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Attribute-Based Shopping [Part 1 of 3]

Posted on Sep 12, 2017 9:17:53 AM

In this 3-part series, Pegasus partners with Hudson Crossing to examine what attribute-based shopping is, what it means for hotels, and what benefits hotels and consumers can expect from this new technology. You can download all parts 1-3 in a full report here.

This article also appears on LinkedIn.

Roukas_.jpg By George Roukas, Partner, Hudson Crossing

Two modes of shopping commonly carry the name attribute based shopping (ABS). In the older form, widely used today, a consumer begins by choosing a hotel and stay dates and receives a list of rates. These rates correspond to combinations of room types and rate plans and each combination has a price. Both the room type and the rate plan for each combination have attributes. For example, a room type might have attributes like the bedding, max guest capacity or whether it has a view or a balcony.  Rate plans have attributes like cancellation policies or whether they include certain features like meals or prepaid Wi-Fi. Customers are not searching for those specific attributes, rather they’re filtering what’s been retrieved to find the best specific room type and rate plan for their needs. We’ll refer to this older shopping mode as attribute-based filtering.

In the new attribute-based shopping model, consumers don’t see the room type or rate plan combinations; they see a list of attributes they can put into a shopping cart to build the product they want. If Jon wants to stay at a particular hotel with his wife and they’re interested in a king bed with ocean view and a balcony, then he can specify those attributes without knowing the room type.  Each time he adds an attribute to the cart, the ABS engine prices that attribute separately and the consumer can see how each attribute affects the total price of the room.

What’s going on behind the scenes is very different from the attribute based filtering example. With attribute-based shopping, the hotel is not promising any particular room type or rate plan.  It must determine the price at which it will offer each attribute, based on the total inventory in the property that meets the consumer’s needs. As each attribute is added to the cart, it narrows the list of rooms that can meet the consumer’s need, so the CRS must determine the price of that next attribute going into the cart in real time. 

This has two immediate effects: first, it allows the consumer to choose exactly what they want to pay for and nothing more, and second, by giving the hotel more flexibility in how it meets those needs, it could offer the customer a lower price than for a specific room type or rate plan while earning higher overall revenue. If the hotel has flexibility in terms of the room types that can be assigned, there are opportunities to optimize the room assignments and improve conversion.  For example, consider the following ‘traditional’ availability picture. For simplicity, let’s assume these are the only rooms in the hotel.

Room #

Bedding

View

Balcony

Cancelable

Wi-Fi

Breakfast

Price

1001

K

Ocean

Y

Y

Y

N

425

1002

K

Ocean

N

Y

Y

Y

400

1003

K

City

N

N

N

N

350

First, let’s assume we’re shopping in the filtering model and we present the consumer with room types and rate plans that include the above options.  If the consumer is interested in an ocean view king that has Wi-Fi and is cancelable, she will choose room 1001, thus removing it from availability for those nights.  If the next consumer wants a king room with ocean view and a balcony, there is no room type left that meets his needs. The second traveler may continue with a different product but they may also search other hotels or chains or other types of accommodations (e.g., home sharing) to meet their needs.

If, instead, the consumers are using ABS shopping for a king room with an ocean view and Wi-Fi, the first will see a price for those attributes, but not a specific room type, that she can book. The room assignment algorithm (which operates as the consumer is shopping, not on the night before arrival) might even tentatively assign her to room 1001. When the second consumer comes in looking for the king room with ocean view and a balcony, the room assignment algorithm can switch the first consumer to room 1002 and assign the second consumer to room 1001, thereby capturing two bookings instead of one.                            

The result of ABS in this case, is that the hotel could offer the consumer the package of attributes they want for less than the price of the RT/RP combo and still make more revenue. Let’s say the hotel offered each of the consumers in the example above the attributes they requested for $25 less than the associated than standard combinations.  In the first case, the hotel would have realized $425 in revenue for one room and in the second case they would have recognized $775 (425 -25 + 400 – 25) in revenue for two rooms.

Note that in the example above, the room assignment optimization algorithm for ABS shopping takes the room attributes into consideration when figuring room assignments, but does not consider the rate plan attributes like cancellation policy or Wi-Fi/breakfast inclusion.  These figure into pricing but not into room assignment because they can be priced and included or excluded as required to meet demand.

Of course, the big question is whether consumers will want to shop this way. The short answer is that, of course, some will want it, but it’s impossible to know how many without testing. Still, two things stand out: pricing and choice.  If, as we expect, the optimization of room assignments allows hotels to offer attribute bundles at less than the price of specific rate plans and room types, then customers will likely enjoy a price advantage. Regarding choice, consider an example where you go to the grocery store to buy bread and milk but the store only sells bags that are already pre-packed with multiple items. You can’t choose to buy just milk and bread, you have to find a bag that has milk and bread in it but you’re also buying other things you don’t need and, in some cases, can’t use.  That’s essentially what hotels are doing today—selling packages of attributes where some customers would prefer to go a la carte. ABS lets consumers buy, and pay for, only what’s most important to them. 

We hear about disruptive technologies, innovations, or companies every day; often with little relation to its original meaning. Giving consumers more of what they do want and less of the fluff they don’t, especially at a better price, has been a key to success for many companies. Just ask Ikea and Southwest Airlines. 

In part 2 we’ll look into the impacts on hotel systems. Or download the full report (parts 1-3) here.

Topics: attribute based shopping, hotel digital marketing, hotel marketing

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