By Hank Marquis

Ace Your IT Satisfaction Scores!

Image Credit: pexels.com/@volkerthimm

Great IT satisfaction scores aren’t about charisma, charm, or clever messaging. They come from understanding how people actually experience their digital workplace. In Completely Satisfied, this means looking at what people perceive every day, what they expect from their tools, and where those two things fall outside their zone of tolerance. That process—done manually—takes time, coordination, and a surprising amount of analytical effort.

Before tools like Hailee existed, teams relied on interviews, surveys, spreadsheets, and long workshops to interpret experience data. Analysts had to map perceptions and expectations, calculate tolerance ranges, compare activity importance, and align all of it with benchmarks and workgroup realities. It's meaningful work, but it isn’t fast or simple—which is why organizations often bring in consultants for months at a time. Hailee automates that same method. The underlying principles don’t change; the time to get the insights does.

Reliability

Reliability reflects the gap between the dependable service people expect and what they actually perceive each day. Outages, inconsistent behavior, and unstable performance push perceptions below reasonable tolerance limits. In a manual assessment, teams would gather uptime data, code survey responses, and compare user perceptions with benchmark values to understand where reliability breaks down. Hailee does the same work automatically, using the workgroup’s perception and expectation distributions to explain where reliability falls short and how that gap affects their ability to get work done.

Assurance

Assurance is about trust—whether people believe the service is safe, accurate, and backed by competent support. Users form expectations about honesty, transparency, and security, and they compare every interaction to those expectations. Traditionally, uncovering assurance issues required detailed interviews, risk reviews, and alignment sessions to trace where confidence was eroding. The work is real and often slow. Hailee applies the same reasoning instantly, comparing perception and expectation patterns and using benchmark values to show where confidence diverges across a workgroup.

Tangibles

Tangibles cover the visible side of the service—interfaces, navigation paths, documentation, and the layout of information. People come in with expectations shaped by every other tool they use, and they immediately notice when a system feels harder than it should. Historically, analyzing tangibles meant usability testing, coding qualitative feedback, mapping friction points, and studying how the interface supports (or blocks) key job activities. It’s essential work, but no small task. Hailee interprets tangibles through experience data the same way: by identifying where perception falls below expectation and explaining how that misalignment affects high-importance activities within the workgroup.

Empathy

Empathy reflects whether people feel understood and considered in how services are planned, communicated, and supported. Users expect their input to matter, and they notice when changes happen without explanation or when feedback disappears into a void. To evaluate this manually, teams traditionally analyze feedback patterns, review engagement scores, reconstruct communication timelines, and align all of it with service decisions—a process that can take weeks. Hailee uses the same framework from Completely Satisfied, looking at how perception and expectation differ and identifying where users no longer feel heard or included in the process.

Responsiveness

Responsiveness reflects how quickly people and systems react when someone is trying to get work done. That includes support interactions, ticket handling, and the speed of the interface itself. Users expect reasonable response times, and when those expectations are not met, friction builds fast. A manual assessment requires collecting response-time data, coding support feedback, reviewing system performance, and linking it all to job-activity importance. It’s thorough, but slow work. Hailee automates the same logic—comparing perception and expectation patterns, factoring in benchmarks, and showing how waiting, delay, or lag impacts the workgroup’s day.

None of this is guesswork. It’s the structured method from Completely Satisfied—experience evidence, tolerance ranges, gap analysis, activity importance, and benchmark comparison. The only difference is speed: what used to take months of manual analysis now takes minutes.

Please comment or reach out and let me know what you think, I'd love to talk with you!

Best,
Hank

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