At first, it was subtle. A slow trickle of mid-range reviews. A few vague comments about “a stuffy room” or “didn’t sleep well.” But over time, a national hotel chain noticed a pattern: guest satisfaction scores were falling. The fall wasn’t dramatic but just enough to raise red flags.
There wasn’t one glaring issue. Instead, it felt like something was off, especially with guests staying on the top floor. Leadership wanted clarity, not guesswork. So, they turned to software for hospitality industry challenges—using Minitab Solution Center to investigate the issue with data.
Step 1: Define What “Satisfaction” Really Means
The operations team began by asking a deceptively simple question: What does “guest satisfaction” actually mean?
Using the CTQ Tree tool in the Brainstorm utility in Minitab Solution Center, they mapped out the core drivers of a great hotel experience. The top-level need was obvious—“Outstanding Stay”—but the next layer took more thought. As they brainstormed, they began to sort guest needs into four core categories: Comfort, Cleanliness, Service Speed, and Amenities:
Each of those categories branched further. For "Comfort," they initially listed items like “quiet rooms” and “comfortable beds.” But they weren’t done yet.
The team clicked Quick Generate, a feature powered by Minitab AI that suggests additional contributors based on patterns from thousands of similar CTQ Trees across industries:
That’s when something new popped up under the “Comfort” branch: “Comfortable room temperature.”
It hadn’t been mentioned in the initial brainstorm, but it immediately resonated.
Front desk staff recalled fielding frequent, informal complaints about warm rooms, especially on the fifth floor. No one had ever logged these as formal service issues, but the moment the AI flagged it, the team knew it was worth exploring.
They now had a measurable hypothesis and a specific data trail to follow.
Learn more about Minitab Brainstorm in our recent blog post.
Step 2: Visualizing the Pattern
The team imported six months of operational and survey data into Minitab Statistical Software, a trusted software for hospitality industry teams looking to turn guest feedback into actionable insights. Using Graph Builder, they created a scatter plot comparing room temperature and guest satisfaction.
The association was hard to miss. As temperatures rose, satisfaction dropped. The effect was especially visible in stays where room temperature climbed above 77°F.
This provided visual proof for an anecdotal observation.
Step 3: Prove it with Regression
To quantify the impact, the team ran a multiple linear regression analysis in Minitab, using Temperature and Floor as predictors of Satisfaction:
Minitab’s built-in AI generated a plain-language summary of the findings. It demonstrated:
Significant Predictors: Both Temperature (p-value = 0.000) and Floor (p-value = 0.000) were statistically significant predictors of Satisfaction, indicating that increases in either variable were associated with declines in guest satisfaction.
Regression Equation: Satisfaction = 12.062 – 0.04605 × Temperature – 0.1996 × Floor
This means each degree increase in room temperature reduced satisfaction by about 0.05 points on average, and each floor higher contributed to an additional 0.2-point decrease.
Model Fit: The model’s R-squared was 13.24%, suggesting that while temperature and floor were important, other factors also influence satisfaction.
Overall Significance: The regression model was statistically significant overall (F-value = 30.29, p-value = 0.000), confirming that these predictors together meaningfully explain variation in satisfaction scores.
This wasn’t just a handful of picky guests; it was a statistically validated, environment-driven drop in experience.
See how Minitab AI can enhance your statistical analysis—even if your team doesn’t have an expert statistician. 
Step 4: Make the Fix
With clarity in hand, the hotel moved quickly. Engineering adjusted airflow to the top floor and replaced dampers that were underperforming during high outdoor heat.
Managers now review CTQ metrics monthly, using live Graph Builder visuals to detect early warning signs before they escalate.
Results That Hold Up
In the eight weeks following the change:
- A full percentage point improvement in overall satisfaction
- Comfort improved, negative temperature reviews dropped by more than 70%
- Hotel's overall rating improved with fewer three-star reviews
Summer travel plans don’t always go as expected, and neither do guest experiences. But just like a smart traveler uses maps, apps, forecasts, and reviews to plan the smoothest trip, hospitality teams can rely on data to guide smarter decisions. This hotel used data not just to course-correct, but to arrive at a better destination: higher guest satisfaction, stronger reviews, and a more comfortable stay for all.
