Pilot Tab 01

Let's Build a Python-Based Pricing Engine

A CSV-backed MVP for the five-hotel group that loads daily demand signals, joins records by hotel and room identifiers, computes rule-based price recommendations, and exports a frontend-ready pricing_output.csv file.

Open Dashboard View Architecture
Working Pilot Dashboard

Sample Frontend Dashboard from CSV Data

This live prototype reads the sample CSV files and turns them into operating metrics, pricing recommendations, and planning visuals for the hotel group.

Revenue Command Center

Patel AI Hotel Group Pricing Pilot

Boardroom-ready pricing intelligence demo for the Tybee Island hotel portfolio with explainable AI-style recommendations and live CSV-backed metrics.

5 Hotels Daily Pricing CSV + Python
Loading live sample data from CSV files...

Upload Pricing Data

CSV or XLS files are stored in Google Cloud Storage (bucket: patel-hotel-pilot-data)
Avg recommended ADR --
Avg occupancy --
Raise actions --
Forecast revenue --

Revenue forecast by hotel

Portfolio view

Occupancy vs. price curve

Daily pricing posture

Forecast accuracy panel

Pilot benchmark vs. realized occupancy
Demand accuracy proxy --
Mean absolute error --
Avg price uplift --
Model note --

Demand heatmap calendar

Hotel-by-date demand intensity

Daily price recommendations

Frontend-ready action table
Date Hotel Room type Base Suggested Change Occupancy Demand Status
01

CSV Relational Layer

No database in the MVP. Linked CSV files act like relational tables keyed by hotel_id and room_type_id.

02

Modular Python Engine

Loader, demand, competitor, pricing, and writer modules keep the system easy to extend and test.

03

Demand-Adjusted Rules

The MVP blends forecast demand, occupancy pressure, local events, and competitor market rates.

04

Dashboard Output

The pipeline writes daily pricing recommendations for table views, action badges, and later dashboard charts.

Architecture Overview

End-to-End Pricing Pipeline

The first version is intentionally simple, production-structured, and ready for future ML upgrades.

Step 1

Ingest CSV Signals

Hotels, room types, bookings, occupancy, events, competitor prices, and demand forecasts are loaded from flat files.

Step 2

Join Daily Context

The loader joins rows by hotel_id and room_type_id to build one daily pricing context per hotel-room combination.

Step 3

Run Pricing Logic

The rules engine applies demand multipliers, occupancy thresholds, event boosts, and soft competitor anchoring.

Step 4

Write Dashboard Feed

The output writer exports pricing_output.csv for the frontend dashboard and recommendation table.

Forecast Model Recommendation

Ideal Forecast Model for the Pilot

For this first working version, the best forecast baseline is Prophet with event and holiday regressors, then later a multi-hotel ensemble can be layered in for higher accuracy.

Best MVP Fit

Prophet Forecasting

It is fast to train, handles daily, weekly, and yearly seasonality, and is especially strong when demand changes around special dates and local events.

Why It Fits Hotels

Event-Ready Structure

Hotel pricing is shaped by weekends, holidays, shoulder periods, and festival spikes. Prophet can explicitly model those drivers through holiday windows and extra regressors.

Upgrade Path

Scale to an Ensemble

After the pilot, keep Prophet as a benchmark and compare it with LightGBM or XGBoost using lead time, competitor price, and channel mix features for each hotel.