Kýma Perissa — Booking experience concept
Making the next booking easier
A booking experience concept for Kýma Perissa. The proposed screen would answer which room fits, what the stay costs, and whether plans can change before a traveler is asked to commit.
+35%illustrative booking uplift
Summary
- ScopeA booking concept for Kýma Perissa. The figures are illustrative, not a measured hotel result.
- ApproachKeep the dates, show the total and the change policy, and add Reserve on the page.
- Intended outcomeMore sessions check availability and choose a room. The later rates stay the same.
Interest without a clear next step
A traveler can love the photographs and still hesitate to book. Which room fits the trip? Is it available? What is the full price? Can they change their plans?
The working hypothesis is that answering these questions earlier could help more interested visitors reach a confirmed reservation.
Room names, the price, and the change policy are illustrative.
Illustrative starting point
Kýma Perissa
- Caldera roomSea view
- Garden roomQuiet
The total and the change policy are elsewhere. Checkout used an external booking flow.
Ask reception
Continue to booking partner ↗
Proposed design
112–16 Jun · 2 guests
Kýma Perissa
- Caldera roomSea view
- Garden roomQuiet
2€1,840 total · 4 nights · 2 guestsTaxes and fees included3Free changes up to 7 days before arrival
Reserve
Ask about this room
- 1Retained dates
- 2Visible total price
- 3Clear change policy
The design question
How might we make choosing and booking a stay feel as reassuring as the stay itself?
The proposed approach has four steps. Clarify where the journey breaks. Regroup the room decision. Make the booking action easier. Test that result before treating it as a finding.
01
Clarify
AEIOU + Journey Mapping
DiscoverCompareCheck datesBookInformation scattered across multiple pages
Journey mapping would identify repeated date entry.
02
Ideate
Systematic Inventive Thinking
- Room details
- Amenities
- Policies
- Total price
Before
Superior Room
- Room details
- Amenities
- Policies
- Total price
Check availability
After
Subtraction would remove unnecessary re-entry.
03
Develop
Fogg Behavior Model
Using the Fogg model, we would prioritize reducing booking effort by keeping the total price and reservation action together.
04
Validate
Critical Questions + Prototypes
Deluxe Room
- King bed
- 2 guests
- Free Wi-Fi
- View details
Select room
A
Deluxe Room
- King bed
- 2 guests
- Free Wi-Fi
- Total price
Select room
B
The test would count confirmed bookings, including those that no longer leave for an external flow.
Methods drawn from supplied design-thinking materials. Proposed application; no completed research or measured hotel outcomes implied.
Investigate the hesitation
The proposed research would combine mobile usability sessions, recurring guest inquiries, website analytics, and booking-system diagnostics.
We would watch travelers check dates, compare rooms, review policies, and try to reserve. Staff conversations would name the questions that keep interrupting a booking.
Technical checks would look for slow availability, failed payments, and reservation errors. A confusing page and an unreliable booking system need different fixes. These methods would test the hypothesis before it is treated as a finding.
Bring the decision together
The starting experience sends guests to reception or an external booking partner. The proposed design brings reservation into the same journey. That is a new capability, not only clearer information. It would need the hotel’s booking system to confirm the room, take payment, and record the reservation.
Ask about this room stays beneath Reserve for guests who still want a conversation. The inquiry would carry the chosen room and dates, so reception does not start the question again.
Illustrative booking impact
Illustrative data, not measured hotel results
Two illustrative eight-week periods, each with 6,000 website sessions, show how a higher share of visits could become confirmed bookings.
More sessions become confirmed bookings
- Before
- After
+0.7 percentage points35% relative increase
| Period | Confirmed booking rate | Bookings | Website sessions |
|---|---|---|---|
| Before | 2.0% | 120 | 6,000 |
| After | 2.7% | 162 | 6,000 |
Where the illustrative improvement happens
- Before
- After
Each rate measures the share advancing from the preceding stage. The scenario improves availability searches and room selection while holding later conversion rates constant.
View counts and assumptions
- Website sessions: 6,000 before / 6,000 after.
- Availability searches: 2,400 / 2,700.
- Room selections: 1,200 / 1,620.
- Checkout starts: 300 / 405.
- Confirmed bookings: 120 / 162.
- Two illustrative eight-week periods with equal traffic.
- These figures demonstrate a possible outcome; they do not establish a measured result or causal effect.
| Stage | Before | After | Note |
|---|---|---|---|
| Website session → availability search | 40% | 45% | +5 percentage points. |
| Availability search → room selection | 50% | 60% | +10 percentage points. |
| Room selection → checkout start | 25% | 25% | Held constant in this scenario. |
| Checkout start → confirmed booking | 40% | 40% | Held constant in this scenario. |
Connect bookings to commercial value
Both periods use the same average reservation value. The cancellations here are refunded reservations, separate from the change policy on the proposed screen. They are treated as full refunds. The chart is booking value after those refunds, not profit. Taxes, operating costs, and payment fees are excluded.
Before
After
Cancellations assumed: 12 in the first period and 16 in the second, each fully refunded at €600. Retained bookings are 108 (€64,800) and 146 (€87,600).
Source: constructed scenario · two illustrative 8-week periods · €600 per booking · no causal inference
Validate the result before claiming success
A real evaluation would match website confirmations to the hotel’s reservation records, and give both periods the same time for cancellations to appear.
The proposed evaluation would connect website sessions to confirmed reservations across both the external and integrated booking flows, excluding duplicates.
Where traffic allows, a randomized test could compare the booking experience. Otherwise a before-and-after comparison would need to account for season, availability, rates, and how people arrived.
Direct booking growth also needs a wider check. Did the hotel gain new business, or did guests move from Expedia and other channels to the website? Both can matter. They are different outcomes.
The impact we would aim to demonstrate
The intended chain is better information, then an easier room choice, then more completed reservations.
For travelers, that means fewer unanswered questions. For Kýma Perissa, it means confirmed bookings and conversations that reservation records could support.