The starting point
Customer feedback is only as valuable as the decisions it influences. At doinstruct, it flowed in from many sources simultaneously — customer calls, Intercom, Slack, Sales — but there was no place where everything came together, and no process that carried it through to concrete product decisions. Valuable insights evaporated, patterns stayed invisible, and it was unclear which topics actually had weight.
I designed and built this system alone.
Customer calls — the source at the root
The qualitative raw material comes from me directly: I run the customer calls where it becomes visible where users get stuck in the product, which workarounds they’ve found, and which features they’re asking for. These direct conversations are the most valuable feedback source — and the starting point for everything else.
Building the Notion infrastructure
I designed and built the research hub “User Research @doinstruct” in Notion — the place where all feedback and research work comes together. This includes structured databases: one for research per product topic, one for customer visits and feedback sessions, and a feedback archive as long-term memory so insights stay findable and aren’t gathered twice.
The weekly feedback report
The operational centrepiece: a recurring report that bundles the feedback of a week. One block summarises my customer calls (based on conversation transcripts), a second covers requests from CS and Sales. Each request is captured as a structured ticket and assigned to a product area — turning individual voices into patterns, making recurring themes visible.
To make daily feedback strategically usable, I map each requested feature to one of the company’s five Strategic Bets — following clear rules I defined for this, including transparent labelling of interpretations and clean handling of topics that belong to multiple bets (no double-counting).
System Flow — How feedback moves from source to product decision
Analysis and visualisation
I analyse which product areas and which Strategic Bets are most requested in any given week and prepare this visually — overviews and charts that show concentrations and distributions at a glance. This makes the picture comparable across weeks and trends become recognisable.
Currently building an additional internal newsletter that carries the most important insights regularly to the wider company — feedback knowledge shouldn’t stay locked in the product team.
The hardest part wasn't building the system — it was defining the rules for how to categorise ambiguous feedback. When a customer request could belong to multiple strategic priorities, arbitrary mapping creates misleading data. Building transparent, consistent rules for edge cases turned out to be as important as the infrastructure itself.
On systems thinking as a design skill
This project sits at the intersection of research and systems thinking. The design challenge wasn’t an interface — it was an information architecture. What made it work was treating the feedback system like a product: with clear users (the product and engineering team), clear jobs to be done, and a deliberate decision about what to include and what to leave out.