This project is a full-scale revenue and pricing intelligence audit conducted on a hospitality listings dataset (3,465 properties | 14 structured attributes), including:
- Room Type
- Bed Configuration & Capacity
- Location
- Price
- Rating
- Premium Features (Sea View, Balcony, Bathroom Type, etc.)
The objective was not to compute averages — but to evaluate whether the revenue engine behind this portfolio is structurally optimised for scalability, pricing efficiency, and long-term growth.
All analysis was performed entirely in MySQL 8, using CTEs, window functions, statistical formulas, and segmentation logic.
- Is revenue overly concentrated in a small cluster of listings?
- Is pricing aligned with customer-perceived value?
- Where does demand start weakening as prices increase?
- Are high-rated listings under-monetised?
- Is inventory mix aligned with revenue density?
- Which features truly drive price premium?
- Which geographic clusters are expansion-ready?
- Where does pricing inefficiency introduce risk exposure?
- Revenue shows concentration within a limited segment of listings, indicating portfolio risk exposure.
- Revenue density varies significantly by room category.
- Location-based revenue quartile analysis reveals uneven economic distribution.
- Weak-to-moderate correlation between price and rating suggests structural misalignment.
- Demand elasticity tightens beyond premium price tiers.
- Identified listings priced above median but rated below median — signalling pricing inefficiency.
- High-rated listings below market median price reveal monetisation opportunity.
- Higher bed capacity does not proportionally increase revenue.
- Certain room categories outperform others in revenue per unit efficiency.
- Supply distribution shows an imbalance between premium and standard inventory.
- Sea View and Balcony features demonstrate measurable price premiums.
- Shared bathroom listings show rating compression and reduced pricing power.
- Feature segmentation highlights which amenities drive true revenue impact versus cosmetic differentiation.
- Revenue efficiency varies across locations.
- Volatility analysis highlights stable vs risk-prone regions.
- Identified expansion-ready zones based on revenue-per-listing performance.
This project includes:
- Revenue decile and quintile modelling
- Median-based pricing inefficiency detection
- Pearson correlation implementation in SQL
- Elasticity approximation via bucketed demand analysis
- Revenue volatility measurement
- Portfolio risk quadrant classification
No visualisation tools were used — all insights were derived directly through SQL logic.
- Strong command over advanced MySQL (CTEs, window functions, statistical calculations)
- Ability to translate structured data into strategic business insights
- Understanding of pricing architecture and revenue concentration risk
- Business-first analytical thinking beyond surface-level metrics
Core Principle: Data analysis should not stop at “what happened.” It should evaluate whether the system itself is economically sound.
Abhishek Singh Data-driven revenue intelligence & strategic analytics.