TruTrip Analytics allows you to take your travel control to the text level by really understanding where your spend is going. One simple dashboard with quick insights on current activity, historical trends but most importantly cost optimisation insights.
This article covers:
- The main TruTrip Analytics Dashboard
- How Cost Optimisation works.
Note: TruTrip Analytics is currently only available on Optimise or Enterprise plans. Reach out to your account manager for access.
1. The Analytics Dashboard
The analytics dashboard shows you a quick set of widgets. We've listed them out in detail below with some further explanations.
Overview:
- Total Bookings: The total number of bookings made in the selected period across all booking types.
- Total Spend: The total monetary value of all bookings in the selected period.
- Avg Cost/Booking: The average spend per booking, calculated by dividing total spend by total bookings.
- Avg Approval Speed (in hours): We look at all bookings that required approval in the selected period and calculate the time in hours from booking request to final approval or rejection. This figure represents the P50 median of all approval durations in that period. A higher number indicates longer approval cycles and potentially greater pricing fluctuations before tickets are issued.
- Booking Type Distribution: A breakdown of bookings by type such as flight, hotel, car rental, airport transfer, group booking, and Airbnb, shown as a percentage of total bookings.
- Booking Window: For each booking in the selected period, we calculate how many days in advance it was made relative to the trip start date, such as departure or check in. Bookings are then grouped into defined advance purchase categories. A higher booking window generally correlates with lower costs and better availability. From a cost optimisation perspective, bookings made 14 days or more in advance are typically preferred.
- Spend by Booker: A table showing total spend grouped by the person who made the booking, including their name and email.
- Spend by Traveller: A table showing total spend grouped by the traveller, including name and email, ranked by total spend.
- Spend by Group: Total spend aggregated by approval group, department, or policy group within the company.
Flight data
- Flight Total Bookings: The total number of flight bookings in the selected period.
- Flight Total Spend: The total amount spent on flights.
- Flight Avg Cost/Booking: The average cost per flight booking.
- Avg Booking Window (Days): The average number of days between booking date and travel date for flight bookings.
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Domestic vs International: Trips are classified based on the invoice profile used for the booking. If any part of the trip takes place in a country different from the country of the business entity linked to the invoice profile, the trip is considered international.
This is different from route based logic. For example, if the business entity is Singapore and a flight is taken from Tokyo to Osaka, the trip is still classified as international because it takes place outside of Singapore. - Trip Type: A breakdown of flight bookings by one way, round trip, and multi city.
Hotel data:
- Top 5 Destinations (Flights): The five most frequently booked flight destinations, ranked by booking count.
- Hotel Total Bookings: The total number of hotel bookings in the selected period.
- Hotel Total Spend: The total amount spent on hotel bookings.
- Avg Nightly Rate: The average price per night across all hotel bookings.
- Avg Stay Length (Nights): The average number of nights per hotel booking.
- Nights Stay Distribution: A breakdown of hotel stays by length, such as 1 night, 2 to 3 nights, 4 to 5 nights, and 6 to 8 nights.
- Refundable Share (WIP): The percentage or share of hotel bookings that are refundable versus non refundable, currently under development.
- Top 5 Destinations (Hotels): The five most frequently booked hotel destinations, ranked by booking count.
2. Understanding Cost Optimisation indicators
The Cost Optimisation section helps you understand whether your team is booking efficiently, not just how much they are spending.
Instead of looking at total spend alone, these indicators focus on behaviour: when bookings are made, how quickly they are approved, and whether the most logical fares are being selected.
Together, these three indicators highlight where meaningful savings opportunities may exist.
Booking window
Booking earlier generally results in lower fares, better availability, and more flexibility.
This widget users historical TruTrip data + industry benchmarks to evaluate how far in advance your team books travel and estimates the potential savings opportunity if more bookings were made earlier.
What this tells you
The booking window savings opportunity is an indicator of how much the company may have overpaid due to short notice bookings. The green bars visualise the potential savings if a larger portion of bookings had been shifted into longer booking windows.
From a cost optimisation perspective, bookings made 14 days or more before departure are typically considered healthier.
How this is calculated
Industry benchmarks show that bookings made closer to departure are typically more expensive:
- 0–3 days before are typically +110%
- 4–13 days before are +80%
- 14–27 days before are typically +50%
- 28+ days before are closer to optimal
We apply these benchmark premiums to the total spend within each booking window.
For example if the total spend for bookings made 4-13days before is $18,000, the benchmark shows this is 80% more expensive due to booking late. So the savings opportunity is $18,000/1.8 = $10,000 by booking earlier.
Approval speed
Similar to booking window. The quicker a booking is approved, the more likely you are to secure a good rate. In general bookings approved within 6 hours are considered “safe”. Any hour longer than that increases risks of price fluctuations, fare availability or issuance failures.
Average approval speed: a simple indicator showing how fast most bookings are approved or rejected.
No. bookings by approval speed: a simple distribution of where most bookings are distributed. In this example, most bookings are approved within 6h.
How this is calculated
We look at all bookings in the selected period that required approval and were eventually approved or rejected. For each booking, we calculate the duration from the moment the booking request was submitted to the moment it was approved or rejected.
The primary metric shown is the median (p50) approval duration. This avoids distortion from extreme outliers and better reflects how most bookings are processed.
Booking behaviour: selecting the lowest logical fares
Real savings do not come from finding a cheaper ticket for the route/hotel. They come from booking differently. Lowest Logical Fare (LLF) widgets give you an indicator on how well your team selects the best fares available.
How this is calculated
With every booking made, the related Lowest Logical Fare is stored (read here to learn more). This means that people ultimately either book the LLF, or they do not. If they don’t, we calculate the price difference as a missed savings opportunity.
Picking the Lowest Logical Fare: This chart shows you how many bookings had LLF data attached to it, in which cases the LLF was selected, and when it wasn’t.
Savings opportunity: The savings opportunity exposes the sum of all the price differences between the fare booked, and the LLF that was available at the time. So in this example below, the business would’ve spent about $30,000 less if everyone booked the Lowest Logical fare.