Data definitions
This report contains data collected from Revinate solutions (Revinate Marketing, Revinate Guest Feedback, Revinate Ivy, and Revinate Reservation Sales) from January 1, 2023 through December 31, 2023.
To create this report, we analyzed 1.8 billion emails, 244 million guest records, 37 million guest reviews, 19 million text messages, and 4.6 million calls from hoteliers located in North America, APAC, and EMEA. Data from Revinate Ivy and Reservation Sales are limited to North America.
Find more information below on how we defined the statistics and calculated the data in this report. We’ve also included definitions for acronyms and information on regional segmentation. Have any questions? Drop us a line — we’re here to help. media(AT)revinate.com
Methodology
This report contains data collected from Revinate solutions (Revinate Marketing, Revinate Guest Feedback, Revinate Ivy, and Revinate Reservation Sales) from January 1, 2023 through December 31, 2023.
To create this report, we analyzed 1.8 billion emails, 244 million guest records, 37 million guest reviews, 19 million text messages, and 4.6 million calls from hoteliers located in North America, APAC, and EMEA. Data from Revinate Ivy and Reservation Sales are limited to North America.
Read the full data definitions to understand our calculations and terminology in detail.
Automation rate
The count of messages resolved by Ivy divided by the total number of messages resolved by Ivy or staff combined.
Average booking value
The total $USD value of reservations divided by the total number of reservations. This represents the average value of an individual booking from a given segment.
Call volume
The average number of inbound phones a hotel receives per month.
Click-through rate
The number of emails where a hyperlink was clicked divided by the number of sent emails.
Conversion rate
The number of completed bookings divided by the number of sent emails.
Database growth
We identified the total number of database records of profiles in each regional segment in 2022 and the number of database records those same customers had in 2023, and calculated the percent change. This method is called same-store sales, and it represents the average growth of a hotelier’s database in that time period.
(The number of database records of hoteliers in 2023 minus the number of database records of those same hoteliers in 2022) divided by the number of database records of the same hoteliers in 2022.
Database records with an OTA-masked email address
The number of raw profiles with a known OTA-masked email address divided by the total number of raw profiles.
Database records with phone numbers
The number of records in a database with a phone number divided by the total number of records.
Database records with valid email addresses
The number of records in a database with a valid email address divided by the total number of records. A valid email address is defined as one not masked by an OTA.
Email capture rate
Total number of non-booked lead calls where emails were obtained in the process divided by the number of total non-booked lead calls.
Hotel class
Hotel classes are defined using the Smith Travel Research (STR) system, including Luxury, Upper Upscale, Upscale, Upper Midscale, Midscale, and Economy.
Hotel rating
The score a hotel receives on a scale of 1-5 on public review sites.
Hotel review volume
The average number of new public reviews a hotel receives each month.
Incoming message volume
Average number of messages that guests send to Ivy per month.
Incoming messages
Messages that guests send to Ivy.
Incremental revenue per room from outbound calls
The additional revenue a hotelier earns per room based on the outbound conversion rate of non-booked leads multiplied by the average booking value of a reservation made on the voice channel.
Lead call conversion rate
Total number of booked inbound lead calls divided by the total number of inbound lead calls.
Lead call volume
The average number of phone calls received from leads that a hotel receives per month. Lead calls are calls inquiring about reservations as opposed to other inquiries.
Median resolution time
The median is the midpoint in the range of times it takes for a guest message to be considered “resolved” – i.e. no further action is needed. Ivy’s resolution time is based on her ability to auto-resolve guest messages, while staff resolution time is based on their time to manually address and resolve messages. We are presenting the median because it excludes extreme outliers — for example, when a hotelier has addressed a guest’s concern but forgets to mark it as resolved.
Merged profiles per database
The number of merged profiles divided by the number of raw profiles in a customer database.
Messaging engagement rate
The number of times a guest responds to Ivy’s first message divided by the total number of first messages from Ivy each month summed across all months in 2023 divided by 12.
Messaging opt-out rate
The percent of guests who decline to receive further messages from Ivy.
Net Promoter Score
Net Promoter Score (NPS) is a measure used to gauge customer loyalty, satisfaction, and enthusiasm with a company that’s calculated by asking customers one question: “On a scale from 0 to 10, how likely are you to recommend this product/company to a friend or colleague?” To get the aggregate NPS scores, we make the following calculations:
#Promoters = # of Surveys with NPS >= 8
#Detractors = # of Surveys with NPS <= 6
#Responders = # of Surveys with NPS >= 0
%Promoters =100 x ( #Promoters / #Responders)
%Detractors =100 x ( #Detractors / #Responders)
NPS = %Promoters – %Detractors
Non-booked lead volume
Average total number of lead calls that do not result in a reservation.
One-time campaigns
Campaigns where all emails are sent to recipients at the same time.
Open rate
The number of emails opened divided by the number of sent emails.
Outbound conversion rate of non-booked leads
The number of booked outbound calls without any associated inbound call within 30 days prior to the booking divided by the total number of non-booked leads.
Outgoing messages
Messages that Ivy sends to a guest. Includes both automated messages and those written by staff.
Recurring campaigns
Campaigns where emails are sent to recipients at different times automatically based on certain events or triggers, such as checking in, a guest’s birthday, or canceling a booking.
Revenue per segment filter
The total average of (revenue of a segmented campaign divided by the number of emails sent).
Room nights per booking (recurring campaigns)
The average number of room nights in an individual reservation from a recurring campaign within a segment.
Review response rate
The average number of new reviews a hotelier publicly responds to.
Room nights per campaign (one-time campaigns)
The total number of room nights reserved as a result of one-time campaigns averaged across all one-time campaigns by segment within the year.
Send size
The number of emails sent in a campaign.
Survey response rate
The number of surveys sent to guests divided by number of surveys submitted by guests.
Upsell categorization
A statistically significant random sample of upsells were labeled to determine the population proportion of categories with 99% confidence and a bound of error of 5%.
Upsell revenue
The average revenue per upsell within a segment, i.e. the total upsell revenue divided by the number of upsell campaign emails sent within a segment.
Upsell utilization
Number of customer accounts who have deployed an upsell campaign divided by the total number of customer accounts within the segment. Only applies to customers with subscription in both 2022 and 2023 for accurate year-over-year comparison.
Value (in USD) of database records with phone numbers
Incremental value is determined by the difference between the average revenue of all bookings with a phone number compared to the average revenue of the remaining bookings without said information.
Value (in USD) of database records with valid email addresses
Incremental value is determined by the difference between the average revenue of all bookings with a valid email address compared to the average revenue of the remaining bookings without said information. A valid email address is defined as one not masked by an OTA.
Region: APAC
Asia-Pacific. Data includes hotels in all APAC micro-regions combined.
Micro-region: ANZ
Data includes hotels located in Australia and New Zealand.
Micro-region: Rest of APAC
Data includes hotels located in Bangladesh, Bhutan, China, Cook Islands, Fiji, French Polynesia, Guam, Hong Kong, Japan, Republic of Korea, Macao, Maldives, Mongolia, Nepal, New Caledonia, Northern Mariana Islands, Pakistan, Papua New Guinea, Samoa, Solomon Islands, South Korea, Sri Lanka, Taiwan, Tonga, Vanuatu, Palau.
Micro-region: SEA
South East Asia. Data includes hotels located in Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, Vietnam.
Region: EMEA
Europe, the Middle East, and Africa. Data includes hotels in all EMEA micro-regions combined.
Micro-region: Benelux
Data includes hotels located in Belgium, Luxembourg, and the Netherlands.
Micro-region: DACH
Data includes hotels located in Austria, Germany, Liechtenstein, and Switzerland.
Micro-region: MEA
Data includes hotels located in the Middle East and Africa.
Micro-region: Rest of Europe
Data includes hotels located in Albania, Andorra, Armenia, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus, Czech Republic, Estonia, Finland, France, Georgia, Gibraltar, Greece, Guernsey, Hungary, Iceland, Italy, Latvia, Lithuania, Macedonia, Malta, Moldova, Monaco, Montenegro, Poland, Portugal, Romania, Russia, San Marino, Serbia, Slovakia, Slovenia, Spain, Turkey, and Ukraine.
Micro-region: Scandinavia
Data includes hotels located in Denmark, Norway, and Sweden.
Micro-region: UKI
Data includes hotels located in the United Kingdom and Ireland.
Region: Global
Data from North America, EMEA, and APAC combined
Region: NAM
North America. Data includes hotels located in Canada, the Caribbean, Central America, Mexico, and the United States.