Overview
New dashboard module where goals, actions and progress connect, and everyone knows who owns what
Role: Sole Product Designer, with 1 PM, 2 engineers & CTO.
Context
Mavue is an early-stage B2B SaaS company that helps small and mid sized manufacturers in Germany create their annual sustainability report, from collecting ESG data and spotting risks to setting goals and writing the report itself. Throughout the year, they collect data on Environment, Social and Compliance topics. After collecting data, they are spotting risks and opportunities, and companies are able to set goals for the coming months and years, and assign ownership to them. This area did not exist in the product yet, so I designed it as a new module.
Problem
Sustainability managers & department heads who own goals, action owners and top management all worked with disconnected tools:
Goals lived in scattered tools like Excel, which led to inconsistencies and poor organization.
Actions were not linked to goals, risks or impacts, so progress was hard to track.
Leadership had no clear view of progress across departments.
Meeting regulatory requirements was time consuming and had no structure.
“We use tools like Confluence or Jira to keep track, but not many people are internally familiar with them.” (Sustainability Manager)
Constraints
8 weeks, small team: 1 PM and 2 developers.
Limited discovery: 5 interviews and 6 usability tests.
Technical limits: complex visualizations did not fit the time, so the design had to stay simple.
Regular and annual use: the tool had to work for weekly tracking and for the yearly report.
Connected modules: it had to link to other modules like data collection, materiality assessment and report writing.
Solution & Key Decision
One dashboard that starts with an overview of topics and leads down to goals and actions. Goals have owners, deadlines, priorities, progress and year on year change, and link to the risks and opportunities identified earlier. Actions are assigned to owners with deadlines, and notifications and email reminders keep people accountable. AI suggestions for metrics and deadlines help people who don’t know where to start.
Decision 1: topics first. In an A/B test, the structure that goes from topic to detail won: “Option 1 feels more logical and clearer because it goes from topic to detail.”
Decision 2: suggestions are proposals. Testers said it is hard to know which metrics to use, especially in unfamiliar areas like social impact: “We need examples or suggestions.” So the AI suggests metrics and deadlines, but nothing is added until the person chooses.
Process
Understand (3 weeks): I reviewed existing workflows with the developers, interviewed 5 users and mapped flows for four groups: top management, sustainability managers, goal owners and action owners. People needed clear progress indicators, visual goal hierarchies and compliance support.
Ideate: I ran a Miro workshop with the team using Crazy 8, and prioritized with an impact effort matrix. The MVP included a central dashboard with owner, deadline, progress and task assignment. Goal hierarchies, timelines, notifications and reminders were next. Filters, uploading existing goals and in depth graphics came later or were out of scope.
Prototype: I validated low fidelity concepts with the developers, who flagged that complex visualizations didn’t fit the time, so I simplified. Then I built high fidelity prototypes in Figma.
Test: 6 A/B usability tests with sustainability managers and action owners. They liked the consistency with the rest of the software, the clear wording and the topic to detail structure. They asked for flexible deadlines (annual versus long term targets), metric suggestions, clearer responsibility and action progress in the side drawer.
Iterate & hand off (4 weeks of design, 1 week of support): I designed email notifications and an onboarding product tour, then handed over with documentation and regular design reviews.
Outcome
Estimated from product usage over the first 3 months after launch:
45% less time spent on manual goal tracking and updates
30% fewer reporting errors
25% higher task completion
70% higher adoption of goal tracking features
"Defining goals and measures is really simple and intuitive.” (HR Department Head)
“The linking of measures is great, it reflects how things often work in projects and goal settings.” (Sustainability Manager)
“The AI recommendations are fantastic! They make everything so much easier and smarter to use.” (Fleet Manager)
Learnings
Involve users early and iterate: it led to a solution people found clear and relevant.
Work closely with developers: it kept the design feasible and reduced rework.
Design for extension: thinking about future needs saved time during implementation.
What I would do differently today
AI tools were still very early when I worked on this. Today I would:
Synthesize research faster: use Claude Projects to collect interview notes and synthesize themes, while I decide what matters.
Explore and test earlier: use Claude Design and Figma Make to produce A/B variations quickly and put more of them in front of users.
Prototype the AI itself: use Claude Code to build working suggestions with real outputs, so metrics and deadlines are tested with real data.
Note: The screens shown were recreated, anonymized and adapted: data, copy and branding are invented to protect confidential product details.
