SaaS / B2B / Complex analytical products

Gemius analytical products

Full case study

UX/Product Design and UX writing work for complex analytical products involving dashboards, tables, filters, metrics, reports, permissions, and data-heavy workflows.

SaaSB2BProduct DesignUX WritingData-heavy UXDashboardsFiltersMetricsDocumentationDesign System

Selected public-safe screens from Gemius product contexts, using example views to show analytical dashboards, rankings, audience measurement, and panelist survey flows.

Role

Product Designer, UX Writer

Status

Ongoing

Relevant for

SaaS Product Designer roles, B2B product roles, UX roles for complex tools, analytical product teams, implementation-aware design roles.

Evidence focus

Complex B2B SaaS, analytical UX, data-heavy workflows, and product communication.

Context

This case study brings together selected UX work across several Gemius product contexts: AdReal for advertising measurement, Explorer for media audience measurement, gemiusPrism for web analytics, and panelist surveys that support data collection for AdReal and Explorer.

Problem

How do you make dense analytical workflows usable without flattening the complexity that expert users need? Across these products, the interface had to support charts, tables, rankings, filters, exports, comparisons, survey participation, permissions, and different levels of domain knowledge.

My role

I worked across UX/UI design, product communication, and documentation: shaping dashboard layouts, table behavior, filters, report views, empty and edge states, onboarding and account flows, interface copy, tooltips, and reusable design patterns.

Key challenges

Helping users scan dense data without hiding important analytical detail
Designing filters, metrics, and comparison states that reflect real product logic
Writing copy that respects domain terminology while staying understandable
Supporting both expert analytical workflows and less frequent users
Coordinating design decisions with product, development, analysts, and business stakeholders

Process

I worked with product managers, developers, analysts, and business stakeholders to understand product logic, map user flows, review data states, prototype interface behavior, and refine terminology. The work often moved between detailed interaction design and system-level questions: what belongs in a dashboard, what should be configurable, what needs explanation, and where the interface should prevent mistakes.

Product contexts

AdReal

Advertising measurement product.

Rankings, metrics, dimensions, filters, report configuration, and advertising-focused analytical views.

Explorer

Media audience measurement product.

Audience analysis, demographic breakdowns, media measurement views, and market-specific reporting logic.

gemiusPrism

Web analytics product, comparable in category to Google Analytics.

Website performance dashboards, traffic sources, visitor behavior, comparison periods, exports, and overview-to-detail reporting.

Panelist surveys

Survey flows for panelists whose data supports products such as AdReal and Explorer.

Trust, clarity, consent, progress, mobile usability, and reducing friction in respondent-facing flows.

What I worked on

Analytical dashboards and reporting

Structuring dense reporting views so users can move from overview to detail: KPIs, charts, tables, rankings, exports, and repeated reporting patterns.

Filters, metrics, and configuration

Designing control logic for analytical products where one selection can affect available data, comparisons, labels, and interpretation.

Panelist and account flows

Designing simpler user-facing flows connected to the data ecosystem: surveys, onboarding, permissions, access, instructions, and status communication.

Product language

Writing interface copy, labels, tooltips, errors, onboarding messages, instructions, and documentation that help users understand complex product behavior.

Key design decisions

Problem

Dense dashboards can become a wall of numbers.

Decision

Group information into clear modules with visible hierarchy between KPIs, charts, tables, and supporting metadata.

Why it mattered

This lets users scan first, then inspect details without losing orientation.

Problem

Analytical users need comparisons, not only current values.

Decision

Expose comparison states directly in charts, tables, deltas, and tooltips instead of hiding them in separate reports.

Why it mattered

Change becomes easier to interpret when current and previous periods stay in the same visual context.

Problem

Product terminology has to be precise, but precision can make interfaces hard to read.

Decision

Use domain language where accuracy matters, supported by labels, hints, and documentation where the concept needs explanation.

Why it mattered

The interface stays credible for experts while remaining usable for people who do not live inside the product every day.

Solution

The work resulted in clearer analytical and panelist-facing surfaces: dashboards that combine overview and detail, comparison views that make change visible, table and chart patterns that support scanning, survey flows that reduce friction, and interface copy that helps users understand what they are looking at and what they can do next.

Visual evidence

Dashboard screen showing current and previous period comparisons across charts, tables, and metrics.

gemiusPrism: comparison states inside a dense dashboard

A web analytics view showing how charts, tables, deltas, and tooltips can work together to make changes easier to interpret.

Advertising analytics report with ranking chart, data notes, dimensions, metrics, values filter, and table.

AdReal: ranking and table-based advertising analysis

A report view where rankings, dimensions, metrics, data notes, filters, and tables need to stay readable without hiding analytical control.

Audience measurement dashboard with demographic charts for gender, age, education, and city size.

Explorer: audience measurement and demographic breakdowns

A media audience view showing how segmentation and demographic data can be exposed through clear controls and comparable chart modules.

Mobile survey question screen with progress indicator, answer options, and privacy/contact links.

Panelist surveys: respondent-facing clarity

A simpler, mobile-first flow connected to the same data ecosystem, focused on trust, progress, and low-friction participation.

What this project demonstrates

complex SaaS experience
analytical product design
data-heavy UX
UX writing for complex flows
documentation
design system work
collaboration with product and development teams

Reflection

This product area sharpened how I design under real constraints. In analytical software, clarity is not about removing complexity; it is about giving users enough structure to move through it with confidence.

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