STEELSERIES GG · CONSUMER SOFTWARE · 2024–PRESENT

Making complex software easier to understand and use.

I design software experiences for SteelSeries GG, a Windows platform with 5M+ monthly active users. My work connects research and product strategy with interaction design, experimentation, and delivery alongside engineering.

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SteelSeries GG software ecosystem with Engine configuration interfaces — SteelSeries GG combines device configuration, audio, capture, training, and game experiences in one Windows platform.

5M+monthly active users

100K+data points analysed

110+external research participants

20+in-depth interviews

PRODUCT OVERVIEW

Make advanced control accessible without limiting experts.

GG supports first-time players and experienced enthusiasts in the same product. I frame the design challenge around that tension: make setup and advanced controls understandable while preserving expert speed and consistency across the platform.

Role: Senior Product Designer
Scope: Engine, Sonar, Moments, 3D Aim Trainer, and new device programmes
Partners: Product, engineering, firmware, hardware, research, and regional teams
Ownership: Discovery, product definition, software UX, validation, design system, handoff, and design QA

My role

I lead design from problem framing through implementation review. With product and engineering, I turn evidence into scope, compare interaction models, agree success criteria, and resolve usability gaps before release.

The work sits within a software ecosystem that connects everyday setup with deeper control.

SteelSeries GG Sonar equalizer with presets and connected audio device — Sonar brings presets and detailed equalizer controls into the GG desktop experience.

SteelSeries GG Engine keyboard configuration and lighting visual — Engine shows the breadth of configuration, from per-key settings to lighting.

01 · DISCOVER

Find the behaviour behind the feedback.

I triangulate behavioural data, surveys, interviews, Discord feedback, and usability tests. Analytics locates the friction; qualitative evidence explains it. Together, they help me distinguish recurring product problems from isolated requests.

100K+

records informing product and research decisions

I used Redash and Snowflake to examine usage, Airtable and Dovetail to organise feedback, and Python and SQL to clean and compare records. Synthesis connected recurring behaviours to specific design questions.

RedashSnowflakeAirtableDovetailPythonSQLDiscordNPSUseberry

Privacy-protected view of SteelSeries Discord community feedback — Community feedback
Recurring questions and language from active players

Privacy-protected view of NPS feedback analysis — NPS and in-product sentiment
Patterns behind positive and negative experiences

Privacy-protected view of in-app feedback organised in Airtable — In-app feedback
Evidence tagged by product, topic, and severity

Privacy-protected view of survey findings — Survey analysis
Preference and behaviour patterns across participants

Internal metrics, participant identities, account details, links, project codes, and verbatim feedback have been obscured. The visuals show method and evidence structure only.

02 · DEFINE

Teach the concept at the moment of control.

Research into advanced configuration exposed a comprehension gap: users could find a setting without understanding its effect. I reframed the problem around when to teach the concept and how much guidance to keep visible during the task.

SignalPlayers misinterpreted advanced settings.
Interviews, community feedback, and usability sessions surfaced the same comprehension gap.
Root causeTechnical controls appeared before the mental model.
More copy alone would increase density without helping users form the right concept.
HypothesisProgressive guidance could teach without slowing experts.
Contextual education would appear only when the user needed it.
ExperimentTooltip guidance versus expandable guidance.
Useberry compared comprehension, confidence, task success, and completion time.
DecisionSelect the pattern against the learning task.
I compare comprehension, confidence, task success, and time, then document which context each pattern serves. The choice balances first-use learning with speed for experienced players.

Ninna facilitating collaborative product workshops — Cross-functional workshops convert research into shared scope and decisions.

Alignment as a product tool

I facilitated FigJam workshops with product, design, and engineering partners to turn competing directions into a shared scope. We compared user impact, implementation effort, dependencies, and technical risk.

The output was a decision the team could act on: the problem to solve, what to prioritise, what to defer, and how to test the chosen direction.

FigJamOpportunity mappingRoot cause mappingPRD

03 · DEVELOP

Compare the cost of interruption with the value of guidance.

I compared two levels of guidance against the same tasks. Tooltips protect interface density; expandable help keeps explanations available while users act. The study examined that trade-off through task success, comprehension, confidence, and completion time.

Option A

Tooltip guidance

Low interruption and easy to reuse, but less discoverable and harder to retain during a complex task.

Option B

Expandable guidance

More persistent context and room for examples, with a larger layout and interaction cost.

Evaluation

Useberry A/B study

Measured task success, comprehension, confidence, and time. Qualitative feedback explained the score differences.

Privacy-protected view of an interactive SteelSeries software prototype — Interactive prototype
Behaviour, states, and realistic task context

Privacy-protected view of usability testing evidence — Usability evidence
Task behaviour and attention patterns compared across options

04 · DELIVER

Carry the interaction through engineering and release.

I translate the chosen direction into buildable specifications: component states, responsive behaviour, edge cases, and acceptance criteria. During implementation, I review the interaction with engineering, report reproducible issues, and follow changes through design QA and release feedback.

Requirements and scope

Connect the product brief to user flows, acceptance criteria, and Jira tickets.

System-ready handoff

Specify shared design-system components, behaviour, states, and edge cases with engineering.

Implementation review

Compare the build with the intended interaction and report gaps with clear reproduction steps.

Release learning

Review alpha, beta, and post-launch signals, then carry findings into the next iteration.

Privacy-protected view of Jira handoff — PRD to Jira
Requirements translated into buildable work

Privacy-protected quarterly planning view — Quarterly planning
Dependencies and programme timing aligned across teams

Privacy-protected Dovetail research repository — Research repository
Findings connected back to tasks and decisions

Privacy-protected alpha and beta testing view — Alpha and beta testing
Internal and player feedback before release

DESIGN SYSTEM

Turn recurring decisions into reusable patterns.

I extend SteelSeries’ existing design system with reusable interaction patterns, states, and documentation. Material Design 3 principles inform the foundations; product constraints shape how components behave. Shared specifications give design and engineering a consistent basis for implementation and QA.

Foundations

Typography, colour, spacing, elevation, and motion rules.

Components

Sliders, switches, selects, tabs, chips, buttons, tooltips, backdrops, and snackbars.

Behaviour

Loading, disabled, warning, error, confirmation, and recovery states.

Delivery

Shared specifications that improve reuse, implementation speed, maintainability, and cross-product consistency.

GG SOFTWARE ECOSYSTEM

One design practice across different player journeys.

My work spans device configuration, audio control, gameplay capture, and training. The interface changes by context, while shared navigation, interaction patterns, and system rules keep the ecosystem coherent.

Engine

Device setup, profiles, advanced configuration, firmware states, and configuration education.

Sonar

Channel mixing, presets, equalisation, and audio processing across game, chat, and microphone paths.

Moments

Gameplay capture, clip editing, audio control, and sharing within a focused creator journey.

3D Aim Trainer

Training discovery, practice activities, and performance feedback within the wider GG experience.

Audio settings also need a coherent language across product and companion surfaces.

Illustrative SteelSeries headset and audio companion interface visual — Illustrative product visual showing device status, microphone settings, and audio controls.

Illustrative mobile headset equalizer visual — Illustrative mobile view of headset connectivity and audio settings.

05 · MEASURE

Connecting usability outcomes to product health.

I separate usability signals from product outcomes. Task success and comprehension help assess an interaction before launch. Adoption, repeat use, NPS themes, and support contacts help the team decide what to improve after release.

Verified scale

5M+

monthly active users across GG

The work operates inside a mature platform where consistency and release quality matter at scale.

Research coverage

110+

external participants

Supported by 20+ interviews and more than 100,000 product and research data points.

Measurement plan

NPS and support signals

Track sentiment themes and support contacts for the affected interaction after release.

Adoption and daily use

Compare feature activation, repeat use and task completion against a defined baseline.

Leading indicators

Task success · comprehension · confidence · completion time · error rate

Lagging indicators

Feature adoption · DAU · retention · NPS · support volume · community sentiment

Release-level lift is not disclosed here. Platform scale and research coverage are shown separately from the measures used to evaluate individual changes.

AI-NATIVE DESIGN WORKFLOW

From confirmed requirements to reviewable design proposals.

I built a Codex workflow that turns confirmed meeting decisions and structured requirements into editable Figma proposals. Design-system rules guide component choices and states. I review coverage, interaction logic, accessibility, and edge cases before handoff, and use the findings to improve the workflow.

Confirmed decisionsStructured requirementsDesign-system rulesEditable Figma draftHuman design QA

58-second workflow demonstration

The demo shows how I structure decisions, apply reusable design-system knowledge, generate an editable proposal, and keep human judgement in the review loop.

REFLECTION

Improve the product and the way the team builds it.

The work strengthens both the experience and the team’s decision process. Research explains the problem, experiments clarify the trade-offs, and shared patterns carry the decision into production. My next iteration starts with what players do after release.

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