Tell me who you were, and I’ll tell you who you are (or almost).
Let’s explore what historical data is, where to start to create it, and, most importantly, how to interpret it to establish base rates. We also need to be clear on the function of historical data. Its primary purpose is to set the starting rates for the following year. Thus, it’s essential to understand ADR (Average Daily Rate) and the related RevPAR at which rooms were sold during analyzed periods to identify occupancy levels for those specific rates and dates. It’s also crucial to assess the impact of Event Dates, the demand increase they generated, and corresponding rates. Additionally, identifying errors like Spillage or Spoilage allows us to analyze causes and adjust policies for the coming year.
Of course, we must remember that other variables—those so-called Distorting Variables—can influence our pricing and are not easy to predict.
Analyzing Basic Historical Data
Let’s start with a simple scenario where only limited daily data is available to develop an effective Revenue Management policy. Essential data includes:
- Segmentation by days of the week
- Total occupied rooms (all types)
- BB production (only the Bed & Breakfast portion)
These basic data points might seem easy to obtain, but it can be surprisingly challenging to acquire them.
Remember that the average Italian hotel is small, family-run, with limited technology and digitalization. This is a major issue for Italian tourism.
When a hotel has an attached restaurant, the available data often becomes “messy” because HB or FB bookings might be recorded as single entries in the management system. Separating these components afterward is complex and time-consuming, if possible.
With such messy data, it becomes impossible to correctly identify even the average selling rate. Therefore, the production data should relate solely to the BB portion (including VAT), and only afterward can assumptions be made for HB or FB rates.
Understanding the number of rooms sold and at what rate helps forecast next year’s sales and adjust rates accordingly, thanks to the Forecast tool.
Building a Simple Historical Data Table
To create historical data, set up a table and include the key data. In the rows, list each day of the week for the entire year, highlighting weekends. For the columns, follow this list:
- Column for day of the week
- Column for day of the month
- Column for total room occupancy (Occup.)
- Column for unsold rooms (Unsold)
- A column with a formula for calculating occupancy percentage (Occup. %)
- Column for BB production (Prod.)
- Column with a formula for calculating ADR (average across all room types)
- Column with a formula for calculating RevPAR (average across all room types)
This is a “homemade” setup using simple Excel sheets with basic, easy-to-use data. Of course, you can improve and enhance it significantly with available technology.
Imagine Revenue Management as a large puzzle with tiny, unique pieces. When complete, it’s extremely resilient, able to withstand shaking like a sheet in the wind without falling apart. But if even one piece is misplaced, the entire puzzle becomes weak and unstable.
Vito D’Amico, CEO MyForecast RMS