Revenue management is finally catching on in the hostel space, and hostels who don’t use it, are practically leaving money on the table. We all know by now that you have have to change your prices frequently and share data to really get conclusive results and that you have price strategically to grow your demand, keeping a pace of bookings coming in through multiple means of distribution. But that is all looking outward and it (the market and the channels) is old news. It is time to move your strategy into something new.
You should be now looking inwards, and seeing what insights your internal data can tell you. To best illustrate this, we’ll talk about how the successful hotels organize and execute their data, I mean revenue management, to boost the most tRev.
The focus for proper revenue management is on the total guest spend or tRev, not just their price for their bed night. This includes their food and beverage (F&B) and other ancillary revenue such as travel sales and services. For hotels its the bar, restaurant, housekeeping, entertainment (spa, golf, media,etc) and yes, you will need a system that tracks that data per guest account to get the most out of RM. The best of hotels to look at in this regard would be resorts, think beach or ski, where there are tons of options for the guests to indulge.
Each purchase a guest makes is a data record that becomes part of their folio. In that record you have many data fields that at least record the when and where. You can imaging each purchase as an item in a shopping basket. Looking at the total purchases of the guest is called and affinity or basket analysis (and for the techie). Basket Analysts is a popular data mining slash business intelligence term, and the focus here is to get more guests to add more products into their basket, which will mean more revenue for you. Combine those transaction records for each guest, you can make a statistical based tree graph showing what decisions happen before of after other decisions, commonly known as a decision tree. You can see what events and purchases lead to other purchases etc. Now we’re doing data modeling. If you add or remove a decision, how it would affect the purchases down the line. The larger resorts even go further, combining the trees of many properties and make a random forest, but that is super far.
Pretend you are a resort owner. Since it is winter, pretend you are a ski resort owner. Say you sell day-passes to the lift, warm beverages, meals and a sauna. You can make a tree and see if guests have a hot chocolate after 6PM they usually usually have a couple of drinks and go to bed, because they are exhausted. You can call that a bottleneck on your tree. Now imagine you offer a special on hot beverages at 3PM, and guests return early and not so exhausted. They have a coffee, then go into the sauna, and after have a meal and some drinks. That can lead to a major boost of the revenue per guests. This is where these major resorts and hotels are doing, looking deep into their operations and how they can be more welcoming to you, and your money.
What does this mean? I means you use data to back up your guests behavior. You are understanding your guests statistically. Sure many hostels don’t even obtain enough data sets to even begin to do some modeling, but you can still think and test small changes you can make that could influence your guests behavior and average spend. Even if you are a small hostel, you need to focus on what your guest is purchasing, when, why and how you can deliver that service to them. Larger hostels and hostel groups can pool their resources and do tests as well.
Now if have the bed-count, or better, headcount because private rooms matter too, and the right tech in place to record the data to play with, you might want to investigate into this further. . If you know, please make a comment. Many major hotels and resorts hire data scientists to do it directly. You can find a data scientist student (most likely a masters degree level) and offer them a part time position. Maybe a RM grad student will enjoy this as well, and bonus points if you anonymise your data and make the project open source on GitHub. You could get major support there. These aspiring data scientists need to know a basic stack and some data skills too. The easiest could be Python, Django, and R-studio, but there are many ways to do this. It’s not easy, but there is a reason data science is one of the fastest growing technical fields, and it is about time it founds its way into hostels and if you do make it open source, send us a link!
If you read this, kind of understood it, but it was still a little over your head, congratulations, you were learning about algorithms and data science. Just remember revenue management is more than numbers, it is more than your bed rate. It is all about your tRev, your total revenue and data science can really help you learn more about your customers.