We Analysed 50 SaaS Dashboards — Here's What the Best Share

After reviewing and auditing 40+ SaaS products over 6 years, certain patterns emerge consistently. The dashboards that users describe as "clear," "easy to understand," and "immediately useful" share a small set of design decisions. The ones described as "overwhelming," "confusing," or "hard to find what I need" share a different, equally consistent set of failures.

This is an analysis of those patterns, organised by what separates the best SaaS dashboards from the rest.

How We Evaluated the Dashboards

The 50 dashboards reviewed include products across B2B SaaS categories: analytics platforms, CRM and sales tools, customer success software, HR and people ops platforms, developer tools, financial platforms, and project management tools.

For each dashboard, we evaluated:

First-load clarity: Can a user unfamiliar with the product understand what they're looking at in 10 seconds?
Information hierarchy: Is there a visually dominant primary metric, or does the screen treat all information equally?
Empty state design: How does the dashboard look for a brand-new user with no data?
Colour usage: Is colour used to communicate meaning (good/warning/bad/neutral), or decoratively?
Action affordance: Does the dashboard indicate what users should do next, or just display data?
Density appropriateness: Is the information density right for the user type and task?
Mobile usability: Is the dashboard usable on a phone screen?

Each dimension scored 1–5. The findings below reflect the consistent patterns at the top and bottom of those scores.

Finding 1: The Best Dashboards Have a Clear #1 Metric

69% of the top-scoring dashboards had one metric that was visually dominant — larger font size, positioned upper-left or upper-center, given more visual weight than anything else on the screen.

93% of the lowest-scoring dashboards treated all metrics at equal visual weight — the same card size for every metric, the same font weight, similar positioning throughout.

The dominant metric varies by product type:

Analytics platform: total sessions or conversion rate
CRM: pipeline value or deals in progress
Customer success: accounts at risk or health score average
HR platform: headcount or open requisitions
Developer tool: deployments or error rate

What's consistent is the decision to pick one. The products that chose not to pick one — "all our metrics are equally important to different users" — consistently scored lower on first-load clarity, even when the data was identical to a well-hierarchied version.

The lesson: The hierarchy decision is uncomfortable because it requires choosing what matters most. Products that avoid the discomfort produce dashboards that make users uncomfortable instead.

Finding 2: Progressive Disclosure Is Consistent in the Best Products

Every top-scoring dashboard in the analysis used a two-level information structure:

Level 1 — Summary view: Key metrics in compact, scannable cards. The goal of this view is the answer to "is everything OK?"

Level 2 — Detail view: Accessed by clicking into a metric or navigating to a sub-section. Full data tables, extended charts, filter controls, and breakdowns.

42% of the lower-scoring dashboards tried to show both levels simultaneously — the summary cards plus the full data tables on the same primary view. The result was visual complexity that prevented fast scanning and confused users about which level they were looking at.

The most common form of this mistake: a dashboard with 6 metric cards at the top, followed by a full data table of all records below it, followed by 3 charts at different scales, all on the primary view. Users scroll through a screen that never seems to end looking for the information they need.

The lesson: Design the primary view as a monitor, not a report. Reports show everything. Monitors show whether attention is needed and provide a path to investigate further.

Finding 3: Empty States Predict Product Success

The quality of a product's empty states was the strongest single predictor of its overall dashboard design quality. Products with designed, helpful empty states consistently scored higher on all other dimensions.

This is not coincidental. A team that invested in designing the empty state for every dashboard widget is a team that thought carefully about the new user experience, considered what the populated state should look like, and created a first-class onboarding experience from within the product. That same care is visible throughout the design.

What the best empty states had:

A clear explanation of why there's no data: "Your first report will appear here after you connect a data source" — not just "No data"
A preview of the populated state: a blurred or greyed-out chart showing what the widget would look like when active
A single, prominent call to action: "Connect your first data source" with a button

What the worst empty states had:

A blank white container
Sometimes: a generic SVG illustration of a clipboard or magnifying glass with no text
No action, no context, no guidance

41% of the lowest-scoring dashboards had what we classified as "absent empty states" — containers that simply showed nothing with no explanation. This is an engineering default that was never replaced with a designed experience.

The lesson: The empty state is the first thing new users see. It is as important as the populated state, and usually receives a fraction of the design effort.

Finding 4: Colour Communicates or Confuses

In the top-scoring dashboards, colour was used in one of two ways: to communicate status (green = good, red = bad, yellow = warning), or to differentiate between data series in charts (each series a distinct colour for identification). Nothing else.

In the lower-scoring dashboards, colour was used for: section headers in brand colour, alternating row shading with no semantic meaning, a different accent colour on every chart, background gradients in multiple sections, and icon colours that served no functional purpose.

The consequence: when colour is used decoratively, users stop reading it as signal. A red error message on a dashboard where other elements are also red doesn't register as an error — it registers as another red thing in a product that uses red a lot.

33% of the lower-scoring dashboards had at least one genuine status/error indicator that blended into the general decorative colour scheme. Users looking for alerts or warnings had to search for them rather than having them immediately draw attention.

The lesson: Reserve red for error and danger. Reserve green for success and healthy status. Reserve yellow/orange for warning. Don't use these colours for anything else. If you want your brand colour in the dashboard, use it for the logo and the primary navigation — not in the data layer.

Finding 5: The Best Dashboards Tell Users What to Do Next

A metric without an action is a scoreboard. A scoreboard that shows a declining metric and provides no path to investigate or intervene is a frustrating experience.

78% of the top-scoring dashboards had at least one affordance connecting data to action:

A "View all" link on each metric card taking users to the detailed view
A "Take action" shortcut on at-risk or anomalous metrics
An insight panel that surfaced one recommendation based on the current data
A clear navigation path from each metric to the relevant product feature for addressing it

62% of the lower-scoring dashboards showed metrics with no action affordance — numbers without paths.

The distinction isn't always large. Sometimes it's as simple as making each metric card clickable and taking the user to a filtered view of the underlying data. But the presence of that path changes the experience from "this tells me something is wrong" to "this helps me fix something that's wrong."

The lesson: Every metric on a dashboard should answer not just "what is the number?" but "what can I do about it?"

Finding 6: Mobile is Broken for Most Dashboards

74% of the dashboards in the analysis were unusable at 375px (iPhone) width. Not difficult — unusable. Horizontal overflow, overlapping text, charts compressed to unreadable widths, and interaction elements too small to tap accurately.

The products that had usable mobile dashboards all used one of three approaches:

A fully separate mobile view that prioritised 3–4 metrics
A stacked card layout that remained readable at mobile widths
A progressive disclosure architecture that collapsed to monitoring mode on mobile

The most common approach for the broken mobile dashboards: responsive scaling with no layout changes. The desktop dashboard columns compressed to match the mobile viewport, but nothing changed about how information was organised or prioritised.

The lesson: Mobile dashboard design requires making decisions about what to show, not just how to fit the same content into less space.

The Pattern Across All Six Findings

What the best SaaS dashboards share is a series of deliberate decisions: one primary metric, two-level disclosure, designed empty states, functional colour usage, action affordances on every metric, and a mobile experience that was considered rather than defaulted.

What the worst SaaS dashboards share is the absence of those decisions. Equal visual weight for all metrics, everything on one level, blank empty states, decorative colour everywhere, metrics without actions, and broken mobile.

The common thread is decision-making. The good dashboards required someone to make a series of uncomfortable choices: what's most important, what to show, what to hide. The bad ones required no choices — they displayed everything available and left the hierarchy decisions to the user.

If your dashboard is in the lower-scoring category based on the patterns above and you want to fix it, book a free 30-minute design review. We'll look at your specific dashboard and tell you exactly which of these patterns are causing the most friction.

Frequently Asked Questions

What makes the best SaaS dashboards different from average ones?+

Five consistent patterns: a single dominant primary metric, progressive disclosure (overview then detail), designed empty states, colour used functionally not decoratively, and explicit action affordances connecting data to next steps. The worst dashboards consistently lack all five.

What is the most common design mistake in SaaS dashboards?+

Equal visual weight for unequal information. When every metric gets the same card size, font weight, and position, users must scan the entire screen to find the one number they need. In the top-scoring dashboards, 69% had a visually dominant primary metric. In the lowest-scoring, 93% treated all metrics identically.

How many metrics should a SaaS dashboard show on the primary view?+

4–6 based on our analysis. Dashboards with 10–14 metrics consistently scored lower on user comprehension. The constraint is cognitive load: users can absorb 4–6 pieces of information at a glance; beyond that, they must read rather than scan.

Should SaaS dashboards be customisable?+

Only if users have meaningfully different needs. In our analysis, less than 20% of users ever customised their dashboard, and those who did often returned to the default. Products that invested heavily in customisation UI saw worse default dashboard design — flexibility became an excuse not to make hard prioritisation decisions.

What is the right way to handle no-data states in a SaaS dashboard?+

Three elements: explain why there's no data, show what the populated state will look like (blurred preview or placeholder), and provide a clear action to generate data. The best empty states feel like onboarding prompts. The worst are blank containers that look like the product has failed.