Smart ways data analytics facilitate hotel revenue management
Hotel revenue management strategy is closely linked to the optimization of room availability, pricing tactics, understanding customer behavior, and other strategically directed activities. The goal of this strategy is to maximize revenue growth through excellent CX, by providing the perfect room, on the perfect cost, to the perfect client, at the perfect time. Basically, this is the so-called “Right order” in the warehousing industry but adapted to the hospitality sector.
Nonetheless, hotels cannot afford to “store their rooms in inventory until they are ordered.” What is more, in the long run, either used or unused, every room is being amortized and depreciated in the financial statements. In other words, with each passing day, the hotel assets have less financial worth, and this is the reason why the business entities have to improve the management of their resources and ensure maximized revenue retention of each hotel room for each night.
This task may sound extremely complex and difficult, but in fact, using a high-quality data integration strategy facilitates the process. Excellent predictive analytics motivate the achievement of successful revenue management, as they enable us to make data-based, evidence backed-up decisions.
Now, let’s see how data integration gives a hand to the hospitality organizations on this matter.
Understand the market
Data analytics make it easy to identify, differentiate, and recognize each separate client group. In addition, they give a clear understanding of the market segments by providing a 360-degree customer view.
Besides, those analytics help hospitality companies to discover the interests, likes, needs, expectations, and wants of their guests, as this knowledge is a prerequisite for providing tailored CX, which to drive the business entity to customer excellence.
And we know that happy customers always result in a great reputation and a sustainable revenue stream.
What's more, data integration helps organizations to identify any risks connected with each customer group in terms of the macro-industrial environment, competitors, micro-environment, etc. This enables businesses to transform risks into evaluated opportunities and take full advantage of them for the purpose of maximizing their revenue.
Provide the best experience possible
After we identify the characteristics of the hotel guests, we need to know more about their buying behavior to be able to treat them accordingly from the start to the finish of the buyer’s journey. In that sense, we need to refer to a few questions:
When do they book?
Knowing when the customers plan their journey and book their stay gives us important insights on how to plan our demand in the long-run and which customers to focus on at a specific time frame. And predictive data analytics provide us with information about demand trends and tendencies based on past events. What is more, they help us to identify when to send promotional content, make discounts, and publish advertising, in order to engage with as many clients as possible.
According to a study conducted by SiteMinder, every hospitality company can recognize two types of customers in terms of buying time: Lower-yield, and higher-yield guests.
- Lower-yield clients book their stay very early, about 6 months prior to the travel date, yet they travel throughout the whole year.
- Higher-yield clients book their stay in the “last minute,” often 5 days prior to the journey. They also tend to travel only in high-season time-frame.
How do they book?
Data integration provides us with valuable insights about the methods that the clients use to book their stay: if it is from third party agencies, company’s website, social media platforms, community websites, etc. This gives us the knowledge on what promotional channels to use in order to target our customers, communicate with them, send them offerings, discounts, engaging content, and interest-relates news.
What do they book?
To plan our resources properly, we need to know the specific types of hotel facilities that our customer segments usually book: rooms, apartments, suites, etc. Advanced analytics give us answers to this question and help us plan in advance the room resources available, taking into account predicted demand for specific requirements, such as sea view, ground floor location, balcony, kitchen, etc.
This planning enables hospitality businesses to make data-based decisions and optimize the revenue stream per room.
What is the length of their stay?
The length of the stay is a key point to take into account, as it allows us to keep the rooms occupied and ensure sustainable revenue flow. Data analytics directly give us the average number of days that each client segment stays; what is more, it gives us enough knowledge to prevent the rooms from generating negative revenue.
What is the revenue per guest?
The data integration process helps us to identify which clients contribute most to our revenue stream. This way, the company knows which guests to strengthen its relationship with and which customer groups are a second priority.
All those data-based answers help the organizations to efficiently identify and plan demand, optimize resources, and reallocate assets.
Predictive analytics help hospitality businesses to maintain a dynamic pricing strategy without bearing the risk of losing control over its revenue inflow and expenses outflow. Data integration provides us with valuable insights that facilitate the data-based decision-making process. Those motivate us to make evaluated changes in pricing on seasonal, monthly, or daily basis, or according to the changing room availability.
What is more, data shows the internal and external factors that have an influence on the pricing trends and makes us understand detailed market dynamics to increase revenue and cut down costs.
To achieve successful revenue management, the different departments in the business entity should work together and be consistent in their short and long-term decisions and planning.
High-quality data integration and successful data governance policy facilitate cross-departmental collaboration by making all data trustworthy and available to all organizational working units. This helps the employees to understand what the other departments are doing, align their own strategy to the corporate objectives and forego any duplicated tasks. Because consistent strategy is an effective strategy.
According to Revxpert, adopting an effective revenue management system results in the following benefits:
- Focus on specific target groups
- Efficiently allocating tasks to the various departments
- Drive down costs
- Innovate in terms of products, services, and offerings
- Keep relevant pricing
- Improve demand forecast
- Gain market awareness
- Teach employees how to manage revenue flow
- Gain a competitive advantage
- Reduce time and costs for daily routine tasks
And taking advantage of those benefits contributes to long-term sustainable growth, as well as to financial and operational success.