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Feedback and research · Jan – Sep 2026
Stop guessing. Start counting.
I built how Lula hears its users. My team watches real sessions, logs every signal from calls and Slack, prototypes answers in hours, studies eight competitors and brings the evidence into the room where priority is set.
- Design
- Me and three product designers
- Readers
- Product, operations, go-to-market, engineering, leadership
- Tools
- FullStory, Notion, Linear, Slack, Netlify, Claude
- Timeline
- Jan 29 – Sep 29, 2026
Prototypes and audits built by the design team. Sample data; competitor views are approximations for internal study.
Summary · 2 minutes
The short version.
1,050feedback items by Sep 29, up from 14 in June
6 daysfrom a FullStory session to a shipped fix
41prototype and tool sites in under five months
42feedback rows cited in the Pro sprint
The problem
Product feedback was scattered across dozens of Slack channels and call summaries. One call could fan out into five messages. The feedback database held 14 items in March and April, and none in May.
The approach
- 1Instrument first, then shipSession replay in place before a launch, so there is something to watch.
- 2Every signal becomes a rowOne tracker, scored the same way, so a loud channel doesn’t outrank a widespread problem.
- 3Prototype before you debateIf an argument runs past ten minutes, build both versions and react to working software.
What we did
01Watched every jobSix fixes from FullStory sessions during the Flat Rate Catalog beta.
02CountedThe Product Feedback Tracker, fed by three weekday listeners.
03Ran the Pro sprintEvidence cards from 100 tracker items set the order.
04ComparedAI audits of eight competitors, clickable before the meeting.
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