
3M
1Y
July 4
Min
$145
Max
$460
Your Unit
$280
Have a question? Ask a Rates Specialist
04
JUL
Owner Hold
14 nights
Last Updated Oct 8, 2023
Owner hold
Guest Stay
Vacasa hold
Your home
Market Range
Services
Taxes
Home Info
Statements
Support Hub
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This chart shows how your home's nightly rate compares to other vacation rentals in your area with similar size and features.
