Building a BI layer for Product Hunt launches — what metrics would you like to see?
One recurring piece of feedback after launching PH Radar was that makers want more context, not just rankings.
I'm currently experimenting with a BI layer that analyzes Product Hunt activity beyond the daily leaderboard.
Current prototype dashboards include::
• Launches by category
• Votes by category
• Hunter activity
• Comment-to-vote ratios
• Historical launch trends
My goal is to help makers answer questions like:
• Which categories are becoming more competitive?
• Which hunters are consistently active?
• When is the best time to launch?
• What engagement patterns correlate with successful launches?
If you could see any Product Hunt metric or visualization, What Product Hunt metrics, comparisons, or historical trends would help you make better launch decisions?
Replies
@m_m_carvalho Interesting idea. I'd personally love to see repeat launch performance , how second , third, or fourth launches compare to a maker's first one. That could reveal a lot about what experience and community building actually contribute over time.
I really like the shift from rankings to context. For launch planning, the most useful metrics for me would be category-specific benchmarks, not just global ones.
I’d want to see things like:
• Median votes/comments by category, not only top launches
• How competitive each category is over time
• Comment-to-vote ratio by category
• Early traction curves: first 1h, 3h, 6h vs final position
• Best launch days/times by category
• Hunters with consistent activity in specific niches
• Similar past launches and how they performed
The biggest value would be helping makers answer: “What does a strong launch look like for a product like mine?” Not just “who won today?”
WebCurate.co
I'd be very interested in seeing the relationship between comments and rankings. For example, do products with higher comment-to-vote ratios tend to perform better? And how early in the launch day do those comments need to happen to make a difference?
I'd also love to see launch difficulty over time. It feels like the number of launches is increasing rapidly, but having actual data showing how many votes/comments were needed to reach Top 5 or #1 each month would be really useful for planning a launch.
I would love to see comparisons between similar products launched in the same category. That would help set realistic expectations.
Upvote velocity by hour (not just total count), conversion from tool page to signup, and where traffic is coming from - organic PH vs. maker's own network. Those three together tell you if a launch is actually working or just getting a popularity boost from the maker's existing audience.
As a data analyst who just launched: the metric I wanted and could not get from the leaderboard was a proxy for conversion, not rank. Final position told me almost nothing about whether the launch actually sent people who signed up. I only learned that by tagging my own links.
PH cannot see off platform conversion, but it can compute proxies that correlate with it. Three I would build:
Maker responsiveness: time to first reply and reply rate in the first 6 hours. Responsive makers convert and retain better, and it is invisible on the leaderboard.
Engagement depth, not volume: unique commenters per 100 upvotes, and how comment-to-vote moves over time rather than one final figure. Thirty real conversations beat three hundred drive-by upvotes.
Velocity shape: points in the first 3 hours versus final rank. The curve shows whether a launch had genuine pull or just front-loaded a network.
Comment to vote ratio is already on your list, which is the right instinct. I would just split it by depth and timing rather than a single number.