Track Occupancy and Flag Overbookings

For hotels, managing booking data is crucial for maintaining profitability and ensuring a smooth guest experience. Manually tracking room availability, occupancy rates, and potential overbookings can be time-consuming and prone to error. But with Protocols, you can automate these processes, flag potential issues in real time, and focus more on guest satisfaction and less on data management.

In this guide, we’ll explore how Protocols can help you manage your booking data, track occupancy rates, and prevent overbookings – all with minimal manual effort.

MongoDB can be used to securely store and manage your booking data, providing you with real-time insights into room availability and occupancy trends.

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The Problem: Manual Data Management Leads to Errors

Hotels often face:
❌ Overbooking and double-booking due to manual data entry
❌ Time-consuming tracking of room availability and occupancy
❌ Lack of real-time insights into booking trends

Time Wasted Without Automation

Hotel staff often spend 6-8 hours a week manually checking and updating booking data. At $30/hour, that’s $1,440 to $1,920 per month spent on tasks that could easily be automated.

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The Solution: Automating Booking Data Management with Protocols

With Protocols, you can:
✅ Automate tracking of room availability
✅ Flag overbookings and underbooked rooms
✅ Track occupancy rates and generate reports for forecasting
✅ Store and access all booking data in MongoDB

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Step 1: Load Booking Data

To get started, load your booking data from an Excel or CSV file that contains information such as room number, guest name, check-in/check-out dates, and booking status.

Example Booking Data (hotel_bookings.csv)

Room Number

Guest Name

Check-In Date

Check-Out Date

Status

101

John Doe

2025-02-18

2025-02-20

Booked

102

Jane Smith

2025-02-19

2025-02-22

Booked

101

Michael Brown

2025-02-18

2025-02-20

Booked

import pandas as pd

# Load hotel booking data
booking_data = pd.read_csv("hotel_bookings.csv")

# Display first few rows
print(booking_data.head())

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Step 2: Track Room Occupancy

Now, let’s calculate the occupancy rate. You can determine the percentage of rooms booked based on the total number of rooms and the number of bookings.

# Calculate room occupancy
total_rooms = 100  # Assume the hotel has 100 rooms
rooms_booked = len(booking_data)

occupancy_rate = (rooms_booked / total_rooms) * 100
print(f"Occupancy Rate: {occupancy_rate:.2f}%")

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Step 3: Flag Overbookings

To prevent overbookings, we’ll flag instances where the same room is booked for overlapping dates. You can use a simple check to identify these conflicts.

# Check for duplicate room bookings
overbooked_rooms = booking_data[booking_data.duplicated(subset=["Room Number", "Check-In Date"], keep=False)]

# Display overbooked rooms
print("Overbooked Rooms:")
print(overbooked_rooms)

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Step 4: Generate Daily Reports

Next, you can generate daily reports that highlight occupancy rates, overbookings, and any rooms with low availability.

# Generate daily occupancy report
low_availability_rooms = total_rooms - rooms_booked

# Display report
print(f"Daily Report: ")
print(f"Occupancy Rate: {occupancy_rate:.2f}%")
print(f"Low Availability: {low_availability_rooms} rooms")
print(f"Overbooked Rooms: {len(overbooked_rooms)} rooms")

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Step 5: Store Booking Data in MongoDB

To securely store and manage your booking data, you can use MongoDB. This enables easy access to real-time information and helps track trends over time.

from pymongo import MongoClient

# Connect to MongoDB
client = MongoClient('mongodb://localhost:27017/')
db = client['hotel_data']
collection = db['bookings']

# Insert booking data into MongoDB
collection.insert_many(booking_data.to_dict("records"))

print("Booking data saved to MongoDB.")

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Step 6: Track Trends and Insights

With all your booking data stored and tracked, you can now analyze trends in occupancy rates, overbooking occurrences, and low availability, giving you insights into potential issues and opportunities for improvement.

# Calculate insights
total_overbookings = len(overbooked_rooms)
available_rooms_percentage = (low_availability_rooms / total_rooms) * 100

# Display insights
print("Booking Data Insights: ")
print(f"Total Overbookings: {total_overbookings}")
print(f"Rooms with Low Availability: {available_rooms_percentage:.2f}%")

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The Result: Efficient and Automated Booking Data Management

By automating your hotel’s booking data management with Protocols, you can:
✅ Save 6-8 hours per week, worth $1,440 to $1,920 per month
✅ Track occupancy rates in real time and prevent overbookings
✅ Automatically flag rooms with low availability
✅ Gain valuable insights into booking trends

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Let Me Automate Your Hotel’s Booking Management

With Protocols, you can automate the tedious process of tracking bookings and room availability. I can build a custom solution for your hotel that tracks occupancy, flags overbookings, and generates daily reports—all with minimal effort. Let me handle the data, so you can focus on providing an exceptional guest experience.

Contact me today to get started!