revenuebot.test
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Rebot
Web based MVP for optimizing
pricing & reporting with AI.
Web based MVP for optimizing
pricing & reporting with AI.
In this project, I designed a responsive web app that helps hotel managers make smarter pricing decisions.
The tool integrates real-time competitor data, AI-generated recommendations, and intuitive dashboards turning complex revenue management into something clear, scalable and actionable.
In this project, I designed a responsive web app that helps hotel managers make smarter pricing decisions.
The tool integrates real-time competitor data, AI-generated recommendations, and intuitive dashboards turning complex revenue management into something clear, scalable and actionable.
B2B
MVP
AI-powered
SaaS
My role:
UX Designer
Timeline:
4 weeks
The problem
Revenue managers face inefficient, manual
workflows when managing hotel pricing and performance.
Revenue managers face inefficient, manual
workflows when managing hotel pricing and performance.
Revenue managers spend up to 4 hours per hotel each week tracking competitor prices across 30 days, limiting updates to weekly.
This makes it nearly impossible to scale beyond 2–3 hotels per day.
Without dynamic pricing, hotels can lose 5–10% of potential revenue
Revenue managers spend up to 4 hours per hotel each week tracking competitor prices across 30 days, limiting updates to weekly.
This makes it nearly impossible to scale beyond 2–3 hotels per day.
Without dynamic pricing, hotels can lose 5–10% of potential revenue
The Design Solution
We designed an AI Revenue web to centralize
and automate revenue management:
Instead of hours in spreadsheets, managers now
get insights to make instant decisions in one place.
The Design Solution
We designed an AI Revenue web to centralize and automate revenue management:
Instead of hours in spreadsheets, managers now get insights to make instant decisions in one place!
Instead of hours in spreadsheets, managers now
get insights to make instant decisions in one place.
Instead of hours in spreadsheets, managers now
get insights to make instant decisions in one place.
Main Features
Heatmap
Heatmap
Traditional heatmaps are common tools for revenue managers,
but they’re often overwhelming, packed with dense grids,
endless rows of dates, and heavy colors. While they show a lot of data,
they make it difficult to scan quickly or spot meaningful patterns.
Traditional heatmaps are common tools for revenue managers,
but they’re often overwhelming, packed with dense grids,
endless rows of dates, and heavy colors. While they show a lot of data,
they make it difficult to scan quickly or spot meaningful patterns.
So I designed a compact widget for quick scans, with the option to expand into a full 30-day view when deeper analysis is needed.
So I designed a compact widget for quick scans, with the option to expand into a full 30-day view when deeper analysis is needed.
Main Features
Scraper Recommendations
Scraper Recommendations
Revenue managers waste hours tracking competitors prices manually, so working with
the dev-built scraper helped me understand how this tool could solve this problem
for the user and translate it into a clear, actionable table.
Revenue managers waste hours tracking competitors prices manually, so working with
the dev-built scraper helped me understand how this tool could solve this problem
for the user and translate it into a clear, actionable table.
So I designed an actionable table where managers can edit, accept, or ignore the scraper’s recommended prices, check the justification behind each suggestion, multi-select dates, and quickly scan key data like occupancy and market averages.
So I designed an actionable table where managers can edit, accept, or ignore the scraper’s recommended prices, check the justification behind each suggestion, multi-select dates, and quickly scan key data like occupancy and market averages.
The Design Process
I designed this MVP in just one month!
I designed this MVP in just one month!
I approached this project with a design thinking mindset, starting with real user pain points,
validating needs quickly, and iterating fast. Along the way, I used AI as a partner to
question assumptions, explore ideas, and speed up smaller decisions.
I approached this project with a design thinking mindset, starting with real user pain points,
validating needs quickly, and iterating fast. Along the way, I used AI as a partner to
question assumptions, explore ideas, and speed up smaller decisions.
• How revenue managers work
• Current tools, frustrations & workflows
• How revenue managers work
• Current tools, frustrations & workflows
• Problem statement
• Business goals
• Problem statement
• Business goals
• Fast wireframes to validate ideas
• AI-powered flows: scraping, PMS sync, smart pricing
• Familiar UI for managers
• Fast wireframes to validate ideas
• AI-powered flows: scraping, PMS sync, smart pricing
• Familiar UI for managers
• Iterated based on real feedback
• Narrowed the scope
• Reduced visual noise
• Clear hierarchy
• Iterated based on real feedback
• Narrowed the scope
• Reduced visual noise
• Clear hierarchy
• Dev team building the MVP
• Internal testing in progress
• Next step: test with real revenue managers.
• Dev team building the MVP
• Internal testing in progress
• Next step: test with real revenue managers.
Understand
Understand
Define
Define
Ideate
Ideate
Build & Iterate
Build & Iterate
Implement
Implement
Understand
• How revenue managers work
• Current tools, frustrations & workflows
Define
• Problem statement
• Business goals
Ideate
• Fast wireframes to validate ideas
• AI-powered flows: scraping, PMS sync, smart pricing
• Familiar UI for managers
Build & Iterate
• Iterated based on real feedback
• Reduced visual noise
• Clear hierarchy
Implement
• Dev team building the MVP
• Internal testing in progress
• Next step: test with real revenue managers!
Understand
• How revenue managers work
• Current tools, frustrations & workflows
Define
• Problem statement
• Business goals
Ideate
• Fast wireframes to validate ideas
• AI-powered flows: scraping, PMS sync, smart pricing
• Familiar UI for managers
Build & Iterate
• Iterated based on real feedback
• Reduced visual noise
• Clear hierarchy
Implement
• Dev team building the MVP
• Internal testing in progress
• Next step: test with real revenue managers!
Design Iterations
This example is one of the iterations that shows how the design evolved,
starting simple in low-fi, testing actions in the first design,
and refining into a clearer final version.
This example is one of the iterations that shows how the design evolved,
starting simple in low-fi, testing actions in the first design,
and refining into a clearer final version.
First Version
First Version
First Version
In the first high-fidelity design, we introduced AI recommendations alongside the three action buttons (Accept, Ignore, Edit). While this added intelligence, the card became visually saturated.
Final Design
Final Design
Final Design
After testing and reflection, I simplified the interaction. The final card has a cleaner layout with fewer but clearer options, allowing managers to quickly understand the recommendation and act with confidence.
The Happy Path
The happy path was designed to give managers peace of mind: add your hotel, trust the AI’s recommendations, and update prices in minutes instead of hours.
Setup
Create hotel profile (name, costs, quality rating).
Add competitors.
Connect PMS to sync occupancy data.
The system is ready to collect and analyze automatically.
Insights
The dashboard centralizes key insights, showing daily competitor prices alongside occupancy trends
so managers can track everything in one clear view.
Action
Managers multiple select desired dates.
Chooses Accept, Edit, or Ignore for the AI proposal.
PMS updates automatically when accepted.
Confirmation appears, showing the changes applied across the selected dates.
What used to take hours is now done in minutes!
Let's keep in touch!
Let's keep in touch!
The Happy Path
The happy path was designed to give managers peace of mind: add your hotel, trust the AI’s recommendations, and update prices in minutes instead of hours.
Setup
Create hotel profile (name, costs, quality rating).
Add competitors.
Connect PMS to sync occupancy data.
The system is ready to collect and analyze automatically.
Insights
The dashboard centralizes key insights, showing daily competitor prices alongside occupancy trends
so managers can track everything in one clear view.
Action
Managers multiple select desired dates.
Chooses Accept, Edit, or Ignore for the AI proposal.
PMS updates automatically when accepted.
Confirmation appears, showing the changes applied across the selected dates.
What used to take hours is now done in minutes!
The Happy Path
The happy path was designed to give managers peace of mind: add your hotel, trust the AI’s recommendations, and update prices in minutes instead of hours.
Setup
Create hotel profile (name, costs, quality rating).
Add competitors.
Connect PMS to sync occupancy data.
The system is ready to collect and analyze automatically.
Insights
The dashboard centralizes key insights, showing daily competitor prices alongside occupancy trends
so managers can track everything in one clear view.
Action
Managers multiple select desired dates.
Chooses Accept, Edit, or Ignore for the AI proposal.
PMS updates automatically when accepted.
Confirmation appears, showing the changes applied across the selected dates.
What used to take hours is now done in minutes!