Share
Daniel McGarry, Regional Director of Revenue Management, Stonebridge, Rising Revenue Optimization Leader Council Member
At a Rising Revenue Optimization Leader Council meeting, I opened our discussion with a simple background. A signal is a meaningful, actionable insight while noise is distraction or a false flag. With so much political and economic uncertainty, even seasoned teams feel that separation is getting harder. So, we asked ourselves: What data do you trust? What data do you ignore? And how do you decide fast enough to matter?
Breakouts came back with a consistent baseline: start with your own house. Short-term pickup, on-the-books, and YOY internal comps earned the most trust. External context (like STR) still helps, especially when you diversify beyond a single comp set and compare notes with peers, but the group stressed reading it through your market reality. Several leaders cautioned against treating any one dataset as gospel; numbers carry bias if we don’t interrogate why a result looks the way it does.
One comment that stuck with everyone: “Noise changes based on what you intend to use it for.” The same datapoint can be a signal through one lens and noise through another. That idea threaded through the rest of the hour.
On tools, the mood was pragmatic. It’s not just “too many tools” versus “too few interpreters,” it’s whether we’re deriving value from what we already own. Some teams are streamlining tech stacks; others are investing in training so people can interpret outputs, not just surface them. There was curiosity about where AI is headed. If an “analyst” layer can sift multiple sources and surface only what matters, speed becomes the differentiator. The group agreed that reacting to your own benchmarks first often wins over chasing competitors.
A clear theme on strategy was that without KPIs, you can’t tell signal from noise. If you don’t know what you’re optimizing for, nothing is actionable. That clarity also matters when “managing up.” Executives don’t always have time for the full story. They want the two or three things that matter now then habitual follow-up to close the loop once a hypothesis proves out (or doesn’t). Roll-ups can blur the picture and sometimes a portfolio trend looks soft while individual markets are healthy. Knowing when to break the data apart came up as a core leadership skill, as well as how to explain that succinctly.
We ended with a couple of candid admissions. Many of us have “noisy” reports we tolerate each budget cycle (CRM rollups for whole regions were a repeat offender), and more than one person admitted to over-analyzing now that dashboards are slick and infinite.
Further Reading
- Signals vs. Noise: How to Identify the Most Important Trends Before They Impact You
- Information Overload: The Importance of Tracking the Right Metrics
- Why Too Much Information Can Sink Revenue Management Efforts
Questions for Discussion
- What data do you always trust when making a decision?
- Have you ever made a bad call following the wrong data?
- What is your personal signal detector—instincts, trends, and tools?
- Does noise get louder when your strategy is unclear?
- FC vs. actuals: is the variance a signal or noise, and how do you define it?
- Does AI/automation help filter signals, or create more noise?