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Feature-based pricing: Sell uniqueness, not just rooms

Updated: May 14, 2021

Simplified rooms inventory enables online sales

Commonly, hotels manage their rooms revenue at the base of room types or room categories. Over the past years – and still today - hotel sales and revenue management teams cluster and re-cluster the rooms inventory into a handful of room categories to provide a digestible offering through online sales channels. And whilst this approach has proved to increase revenues and conversion (selling 5 room categories versus previously 18 or more categories), the uniqueness of each of the rooms and differentiators gets lost and as such becomes a nonvisible inventory item or feature for the guests.

Every single hotel or hotel group may set the same standard definitions for their room categories, however there is no global definition on what to expect when booking a standard, superior, deluxe room or suite. End consumers are often puzzled to see the big difference between hotel rooms whilst booking the same room category. Brand standard-built hotels - such as Motel One - have a competitive advantage when offering the exact same room sizes and features within each of their offered categories providing the same look and feel across all their hotels. A guest knows exactly what to expect and there are no “unpleasant” surprises or disappointments – client expectations and brand promise are easily matched.

However, the majority of hotels are independent and individually built properties and as such, the challenge remains when clustering rooms of different look, feel and size into a limited number of room categories. This practice creates guest expectation gaps when staying in those rooms and can lead to numerous operational issues, positive or unfortunately also negative customer feedback.

The challenge with common room categories

A basic principle within revenue management is to set up different price points per room category, linked to occupancy and demand. The price structure set up (particularly if managed manually without any sophisticated revenue management tool) often does not consider minimum length of stay or dynamic variations of rate adjustments between the room categories. Price points between the different categories are often too high which results in early dropouts[1] and the revenue is lost.

The average revenue per available room (RevPar) of a hotel, is often linked to the rates of the lowest room category itself, particularly if this category reflects the majority of the hotel inventory. Not only are the room categories with the lowest price points used as general reference rate, they are the rate being looked at when comparing against the competitive set and used for most sales negotiations, including contracted rates with corporates and TMCs.

As result, the lowest room categories are the main ones being requested and booked, regardless of their true inventory count, resulting in regular overbookings and often free upgrades to higher priced room categories. Hotels may be tempted to re-cluster their room inventory and add even more inventory to the lowest category, which creates another increase in physical room variations within this room category.

Apart from room categories, rate products offer an additional layer that impacts the RevPar performance like best available rates, non-refundable rates, packages, etc. For the purpose of this article though, the outline will remain solely on the impact linked to rooms inventory.

How many room types and price points are yielding the optimum return?

The answer to this question always creates trade-off implications which need to be considered:

· Simplification of room types into commonly known categories improve conversion, however it also commoditizes the hotel experience and creates many physical room variations within the same category impacting the guest’s feedback and experience.

· Keeping the amount of different price points (and rate conditions) to a minimum in order to not overwhelm the booker, may result in too large variations within the price points of the different room categories and increases the potential opportunity costs due to additional reservation dropouts.

· What categories to be sold through direct sales channels and which ones to be sold through third parties (online tour operator, channel manager etc.)? Making all rooms available on all channels is restricted due to system limitations of third parties and can cause naturally a rate discrepancy between channels. In absence of interface connectivity between sales channels, the additional manual workload creates another layer of conflicts that may not trade off with the additional revenue expected to be gained (eg. resource limitations, confusion of the overall offering for the booker, potential conflicts linked to contractual agreements).

These questions are known dilemmas, where OTA’s have taken the lead in commoditizing the hotel’s room inventory into simple categories, making it easy for customers to book at a higher conversion rate than on the hotel’s own website.

Can those trade-offs be eliminated?

So how can this be changed and trade-offs overcome? The first point we need to make here is, that this is not only a pricing or inventory question, yet it is a way how we craft the booking experience for our guests. We suggest using the word retailing to include this skill set. On one side we know, guests like an easy booking process and on the other side we understand they want personalized experiences and not be surprised by inconsistent room type labelling. In order to provide both, we have to finally let go from classifying our rooms into the typical definitions of standard, superior, deluxe room etc. and come up with a very different way of inventory labelling and booking process which is intuitive and easily understood by customers.

Why would a guest not book a specific room including their personal preferences if it is easy and intuitive?

We believe, only if you can provide a new retail experience, we can also bring a real new pricing approach to live. While adding upsell opportunities for customers to the booking process offering various room features, we need to consider the danger of making the process too complex and therefore suffer conversion loss. After all, the OTA’s have shown us the way that easy booking processes - taking away all the hazzle - just convert better. So how does this new retail experience need to look like?

We need a new inventory codification!

At GauVendi we have been testing a novel codification system for hotel rooms inventory, combining it with a new guest demand matching logic, making the retail experience simple and quick. In numerous consumer tests we found, that the core issue to provide an intuitive booking process as well as personalization, requires a new language of how we cluster and retail rooms inventory[2]. The new inventory codification tested extremely well with bookers and allows to price each feature per day individually.

As a result, each individual room can have a different price point subject to the features physically identified per room. Guests can now actually pick their preferred room themselves. This is a similar process as booking an airline ticket, where customers can also pick their row and aisle. Only that airlines seats have location and space as a main differentiator, hotel rooms vary much more with many different features and attributes per room.

What is the impact of feature-based pricing?

We like to demonstrate this with an example of a simple 10-rooms hotel: 5 standard rooms, 3 deluxe rooms and 2 suites. To avoid making it too complex, we ignore any potential connecting doors or special room configurations. For our use case we zoom into a price set up of one single day in a year.

With a simple revenue management approach this suggests that you have 3 price points on a specific day. In contrast, with a feature-based pricing approach you would potentially have 10 price points, since all rooms might be a bit different, including features which are priced individually. This does not suggest that all rooms and price points are shown at the point of sales (POS) or at the same time.

See graph 1. Common price vs GauVendi Retail System (GRS) approach for a 10-rooms hotel example

As shown in the table, you can yield a higher total revenue per available room when selling the entire inventory with a feature-based pricing approach using much more subtle price jumps.

While you only have 3 price points with the common approach, with the feature-based pricing you could put 10 price points on the shelf without showing them all at the same time at POS (which is usually the issue with current booking engines on the market).

Since the price points are a lot closer to each other and room categories do no longer apply, it reduces the potential of dropouts and overbooking of the lowest room category. Price jumps and room availability is rather driven by demand of requested features or attributes included in the rooms.

Big data to support pricing decisions!

The main question for a feature-based pricing approach is however, what features should actually be priced, for which ones are customers willing to pay for and how much?

Whilst demand and pricing can go up or down, just the increase in data points using feature-based pricing will allow for making much smarter pricing decisions moving forward. Think about the revenue management options calculating the price elasticity for each feature, which is the measure of the change in the quantity demanded in relation to the price[3].

We are at the forefront finding this out in the hospitality sector and results might be very different by hotel and travel segment. As a simple example, customers might be interested in getting a room with balcony. During a cold season, guests might not want to pay for the balcony but during a hot season they might do. Or even if cold, they might be prepared to pay for a room with balcony depending on the hotel and room location (ski resort, sunset, sunrise), purpose of their stay or if they are a heavy chain smoker. Big data and the opportunities of setting up a new inventory codification combined with a feature-based pricing approach will ultimately lead to much more scientific ways in pricing hotel room inventory. Just imagine the various correlations and insights gained knowing which room features are requested by season, travel purpose, people travelling, nationality and so forth.

Beyond the revenue management benefits of a feature-based pricing approach, new insights gained from consumer buying behavior will ultimately be of great value to hotel operators leading to better decisions for future hotel investments, target segmentations and also impacting promotional mix decisions. And for hotel guests it has the potential of creating unique experiences increasing guest satisfaction that ultimately has a correlation on the price points ranges a hotel is able to request for[4].

Systems with open API’s are needed!

In order to maximize those concepts, it is critical that Property Management Systems (PMS) or other new technology providers can be connected easily. Open API’s are a pre-requisite for the GauVendi approach to ensure a solid two-way interface, making the life of hoteliers easier and allow them to differentiate themselves from their competition.

The proprietary GauVendi Standard Interface is built with microservice architecture and API-first approach, enabling partners to easily integrate into the GauVendi ecosystem. For that reason, apaleo PMS is our preferred choice when selecting the first PMS for the pilot program. We leverage on apaleo’s comprehensive APIs such as Inventory V1 and Settings V1 to create an automated onboarding process for hotels, reducing manual data input and improve the hotel onboarding experience. Their two-way interface allows both systems to exchange information seamlessly, by using RatePlan V1 and Booking V1 endpoints, GauVendi can push the rates plan and complete the booking process with a room pre-allocation in real-time.

[1] Early drop outs often happen, when the offering of the lower priced room category does not meet the booker’s expectations but the components of the next higher category do not justify the higher price point in the eyes of the booker [2] To find out more about the GauVendi Retail System click on apaleo store here [3] Price Elasticity of Demand = % Change in Quantity Demanded / % Change in Price [4] See various articles on guest survey satisfaction suggest that positive customer responses allow for more flexibility in requesting higher price points

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