How to compare booking growth over different periods?

✅ Analyze booking volume over different months, quarters, or years
✅ Compare revenue trends and average trip values
✅ Track changes in client behavior and demand fluctuations
✅ Identify the impact of promotions or external factors on sales

🚀 Objective: Learn how to extract and analyze booking data to compare growth over time and make data-driven business decisions.

Extracting booking data for period comparison

To compare booking trends:

Go to Client Folders in the left-hand menu

Click on Extract to CSV (top right)
Use the booking period filter to select:
- A specific month, quarter, or year
- A custom date range for comparison

Download and open the report in Excel or Google Sheets

💡 Extract data for at least two different periods (e.g., Q1 2023 vs. Q1 2024) to compare growth effectively.

Key data fields for booking growth analysis

Data Field	Description
📅 Booked On	Tracks when bookings were made, helping analyze sales cycles
📅 Start At	Identifies travel dates to compare seasonal demand
💰 Total Price	Helps track revenue growth over different periods
🏨 Hotel Name / Product Name	Identifies which hotels or experiences gained or lost popularity
🌍 City	Shows destination trends over time
📂 Folder Name	Groups multiple bookings under the same trip to measure overall trip growth
🔢 Number of Bookings	Tracks the total number of bookings in a period
👥 Number of Guests	Measures total travelers, useful for tracking group vs. FIT trends
🤝 Partner Name	Identifies which DMCs contributed most to sales in different periods

📌 Example:

A TO analyzes Q1 2023 vs. Q1 2024 and finds that total revenue increased by 15%, but the number of bookings remained the same — indicating higher average trip prices.

💡 Use “Booked On” vs. “Start At” to compare booking lead time changes year over year.

Measuring booking volume growth

To analyze whether booking numbers are increasing or decreasing:

Sort the report by "Booked On" to group bookings by date
Count total bookings for each selected period
Calculate the growth percentage:

Compare periods (e.g., Q1 vs. Q2 or Year-over-Year)

📌 Example: A TO finds a 20% increase in bookings from January to March compared to the previous year, suggesting strong demand growth.

💡 Track quarterly booking growth to spot early trends and adjust marketing strategies accordingly.

Comparing revenue growth across periods

To measure revenue trends:

Sort data by "Start At" to track when trips occurred
Sum the "Total Price" field for each period
Compare revenue between selected periods using the formula:

Analyze revenue per booking to see if average spend increased.

📌 Example: A TO sees that total revenue in Q3 2023 was higher than in Q3 2022, but fewer bookings were made — indicating higher-priced trips or longer stays.

💡 Use “Average Revenue per Booking” = Total Revenue ÷ Total Bookings to compare trip values over time.

Analyzing destination and hotel growth trends

To identify which destinations and accommodations are growing in popularity:

Filter the report by City
Count the number of bookings per city for each period
Compare growth percentages across periods
Repeat the same analysis for Hotel Name / Product Name

📌 Example: A TO discovers that a boutique hotel in Bangkok saw a 30% increase in bookings in 2024, while a resort in Phuket declined by 15%.

💡 If a destination is trending, negotiate better rates with suppliers or expand marketing efforts.

Identifying trends in traveler behavior

To understand how customer preferences change over time:

Compare Number of Guests to track growth in total travelers
Analyze Number of Adults vs. Number of Children to see if family travel is increasing
Check Room(s) Name(s) to identify shifts in accommodation preferences (e.g., more suites vs. standard rooms)

📌 Example: A TO finds that group bookings (6+ travelers) increased by 25% year-over-year, signaling a rise in multi-generational family travel.

💡 Adjust product offerings based on whether solo travelers, couples, or families are booking more frequently.

Evaluating the impact of promotions and seasonality

To assess whether discounts or external factors influenced bookings:

Filter by Promotions / Supplements
Compare how many bookings included a promotion per period
Analyze how promotions impacted total revenue

📌 Example: A TO notices that a 10% discount campaign in October boosted bookings by 40%, but average revenue per trip dropped.

💡 Use this insight to adjust future discounts, ensuring they drive volume without hurting profitability.

Forecasting future booking trends

Based on past growth analysis, TOs can:

✅ Predict high and low booking periods to adjust inventory and pricing
✅ Allocate marketing budgets effectively based on past performance
✅ Strengthen relationships with high-performing DMCs and hotels
✅ Identify early signs of market shifts to stay ahead of competitors

📌 Example: A TO sees that historically, Q2 bookings grow by 10% year-over-year. They prepare by negotiating more hotel allotments and launching campaigns earlier.

💡 Use historical data to make data-driven forecasts and strategic decisions.

Takeaways

✅ Compare booking volume across months, quarters, or years to measure growth
✅ Analyze revenue trends to see if trip values are increasing or decreasing
✅ Identify top-performing destinations, hotels, and experiences
✅ Track customer behavior changes, such as solo vs. family travel growth
✅ Evaluate the impact of discounts and promotions on sales
✅ Use historical data to predict and plan future business strategies

FAQ

Q: Can I compare growth for different types of bookings (Hotels vs. Experiences)?
🧭 Yes! Filter the data by "Hotel Name / Product Name" to compare trends by category.

Q: How often should I analyze booking growth?
📆 Monthly and quarterly comparisons help spot early trends, while year-over-year analysis provides a long-term view.

Q: Can I see which clients book the most trips over time?
👤 Yes, by sorting by "Client Folder Name", you can track repeat travelers.

📌Need help? Visit the KOOB Knowledge Base or contact KOOB Support 🚀