UX Researcher & Product Designer
Uncover. Connect. Design.
I uncover root causes, connect perspectives, and translate research into clear product direction, helping teams build products grounded in evidence rather than assumption.
About
I didn't start my career in UX.
For more than a decade, I worked in industrial design and product development, collaborating with engineers, manufacturers, and cross-functional teams to bring products from concept to production. That experience taught me to see products as systems, where technology, people, and processes continuously influence one another.
Good design starts with understanding the system, not just the screen.
Moving into UX Research didn't feel like changing disciplines. It felt like applying the same systems thinking to a different material: digital products instead of physical ones. I became interested in how people make sense of complexity, how they make decisions, and how thoughtful design can reduce uncertainty.
That shift also pulled me closer to the decisions around the research, not just the research itself. I started paying attention to product direction, to what stakeholders needed to align on, and to how user needs and business goals often pull in different directions at once. Evidence became less about proving a point and more about giving teams enough clarity to decide with confidence.
Research only creates value when it shapes what a team prioritizes and builds next.
Today, I research how people work with complex, often AI-driven products, uncover patterns in behavior and trust, and transform research into structures that help teams prioritize, align, and design with greater clarity and confidence.
Whether I'm evaluating an enterprise platform, improving a research workflow, or designing for complex product ecosystems, I'm driven by the same question:
How can complexity become something people can confidently use?
Skills
Toolkit
The tools change depending on the project. The thinking and process stay consistent.
View the toolkitFrom discovery and synthesis to information architecture, interaction design, wireframing, and interactive prototyping.
Organizing research, documenting decisions, managing projects, collaborating across teams, and communicating insights throughout the product development process.
Applying AI across research synthesis, concept exploration, documentation, interaction design, and rapid functional prototyping. AI accelerates execution while product thinking, design decisions, and evaluation remain human.
Where I've Worked
Supporting enterprise UX research through structured synthesis, AI product evaluation, and evidence-based product decisions.
Evaluated an AI-powered research tool for reliability and evidence quality, then improved its configuration to make insights more traceable for product teams.
Led cross-functional product development for automotive components, aligning design, engineering, and production teams from concept to manufacturing.
Designed automotive mechanical components from concept through technical documentation, collaborating with engineers and production teams throughout.
A selection of projects where research, systems thinking, and design come together to solve real problems.
The problemEvery team measured UX in its own way, so no one's numbers held up outside their own team.
Designed a reusable UX measurement framework for enterprise product teams, combining behavioral metrics, survey results, and expert evaluations into a consistent decision-support layer.
The problemInternational students arrive to a system that assumes they already know how it works.
Full UX design process for a mobile app helping international students navigate their first weeks in Germany. From 4 qualitative interviews and 38 survey responses to a tested, high-fidelity prototype.
The problemResearch existed across the organization, but the evidence was too fragmented to guide product decisions.
Synthesized interviews, prototype evaluations, support conversations, and user feedback into recurring UX themes, product risks, and actionable opportunity areas.
The problemA Microsoft Copilot Agent that sounded confident even when it was wrong.
Designing and evaluating a Microsoft Copilot Agent for reliable, source-grounded access to internal UX research knowledge.
The problemSmall friction points scattered across a grocery app were quietly costing user trust.
Moderated usability evaluation with 8 participants using think-aloud protocol. Identified critical issues across list creation, scan feedback, search relevance, and language accessibility, with targeted redesign proposals.
The problemPeople don't distrust AI because it's wrong. They distrust it because they can't tell when it might be.
Designed and ran a controlled, between-subjects experiment measuring how explainable AI reasoning shapes user trust, perceived usefulness, and intention to use.
Contact
I'm currently seeking full-time UX Research and Product Design opportunities in Germany and across Europe. If you're building products that solve complex problems through thoughtful research and human-centered design, let's talk.
Understanding the Existing System
The organization did not have a UX data problem. It had an interpretation problem.
Product teams already worked with established UX evidence, including survey metrics, behavioral metrics, and expert evaluations. These signals were valuable and were intended to remain unchanged — each metric already had its own purpose, calculation method, benchmark logic, and detailed scorecard.
Defining the Framework Opportunity
The framework was designed as a shared layer above existing UX metrics, not as a replacement for them.
The concept introduced one reusable interpretation system that connects existing survey, behavioral, and expert evidence while preserving the detail and context of each source.
The framework adds a structured summary layer that helps stakeholders understand:
The detailed scorecards remain the primary evidence layer. The unified framework acts as the interpretation and decision-support layer above them.
The framework connects fragmented UX evidence without compressing every signal into one oversimplified score.
Framework Evolution
The concept evolved through three stages.
The initial focus was understanding the existing measurement ecosystem. It then shifted toward connecting evidence through a shared interpretation layer, before finally defining the information hierarchy required for stakeholder decision-making.
Understanding where interpretation became difficult
The first sketch mapped the current UX measurement landscape.
Survey metrics, behavioral metrics, and expert evaluations already existed, but they were presented separately. The exercise made the central problem visible: stakeholders had to manually connect multiple signals before reaching a conclusion.
Mapping separate evidence sources, manual interpretation effort, missing confidence visibility, and limited trend comparison.
Connecting evidence without replacing it
The second sketch explored how existing UX signals could feed into one overall interpretation layer.
The concept introduced an overall UX summary, confidence visibility, comparison insights, conflict detection, AI-supported interpretation, and access to detailed evidence.
This was the point where the project shifted from designing a scorecard to defining a reusable evaluation framework.
Exploring how survey, behavioral, and expert signals could contribute to one transparent interpretation system.
Structuring the framework around stakeholder questions
The third sketch translated the framework concept into an initial information hierarchy.
The layout prioritized the questions stakeholders needed to answer first:
Testing the hierarchy of summary, confidence, evidence sources, comparison, and detailed interpretation before visual design.
Framework Specification
Before any interface was designed, the evaluation framework itself had to be defined. A detailed specification established how existing UX evidence should be aggregated, interpreted, compared, and communicated consistently across products. This document became the foundation for every design and implementation decision that followed.
From Framework Logic to Interaction
Once the framework logic was defined, I translated it into an interaction model through parallel sketching and low-fidelity exploration.
The goal was to test how stakeholders could move from an overall UX assessment to confidence, conflicting signals, metric-level evidence, and comparison over time without losing context.
These explorations helped define the page hierarchy, expandable scorecards, evidence relationships, and progressive disclosure before the concept was developed into an interactive prototype.
Rapid Interactive Prototype
The low-fidelity interaction model was developed into a complete interactive prototype to test the framework as a connected experience rather than as a set of isolated screens.
The prototype was first generated rapidly to accelerate exploration, then reviewed and refined in Figma. I adjusted the information hierarchy, interaction states, visual structure, and component behavior so the result reflected the framework requirements rather than the limitations of the initial AI-generated version.
It was used to evaluate:
The refined prototype became the shared reference for stakeholder discussions and engineering implementation.
Overview — overall UX grade, AI summary, and data confidence at a glance.
The refined interactive prototype became the foundation for implementation.
Throughout development, I worked closely with the developer to review and improve interaction details, information hierarchy, edge cases, confidence behavior, comparison logic, and component consistency.
The implemented experience continued to evolve through iterative design reviews and refinement during development.
Design Principles
Project Outcomes
Rather than introducing new UX metrics, the framework changes how existing survey, behavioral, and expert evidence is structured, connected, interpreted, and communicated.
It provides one shared model for understanding UX quality while preserving the context, confidence, and detail behind each conclusion.
Reflection
The project began as a challenge around presenting UX metrics more clearly. As the work progressed, it became clear that the deeper problem was not visualization, but interpretation.
The organization already had established survey metrics, behavioral evidence, expert evaluations, and detailed scorecards. What was missing was a shared way to connect these signals, communicate confidence, preserve contradictions, and compare UX quality consistently over time.
The most important design challenge was balancing simplicity with transparency. The framework needed to support quick stakeholder understanding without hiding evidence, uncertainty, conflicting results, or data limitations.
This shifted the work from designing a dashboard to defining a reusable evaluation system, including its interpretation model, information architecture, interaction behavior, confidence logic, comparison rules, and implementation specifications.
Evaluating and improving a Microsoft Copilot Agent through structured scenario testing, prompt architecture refinement, source grounding, and knowledge-boundary design.
Due to confidentiality, internal prompts, proprietary research content, product-specific examples, and company data have been generalized.
Project at a Glance
The agent needed to do more than retrieve information. It needed to distinguish supported knowledge from missing evidence, preserve traceability to original research sources, and communicate limitations without creating false confidence.
Design Challenge
The organization already had a large internal repository of UX research. The challenge was helping employees retrieve useful evidence through a Copilot Agent without creating false confidence.
The agent could generate fluent responses even when the supporting evidence was incomplete, weak, or absent. This created a risk that interpretation would be mistaken for documented research.
The design challenge was therefore not only improving retrieval speed. It was defining how the agent should search, cite, summarize, communicate uncertainty, and acknowledge when the repository could not support an answer.
System and Interaction Model
I structured the interaction flow so that the Copilot Agent moved from user question to evidence retrieval, source verification, synthesis, citation, and knowledge-boundary handling. The objective was to make every answer traceable to available internal research.
Design Process
Evaluation Framework
Every scenario response was reviewed against the same six dimensions, so that reliability was judged as an interaction quality rather than a single pass-or-fail check.
Key Findings
The project treated reliability as an interaction and system-design challenge. The underlying language model could generate fluent answers, but the Copilot Agent still required explicit rules for retrieval, evidence use, citation, uncertainty, and non-answer behavior.
The agent's reliability depended on how its instructions, source hierarchy, response structure, and knowledge-boundary logic were designed. The metrics below are evaluation evidence from the seven structured Copilot Agent scenarios, not general system statistics.
Design Decisions
Prompt Refinement
Evaluation findings were translated into a revised prompt architecture, refined from version 1.2 to version 1.3. The change was in the design logic of the instructions, not in any reproduced prompt text.
Project Outcomes
The Copilot Agent created a clearer entry point into the internal research repository. Instead of searching across separate documents manually, stakeholders could ask a question and receive a structured, source-linked response with limitations left visible.
Design Trade-offs
Project Contribution
Potential Organizational Value
Labeled here as potential or expected value, not measured business impact.
From Agent Behavior to User Trust
The project suggested that these behaviors may shape whether users perceive the agent as credible and decide to rely on it.
Beyond the Project
The practical project focused on how the Copilot Agent behaved. It revealed a deeper question: how do users perceive an AI agent that explicitly acknowledges the limits of its knowledge?
This observation later became the basis for my academic thesis research on user perceptions of knowledge-boundary acknowledgment in AI-supported knowledge systems.
Reflection
This project was not simply about writing prompts or building a chatbot. It was about designing the behavior of a Copilot Agent that people could use to navigate internal UX research responsibly.
The central challenge was balancing usefulness with epistemic honesty. The agent needed to provide concise, actionable answers while remaining traceable to evidence and transparent about what the repository could not support.
The final result was a refined Copilot Agent framework with clearer retrieval logic, stronger source grounding, structured response behavior, and explicit handling of missing evidence.
Detailed Academic Report
The full report documents the research context, evaluation method, scenario design, prompt iterations, findings, and limitations in greater detail.
Read the Full Academic Report
Competitive Analysis
I explored existing apps and services for international students and newcomers in Germany to uncover gaps in usability, tone, and feature focus, and to define what an effective, student-centered onboarding experience could look like.
Free, official, comprehensive, suitable for newcomers.
Fragmented across regions, mostly in German, complex UX.
A student-specific experience with checklists for registration, bank account, insurance.
Comprehensive financial and insurance services, easier digital setup.
Narrow focus on legal setup, commercial rather than supportive tone.
Expand beyond finance into a friendlier, student-oriented tone.
Official and directly connected to universities.
Fragmented across regions, mostly German, complex experience.
Unified, simplified, translated information for international students.
Outcome Benchmark
I evaluated how well current apps deliver key outcomes international students need (clarity, guidance, momentum). Instead of listing features, I measured coverage and quality for each outcome.
| Outcome / Need | Ankommen | Expatrio | Studierendenwerk |
|---|---|---|---|
| Multilingual guidance | Good Clear multilingual content in English, Arabic, and others |
Partial English only |
Partial English only |
| Guided flow that prevents bottlenecks | None Static information |
None Focuses on financial products |
None Fragmented info by topic |
| Student-specific focus | None General newcomer guidance |
Partial Finance only |
Partial Fragmented — covers housing and enrollment in some regions but inconsistent overall |
| Localized, trustworthy instructions (city-specific, up-to-date) | None Provides general national info |
None National-level info |
Partial Local only — each local Studierendenwerk publishes separate info; varies in quality and accuracy |
| Actionable reminders & follow-ups | None | None | None |
| Guidance on what’s next (sense of completion) | None | None | None |
Survey Results & Insights
The survey collected both quantitative ratings and open feedback from international students. Below is a selection of the main findings, visualized to highlight the most critical challenges, sources of information, and feature priorities.
User Interviews
To complement the survey data, I conducted four semi-structured interviews with recently arrived international students. These conversations revealed emotional and practical struggles that numbers alone couldn't show.
Students don't just need a checklist. They need contextual guidance: required documents, the order of dependencies, and what happens if a step is missed.
Market Gap
Seven takeaways from the review shaped where Startklar's onboarding experience could set itself apart. Tap to read them.
User Persona & Stories
Interview and survey patterns converged into one primary persona, used to keep every design decision grounded in a real, specific person rather than an abstract user.
Job Stories
Framed as Jobs-to-be-Done: Sara's situations, motivations, and the outcomes the product needs to deliver for her.
Empathy Map
Translating interview and survey data into what Sara sees, does, hears, thinks, and feels, to separate her surface frustrations from what she actually needed.
Customer Journey Map
Mapping Sara's journey from preparing to leave her home country to settling into studies revealed how interconnected each bureaucratic step is. A single delay, such as waiting for an appointment, cascades through all following steps, amplifying stress and uncertainty.
Experience Mapping
Sara's experience shows a critical dependency chain: when one step is delayed, all following steps collapse. This domino effect leaves students stuck, anxious, and unable to proceed with their daily life.
The storyboard visualizes Sara's journey, from hopeful arrival to feeling stuck in paperwork.
Delays and unclear rules create a domino effect. Students feel stressed and isolated when they cannot progress independently.
Final Synthesis
Students don't fail because they are unmotivated, but because the bureaucratic steps are highly interdependent. Missing one step creates a chain of consequences that blocks others.
Students rely on fragmented sources like Telegram groups, informal advice, or vague official instructions. This patchwork of information fuels confusion and repeated mistakes.
Beyond everyday confusion, some students face extra barriers like sanctions, cash-only workarounds, and bank rejections, making already fragile timelines even more stressful.
Students arrive hopeful but quickly feel isolated, anxious, and unsupported. The uncertainty drains their focus and energy for academic life.
The issue isn't motivation; it's the absence of a clear roadmap. Students need a dependable, step-by-step guide rather than scattered encouragement.
Each city and university follows its own procedures. What works in one place can easily fail elsewhere, leaving newcomers lost between systems.
Since students were already overloaded with information, each iteration removed complexity instead of adding it. The goal became the lightest possible interface, so users could act without having to think.
Translating research insights into a clear product structure and two complementary navigation paths.
Information Architecture
Startklar sequences bureaucratic tasks by real dependencies and gives a browsable, trusted knowledge base, so students always know the next step.
To address the tension between sequential bureaucratic steps and the need for quick reference, I designed a hybrid navigation model.
A topic library for browsing by category (Banking, Housing, SIM, Community). Each topic page may reference related guided steps, but it never replaces them, so users can explore freely without losing the structured flow.
A linear timeline that keeps dependent tasks in the correct order. Later steps remain locked until prerequisites are complete. Each guided step opens a structured micro-flow with documents, appointments, and completion tracking.
After signing in, students choose between Guide Mode and Explore Mode, depending on how far they are in their onboarding journey.
Students indicate which steps they've already completed. The timeline automatically jumps to their current position, while later steps stay visible but locked. Each guided step opens a structured micro-flow with documents, appointments, and completion tracking.
A topic library for browsing by category (Banking, Housing, SIM, Community). Each topic page may reference related guided steps, but it never replaces them, ensuring users can explore freely without losing the structured flow.
This flow reduces confusion and prevents missed bureaucratic steps by locking later tasks until prerequisites are completed.
The Bank Account flow demonstrates the app's logic and interaction consistency.
Once a step is completed, it unlocks the next one in the timeline, creating a sense of progress and control.
Hand Sketches
Low-Fidelity Wireframes
High-Fidelity Prototype
Welcome & Onboarding Screens
Key App Screens
Bank Account Setup Flow
Usability & Iteration
Due to time constraints, I couldn't run full usability tests with real newcomers. Instead, I applied three complementary evaluation methods to uncover usability issues early and validate core interactions:
Heuristic Evaluation
Process: I created a matrix mapping Nielsen's heuristics against key screens and interactions. Each flow was reviewed step by step, friction points were noted and rated by severity, then clustered into themes like missing feedback or inconsistent labels. Findings were validated with two peers to confirm recurrent issues before iteration.
| Heuristic Principle | Observation | Severity | Action Taken |
|---|---|---|---|
| Visibility of System Status | Users couldn't tell when a sub-step was completed | Moderate | Added progress indicators and checkmarks after each task |
| Match Between System and the Real World | Bureaucratic terms (e.g., “Anmeldung”) were unclear for non-German users | Moderate | Added short plain-language explanations and translations |
| User Control & Freedom | Users couldn't easily edit or go back to previous inputs | Minor | Added a “Back” option and editable notes before submission |
| Consistency & Standards | Icons and navigation labels didn't match across flows | Minor | Unified icon-label pairs and adjusted hierarchy |
| Error Prevention | Users could skip dependent steps (e.g., opening a bank account before registration) | Critical | Locked later steps until prerequisites were completed |
| Recognition Rather Than Recall | Key navigation items were hidden inside menus | Moderate | Moved essential actions to the main navigation bar for better visibility |
The heuristic findings above formed the basis for the next validation phase: a cognitive walkthrough focused on first-time user behavior.
Cognitive Walkthrough – Simulating First-Time User Behavior
To simulate first-time user behavior, I performed a guided walkthrough of the prototype's key tasks (e.g., completing the “Bank Account Setup” step). Each subtask was assessed using three diagnostic questions:
Notes were attached as Figma comments, turning each friction point into a direct design annotation. This made it easy to visualize where clarity or feedback was missing inside the flow.
Peer Review & Mini Usability Tests
I invited three peers with similar relocation backgrounds to complete assigned tasks (e.g., “find bank setup information”). Each session followed a think-aloud approach, allowing me to observe real-time confusion points and compare them with heuristic notes. While limited in scope, these mini tests confirmed that the added feedback and simplified flows significantly improved comprehension and control.
Example Observation (from Evaluation Notes)
Visibility of System Status
Design Iterations Based on Findings
The evaluations directly led to several design improvements aimed at reducing friction and improving flow clarity:
Replaced redundant pages with modals to reduce context switching.
Added quick access from profile for frequent steps.
Added checkmarks and micro-confirmations after each task to reinforce confidence.
Integrated “Profile” in bottom navigation for predictable reachability.
Updated icon labels for better comprehension and consistency.
Planned Usability Metrics
While full user testing is planned for the next iteration, early heuristic and peer evaluations already indicated clear improvements:
These early metrics suggest that simplifying flow and adding micro-confirmations effectively reduce confusion and hesitation.
Accessibility Review
With limited time, I conducted a light accessibility audit focusing on color contrast, text readability, icon clarity, and keyboard navigation. Even these quick checks helped identify low-contrast elements and improved overall inclusiveness.
Outcome
The project evolved from an abstract idea into a structured onboarding system that provides clear direction when newcomers need it most. It demonstrates how clarity and small confirmations can reduce cognitive load and build emotional confidence during bureaucratic tasks. Designing and iterating in Figma helped transform a concept about “reducing chaos” into a tangible product ready for validation with real users.
Reflection
This project taught me to analyze user flows as attentively as emotions behind them. I learned that UX isn't just about clarity, it's about reducing stress through guidance and reassurance. Turning bureaucratic confusion into calm made me realize that empathy and structure can coexist, usability and emotional trust are equally important.
A moderated usability study with 8 participants using think-aloud protocol identified critical failure patterns across list management, barcode scanning, and price visibility. Targeted redesigns were proposed for each.
EDEKA is one of Germany's largest grocery retailers. Its app is used daily by millions, yet usability testing revealed a persistent gap between what users expected and what actually happened, particularly during list creation, product scanning, and purchase planning.
Usability issues in consumer apps are often trust issues in disguise. When users receive no feedback, they don't just get frustrated. They start to question whether the app is working at all.
Behind the scenes of a moderated think-aloud session: scenario script, live screen recording, and participant device.
Users landed directly on an existing list instead of a list overview, leading them to delete items and reuse the same list. The multi-list feature remained undiscovered throughout sessions.
No feedback during barcode scanning caused repeated attempts and confusion. Users assumed the feature was broken rather than simply slow, reducing trust in the entire app.
Price information was absent during list creation, making it impossible to plan spending in advance. Users returned to paper lists where they felt more in control.
Introduce a list overview as the default entry point, allowing users to immediately view all existing lists instead of landing directly on a previously used one. This surfaces the multi-list feature and lowers cognitive load during task execution.
Redesign the scanning experience to provide clear guidance and continuous feedback throughout the interaction. Show active scanning state, confirm when an item has been successfully added, and build confidence in the reliability of the feature.
Integrate price information directly into the shopping list experience, allowing users to view individual item prices while building their list and understand their overall spending. This enables informed decisions during the planning phase.
The most important insight from this project: usability issues in consumer apps are frequently trust issues in disguise. When the system provides no feedback, users don't just feel uncertain about the feature. They begin to doubt the entire product.
This project reinforced that feedback loops are not a nice-to-have. They are the foundation of a trustworthy experience. Every interaction without feedback is a missed opportunity to build user confidence.
A controlled, between-subjects experiment testing whether explainable AI reasoning shapes user trust, perceived usefulness, and intention to use, within a broader program of empirical Human-AI Interaction research.
Context
AI systems increasingly influence how people evaluate information and make decisions, yet most conversational interfaces present answers without revealing how those answers were produced. This project investigated whether explaining AI reasoning changes users' trust, perceived usefulness, and willingness to rely on AI-generated responses, isolating explainability as the sole independent variable in a controlled experiment.
"The experiment did not test whether people liked explanations. It tested whether explanations changed what people were willing to do with the answer."
Study Design
Research Process
Key Findings
Research Decisions
Research Contributions
Reflection
Trust in AI is not built by accuracy alone. It is built by whether people can see enough of a system's reasoning to judge whether the output deserves belief. As AI systems take on more decision-support roles, that judgment becomes harder to make and more consequential to get right.
Synthesizing fragmented UX evidence into shared product direction.
I synthesized findings from four independent UX studies into a shared evidence model, revealing recurring issues that were not visible within individual reports.
My Role — I turned four disconnected research efforts into one evidence-backed narrative. That meant extracting comparable evidence, coding across studies, resolving contradictions, and translating the patterns into strategic recommendations.
Due to confidentiality, product-specific details, internal materials, and proprietary findings have been generalized. This case study focuses on my research process, reasoning, and contribution.
The Synthesis, at a Glance
The Challenge
Each study contained useful findings, but the evidence remained distributed across separate reports, research questions, and formats.
Recurring issues identified, patterns compared across studies, and one coherent product-level view.
My Role
The goal was to create a structured synthesis that preserved the context of each study while making relationships across sources visible. The synthesis needed to show where findings converged, where they differed, and what those patterns could imply for future product and research decisions.
Why the Synthesis Was Difficult
The synthesis combined four independent studies with different methods, scopes, and levels of detail. Rather than treating every observation as equivalent, I preserved its original source, study context, and evidence type throughout the analysis.
My Synthesis Process
From Evidence to Product Implication
Observations were never treated as isolated facts. Each one remained connected to its original study, user context, journey stage, and supporting evidence. This reduced the risk of merging superficially similar findings that had different underlying causes.
Key Cross-Cutting Themes
Cross-study comparison revealed issues that appeared repeatedly across different research contexts. These patterns were more strategically meaningful than any single observation because they showed where friction persisted beyond one task, feature, or study.
Research Impact
The final synthesis brought recurring issues, supporting evidence, affected journey stages, and source relationships into one structured view. This gave stakeholders a clearer way to understand which themes were isolated and which appeared consistently across the research landscape.
Product and Business Implications
The synthesis did not measure whether the proposed directions improved user behavior or business performance. Its role was to consolidate existing evidence, identify recurring patterns, and translate those patterns into informed implications for future research and product decisions.
This distinction was important to avoid presenting research interpretation as validated impact.
These are research-grounded implications, not measured outcomes. Several could contribute to the consequences above; none were independently verified against business metrics.
Opportunity Areas
The synthesis produced a consolidated view of recurring UX barriers, their supporting evidence, affected journey stages, and recommended areas for future research and product improvement.
Designing the Research Deliverable
The synthesis supported both executive scanning and detailed evidence review. Stakeholders could quickly understand the major themes, while researchers could trace each interpretation back to its supporting observations and source studies.
The report used clear hierarchy, progressive disclosure, and repeated connections between evidence, interpretation, and impact.
Reflection
This project showed me that synthesis is not simply summarizing several research reports. It means preserving context, comparing evidence across sources, and making interpretation transparent enough to trust.
The hardest part was balancing simplification with traceability, giving stakeholders a clear narrative that stayed connected to its original evidence.