Why Vote Count Is Misleading
Consider two features on your board. Feature A has 200 votes, mostly from free-tier users. Feature B has 20 votes, all from subscribers paying $50/month. If you sort by votes, you build Feature A. If you sort by revenue, you build Feature B — and protect $1,000/month in recurring revenue. MRR tracking makes this distinction visible.
How Revenue Weighting Works in Practice
When users vote on features, FeaturePulse looks up their subscription tier and sums the MRR across all voters. Each feature request displays both its vote count and its total revenue weight. You can sort your board by either metric — but sorting by revenue consistently leads to better retention outcomes.
- Pass subscription data via the SDK or API
- FeaturePulse associates each vote with a revenue amount
- Feature requests display total MRR from all voters
- Sort your board by revenue to find the highest-impact items
Combining Revenue with Engagement Data
Revenue alone tells you who pays, but not who's at risk. A $99/month subscriber who hasn't opened your app in two weeks is a churn risk. Their feature request is urgent. Combine revenue data with power user and ghost user tracking to identify which high-value users need attention now.
Don't ignore free users entirely. Some free users are future paying customers. Use revenue weighting as a primary signal, not the only signal. Track churn indicators across all segments.
Setting Up Revenue-Based Prioritization
Connect your subscription data to FeaturePulse by passing payment information through the SDK. If you use RevenueCat, the MRR integration makes this a one-line setup. For custom billing systems, use the REST API to sync subscription amounts.