Impersonal email campaigns yield low conversion rates but the only way to personalize is to ensure your data is clean and complete. Incomplete data carries high opportunity costs such as missed revenue opportunities, decreased guest loyalty (lifetime value), ineffective marketing campaigns, and increased OTA commissions. 

Let’s look at an example.  If you receive bookings and virtual payments through Expedia you know that some of these reservations show the Expedia headquarters as the guest’s billing address. This could lead you to see a misleading share of guests from Seattle, where the Expedia head office is located. Likely, those guests actually booked from locations across the country (or the world). And if you were to use this data to send a targeted email to Seattle-based guests that says “Escape the grey Seattle weather and come visit us for a long weekend!” your campaign is unlikely to generate strong results.

Similarly, inaccurate guest profile data can throw off your personalized emails for birthdays, anniversaries, or loyalty-related offers. Nothing is more off-putting to a guest than receiving a “happy birthday” email nowhere close to their birthday, and a “we miss you” email that hits a guest’s inbox the day after they check out is probably not going to move the needle. These emails can come across as unprofessional and may cause your guests to unsubscribe altogether. Here’s where Revinate’s Advanced Profile Synthesis comes into play.

Given how costly messy data can be, it’s important that you have a tool in place that automatically cleanses your database and provides you with the insights you need to monetize it. We mentioned that Revinate uses a sophisticated artificial intelligence and machine learning process, called Advanced Profile Synthesis, to do just that. But, what exactly is Advanced Profile Synthesis?

Most hotel databases contain plenty of typos, duplicates, and incomplete information about their guests. If you have thousands of profiles in your database, it’s impossible to manually clean and maintain this information. Some software allows you to simply merge duplicate profiles or exclude incomplete records, but guest profiles are nuanced and sophisticated hotel marketers know they need more than this basic functionality. For example, the same guest could have two different email addresses (e.g. work and personal). Those two email addresses should not belong on two separate guest profiles.

Revinate’s Advanced Profile Synthesis takes a more holistic approach to cleaning guest profile data and merging or removing duplicates. Using natural language processing and machine learning, the system sorts through guest profiles in a more human way. Rather than automatically categorizing two unique email addresses as two unique guests, Advanced Profile Synthesis considers numerous other fields in the profile. For example, if two guest profiles have the same name, address, and phone number, but different email addresses, Advanced Profile Synthesis will understand that these profiles represent the same guest and merge them. By contrast, most CRM systems would keep them separate, since it would require all information within the two profiles to be an exact match.

The Advanced Profile Synthesis process occurs automatically and in the background, so you can focus on putting your up-to-date guest profile data into action rather than sifting through it.

What’s happening behind the scenes as Revinate’s Advanced Profile Synthesis scrubs your guest database? The software follows a three-step process: standardization, clustering, and learning.

Data that gets ingested into your PMS is often logged in unique formats and includes typos. This makes it difficult to match data so, in order to standardize data, Revinate first puts all the information it ingests on an even playing field in terms of formatting. For example, guests can enter their phone numbers in a variety of formats. Some write 123-456-7890. Others write (123) 456-7890, or maybe even 123.456.7890. Revinate’s functionality automatically removes all punctuation from phone numbers to avoid miscategorization. The system will also standardize addresses to make 123 1st St. match 123 First Street, and it will remove placeholders like “n/a,” “null,” or “555-555-5555.” Finally, the standardization process ends by assigning “tags” to each field, like “phone number” and “email address,” so that it can compare fields in an apples-to-apples way.

The next step is to cluster similar values that fall within the same tag. For example, profiles with names like “Catherine Smith,” “Cathy Smith,” and “C. Smith” would get grouped together. Then the system will analyze additional elements of those guest profiles to determine if they’re a match or if they are indeed unique guests. If “Catherine Smith” and “Cathy Smith” share the same street address and phone number, then the system understands that the two profiles are likely the same person. But if none of the profile data for “Cathy Smith” aligns with data for “C. Smith,” then the system will assume they’re different people. Revinate’s algorithm also incorporates known nicknames, so profiles with first names “Mike” and “Michael” will be grouped together, while “Ryan” and “Bryan” will not. In addition, Revinate weighs the popularity of names, so it will be more picky when considering grouping “Mike” profiles versus profiles with less common names. For example, if there are two guest profiles with the name “Eustace” in a database, the likelihood of that being the same person is much higher than “Mike” and “Michael” being the same person, given that the latter name is so common.

The third element in Revinate’s process is that it’s technology is continually learning. The system constantly analyzes the profiles that it has matched successfully to find patterns or similarities in your unique database. By doing so, it becomes more accurate over time, which means that you’ll be able to target narrower segments of guests with more engaging and personalized offers.

Clean data allows hotel marketers to get creative with campaigns and maximize conversions at the lowest possible cost by getting incredibly specific with targeting criteria.

Let’s say you want to invite back families who visited your hotel over spring break in the last three years. With your cleansed database, you could easily create a segment of guests who traveled for leisure, booked a suite with twin beds, stayed for at least 5 nights during March and April from 2018-2021, and left a positive review post-check out. In this way, you can be confident your spring break offer will reach the right audience. On the other hand, perhaps you want to inspire past guests who originally visited your hotel for business to instead book a vacation with you once they’re ready to travel again. By targeting guests who booked as part of a conference or with a specific negotiated corporate rate, you will likely get better engagement than if you were to blast your entire database.

Running Advanced Profile Synthesis also tends to yield a surprising result which is that you have a more accurate view of the average number of stays per guest. Having an accurate understanding of this data point is critical for targeting. It will help you understand who your true loyal guests are so that you can drive campaigns to them and also learn more about them so that you can target similar guests who have the potential to become loyal as well.

Here’s why the average number of stays tends to increase: Once a database is cleansed, guest profiles that were previously counted as unique, but who were, in fact, the same person, will have been merged. Let’s say that the profiles of “Catherine Smith” and “Cathy Smith” each had two stays totaling 7 nights, and the profile of “C. Smith” had one stay totaling 5 nights. If Advanced Profile Synthesis was able to determine that those three unique profiles were the same person, your database would now consist of one Rich Guest Profile for “Catherine Smith” with her entire guest history. Her new, complete profile would show that she has stayed five times at your hotel for a total of 12 nights. If you’re building a personalized campaign using # of nights stayed as a variable, having an accurate view of this data is invaluable.

Accurate guest profile data is key. Without the right data, you might have the wrong idea of who makes up your largest or most profitable segments. 

So how can you maximize the potential of your guest database? The first step is to clean your data and consolidate duplicate guest profiles. Then, you can begin to analyze your segments and tailor your marketing messages based on those segments that truly align with your hotel’s overall strategy.

Regardless of which segments you target, these highly tailored campaigns will garner better engagement and generate more conversions than campaigns sent to a more generic audience. In fact, hotels that use Revinate’s advanced segmentation tool see a five-fold increase in booking rates, on average, compared to hotels that launch less specific campaigns. Employing sophisticated segmentation will not only increase direct bookings, but also decrease the amount of marketing budget spent on emails sent to invalid or duplicate email addresses. Furthermore, a tool like Revinate doesn’t charge extra to leverage advanced targeting, segmentation, or A/B testing. Nor does it charge for database size or email volume thresholds. So you are free to build, test and optimize without hurting your budget.

Whether your goal is to decrease marketing costs, drive guest loyalty, or increase conversion of your campaigns, leveraging the power of a clean guest database thanks to Advanced Profile Synthesis will help you realize the full potential of one of your hotel’s most valuable assets: your guest database.

This content was created collaboratively by Revinate and Hotel Tech Report.