As hoteliers, you want to take the insights you gather from your surveys to make operational decisions. Revinate Surveys is making this even easier by adding a “Guest Segments” filter in our Satisfaction Reports, allowing you to see the responses for certain types of guests and how those change over time.
This filter can be accessed on the Satisfaction Score and Satisfaction Trend reports located under “Satisfaction Reports”.
Below, we will outline some of our common use cases for this filter.
USE CASE #1:
Let’s say Hotel A has the following custom question on their survey:
“If you experienced an issue at our hotel and it was reported to the team, was the issue resolved?”
This is a valuable question that can be used with this new Guest Segments filter to identify how the likelihood to recommend (NPS) score, among others, were affected.
To see the results of this question:
Locate the “View By” Filter at the top of the page and select “Challenge Resolution %” based on your custom questions. Next select your calculation. The calculation will automatically be set to “Score (0-100)”, allowing you to view the scores of any question type, normalized on a 0-100 scale. The “Guest Segments” filter can remain “All Guest Segments” for now.
Once your filters have been selected, you will see the first row showing you the “likelihood to recommend” score for each multiple choice options:
- Yes, the guest’s issues were resolved
- No, the guest’s issues were not resolved
- Not applicable
- No responses to this question
In this situation, it is clear that the guests who had their issues resolved by the staff were far likelier to recommend the hotel to other’s than those who’s issues were not resolved.
We can then take this one step further to see scores for those guests who stated that their problem was not resolved by selecting the “Guest Segments filter labeled, “Challenge Resolution > No” and select the “View By: Months” filter.
This will now display the scores for all the questions on Hotel A’s survey for the guests who responded that no, their issue had not been resolved. In this situation, we can see that the majority of the rating metrics such as sleep quality, location, rooms value and cleanliness all decreased; However, the service rating increased.
Hotel A can then dig further into these areas through our Sentiment Analysis reporting.
USE CASE #2:
Hotel B wants to identify how first time guests versus return guests experience the property. The hotel asks the following question on their survey:
“Was this your first visit to our hotel?”
Using the Satisfaction Scores report, the hotel can filter for the following in order to identify how first time guests viewed the property versus how return customers experienced the hotel:
View by: First Stay Guests
Guest Segments: All Guest Segments
The Scores report will display all the scores for each question asked on the survey with each column representing the responses to the question: yes, this was their first stay, no, they were return guests, not applicable or not answered.
In this situation, return guests had higher scores across the board. For example, the likelihood to recommend (NPS) was eleven points higher for this time period and return guests experienced much higher levels of service than the first time visitors.
We can then delve deeper into these guests who are first time visitors to understand if their slightly more negative experiences are trending by selecting the “Guest Segment” filter: “First Stay Guests: yes” and viewing by “month”.
For this particular property, we can identify that the experience for first time guests has slowly been deteriorating over the past few months. For example, the first time guests are seeing less value in the property, perhaps due to the lower sleep quality and lower cleanliness scores.
Now that Hotel B has identified that first time guests are experiencing a lower quality stay overall, they can work towards improving the guest experience for their first time visitors.
There are a multitude of use cases that hotels can take advantage of in order to deeply understand their guest experience.