Dear WuBookers, amongst the English terms that populate the world of hotel revenue management is Same Time Last Year: a metric that compares a property’s performance against the same period in the previous year. This is an important assessment for understanding whether the sales strategy is working or whether there is room for improvement. Here’s how it works and what it’s used for.

Same Time Last Year: a definition

‘Same Time Last Year’ (STLY) refers to the comparison between reservations received up to a certain date in the current year for a future date, and the same period in the previous year. In other words, it is used to compare the year-on-year reservation trend for the same time period.

For example, it is 4 May 2026 and a hotel might wish to compare the reservations received for July 2026 with those received on the same date in May 2025 for July 2025. By doing so, it automatically excludes all sales made after 4 May 2025 that would ‘skew’ the data, as they do not yet feature in the figures for 2026. The STLY analysis therefore does not show the actual value of all sales achieved over a given period, but rather the trend in reservations over a specific timeframe. This allows us to check whether, compared to the forecast and past performance, we are on track or need to adjust our offering in some way, for example, by setting discounted rates that could boost sales.

The DOW and SPIT approaches

The hotelier might also decide to assess the trend for a specific day of the week, rather than a specific calendar date, such as the first Monday in May, the date of which can vary from year to year (in 2026 it fell on 4 May, but in 2025 it was 5 May). This approach is known as DOW (Day of Week) and is very useful for properties with a reservation pattern that varies between weekdays and weekends, perhaps dictated by different customer segments, such as business and leisure travellers.

A further method of analysis concerns the time gap relative to an event, such as Easter, New Year’s Eve, Christmas and so on. The so-called SPIT (Same Period in Time) method examines a specific period prior to the event: typically 30, 60 or 90 days beforehand, regardless of the calendar date or the day of the week on which the event falls. This ensures a linear comparison and allows for objective assessments.

Differences between STLY, pickup, booking pace and other metrics

As we mentioned, there are many technical terms, and they may sometimes refer to very similar concepts, so it is easy to get confused. To prevent this from happening, let’s clarify a few things:

• OTB index (On The Books), where ‘books’ refers to the reservation register. This metric provides a snapshot of what is confirmed today for future dates, that is, the rooms already booked for a specific period, as of today;
• pickup: this is the net change in reservations for a future date over a defined time period (for example, the last 7, 14 or 30 days). It answers the question: “How many more (or fewer) rooms do I have compared to the last time I checked?”. It therefore includes new reservations, cancellations, amendments and changes;
• booking pace: this is the speed at which you receive reservations for a future date, compared with a historical benchmark (usually the same point in time in the previous year). The underlying question is: “Am I selling faster or slower than expected/compared to the past?”;
• booking window or booking window: the time interval between the reservation and the first day of the actual stay.

So are STLY and booking pace the same thing? Not exactly, but they are closely related. Booking pace is the general concept, the ‘pace’ at which reservations accumulate compared to any other period. Same Time Last Year (STLY) is the most common method (the specific benchmark) for measuring that pace, where the point of comparison is the same period last year. In practice, when you say “we’re on a par with last year”, you’re making an STLY comparison. But, as we’ve seen, the comparison isn’t always just STLY. For example, you could also compare the sales rate against a historical average over several years or against a variable event, such as Easter, which falls on a different date each year, thus using the SPIT comparison.

Let’s look at an example

Let’s apply what we’ve seen to a practical case study, which will help us better understand the differences and the importance of the various parameters.

Let’s take a 100-room hotel, and put ourselves in the shoes of the hotelier who, today, 15 August 2026, is analyzing Saturday 13 February 2027 (a future date approximately six months away).

The OTB shows that 62 rooms have been sold for 13 February 2027. This is not a qualitative assessment (high or low), but a purely quantitative one.

If they wished to measure the pickup, they could do so, for example, by looking at the week just ended (8 August): the OTB for 13 February on that date was 55 rooms. Today, however, it stands at 62, so the pickup over 7 days is 7 rooms (62, 55). This figure indicates that, during the period under review (the last week), a certain number of additional rooms were sold, or not, for the same date (in this case, 7). If, in the following week, the pickup falls to 2 rooms, it means that demand is slowing down, even if the OTB continues to rise.

This brings us to the booking pace. Looking six months ahead of arrival, a Saturday in February usually averages 50 rooms sold, compared with today’s figure of 62. This is therefore 12 rooms ahead of the usual pace (+24 per cent), ahead of the previous year. This suggests that the hotelier can afford to raise the rate, as demand is coming in earlier than usual.

And finally, here we are at STLY: our hotelier checks the figures. The question is: on 15 August 2025, how many rooms had been sold for 13 February 2026 (the same day as the previous year, the same time before arrival)? Answer, for example: 48 rooms. The Same Time Last Year figure tells us that today he has 62, which is 14 more rooms (+29 per cent) compared with exactly the same point a year ago.

This figure differs from the final tally: if you were to look at how many rooms had been sold in total by 13 February 2026 (let’s say 88, including last-minute bookings from January), the comparison would be misleading because today, six months beforehand, it is obvious that the figure is below that final total. STLY, on the other hand, compares only the portion of reservations received by the same point in the booking window, making the comparison accurate and consistent.

To summarise our simulation:

• OTB (today): 62/100 rooms. The current status;
• Pickup (7 days): +7 rooms, picking up pace this week;
• Booking pace (vs historical average): +12 rooms (+24 per cent), ahead of the typical pace;
• STLY (vs 15 August 2025) +14 rooms (+29 per cent), much stronger than last year for the same booking window.

How does all this translate in practical terms? All four indicators point in the same direction: strong demand, ahead of schedule. The hotelier, seeing this picture, might decide to raise the rate for 13 February, because both the recent pickup and the historical comparison (booking pace and STLY) confirm that demand does not need to be stimulated with discounts.

If, on the other hand, the pickup had been weaker (for example, just +1 room this week) despite a positive STLY, the picture would have been less clear-cut. The hotelier might have noted that, historically, the trend is positive for that date, but recent demand has stalled and therefore needs to be monitored closely.

How to calculate STLY using the PMS by WuBook

To obtain the Same Time Last Year (STLY) figure, there is no need to carry out complex manual calculations, nor to use systems designed solely for revenue management. All you need is the data from the PMS by WuBook, the property management software for properties, which is collected and organised in a dedicated section. Within the Statistics section, you can view all the key performance indicators (KPIs), updated in real time, and carry out customised queries and comparisons.

For example, you can carry out specific searches for individual dates, periods or days of the week on a year-on-year basis, and easily compare the results to set the best pricing strategy. Furthermore, using the ‘Compare with other dates’ function, the software automatically displays actual and forecast data compared with predefined periods (1, 3 or 6 months prior and up to 5 years back, or 1 year ahead).

Reading the data is also convenient and straightforward: as well as being able to export it to spreadsheet files (CSV), you can view it on screen as bar or pie charts, giving you a clear overview at a glance.

All this, combined with a comprehensive, reliable and user-friendly system, greatly simplifies the work of hoteliers and industry professionals. The performance metrics enable users to make even more informed and targeted strategic decisions, thereby improving the property’s profitability.