“Study the past to define the future.” — Confucius
This post is to shed some light on how to get the most value from a hotel revenue pickup report.
Be Diligent
The first step that needs to be mentioned is how important it is to be diligent in when you gather the information. The pickup report is meant to analyze previous data by comparing apples to apples. If you skip a few days, you will have gaps in your data that is not beneficial to you. It may seem like a pain to build the habit of entering the data on a daily basis, but the payoff will be immense.
After you go through the process of collecting the rooms and revenue on the books, you will open doors to what kind of information you can extract from it.
Setup Your Rules
Looking at all the numbers can be confusing and dizzying. The pickup sheet has some of the rows highlighted to make it a bit easier, but for analyzing the numbers you will want to setup conditional highlights.
Conditional highlights are a way of creating rules for each cell and then highlighting the ones that match the rule. The rules are going to be more specific to your property (i.e. the rules created for a 32 room hotel will be different than a 100 room hotel), which are why they aren’t built into the pickup report already. The rules can be created for any of the sheets, depending on what information would be valuable to you.
Some examples:
- highlighting occupancy in the data set (red for sold-out, yellow for 80-95% full, green for 65-80%.)
- highlighting revenue in the data set being over set intervals
- highlight revenue based on the ADR (conditional highlights can include formulas that involve other cells)
- highlight rooms picked up in the monthly sheets
- highlight revenue or ADR in the monthly sheets
The conditional highlights let you see patterns more clearly than by looking at the plain numbers. By having some leeway with the numbers, patterns will be more visible.
Pattern Recognition
Once your conditional highlights are setup, you can easily zoom in on the data to spot trends. For the sample data above, I created a separate grid to put more emphasis on the weekends as that is what I am more generally interested in. Looking across the 6-8 lines in the spreadsheet above, i can notice a trend of a general lead-time for most reservations of 4-5 days, plus more activity the day before the arrival date. This allows me to instruct the Front Desk agents to not offer discounts 4 days in advance, potentially discount 2-3 days out, and then remain firm with the rates with last minute reservations.
I can also go back and see that the bulk of the reservations are likely one night only given the number of gaps in the highlighted cells (when looking down the column.) When I look down the column for the 4th, I can see that I had a string of days of 4, 1, 2, 2, 3, and 0 reservations. That tells me that I had at least 3 single night reservations the first night, and 1 on the fifth night. Depending on the overall occupancy levels, incentives could be created to encourage people to stay additional nights.
Another thing I could pick up on is very few people are booking both Friday and Saturday nights. When looking down the columns, I can see more reservations being made for Friday than Saturday, and vice versa. If that trend continued (which it does in the first image), a weekend package could be created to help encourage couples to have getaways to help fill up the hotel.
While the pickup report can give you a lot of information in these regards, you still need to compare it to your actual stay information to make the best decision. For example, with one hotel I work with, they host international flight crews on the weekends that fill up the hotel. That makes it impossible to offer weekend getaways because no one will be able to book it. That kind of information isn’t going to show up in the pickup reports.
Planning For Next Year
Analyzing the data from the current year is useful to notice short-term trends (i.e. lead time for reservations), but it can also be very instructive for longterm planning. By using the monthly pickup sheets (again, being diligent about entering the data every month), new trends will emerge. The trends are much easier to pick up on so I haven’t highlighted the cells this time.
In the monthly pickup sheet, the blue columns are the difference between that month and the data the month prior, giving you the pickup rooms and revenue for reservations made in that month. Looking across the May-July lines, you can see that in February they didn’t have much activity, lots of activity in March and April, and then relatively little in May. That would allow me to be more firm in pricing for March and April when taking reservations for the summer months. The closer I was to the month, I may have to lower rates or come up with other promotions to encourage people to book and fill the hotel up.
Again, it will be important to compare to your actual stay information and be aware of any large group blocks being entered. Maybe a large convention booked for June in March, but it only occurs once a year, or maybe March is when the international flight crew books their rooms for the summer months.
There are a lot of variables in play, but the monthly pickup sheet helps highlight those potential areas that requires further investigation.
After filling out your pickup report on a regular basis, you will have a lot of information that will help guide your hotel operation. All of the information you extract from this report should be shared with other team members, so that collectively you can shape a revenue management strategy that aligns with the hotel’s needs and works well with the marketing and sales plans. Your competition is unlikely to be using a report like this, so use it to your advantage and watch your revenues grow.
If you haven’t already, you can grab a copy of the pickup report below, and if you want to learn more about reading forecast and pickup reports, check out the book below. It’s a fantastic resource for revenue managers that are new to the profession, or general managers that want to learn more.