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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Developed a sentiment classifier for hotel guest reviews (Positive / Negative / Neutral) and automatically extracted common complaint themes such as cleanliness, staff, service, and pricing to support guest experience decisions.
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Built a machine learning model that predicts the likelihood of hotel booking cancellations and no-shows. Helps hotels optimize overbooking strategy and protect revenue.
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AI-Powered Guest Segmentation for hotels.
Analyzed 83,590 real hotel customers and grouped them into 4 actionable segments using K-Means clustering to support targeted marketing and guest experience strategies.
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