StoriesOnBoard Research¶
Strategic research and analysis for StoriesOnBoard product development.
Market Research: AI Agent Differentiation¶
Central Question: How do we differentiate our AI agent in a market where everyone has agents?
Key Findings¶
| Gap | Opportunity |
|---|---|
| Story Mapping AI | No competitor has methodology-aware story mapping |
| Feedback → Story Maps | Productboard stops at flat features, not journeys |
| Cross-Project Learning | No tool learns across customers' projects |
Shortlisted Directions¶
- Story Mapping Methodology Agent — "The only AI that understands story mapping"
- Feedback → Story Map Pipeline — "Turn customer voices into user journeys"
- Cross-Project Pattern Learning — "Learn from thousands of story maps"
Reports¶
| Report | Description |
|---|---|
| Executive Summary | 1-page overview of findings and directions |
| Agent Landscape Analysis | Full competitive analysis (14 tools) |
| Strategic Directions | 5 directions with assessments |
| Competitor Research | Individual tool analyses (14 files) |
Feedback Research: Customer Feature Requests¶
Analysis of customer feedback from two sources to extract feature requests and product insights.
Data Overview¶
| Source | Raw Input | Feature Requests | Unique Topics | Date Range |
|---|---|---|---|---|
| Intercom | 2,482 conversations (1,730 after noise filtering) | 708 | 487 | Jan 2025 - Jan 2026 |
| Slack #feedback | 92 daily threads (staff-curated) | 144 | 141 | Jan 2025 - Jan 2026 |
| Total | 852 |
Methodology¶
Intercom (direct customer conversations):
- Export full conversation threads from Intercom API
- Noise filtering: Removed 752 auto-replies, spam, system emails (30%)
- LLM-assisted extraction of explicit and implicit requests
- Customer enrichment (plan, workspace) from Intercom contacts
Slack (staff-curated feedback):
- Export from internal #feedback channel where staff shares customer requests
- LLM extraction handling bilingual content (Hungarian discussion + English quotes)
- Staff-highlighted flag as prioritization signal
Explicit vs Implicit: We capture both direct asks ("please add X") and implicit signals (complaints, workarounds, churn reasons).
Reports¶
| Report | Description |
|---|---|
| Intercom Requests | 708 requests with customer segmentation and source links |
| Slack Requests | 144 staff-curated requests from #feedback channel |
Last updated: February 2026