THE FUTURE OF PRICING IS AUTOMATION

Not only will automation produce these thousands of pricing decisions at the speed of business, but it will also free up the revenue manager for more strategic activities. Yet, according to Skift in a 2019 study, only about 17 percent of hotels use any kind of automated system for daily pricing decisions. Industry and the technology share blame for the lack of penetration of automated revenue management solutions. Industry has been fearful of turning crucial revenue decisions over to a system that they don’t fully understand, or at times, have suffered from inertia, unwilling to make any changes to the status quo. On the tech side, systems were originally designed for the full-service hotel, and therefore, weren’t always a great fit for other industry sectors. Fortunately, there has been a great deal of innovation in systems over the last five to 10 years. Tech has made great strides in broadening the scope of solutions, as well as making them easier to install and use. This means that solutions are now available to more hospitality sectors. For the remainder of this article, we’ll describe three emerging pricing techniques that have broadened the reach and extended the value of revenue management solutions. We will also describe who can take advantage of these techniques and how to prepare the organization to implement them. These techniques are mathematically complex and data intensive, and therefore, are only available through automation

PRICE OPTIMIZATION

The first emerging technique is price optimization. While this term has been overused, we refer specifically to calculating price sensitivity of demand and using that to produce the optimal price, down to the room-type level. It is also possible to incorporate the impact competitor price effects on your demand. Price optimization has two advantages. First, it overcomes the weakness of traditional displacement-based methodologies, where you determine who to say yes to by understanding what revenue you will give up if you accept a particular booking. Displacement works best at high occupancy, where you would discriminate among bookings and when demand comes from many segments with different “values” (transient, wholesale, contract, group, etc.). Price optimization is effective at any level of occupancy, and with limited segments. Price optimization, therefore, provides lift for full-service hotels, and as well made it possible for economy and limited service hotels to automate pricing. Price optimization is used for the “public” price, so any hotel that has a significant amount of transient demand and operates in a competitive environment can use it. Even better news for hotels, price optimization is available in many revenue management systems, and is relatively easy to implement in current selling systems. It can be deployed in a traditional BAR spectrum environment, but many hotels will see benefit from removing the constraints of a pre-defined spectrum to allow the system to recommend any price within marketing constraints (open pricing or continuous BAR pricing). The primary challenge for hotels will be change management. In manual environments, revenue managers manage price directly, review the impact on demand, and then recalculate price. With price optimization, the system manages the price, and the revenue manager becomes responsible for ensuring the system has the correct picture of demand. This can be a hard adjustment for the revenue managers to make. Yet, if the revenue manager continually overrides price, rather than managing demand, the system performance will degrade over time.

PROFIT OPTIMIZATION

Profit optimization is another term that has been overused. We refer here to the concept of pricing considering total expected guest spend, not just rooms revenue. Properties should encourage bookings from customers who will spend on more than just the room. This then has both top and bottom line impacts on the business. Of course, when you are going to consider multiple revenue streams, you need to consider the individual margins of each, as these can vary greatly. Profit optimization is a crucial functionality for casino resorts, who incentivize activity on the casino floor from their gaming segments by offering discounted or free rooms. If the system only considers hotel revenue, when demand is high, it will “yield out” these discounted and free rates, at a loss of casino revenue, a huge impact to the property bottom line.

Profit optimization is best suited for properties that have significant non-rooms revenue. It would be natural to think that profit optimization could also help in managing distribution costs, however, this is not necessarily the case. With rate parity and last room availability agreements, systems are constrained in their ability to price discriminate by channel, negating the value of including channel costs. Channel management strategy is best handled outside or alongside the revenue management system, using different techniques, such as marketing optimization, which would help to optimize marketing spend based on channel production against channel costs. Moving to profit optimization requires being able to tie nonrooms revenue spend and margin to segments, new data which can be extremely challenging to collect. Plan on a significant data effort. Further, all testing so far shows that profit optimization, logically, tends to recommend sacrificing some ADR in favor of volume/ occupancy. This could negatively impact RevPAR while improving bottom line profits. The entire organization, and external stakeholders, need to be aligned around this change. Finally, profit optimization across all segments, considering known and unknown guests, is not fully available yet. Changes need to be made both in revenue management and selling systems to accommodate full profit optimization. However, this capability is under rapid innovation, and should be available very soon.

ATTRIBUTE-BASED SELLING

Attribute-based selling refers to the notion that rather than selling rooms as a combination of room type and rate plan, we break rooms and rates into their component parts, and allow guests to create a customized package at time of booking. The hospitality industry has become quite excited about the potential offering guests more choice and control, while driving incremental revenue through pricing according to customer value at the attribute level (attribute-based pricing). More complex hotels with many, clearly differentiated, room type attributes (bed type, view, balcony, room size), and rate options (advanced purchase, bed and breakfast) will be able to take this approach. Properties without sufficient variety of valued attributes won’t be able to make the effort of pivoting to this approach worthwhile. Attribute-based selling requires significant changes to every system that touches a booking. It could also have a negative impact on consumer perceptions if implemented incorrectly. Based on reactions to the airlines’ unbundling initiatives, there is a risk perception of price gouging. Adding too many attributes without  clear distinctions could potentially add friction to the booking process. Therefore, the strategy of attribute selection, and the operationalization of this during the selling process will be critical. It is also worthwhile to note that the analytics required to do customer-value based, dynamic attribute-based pricing are very new to hospitality, so it will take some time and testing to develop best practices. Regardless of whether a hotel uses these new pricing techniques or not, it is clear that in order for revenue management to continue to deliver value, it is crucial to automate routine decisions, leaving revenue managers free to focus on strategy. The automation path is not easy. In addition to the technology investment, it requires a clear strategy, organizational alignment, and people and process changes. However, with the proper investment, the benefits will far outweigh the effort.

This article is taken from a white paper and presentation produced for HSMAI Europe for their ROC event on January 28, 2020.