Company
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Survey
How the Seattle Mariners Transformed Fan Feedback into Structured Intelligence
About the Company
Each season, the Seattle Mariners host 81 regular season home games, welcoming 31,000+ fans per night and collecting massive amounts of fan feedback through post-game satisfaction surveys.
Over the course of a season, that results in hundreds of thousands of structured ratings and open-ended responses describing every aspect of the ballpark experience — from concessions and crowd flow to in-game entertainment and overall satisfaction. The volume of feedback was never the issue. The challenge was translating that scale of fan language into clear, actionable intelligence that could explain performance shifts from homestand to homestand.
Recognizing that the real insight lived inside open-ended responses, the Mariners partnered with Dimension Labs to transform unstructured fan comments into structured, queryable data. With the platform in place, they made a strategic shift in their survey program — leaning more heavily into open-ended feedback and capturing richer qualitative input, confident they now had the infrastructure to analyze it consistently at scale. What began as a reporting challenge evolved into a broader fan intelligence strategy, redefining how the organization collects, structures, and acts on feedback across an entire season.
Before Dimension Labs
“We have all these scores and numerics… we really don’t understand why they go up and down.” — Chris Kennedy, SVP of Business Strategy & Analytics
Prior to Dimension Labs, the Mariners relied on a combination of manual review and basic modeling to analyze open-text survey responses. The team could read comments and observe when satisfaction scores moved from game to game or homestand to homestand. But understanding why those shifts occurred was far less clear.
Key limitations included:
Manual labeling that required significant analyst time across the season
Inconsistent categorization of themes
No scalable way to quantify which issues were widespread versus isolated
Difficulty distinguishing baseline complaints from true performance drivers
Internal debate driven by anecdotes rather than evidence
Even transitioning from analyst labeling to model-assisted labeling still lacked the nuance needed to analyze complex comments that included both praise and friction in the same response. The insight existed — but it was expensive, inconsistent, and difficult to scale across all 81 home games.
With Dimension Labs
The Mariners transformed open-ended fan feedback from a manual reporting exercise into a structured intelligence system operating across all 81 home games.
Where analysis was once manual and inconsistent, the team now automatically processes hundreds of thousands of open-text responses using a consistent, multi-dimensional framework. Each comment is structured across category, issue and success drivers, specific experience aspects, and predicted satisfaction impact — turning fan language into measurable signals tied directly to performance.
The shift directly addressed the limitations they faced before:
100% of open-text responses are analyzed consistently at scale across the full season.
Categories, issue drivers, and experience aspects are structured using predefined and dynamically generated labels.
Statistically significant movement between games and homestands can be isolated with confidence.
High-volume complaints are separated from drivers that materially impact satisfaction.
Fan language is directly connected to movements in overall satisfaction and entertainment scores.
Implementing Dimension Labs also enabled a strategic evolution in the Mariners’ survey program. Confident that open-ended feedback could now be structured and quantified, the organization leaned further into qualitative questions to capture richer insight. Open-text responses are no longer supplemental to structured ratings — they are the central context to understanding fan feedback.
What was once reactive reporting has become a scalable intelligence layer that informs operational and strategic decisions throughout the season.
Use Case 01
Explaining and Validating KPI Movement
“Dimension Labs unlocks the ability to maximize the potential usage of a survey.” — Kobe Sarausad, Analyst of Revenue Insights
The Mariners track structured KPIs such as overall satisfaction and in-game entertainment score. Previously, score movement was visible but not clearly attributable.
Now, every open-ended comment is analyzed across multiple dimensions, including:
Category (e.g., concessions, venue satisfaction, game presentation)
Issue reason and success reason
Specific aspects such as food quality, drink variety, noise levels, and exit flow
Predicted satisfaction impact
This enables the team to connect unstructured fan language directly to KPI changes.
With Dimension Labs, they can now:
Quantify which categories correlate with score movement
Identify the top drivers per homestand
Filter out minor fluctuations using statistical significance testing
Validate predicted satisfaction against actual ratings
Across a full season of 81 home games, this means:
100% of open-text responses analyzed consistently
Clear attribution of score movement
Reduced internal speculation
Faster post-game reporting
Rather than debating anecdotes, the team can state with confidence which 2–3 experience variables influenced performance shifts.
Use Case 02
Separating Noise from True Impact
Not all high-volume feedback drives measurable outcomes. As Chris noted, “They’re the loudest people, but it’s not the most impactful thing.”
Dimension Labs enables the Mariners to distinguish between:
Recurring baseline complaints
Minor statistical variation
True anomalies that cross meaningful thresholds
Through significance testing and trend analysis, the team avoids reacting to every 3–5% fluctuation. Instead, they focus on shifts that materially influence satisfaction and experience metrics.
Across an 81-game season, this precision prevents operational churn and ensures resources are directed toward issues that genuinely move fan perception. The result is smarter prioritization, better resource allocation, and clearer ownership.
Use Case 03
Aligning Departments Around Structured Insight
“Ultimately, it allows us to quantify the qualitative and give context and reasoning to KPI movements.” — Seattle Mariners
Fan experience spans multiple internal teams:
Ballpark operations
Concessions and food & beverage
Marketing and communications
Game presentation
Executive leadership
Each group requires concise, outcome-focused insight rather than raw comment feeds.
Dimension Labs enables the Mariners to deliver structured summaries aligned to departmental KPIs. For example:
Concessions leaders can quantify how food quality or drink variety impacts satisfaction.
Marketing teams can isolate drivers influencing entertainment scores.
Operations teams can identify crowd flow or exit friction affecting experience.
Because the platform analyzes open-ended responses across predefined aspects, stakeholders receive targeted intelligence without requiring additional survey questions. This has strengthened cross-functional alignment and reduced internal debate over what matters most.
Conclusion
Conclusion
For the Seattle Mariners, fan feedback is no longer a reporting artifact but an operating system for experience strategy. By restructuring their survey program to capture richer open-ended insight and deploying Dimension Labs to structure that language at scale, the organization transformed hundreds of thousands of fan comments into a continuous signal layer. What once surfaced as disconnected scores and anecdotes is now a measurable understanding of what drives satisfaction, fan engagement, and return intent.
At an executive level, this shift changes the conversation from “How did we perform?” to “What is shaping the fan experience, and where should we invest?” The Mariners have moved from reactive reporting to proactive experience management, aligning operations, marketing, and leadership around evidence rather than opinion. In a league where fan loyalty and in-venue differentiation matter, structured intelligence from unstructured feedback becomes not just a tool, but a strategic data advantage.
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