Guest satisfaction is a score. Returning is a decision.
Explore how a regional hotel operator could move from guest feedback to property level questions, commercial scenarios, and a clear brief for its data team.
This is a demonstration, not a client case study. Banyan Coast Hospitality and its five properties are invented, and every review and figure is synthetic.
The operating question
Most hotel reporting stops at a satisfaction score. That is useful, but it is not the same thing as a guest deciding to book you again. A guest can give you four stars and never come back. Another can complain about the pool and still say they will be back in December. Those are three different things: how the stay felt, whether the guest says they will return, and whether they actually do.
This demo separates them, and it keeps the evidence attached so you can check any number yourself.
- Where are guests explicitly saying they would return?
- What themes appear alongside that intention?
- What should leadership ask its data team to investigate next?
Try it
Five invented properties, six months, 900 invented reviews. Pick a property, change the months, filter by theme, and open the records behind any number. There is also a full page version at the demo page.
Guest Return Signals
Banyan Coast Hospitality, a fictional five property group
- Bangkok Riverside, Urban business hotel. Approximate location: Bangkok.
- Chiang Mai Garden, Leisure retreat. Approximate location: Chiang Mai.
- Phuket Cove, Beach resort. Approximate location: Phuket.
- Krabi Bay, Family resort. Approximate location: Krabi.
- Samui Retreat, Upscale island resort. Approximate location: Koh Samui.
Return intent
- Would return486 of 900 (54.0%)
- Would not return128 of 900 (14.2%)
- Conditional or unclear150 of 900 (16.7%)
- Not stated136 of 900 (15.1%)
Would return by month
Themes appearing alongside the intent
These are things mentioned in the same review. They are not proof that the theme caused the intent.
| Theme | Reviews | Would return | Filter |
|---|---|---|---|
| Food and beverage | 210 | 138 of 210 (65.7%) | |
| Location | 209 | 140 of 209 (67.0%) | |
| Pool and grounds | 193 | 137 of 193 (71.0%) | |
| Cleanliness | 191 | 100 of 191 (52.4%) | |
| Value | 152 | 65 of 152 (42.8%) | |
| Front desk | 150 | 88 of 150 (58.7%) | |
| Room condition | 136 | 62 of 136 (45.6%) | |
| Noise | 114 | 19 of 114 (16.7%) | |
| Booking and billing | 101 | 15 of 101 (14.9%) |
The reviews behind the numbers
Revenue scenario
You type the assumptions and the page multiplies them. Nothing here is derived from the review numbers above. It is not a forecast, not profit, and it assumes the rooms are available and no other booking is displaced.
Brief for the data team
Written in your browser from what you selected. Nothing is sent anywhere.
Methodology and limits
- Every property, guest, review and number in this demo is invented for the demo.
- Review samples are not automatically representative of your guests.
- A positive rating is not the same as saying you would come back.
- Saying you would come back is not the same as a booking.
- Themes that show up alongside an intent are associations, not causes.
- A real build needs access to your data and a round of validation before anyone trusts a number.
Fictional demonstration built by SolisMatica. Banyan Coast Hospitality and its five properties do not exist, and all figures are synthetic.
How the workflow works
Select a property
Start with one hotel or the whole group, and pick the months you care about.
Inspect the guest signal
See how many guests said they would come back, out of how many reviews. The count is always next to the percentage.
Review the underlying evidence
Click through to the actual records behind any number, so nothing has to be taken on faith.
Prepare a data team brief
Turn what you found into a short written request your own analysts can pick up and run with.
What this could open up
These are investigations worth running, not results. Nobody can tell you what your guests are saying until they have read your data.
- Look at feedback around a renovation you already documented, and see what guests mention before and after.
- Pull out the service themes that keep showing up next to people saying they would not come back.
- Put this beside your own guest survey results and see whether the two tell the same story.
- Read it next to occupancy and rate, which is where the commercial conversation actually happens.
The revenue scenario
The demo includes a small gross room revenue calculator you can edit. You type in how many extra stays a month you want to test, how many nights each one runs, and your average daily room rate. It multiplies them and shows the number. That is all it does. It does not turn a review percentage into predicted bookings, it is not profit, and it is not a forecast. It exists so a conversation about guest feedback can be held in the same units as the rest of your P and L.
What a pilot could include
- Agree on one business question worth answering.
- Pick three properties.
- Check what data you are allowed to use and can actually get.
- Agree what each metric means before anyone builds anything.
- Hand check the classifications on a sample.
- Build one report where every number can be clicked open.
- Write a handoff brief so your internal team can carry it forward.
Methodology and limits
- All hotel performance data shown here is synthetic.
- Review samples are not automatically representative.
- A positive rating is not return intent.
- Saying you would return is not an observed booking.
- Associations between themes and intent are not causal effects.
- A real deployment needs data access and validation first.
What would you want to know across your properties?
Independent SolisMatica demonstration. All hotel identities, guest examples, and performance figures are fictional or synthetic. No client engagement, hotel endorsement, or measured business result is implied.