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Understanding Discrepancies Between STLY Revenue and Booking Curve Data
Understanding Discrepancies Between STLY Revenue and Booking Curve Data
Ashley Dehertogh avatar
Written by Ashley Dehertogh
Updated over a week ago


In the dynamic world of hotel revenue management, accurately tracking and analyzing revenue pacing is crucial for strategic decision-making. Two key metrics in this analysis are Same Time Last Year (STLY) revenue and booking curve data. However, users may sometimes notice discrepancies between these two data sets. This article explores the reasons behind these discrepancies and offers guidance on how to interpret them effectively.

What are STLY Revenue and Booking Curve Data?

  • STLY Revenue: This measure reflects the revenue generated for a specific stay date last year, as of the corresponding date (or day of week) last year. It's a crucial metric for year-over-year performance comparison.

  • Booking Curve Data: This shows the accumulation of bookings (and associated revenue) for a future stay period, as they are made over time. It helps in understanding booking patterns and pacing.

Reasons for Discrepancies

1. Post-Stay Date Revenue Adjustments

  • Explanation: After a guest's stay, there might be updates to the revenue recorded, such as additional charges or adjustments. For a single stay date, the booking curve treats any revenue updated after the stay date as if it was booked on the stay date itself. However, when analyzing revenue for a whole stay month, this approach can lead to slight differences between the booking curve and STLY revenue metrics.

2. Aggregation over Periods

  • Explanation: When comparing revenue over a stay month or other extended stay periods, the way data is aggregated can cause discrepancies. STLY revenue for a stay month is a straightforward comparison to the same month last year, but the booking curve for a whole stay month aggregates daily bookings, which might include post-stay adjustments differently.

How to Interpret These Discrepancies

Understanding the nuances behind these discrepancies is key to effective analysis. Here are some tips:

  • Context Matters: Recognize that discrepancies are more likely when looking at aggregated data over longer periods, such as a stay month. Daily comparisons tend to be more straightforward.

  • Adjustment Awareness: Be aware that revenue adjustments after the stay date are a common reason for differences. When analyzing booking curves, consider how post-stay adjustments might impact the data you're reviewing.

  • Use Both Metrics: Instead of relying solely on one metric, use both STLY revenue and booking curve data in tandem to get a fuller picture of your revenue pacing and performance.

Examples for Clarity

Example 1: Single Stay Date Analysis

  • STLY Revenue for the stay date March 5, 2023, queried at EOD March 5, 2024: $199,200

  • Finalized Booking Curve Revenue for March 5, 2023: $200,000

  • Post-Stay Adjustments: Additional $800 recorded on March 6, 2023

In this case, the initial discrepancy is clarified once post-stay adjustments are considered, aligning both metrics.

Example 2: Whole Stay Month Analysis

  • STLY Revenue for the stay month March 2023, queried on March 6, 2024: Shows a cumulative $8,344,495.49 as of EOD March 5, 2023

  • Booking Curve Revenue for March 2023: Shows a cumulative $8,339,893.49 as of EOD March 5, 2023.

  • Post-Stay Adjustments recorded after March 5, 2023: -$4,602

The discrepancy here arises from how the booking curve aggregates data, as the -$4,602 recorded after March 5, 2023 is attributed to days on or before March 5, 2023 and therefore is included in the Booking Curve Revenue:


Discrepancies between STLY revenue and booking curve data can initially seem confusing, but understanding their root causes is key to making informed decisions. By considering the impact of post-stay adjustments and the aggregation of data over time, revenue managers can use these insights to refine their strategies and improve performance.

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