Attribute Based Buying (ABS) is a hot topic these days for hotels.
In a recent panel at HITEC, panelists discussed a NYU and StayNTouch-sponsored survey of more than 1,000 travelers. Among others, the survey addressed two of the questions we hear most frequently: Will travelers use it? And what will he do with the hotel income?
The survey had good response data for both, but did not address the inevitable final questions: Where can I see ABS in action? And who is using it in production today?
GauVendi is a small company based in Germany with an internet booking engine attached to a PMS and CRO module; both are ABS-ready and are in production at a small number of properties.
This product represents a point solution for one property at a time rather than the brand-level solution that the big CRS companies are building. But its early data could provide an early orientation to questions about whether customers will like/use it and whether hotels will see increased revenue from it.
For reference, here are some quick facts about the data collected for this analysis:
- The data set includes 14 properties with 1,893 total net reservations made and consumed during the period April 1 to August 31, 2022.
- All properties are located in Europe.
- The booking tools allow travelers to select an ABS route or more traditional options from the best available rate, the most popular rate (actually a rate the hotel wants to promote), and other room type/rate plan combinations.
- All data is reported by GauVendi from its proprietary database.
Are these real purchases based on attributes?
ABS has the following characteristics:
- Allows buyers to define the room experience they want by selecting the specific room level attributes (e.g. view, bedding, room size, high floor, late checkout, etc.) they want to buy
- Guarantees that the room they purchased will have the attributes they purchased; attributes are not “on demand”.
In the image above, we can see that there are several options a hotel shopper can choose from, including attributes at the top followed by lowest price, most popular, and other room type/rate plan combinations.
Note that this particular implementation of ABS is different from the shopping cart metaphor often described in ABS discussions. Here, the shopper selects all the attributes they are interested in before the purchase occurs and sees a price for each recommended bundle instead of seeing the prices for each attribute as it is added to a cart.
When the shopper chooses attributes, the shopping engine scans all rooms to find those that have at least some of the required attributes to find one or more “best fits” for the shopper.
The buyer can see which options best match their list and choose from them and, as is crucial to ABS, they are guaranteed to get what they paid for.
Another significant aspect of this user interface is that it does not present a price for each attribute individually. Instead, hotels are testing bundles of attributes that they believe the shopper is most likely to choose, with an associated total price for each bundle.
As ABS goes into production with other systems and is further tested, we will likely see many different implementations as travelers get used to the new way of shopping and hotels collect actual conversion and revenue data on different layouts. There is no single correct way to present ABS options to travelers.
Results to date are encouraging
Now that we’ve outlined the customer experience, we can look at some of the early results to answer our questions. Note that these results are relative to this specific solution.
Are travelers even interested in ABS to book hotels? Will they use it for booking? Will they understand what they are looking at? Survey results indicated that 97% of respondents indicated some level of interest with 37% very interested and 20% extremely interested.
In practice, when presented with this interface, buyers understood and chose ABS without any direct indication. In fact, 17.4% of all Internet Booking Engine (IBE) bookings came from the ABS route, roughly the same number as those who chose the “most popular” option.
Would customers use ABS to book the standard standard room without any additional attributes to get the lowest price, thereby reducing hotel revenue? In the survey, “63% of travelers who pay $251 or more and 48% of those who pay between $151 and $250 for their daily room rates are willing to pay more for preferred room features.”
In practice, looking at actual bookings from the GauVendi system, if we index the average daily rate for non-IBE channels (which includes OTAs and group bookings) by 100, IBE rates are indexed as follows:
- Non IBE – 100
- Best Available Rate – 110
- Most popular – 118
- Highest value RT/RP – 131
Some final thoughts
While this is not a conclusive study (we would have preferred to be able to create a non-ABS control group to run alongside the standard UI, but none of the hotels we asked were willing to give up the ABS route for a test) , we think it points in some generally positive directions for ABS.
When an ABS option is presented to buyers, a significant portion of them can understand it, use it, and will buy through it. In addition, buyers are not using ABS to try to find a cheaper regular rate, but rather are attaching attributes that add up to a significantly higher average daily rate than the lowest priced room type/rate plan combination or category. OTA rate.
- Although the initial results are promising, there is ample room for improvement. An important area would be the presentation of individually priced attributes so that properties can test how much buyers are willing to pay for each in different contexts. A balcony will have a very different value in the summer versus winter; a fireplace can have a very different value in winter for a couple than in summer for a family; etc.
- The system optimizes room assignments periodically throughout the day to free up unpurchased attributes; doing this more frequently (eg before each store) and applying increasingly smarter dynamic room assignments would likely increase ADRs for ABS bookings as more attributes become available for sale.
- Note that this implementation highlights that ABS is a solution for direct channels, driving more bookings to the hotel’s booking engine or call center.
- ABS is also difficult for automated fare shoppers to decode. While capturing the standard lowest price or recommended room type/rate plan combinations is relatively widespread, attempting to capture all of the various attribute combinations available to a buyer would quickly become a daunting task. This makes it easier to avoid detection by offering personalized rates for customers.
- Continued use of ABS will generate a wealth of new customer-specific preference data that hotels can use to personalize recommendations in new ways. For example, high-traffic buyers who express an interest in ABS may see attribute packages tailored to them instead of the traditional lowest price and recommended categories.
- All shopping attributes, pricing, and shopping data will be a boon to revenue managers and sellers. In addition to testing the price elasticity of individual attributes versus prepackaged packages, marketers can explore differences in descriptions of each attribute in different contexts. Personalization takes on a whole new dimension with individual attributes in the mix.
It’s still very early days for ABS, but there are some encouraging proof points. Hopefully this new shopping method can help travelers get more of what they want and keep them coming back (straight) for more!