Over the next few months, we will be rolling out Wheelhouse Pro — our most robust and professional pricing platform to date.
Wheelhouse Pro is designed for ambitious operators with growing portfolios, prioritizing deep data insights and better portfolio tools, all while delivering even stronger revenue performance.
Additionally, Wheelhouse Pro will further our own goals of providing increased transparency to our customers and community.
We believe transparency is the key to empowering our users to make informed business decisions, and it is high time our space moved from ‘making recommendations’ to ‘providing explanations’.
Therefore, in the following pages, we are going to detail exactly how our dynamic pricing engine works — how it leverages data, unique statistical approaches, and (yep!) machine learning to calculate accurate prices for your unique properties.
Over the course of this write-up, we will leverage visuals, text and occasionally equations (😳) to articulate the models, quality checks, filters and data underpinning Wheelhouse’s revenue management platform, that today increases revenue by an average of 22%, per unit.
At worst, should our write-up prove to be a bit too dry or detailed, we hope detailing the model will illustrate our deep commitment to your success, and to the transparency we believe our space so desperately deserves.
At best, we hope detailing our model will give you new insights that you can leverage to drive your business’s success.
Pricing Engine: Components
The image at the top of the page illustrates the essential components of the Wheelhouse pricing engine. The three most foundational models included in the illustration above are:
The Base Price Model
The Predictive Demand Model
The Reactive Demand Model
As you explore this write-up, you will learn how these high-level components either include, or are amplified by, multiple additional models and statistical approaches, including:
Impact of Prior Bookings
Price Response Function
Last Minute Discounting
While complex, we believe that the 22% increase in revenue these models have driven for our users has made our efforts worthwhile!
Additionally, in this write-up, we have decided to break out two important models to explore them in greater detail:
Competitive Set Model
Dynamic Pacing Model
Each day, our platform ingests, cleans, and processes ~11 million new data points for every unit, in order to inform a continuously relevant and accurate set of recommendations. All of the models above play a role in that process!
Now, let us step through these models, starting with our pricing engine’s foundation, the Base Price Model.