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Project Title: Hotel Revenue & Margin Optimization Engine
Role: Data Analyst / BI Developer Tools: Tableau, SQL (SQLite)
The Overview Most hospitality businesses celebrate top-line gross revenue but lack visibility into the millions bleeding out through third-party OTA commissions. I built an end-to-end data pipeline and interactive executive dashboard to transition a hotel’s focus from Gross ADR to true Net Profitability.
The Problem The client had a pipeline of 119,000+ bookings generating $26M in gross revenue. However, they had no automated way to track the $3.8M lost to commission leakage, nor could they pinpoint which customer booking behaviors were driving these losses.
My Solution I engineered a Dynamic Pricing & Revenue Engine designed for senior leadership:
Data Modeling (SQL): Wrote complex queries utilizing Common Table Expressions (CTEs) and Window Functions to dynamically calculate Net ADR and aggregate commission costs across multiple distribution channels.
Executive Dashboard Design (Tableau): Developed a low-cognitive-load, Dark SaaS-themed UI. I applied strict semantic color coding (Teal for retained profit, Muted Coral for commission loss) to instantly guide executive attention to critical margins.
The Impact & Insights
Isolated exactly where the $3.8M in revenue leakage was occurring.
Revealed a major vulnerability in "Long Lead" bookings (90+ days in advance), empowering the management team to adjust dynamic pricing and minimum length-of-stay restrictions.
Provided an interactive, filterable tool for regional managers to assess profitability between City and Resort properties in real time.
Need clarity on your business data? I combine hands-on operational hospitality experience with advanced data analytics to build tools that drive strategic decisions. If you need to uncover hidden revenue or upgrade your reporting infrastructure, let’s collaborate.
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LEAKLENS - revenue-risk dashboard
What it does
LeakLens is a revenue-risk dashboard that turns raw billing and usage data into a live account health view, flagging which customers are quietly drifting toward churn before it shows up in MRR.
The problem it solves
According to MGI Research, 42 percent of businesses experience revenue leakage, and SMB-focused SaaS companies face annual churn as high as 31 to 58 percent, often because billing and usage signals sit in disconnected systems no one is watching in real time. LeakLens closes that gap by turning scattered data into one clear risk view, before the cancellation happens.
How I built it
Built entirely in Bubble, using Bubble AI to scaffold the initial page layout and design system, then hand-refined the data model, workflows, and logic. The core architecture mirrors patterns from tools like Stripe (billing event ingestion) and Amplitude (usage event tracking). Two workflows handle CSV upload and parsing, feed the data into a unified account record, and trigger the dashboard build once both sources are loaded.
How to use or test it
There are two ways to get a live dashboard running:
Demo mode: Click Create Dashboard, then click Load Demo Data. Once the confirmation reads "Demo Data Loaded," click Build Dashboard.
File data: Upload the billing CSV and usage CSV (Download CSV here), click Upload CSV. Once it reads "Billing and Usage Data Loaded," click Build Dashboard.
Click on the account in Dashboard to get more details and recommendation on it.
What's next
LeakLens is currently an MVP, built to prove the core risk-detection logic end to end. The roadmap includes:
Live integrations with Stripe and Amplitude, replacing manual CSV uploads with real-time billing and usage data feeds
An AI response layer that turns each risk flag into action: drafting a personalized outreach email, suggesting a check-in call, or pinging the account's CSM directly, based on what the risk signal actually indicates.
Thank You!
😍 #BuiltWithBubble #NoCode #RevOps #ChurnPrevention
Impressive! Works very well...Wish you the best.
In this project, I worked on cleaning raw sales data and transforming it into meaningful insights using Microsoft Excel. I applied pivot tables and interactive filters to organize information by city, product, payment method, and customer. The analysis highlighted key performance metrics such as total quantity sold, total revenue, and profit across five major cities.
Mumbai emerged as the top-performing city with the highest revenue and profit, while Chennai showed the lowest figures. The final dashboard included a 3D bar chart for clear visualization, making it easy to compare city-wise performance at a glance.
This project demonstrates my ability to:
Perform data cleaning and structuring
Build pivot tables for multi-dimensional analysis
Apply filters for dynamic exploration
Create professional charts for business insights
Impact: The analysis provided actionable insights into regional sales performance, helping identify strong markets and areas for improvement.
