Feature Request Prioritizer

The Challenge

Feature requests come from everywhere: support tickets, sales calls, customer feedback. Prioritizing them fairly based on impact and effort requires extensive analysis.

The AI Desk Solution

AI Desk scores feature requests by analyzing customer segments, revenue impact, and implementation complexity.

The Workflow

Step 1: Request Aggregation

Sources: Support, sales, NPS, Productboard
Scope: All open feature requests

Step 2: Impact Analysis

• Customer segment weighting
• Revenue correlation
• Effort estimation

Step 3: Priority Stack

📊 Feature Priority: April 2026

REQUESTS ANALYZED: 156
NEW THIS MONTH: 23

TOP PRIORITY (High Impact, Low Effort)

#1 Mobile App Notifications
├── Requests: 89 customers
├── Revenue at stake: $420K ARR
├── Segments: Enterprise (60%), MM (40%)
├── Effort: 2 sprints
├── RICE Score: 892
└── Recommendation: Next sprint

#2 Custom Dashboard Widgets
├── Requests: 67 customers
├── Revenue at stake: $310K ARR
├── Segments: Enterprise (80%)
├── Effort: 3 sprints
├── RICE Score: 645
└── Recommendation: Q2 roadmap

#3 Slack Thread Support
├── Requests: 54 customers
├── Revenue at stake: $180K ARR
├── Effort: 1 sprint
├── RICE Score: 580
└── Recommendation: Quick win

EVALUATE (High Impact, High Effort)
├── API v2 redesign (45 requests)
├── Multi-language support (38 requests)
└── Offline mode (29 requests)

DEPRIORITIZE (Low Impact)
├── Dark mode (cosmetic only)
├── Custom fonts (3 requests)
└── Legacy export format (deprecated)

SEGMENT INSIGHTS
├── Enterprise wants: Dashboards, SSO
├── Mid-market wants: Mobile, integrations
└── SMB wants: Simplicity, pricing

Value Proposition

• Time Saved: 4 hours per cycle
• Data-Driven: Objective prioritization
• Customer-Centric: Real demand signals