Research: What are our problems?

The existing dashboard provided access to valuable experience data, but understanding the overall situation required users to interpret multiple metrics and navigate between different views.

Through customer feedback, product insights, and analysis of the existing experience, we identified an opportunity to shift the dashboard from presenting data to guiding attention.

The challenge wasn't to show more information. It was to create a clearer hierarchy that helped users answer three questions:

1. How are we doing?
Understand the current state and how experience is evolving.

2. What needs my attention?
Surface meaningful changes, employee feedback, and areas for improvement.

3. Where should I investigate next?
Create direct paths from high-level signals into deeper analysis.

Overview first, analysis second.

The dashboard was redesigned around a simple principle: users shouldn't need to analyze everything to understand where they stand.

A consistent six-month overview brings together Happiness, trends, response volumes, and key organizational dimensions, giving users an immediate picture of performance.

When something requires attention, users can move directly into deeper analysis while keeping the same context and timeframe.

This last behavior is actually important: Dashboard drill-downs preserve the six-month window when moving into Experience, Identify, or Feedback. HappySignals also intentionally removed manual Dashboard filters to keep this overview focused and clear.

From overall scores to the reasons behind them.

Knowing whether an experience is good or bad isn't enough. Users need to understand what is contributing to it.

Experience Drivers group related employee responses into meaningful areas and use visual hierarchy to show which topics represent the largest share of selected experience indicators.

Users can expand each driver to reveal the individual indicators behind it and understand where experiences are positive or need improvement.

Design principle: Reveal complexity progressively.

HappySignals defines Experience Drivers as groups of related Experience Indicators. Width represents their relative importance based on response share, while expanded indicators show positive versus negative and neutral experiences. Benchmark differences can also reveal where an organization differs significantly from the global benchmark.

Using AI to surface what employees are really saying.

Quantitative data shows what is happening. Open-text feedback often reveals why.

But manually interpreting hundreds or thousands of comments makes recurring problems difficult to identify.

Focus Topics use AI to classify recent employee feedback into recurring themes and surface the three most prominent topics directly on the dashboard.

Users can understand what employees repeatedly ask IT to improve, then access the underlying comments when they need more context.

Design principle: Use AI to reduce complexity, not add another layer of it.

Technically, HappySignals' AI recognizes roughly 10–30 global themes for each Measurement Area, classifies the organization's previous six months of feedback against them, and surfaces the top three.

Looking beyond averages to reveal meaningful differences.

Overall experience scores can hide important differences between employees.

Employee Analytics adds context by connecting feedback with anonymous information about employees' roles, responsibilities, and ways of working.

This allows users to identify which employee groups are happier, which are struggling, and where improvements could be targeted instead of applying the same solution to everyone.

Design principle: Turn aggregate data into relevant human context.

The feature links employee attributes to both current and historical feedback while preserving anonymity, allowing organizations to compare groups based on work patterns, roles and responsibilities.

From more data to better decisions.Outcome and learnings

The renewed Dashboard created a clearer starting point for experience management at HappySignals.

By bringing performance, Experience Drivers, AI-analyzed feedback, and employee context into a clearer hierarchy, users can understand where they stand, identify what deserves attention, and move naturally into deeper analysis.

Key Learnings:

Clarity comes from prioritization
More data doesn't necessarily create more understanding. Deciding what users need to see first was fundamental.

Progressive disclosure makes complexity manageable
A strong overview gives users confidence while keeping deeper analysis available when they need it.

AI works best when it reduces effort
Focus Topics turns large volumes of qualitative feedback into clear areas of focus without removing access to the underlying data.

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