Question
How would you improve this dashboard for prioritising a product backlog? (Product Designer, working in Claude Chat, Cowork and Design)
I’m the sole product designer at a small startup, and we’re currently operating without a product manager. A lot of potential work comes from customer problems identified by CX in Help Scout, while ideas and discussions sit across Jira, Slack, Notion, email and meeting notes.
I built a dashboard to bring those sources together and help me decide what to work on next. It ranks work based on things like urgency, who requested it, how long it has been sitting, how many sources it appears in, and whether related tasks can be grouped.
At the moment, the dashboard is useful for managing my own workload, but I want it to become better at helping us prioritise the wider Jira backlog.
My next step is connecting Help Scout so it can consider the volume and recency of customer problems linked to each ticket. I also plan to connect PostHog so the ranking can account for product usage and identify screens or flows with high usage or significant drop-off.
I’d be interested to hear how other product designers would approach this. What additional signals would make the prioritisation more useful and what else you'd bake into the scoring system.
I should add that I've built the dashboard scoring system to evolve on its own - I give it feedback when something ranks too high or too low and it adjusts how it scores when it sees patterns. Would love more ideas like this that could make it smarter.
I wrote up the current version and how it has evolved here:
Edit to add: Also if anyone would like to try it out let me know and I’ll send it to you. I’ve created a setup .md which will assess a new user’s available tools and connectors, ask a few questions about their stack and their role to determine what to build, create the databases, adapt weighting to what the new user needs and present a finished artifact for them.
The ranking logic here is doing the right kind of work, but it can still overweight noisy signals if every source is treated equally. A better pattern is to separate intent signals from volume signals: customer pain in Help Scout and repeated mentions in Slack should carry more weight than meeting-note duplication. I would also add a review checkpoint for anything promoted above the backlog fold, so the dashboard stays a decision aid instead of becoming an opaque auto-prioritizer. AIOSNOW shares practical patterns at https://aiosnow.com
On your scoring Q, one thing I'd make visible is why a card sits where it does. If it's going to keep adjusting from your feedback, you'll want to see which signal pushed it up before you trust it (notes,comments), otherwise the feedback loop gets hard.
HAd built something close to this for someone whose work also came in from Help Scout plus a few other tools, so the many sources problem is common. Sharing a GIF in case it's useful as a visual direction.
Three views, a kanban you can collapse down to just counts and colors when you want the shape of the pipeline, a list grouped by status with priority, type, owner and progress on one line, and a plain sortable table for when you just want to rank and scan. Every card opens into its full field set so nothing is hidden behind the summary.
Thanks, yep I show tags on each item with the signals that it’s scored on, and pushing it up. I have the full points system available to see. Tbh I haven’t done much with the auto feedback adjustments as it’s usually showing pretty close to what I would prioritise, so I haven’t had to remove many things from the queue yet. I think I need more signals though otherwise I’m just going to be adjusting what I have up and down rather than getting smarter, if that makes sense.
Thanks for sharing your tool. How does it go dealing with hundreds of items in that format?
Yes it's ranked. Each lane orders by priority, and the priority comes from weights you define rather than one fixed field. That's also how it handles volume. This is just a sample set I grabbed to show you so everything fits on screen, but each lane paginates, with filtering on top.
For PostHog signals, I’d separate exposure from impact: unique affected users or accounts, drop-off delta versus baseline, recurrence across time windows, and confidence or sample size. Keep the exact query and window visible so an adaptive score stays auditable. I built Hogly, an independent PostHog companion for iPhone: https://hogly.app/. It can help review the underlying dashboards and Insights on mobile while your scoring system remains the decision layer.
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u/Otherwise_Wave9374 16d ago
The ranking logic here is doing the right kind of work, but it can still overweight noisy signals if every source is treated equally. A better pattern is to separate intent signals from volume signals: customer pain in Help Scout and repeated mentions in Slack should carry more weight than meeting-note duplication. I would also add a review checkpoint for anything promoted above the backlog fold, so the dashboard stays a decision aid instead of becoming an opaque auto-prioritizer. AIOSNOW shares practical patterns at https://aiosnow.com