What Travel Customer Data Taught Me About Business Signals

Support contacts are a signal about the business, not only a workload number. The way I read them is simple: measure the contact rate per booking, split the contacts by reason and by partner, then fix the source of the reason that grew the most.

Contact rate = contacts ÷ bookings × 100. You can work yours out in a few seconds with the contact rate calculator.

Why does contact rate tell you more than ticket volume?

Take a travel company with 10,000 bookings a month. In January it gets 800 support contacts, so the contact rate is 8% and CSAT sits at 87%.

Three months later bookings are flat, but contacts are up to 1,200. The contact rate is now 12% and CSAT has dropped to 81%. A standard dashboard says "volume up 50%" and someone asks for more agents. The better question is why the same number of customers needed us 50% more often.

Which contact reasons grew?

Split the 1,200 contacts by reason and the picture changes fast:

In this example, 45% of contacts are about confirmations and availability. That does not mean the support team is slow. It means something earlier in the journey is creating friction, and support is where the customer finally reports it.

Which partner is behind the increase?

Now group the same contacts by supplier. Say one supplier handles 18% of bookings but causes 31% of the availability complaints. At that point the problem has left support. It belongs to partner operations.

I learned this on a travel-tech team I worked with. Confirmation tickets kept climbing and we kept adding people to the queue. When we finally grouped the tickets by partner, about a third came from a single hotel group whose availability feed was updating late. One call with that partner's operations contact did more than two extra agents had done. The lesson I kept: before you staff up, find out who is sending you the work.

Which signals move before CSAT does?

CSAT, NPS and churn tell you what already happened. By the time post-trip CSAT falls, the damage is done. These move first, so I check them weekly:

• Contact rate per booking
• Repeat contacts on the same issue
• Cancellation and modification requests
• Supplier escalations and confirmation failures

If confirmation failures go from 3.5% to 6%, I start looking that week. I do not wait for satisfaction scores to confirm it. The customer experience metrics guide explains how to measure each of these.

Try this this week

• Pull last month's tickets and work out your contact rate per booking.
• Group the tickets by contact reason, then by partner or supplier.
• Pick the one reason that grew most and trace it back to where it starts.
• Note your current score with the CSAT calculator so you can see next month whether the fix helped.

In the next edition, I look at what comes after you find the signal: turning it into an action you can measure.