As part of our release of Advanced Profile Synthesis, we’re also launching two new reports that will help you visualize the work that our algorithm is doing in the background:
- Profile Synthesis Report for Properties
- Profile Synthesis Report for Groups
These reports will provide valuable insights, such as:
- how many profiles Revinate ingested from your data sources
- how many profiles remain after cleansing and merging duplicates
- how many profiles lack an email address
- how many profiles had a masked OTA email address for which we found a real email
- the average stays of your guests at your hotel, or across your portfolio
Profile Synthesis Report for Properties
Key Benefits of the Profile Synthesis Report for Properties:
- Understand the true loyalty of your guests
- Keep track of the health of your guest database over time
- See how many guest profiles are missing an email address, and how many times those guests have stayed at your property
- Easily create marketing segments based on the number of stays
Let’s take a look at each section of this report.
Top Dashboard
Total (Raw) Profiles
“Total (Raw) Profiles” are the unique profiles we ingest from your PMS or from a contact list upload. If there are two profiles with the same first name, last name, and email, they will each be counted as a unique, raw profile before we merge them.
Clean Profiles
“Clean Profiles” shows the number of unique profiles that remain after we’ve cleaned, deduplicated, and merged profiles.
Merged Profiles
“Merged Profiles” shows the number of unique profiles that are made up of multiple profiles. For instance, if there are 3,421 “Merged Profiles,” this means that there are 3,421 unique guests whose Rich Guest Profile is made up of multiple profiles.
No Email Profiles
“No Email Profiles” showcases the number of “Clean Profiles” that do not contain email addresses.
OTA to Guest Emails
“OTA to Guest Emails” shows the number of Rich Guest Profiles that have a real, valid email address, and that have one or more merged profiles that also contain an OTA email address.
Essentially, this number represents the amount of people that had junk or masked emails and that you may have believed were individuals. Our algorithm was able to find a real, valid email address for them in your database. We had enough data on these people, besides a junk email address, that allowed us to merge the profiles containing the OTA email addresses with the profile that contained the real, valid email address.
Average Stays
“Average Stays” shows the average number of stays per guest at the property. This number is calculated using the number of “Clean Profiles” (i.e. post-merge). It equals the number of profiles who have a stay, divided by number of stays. We don’t include “no stay” profiles in that division, only people who have stayed 1 time or more.
As we merge more and more profiles over time, the “Average Stays” number should grow. Whereas you may have previously thought that you had multiple guests who stayed only once, after we merge profiles, you’ll find that you had fewer guests who stayed more times. Therefore, as the number of “Clean Profiles” decreases compared to the number of “Total (Raw) Profiles,” the “Average Stays” number should increase.
Profile Distribution by Stays
In this graph, you can toggle between clean profiles and profiles that do not contain email addresses to see the average number of stays for both options, as well as the percentage change for each month-over-month.
The “Clean” option uses the number of “Clean Profiles” found in the top dashboard of this report, and it shows:
- How many guests have no stays – this includes people who signed up on your hotel’s website to receive a newsletter, for example, or they might be part of a certain list, or they might have made a reservation but then canceled it and never arrived
- How many guests have 1 stay – this includes future stays
- How many guests have 2+ stays – this includes future stays
The “No Email” option uses the number of “No Email Profiles” found in the top dashboard of this report, and it shows:
- How many profiles have no email and no stays
- How many guests have 1 stay and no email
- How many guests have 2+ stays and no email
Trends
The Trends graph offers a snapshot of the health of your property’s database over time by comparing “Total Profiles,” “Clean Profiles,” and “No Email Profiles.” The graph should always trend upwards since you will continue to add new people as time goes on.
You can hover your mouse over any date on the graph to get a more detailed breakdown, including average stays. You can compare results month-over-month, or set the period to:
- All-time
- Last 6 months
- Year to date
Profile Synthesis Report for Groups
Key Benefits of the Profile Synthesis Report for Groups:
- Understand the true loyalty of your guests across all of your properties
- Keep track of the health of your guest database over time, both at a corporate and property level
- See how many guest profiles are missing an email address, and how many times those guests have stayed at your property
- Easily create marketing segments based on the number of stays
Let’s take a look at each section of this report.
Top Dashboard
This top dashboard offers you a concise overview of your entire guest database across all properties.
Total (Raw) Profiles
“Total (Raw) Profiles” are the unique profiles we ingest from all of your properties’ PMSs or from contact list uploads. If there are two profiles with the same first name, last name, and email, they will each be counted as a unique, raw profile before we merge them.
Clean Profiles
“Clean Profiles” shows the number of unique profiles that remain across all properties after we’ve cleaned, deduplicated, and merged profiles.
Merged Profiles
“Merged Profiles” shows the number of unique profiles that are made up of multiple profiles. For instance, if there are 3,421 “Merged Profiles,” this means that there are 3,421 unique guests whose Rich Guest Profile is made up of multiple profiles.
No Email Profiles
“No Email Profiles” showcases the number of “Clean Profiles” that do not contain email addresses.
OTA to Guest Emails
“OTA to Guest Emails” shows the number of Rich Guest Profiles that have a real, valid email address, and that have one or more merged profiles that also contain an OTA email address.
Essentially, this number represents the amount of people that had junk or masked emails and that you may have believed were individuals. Our algorithm was able to find a real, valid email address for them in your properties’ databases. We had enough data on these people, besides a junk email address, that allowed us to merge the profiles containing the OTA email addresses with the profile that contained the real, valid email address.
Average Stays
“Average Stays” shows the average number of stays per guest across all properties. This number is calculated using the number of “Clean Profiles” (i.e. post-merge). It equals the number of profiles who have a stay, divided by number of stays. We don’t include “no stay” profiles in that division, only people who have stayed 1 time or more.
As we merge more and more profiles over time, the “Average Stays” number should grow. Whereas you may have previously thought that you had multiple guests who stayed only once, after we merge profiles, you’ll find that you had less guests who stayed more times. Therefore, as the number of “Clean Profiles” decreases compared to the number of “Total (Raw) Profiles,” the “Average Stays” number should increase.
Total Clean Profiles
This graph shows the total number of “Clean Profiles'' after Advanced Profile Synthesis. The top bar (in blue) represents the the group (i.e. Avertine Group), and the bottom bar is divided by property (i.e. Avertine Chicago, Avertine Paris, etc.) and includes the size of each property database.
Why is the total number of group profiles fewer than the sum of the properties?
This is because groups have guests who have stayed at more than one of their properties. For example, if Caroline Brettel stayed at Avertine Chicago and Avertine Paris on different occasions, she will be counted once in each property’s breakdown of profiles. However, at the group level, because she is one person that has stayed at two properties, she will only be counted once.
Cross-Property Profiles
In the Cross-Property Profiles graph, you can see how many guest profiles exist in the databases of two or more properties in your group. This graph helps you understand the number of customers who are loyal to the brand.
When you hover your mouse over each column, it’ll show the amount of profiles in that column, as well as a distribution of stays. So, for example, in the “3 Properties” column, there are 10,000 profiles. Of those 10,000 guest profiles:
- 70% have 3 stays
- 20% have 4 stays
- 5% have 5 stays
- 5% have 10+ stays
From here, you can jump straight to the segment builder to create a segment of those 10,000 profiles.
Profile Breakdown
This graph shows your hotel group’s top 5 properties in terms of database size, each broken down by number of “Total,” “Merged,” and “No Email” profiles. You can hover over each property to see the full breakdown.
Profiles Trend
The “Profiles Trend'' graph offers a snapshot of the health of your group’s database over time by comparing “Total Profiles,” “Clean Profiles,” and “No Email Profiles.” You can see “All Profiles'' across the group, or drill down into each property’s metrics. The graph should always trend upwards, since each property will continue to add new people.
When you hover your mouse over a specific point in time for “All Profiles” in the group, you’ll be able to see:
- Total profiles
- Clean profiles
- No email profiles
- Average visits
- Averages stay length (nights)
- Average lead time (days)
You can compare month-over-month, or set the period to:
- All-time
- Last 6 months
- Year to date
Profile Distribution by Stays
In the “Profile Distribution by Stays” graph, you can toggle between clean profiles and profiles that do not contain email addresses to see the average number of stays for both options, as well as the percentage change for each month-over-month. You can view this for “All Properties,” as well as for each individual property.
The “Clean” option uses the number of “Clean Profiles” found in the top dashboard of this report, and it shows:
- How many guests have no stays – this includes people who signed up on a property’s website to receive a newsletter, for example, or they’re part of a certain list, or they made a reservation but then cancelled it and never arrived
- How many guests have 1 stay – this includes future stays
- How many guests have 2+ stays – this includes future stays
The “No Email” option uses the number of “No Email Profiles” found in the top dashboard of this report, and it shows:
- How many profiles have no email and no stays
- How many guests have 1 stay and no email
- How many guests have 2+ stays and no email
Profile Synthesis Report FAQ
Is it possible to have clean profiles that don’t contain email addresses?
Yes. Email address, while an extremely important profile field, is not the only one that our algorithm takes into consideration when merging profiles. Therefore, it’s not only possible to have clean profiles that don’t contain email addresses – we actually tell you how many of these exist. You can find this information in the Profile Synthesis Report, in the top section under “No Email Addresses.”
Will I see a reduction in my guest profile count?
Whether or not you see a reduction in your total profiles depends on how clean your guest database is when we run our Advanced Profile Synthesis on your data set. The cleaner the data, the less profile merges needed (that’s a good thing!). The dirtier the data, the greater the reduction in total profiles as our algorithm merges multiple profiles into unique, clean profiles (this is also a good thing!).
In the case of having very dirty data, while the number of total profiles may decrease, the number of average stays per guest should increase, thus giving you a more accurate picture of how many loyal guests you actually have. The more information our algorithm gets and, therefore, the better the algorithm gets, the more changes you’ll see over time.