Why trip changes drove support calls — Ansaria J. Mohammed
Ansaria J. Mohammed
SELECTED WORK / 04
Case study · Contact analysis

Why guests and hosts kept calling support to change a trip

Trip changes were the biggest single driver of calls to Turo support. I analyzed 100 of them and found a product problem, not a support one. The breakdown gave a newly formed team its starting roadmap.

Company
Turo
Role
Lead UX Researcher
Timeline
2 weeks
Methods
Contact analysis
Affinity mapping
Business goal
Reduce Customer Support contacts in the “trip modification” category
Affinity map of 100 trip-modification support contacts

Background

Trip modifications were the largest driver of inbound calls to Customer Support, and the 7th largest category across contacts of every kind, counting calls and chats, inbound and outbound. The goal handed to a newly formed product team was to bring those contacts down.

Features already existed to let guests and hosts change a trip on their own, so the question was why so many people were still calling. I reviewed the calls to find out.

Research design

I analyzed 100 inbound calls and 50 chats in the trip-modification category, working from the agent notes attached to every call, plus transcripts I generated when a call needed a closer read. I affinity-mapped them into patterns. Reading a wide set of shallow records like these shows the range of reasons people call, and roughly how that range breaks down. No recruiting, no incentives, no waiting on participants: the evidence was already sitting in the support queue.

The chats settled a scoping question early: 61% of them did nothing more than tell the user to call in. Since chat was mostly a referral step on the way to a call, I focused the depth on the calls, where the issues actually got worked through.

100
inbound calls read as one body
50
chats reviewed alongside them
61%
of chats only told the user to call in

What I found

Three-quarters of the calls came from guests, a quarter from hosts. Almost everyone had tried to make the change in the app first and called because they couldn’t.

The same handful of failures came up over and over: a start time that could no longer be edited because it had technically passed, an extension blocked because the host had snoozed the car, a request sitting unanswered until its window closed, a whole extra day charged for moving a pickup by an hour. Call after call traced back to the same short list of product gaps.

The product was generating the calls

Reading the calls as one body let me size the causes instead of only listing them. Of the calls with an identifiable reason, here is roughly how they broke down.

Blocked by product logic or system errors40%
Manual, request-based approval system20%
Inaccurate trip start and end times20%
Error messaging that didn’t aid recovery15%
Pricing structure (charging by day, not hour)8%
Missing error prevention7%
Location-change gaps in booking and modification6%
Percentages exceed 100% because a single call often contained more than one issue.

The largest share, 40%, came from the product actively blocking a change someone was trying to make. Host availability, snooze periods, buffer times, and calendar blocks stopped requests from going through. A vehicle restricted for today blocked a modification on a future reservation. A start time couldn’t be edited once it had passed, even by minutes. Often the block came with an error that gave no way to recover, so a guest who might have solved it alone ended up on the phone instead.

One trip change could pull in the guest, the host, and an agent, generating several contacts before it resolved.

This was the bigger driver of volume: fewer calls started here, but each one multiplied. The request-based system needed everyone to act in real time, so one change could set off a chain of contacts before it went through. Hosts drove this especially: a host calling on a guest’s behalf usually sent the agent off to reach the guest in turn.

One call in the sample · 20 minutes
Guest → Agent

Guest wants to extend a trip the host has already agreed to, but the app won’t let them.

Agent → Host

The agent finds the host has snoozed the car, and calls to free up the dates.

Agent → Guest

The booking fails again. The car still isn’t marked available.

Agent → Host

A second call to the host finally clears the availability block.

Guest → Agent

The new total comes back higher than expected. The guest thinks they’re being charged a second deposit.

Resolved

Everything cleared, the guest realizes they can just make the change themselves in the app. Which is what they do.

The way to bring the number down was to remove the back-and-forth the system required, since each fix there would prevent more than one future call.

What I recommended

The recommendations followed the sizing.

Move from a request-based system to dynamic approval
A modification that falls within a host’s stated availability should be granted automatically, with no waiting. Only requests that fall outside it, into a snooze or a calendar block, should route to the host for review. That removes the coordination behind most of the multiplied contacts.
Rework extension pricing toward prorated charges
Charge for the hour added rather than a full extra day, and state plainly what an extension or a shortened trip will cost or refund.
Capture accurate trip start and end times
So the platform can adjust reservations on its own, instead of relying on calls to correct the record.
Fix the recoverable basics
Error messages that suggest a path forward, prevention before consequential actions like checkout, and clearer location options at booking.

Outcomes

01

The analysis turned a support metric into a product backlog. Each slice of the contact volume pointed to a specific product gap a team could close.

02

A newly formed product team, chartered to bring support contacts down, adopted the breakdown as its starting roadmap, beginning with the largest slice: the 40% of calls blocked by product logic and system errors.

Up next · 01Understanding Turo’s airport experience from the inside out
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