2.1. Dynamic Signals Scores

ADARA offers a range of scores created from data from hundreds of data partners in our consortium, including companies in travel, real estate, finance, and more.

2.1.1. Traveler Value Score (TVS)

Not all travelers behave similarly. There is significant asymmetry between travel booking volume and profits. A small fraction of the travel population, the most elite travelers, generate a fraction of the bookings, but generate a very substantial amount of revenue and a very healthy portion of the profits. The less frequent, more casual traveler, is a larger segment that together generates more bookings on the whole, but each booking contributes less revenue and profit on a per-booking basis.

Many brands spend a great amount of time and money marketing to travelers en masse with a relatively generic message accompanied by a simple conversion objective. Simultaneously, they acknowledge that not all converters are equal; they place higher value on reaching and capturing those with longer customer lifetime, higher lifetime revenue, and higher lifetime gross profit, but also want to invest appropriate time and attention on lower frequency travelers that provide a steady stream of business.

ADARA can help brands identify, maintain and ideally grow market share / share of wallet with different types of travelers based on those travelers’ specific needs. The Traveler Value Score (TVS) serves as an easy-to-understand, single compound metric that represents the traveler’s spend potential. TVS takes into account many factors of travel activity, including:

• Past travel searches and bookings across travel vertical (flight, hotel, car, cruise, rail)
• Travel frequency
• Total travel spend
• Loyalty status across brands

A higher score signals a greater anticipated level of spend, and a higher lifetime value. There are three basic tiers for TVS:

• Basic (range: 0 to 299): Travelers who make 2-3 trips a year, spend $1K-$5K per year, have no loyalty status, primarily research on OTA _
• Mid-Tier (range: 300 to 600): Travelers who make 1-2 trips per month, spend $15K-$20K per year, have Elite Loyalty status, research on OTA and direct supplier
• Elite (range: 601 to 800): Travelers who make 3+ trips a month, spend >$60K per year, Multiple Elite loyalty status, mostly book direct Traveler Solutions

2.1.2. Max Traveler Value Score

The Max TVS Score is a longer-term longitudinal version of the Traveler Value Score, representing a customer’s highest TVS value over the past six months. While the TVS reflects relatively recent travel trends, the Max TVS is persistent and stable over time. However, Max TVS captures the same dimensions as TVS, and provides many of the same benefits, while providing higher coverage.

2.1.3. Any Travel Intent Score

The Any Travel Intent Score evaluates how likely a consumer is to travel in the future, based on their past online behavior. This score looks at any consumers with travel-related activity in the past two months, from page views on car rental websites to hotel bookings, and gives a probability assessing whether the consumer intends to travel in the near future. As a broad, high-coverage score, Any Travel Intent does not take into account the traveler’s origin or destination, only their intent to travel.

The Any Travel Intent Score is modeled based on a large number of features built to indicate interest in travel. Some major components include:

• Hotel, flight, and car rental searches and bookings
• Type of activity (direct, metasearch site, online travel agency, etc.)
• Loyalty status and counts in different travel verticals
• Number and type of travel companies with activity

The resulting score ranges from 1 to 100, where a score of 100 indicates near-certain upcoming travel.

2.1.4. Total US Travel Intent Score

The US Travel Intent Score, similar to the Any Travel Intent Score, evaluates the likelihood that a consumer will travel in the near future. However, this score is specifically for domestic travel in the United States; consumers are given a high score only if they have a high intent to travel with both origin and destination in the US. For example, a given traveler may have a high Any Travel Intent Score and low US Travel Intent Score if their planned trip is in Europe.

The US Travel Intent Score is modeled based on a large number of features, including:

• Hotel, flight, and car rental searches and bookings
• Searched and booked trip locations
• Type of activity (direct, metasearch site, online travel agency, etc.)
• Loyalty status and counts in different travel verticals
• Number and type of travel companies with activity

2.1.5. Trip Browsing Index Score

The Trip Browsing Index Score indicates the breadth and amount of activity a consumer engages in before booking. With this composite score, you can know if a given user tends to browse more options or less than the average shopper when showing intent for a trip purchase. This score takes into account:

• Travel search and booking activity
• Number of unique trips for which there are activity
• Number and length of online browsing sessions in a given travel vertical
• Overall amount of time from first to last browsing session for a particular trip
• Number and type of travel companies with activity

The value of this composite index is in identifying informative consumer behaviors. For example, the score helps you spot heavy browsers who run a lot of searches, only a few of which result in bookings, as well as heavy browsers who almost never end up booking. Significant browsing above the norm (particularly with corresponding low TVS or Travel Intent scores) likely indicates deal-seeking behavior, while significant browsing above the norm may indicate strong travel preferences where the consumer is only interested in finding the best fit for them. The Trip Browsing Index for a given consumer can also help you understand the overall likelihood that any activity from that consumer will convert into a booked transaction, and how difficult it is to secure a purchase from this individual.

2.1.6. Hotel Consideration Breadth

The Hotel Consideration Breadth score indicates how much someone considers multiple brands or tends to book with a single provider of accommodations when making a hotel purchase for any given trip. Consumers in the marketplace have brand preferences, ranging from strong to weak, as well as a certain level of openness to using a new brand versus one they have used before. Based on a given consumers’ past online behaviors, we can evaluate how strong their preferences are, and how much their preferences can be changed. The Hotel Consideration Breadth score captures the amount of consideration consumers give to which hotel brand and they use, as well as their willingness to substitute one hotel brand for another and try different brands in the future.

This modeled score considers a large number of features, including:

• Hotel, flight, and car rental searches and bookings
• Type of activity (direct, metasearch site, online travel agency, etc.)
• Loyalty status and counts in different travel verticals
• Number and type of travel companies with activity
• Trip date, location, and brand flexibility
• Homogeneity of past hotel, flight, and car searches and bookings

Overall, this score captures a traveler’s willingness and availability to change what hotel brand they book when traveling.

2.1.7. Experience Value Score (EVS)

The ADARA Experience Value Score (EVS) is a composite metric that assesses a customer’s potential value based on their behaviors around events and experiences, including dining, concerts, sports, and arts and leisure. The score evaluates how driven by experiences a given consumer is, and how much they are willing to spend for those experiences. Using the EVS, you can deliver specific programs to targeted customers, or personalize consumers’ level of engagement with the experience economy.

The Experience Value Score uses data from across our experience and travel partners to measure who spends the most on experiences. Features include:

• Number of companies with which the consumer had experience-related activity
• Number of events booked
• Amount spent on events
• Premium events booked
• Group events attended
• Recency of event-related activity

The scores are computed over the past year and given on a 1-100 scale, with a high score indicating high value and spending on experiences.

2.1.8. Home Purchase Intent Score

The Home Purchase Intent Score evaluates the likelihood of a consumer purchasing a new home in the next 3 months, 3-6 months, or 6-12 months. ADARA’s real estate consortium includes data from multiple listing services and real estate brokerages, representing in total over 150 thousand national and regional broker sites such as Century 21, Coldwell Banker, Sotheby’s International, and Berkshire Hathaway. These first-party audiences represent over 90% of all real estate transactions. Using this real estate data, ADARA can assess which consumers are qualified, in-market for a home, and engaged with real estate professionals to buy or sell a property.

This data is used to build features including:

• Real estate website browsing activity
• Current homeowner, mortgage, and construction status
• Financial situation and consumer/personal loans
• Current home location
• Searched home locations and travel locations

All of this information and more is used to model the probability that a consumer will purchase a home in the future in varying time horizons; the resulting probabilities are used to create the Home Purchase Intent score.

2.1.9. Refinance Intent Score

The Refinance Intent Score assesses how likely homeowners are to refinance their home in the near future. ADARA’s consortium of real estate data can generate a large audience of homeowners who have no recent refinance; each of these consumers are scored from 1-100, where 100 indicates that they are highly likely to refinance in the near future.

Refinance Intent is a modeled score that takes into account features such as:

• Homeowner status
• Home value and home equity
• Financial situation, including wealth and mortgage status
• Luxury purchase behaviors
• Amount of time since home purchase
• Recent home construction work or remodeling