01 What they said
74%
“A room that fits my trip matters more than the price”
Share of respondents · Survey n = 5,200
Agoda
Room UpsellTurning a price-driven choice into a value-based one
Over 80% of all bookings came from the cheapest room. When I dug into the problem, I found that users were booking the cheapest option right away, without exploring or comparing the other rooms.
I redesigned the Room Selection experience so users could understand and compare rooms by value rather than price, and choose the room that fits them. I led the work from hypothesis through experimentation to global launch.
Background
80.87% of all bookings came from the cheapest room,
and that was limiting the business opportunity
Most users book the first room they see, which is the cheapest one. On our platform the cheapest room is usually the lowest-margin product, so the more bookings concentrate there, the smaller the business opportunity. I set out to solve this business problem while also uncovering the user problem connected to it.
Room position, sorted by price (low to high) · n = 12M sessions
The Gap
Users said they were willing to upgrade to a better room,
but it never turned into a booking.
I looked into users' price sensitivity through research.
Most users said that after comparing room types, they would pay more for a room that suits their trip better.
But that intent never carried through to actual bookings.
02 What they did
20%
Booked a non-cheapest room
n = 12M sessions
Root cause
Why do users open to upgrading still pick the cheapest room?
The survey showed the problem was in how users explore and compare. They couldn't understand the differences between room types or the value of upgrading, so they kept going straight to the cheapest room.
Insight 1
Can't tell room types apart
Users couldn't see how room types differed or what an upgrade was worth, so they ended up choosing the cheapest room
Insight 2
The benefit feels too small for the price gap
Users understood how the rooms differed, but didn't feel the upgrade was worth paying extra for
Survey · n = 5,200 · multiple answers
Reason 1 · Users can't tell room types apart
Unexplained recommendations and a cluttered list
were making rooms hard to explore and compare
To find where users struggled while exploring and comparing room types, I ran user interviews and an internal UX audit. This surfaced the core problems blocking their behavior, which then shaped the design direction and hypothesis.
- 01
Key differences buried in too much information
Benefits, discounts, cancellation rules and payment terms pile up at once, so the room difference is hard to spot
- 02
Hard to compare room types
Long scroll, so users compare from memory
- 03
No reason given
No basis for the recommendation
Reason 2 · 32% · The upgrade benefit feels small for the price gap
Unclear upgrade benefits and too big a price gap
were blocking upgrades to non-cheapest rooms
To find the balance between value and price premium, I analyzed which benefits users value most and how much extra over the cheapest room they accept as reasonable. Based on this, I defined the recommendation criteria for when an upgrade feels worth it.
Which benefits would make users
willing to pay more?
Users valued benefits they can feel right away, like room size or view, more than abstract ones.
Survey · n = 5,200 · multiple answers
How big a price gap will users
accept for an upgrade?
Existing booking data showed that 60% of non-cheapest bookings happened at a price premium of 15% or less over the cheapest room.
Booking data · 1 week
Users don't always want the cheapest room.
They upgrade when the value is tangible and the price gap feels reasonable.
Set before the experiments
Keep the experience of price-sensitive users,
solve the problem for value-oriented users
Rather than giving every user the same experience, I defined a clear goal for each segment. The success criteria followed the segments too: metrics we expected to move were set as Primary, and metrics that had to stay unaffected as Guardrail.
Target segment
Value-oriented users
They were willing to pay for a better room, but the existing experience didn't give them the information to compare rooms and judge their value.
Primary
Non-cheapest booking rate
Bookings of every room except the cheapest
Protected segment
Price-sensitive users
For them an upsell isn't added value but a distraction. So choosing the cheapest room had to stay exactly as before.
Guardrail
- Cheapest room booking rate
- Overall booking rate
Business
- ADR (Average Daily Rate)
- Daily bookings
How might we
How might we help users easily explore and compare room types,
so they choose the room that fits them best instead of the cheapest one?
Hypothesis
If we show the value of each room type clearly and make comparison easy,
users will choose a room that fits their needs instead of the cheapest one.
Experiment 1 · Initial validation
I validated the hypothesis first with minimal design.
Would highlighting the recommended room's value change users' choices?
Before building complex UI, I tested the hypothesis with the smallest possible design change. The goal was to see whether users respond to a recommended room with valuable benefits at a reasonable price, and whether that interest could turn into actual bookings.
“23% bigger room with ocean view, just ₩68,000 more per night”
Show the recommended room's value at a glance
I showed room size and view, the benefits users care about most, side by side with the cheapest room, so users could see at a glance how much bigger the room is and what view it offers.
Highlight a reasonable upgrade cost
By highlighting the extra cost over the cheapest room, users could easily see what a small price difference gets them.
Within the price range users accept (up to +15% over the cheapest room), I recommended rooms that offer highly preferred benefits like view or room size.
The banner click didn't turn into bookings right away,
but it validated interest in the recommended room and the potential of the experience, laying the groundwork for the next experiment
2.3%Banner CTR
+0.24%Non-cheapest bookings
Experiment 2 · Sharpening the hypothesis
How might we make the value of the recommended room clearer and easier to understand?
Having confirmed interest in the recommended room, I designed a comparison experience for the next step, so users could compare room types directly and understand the value of upgrading.
Hypothesis
If the recommended room is shown side by side with the cheapest room,
users will easily see the difference and recognize the value of upgrading.
Solution
Tapping the recommended room banner
opens a comparison table with the cheapest room, showing the benefits and price difference at a glance
- Side by side with the cheapest room, so size and view are clear at a glance
- Gives the specific reason a room is recommended, and which benefit earned it
Experiment 2 · Result
Once users understood the difference, they chose the room with more value
Users who compared room types directly and understood the difference chose the recommended room more often. Not only did interest (CTR) grow, non-cheapest bookings and Long-term Value rose too, validating the hypothesis.
Interest
5.2%Banner CTRBehavior change
+3.1%Non-cheapest booking rateBusiness impact
+9%Long-term valueExperiment 2 · The next problem the data revealed
The comparison only reached users who scrolled down to the recommended room
The comparison worked, but the banner could only reach users who scrolled as far as the recommended room. Most users only looked at the top two rooms in the list and barely explored anything past the third.
| Master room index | Seen rate | Click rate |
|---|---|---|
| 1st room (cheapest, no banner) | 44% | - |
| 2nd room | 22% | 72.76% |
| 3rd room | 19% | 20.56% |
| 4th+ room | 12% | 4.30% |
70% of users
never scroll past the 2nd room
If we surface the recommended room at the top of the list so users can compare it with the cheapest room without scrolling,
more users will reach the comparison and non-cheapest bookings will rise
Experiment 3 · Expanding reach
How might we help users discover the recommended room without scrolling?
This iteration aimed to raise the reach of the comparison. If users could compare the cheapest and recommended rooms at a glance without scrolling, more of them would start comparing.
- Cheapest room
- Recommended room 1
- Recommended room 2
I placed a carousel summarizing the cheapest and recommended rooms at the top of the room list. Tapping a card scrolls straight to that room.
Experiment 3 · Result
Raising the reach of the comparison got more users to choose the recommended room
Once the validated comparison moved to the top of the list, more users started exploring the recommended rooms. As a result, not only carousel card clicks but also non-cheapest bookings went up.
- Cheapest room10%CTR
- Recommended room 123%CTR
- Recommended room 218%CTR
+3.9%Booking conversion after a carousel tap
+2%Non-cheapest bookings
Impact
Value-oriented users upgraded, and price-sensitive users stayed
Across three experiments, we improved how users explore and compare non-cheapest rooms, and that led to real booking growth. At the same time, the guardrail metrics, cheapest room booking rate and total bookings, held steady. We made a meaningful impact without hurting the experience for price-sensitive users.
Primary
Non-cheapest booking rate
+32%
22% → 29% · +7%p absolute · p = 0.002
Users book non-cheapest rooms when they can clearly see the value
Guardrail
Cheapest room booking rate
-0.3%
No statistically significant change
A user-initiated pattern doesn't disrupt price-sensitive users
Business
Bookings per day
+1,155
Bookings per day · ADR +0.19–0.23%
Upgrades that fit users' needs drive measurable revenue growth
Cumulative average across all experiments
Three experiments
Rather than one big design, we got closer to the answer through repeated small experiments.
We tested hypotheses fast with small experiments, found new problems in the results, and evolved the solution. Each experiment set the direction for the next.
Experiment 1 · Initial validation
Will users show interest if we show them the recommended room we defined?
Banner CTR 2.3%
→ A clear benefit at a reasonable premium drew interest
Experiment 2 · Sharpening
Will bookings rise if users can easily compare the recommended room with the cheapest one?
Non-cheapest booking rate +3.1%
→ Users upgrade when the value is clear
Experiment 3 · Expanding reach
Will more users choose it if they reach the comparison without scrolling?
Non-cheapest booking rate +2.0%
→ Wider reach raised the non-cheapest booking rate with it
Next step · Offer upsell
We extended the pattern validated on rooms to offers.
The same problem showed up in offer selection. Users took the cheapest default offer without exploring or comparing, and struggled to see how offers differed or what an upgrade was worth. So we applied the principle we had validated on rooms.
Can the same choice principle work for offer upsell?
| Room · Validated | Offer · Next step | |
|---|---|---|
| Default | Cheapest room | Cheapest offer (non-refundable · breakfast not included) |
| Highlight | Bigger room with a better view | Free cancellation · breakfast included |
| Compare | Side by side with the cheapest room | Side by side with the cheapest offer |
What this project left behind
One experiment became a design principle applied across the product
Clear value + reasonable premium + easy comparison = value-based choice
Learning fast through small experiments, and adjusting direction as we went
Rather than trying to find the right answer from the start, I learned fast through small experiments. The early experiments focused on validating direction over big wins, and by iterating the hypothesis and solution on the signals we got, we reached a meaningful result.
The validated principle carried over to other problems
Users make better choices when valuable benefits are clear and a comparison is right there. That principle was reused across other upsell projects and teams.