How opportunities are created
ClosedLoop AI automatically discovers and groups related insights into opportunities using semantic similarity. The system runs daily, finding new themes and updating existing ones as new data arrives — with zero configuration. Large opportunities may contain features — more specific breakdowns within the broader theme.RICE scoring (without the E)
If you’ve used RICE prioritization (Reach, Impact, Confidence, Effort), ClosedLoop AI computes the first three automatically — the parts that come from customer evidence. Effort is intentionally excluded because only your engineering team knows what things cost to build.| Dimension | What it measures |
|---|---|
| Reach | How many customers are affected — computed from unique customer count across linked evidence |
| Impact | How severe the problem is — derived from severity distribution, frustration scores, and deal blocker flags |
| Confidence | How consistent the evidence is — based on signal diversity across sources, time periods, and customer segments |
Velocity
Each opportunity has a growth trend showing whether the problem is getting better or worse:| Status | What it means |
|---|---|
| Accelerating | More customers are raising this — the problem is growing |
| Active | Steady stream of evidence — ongoing issue |
| Cooling | Fewer mentions recently — may be resolving |
| Dormant | No recent evidence — likely resolved or deprioritized |
Revenue impact
Each opportunity shows the business context:- Customer list — every affected customer with their deal value, so you can see the revenue at stake
- Total won deal value — aggregate revenue across all affected customers
- Deal blockers — how many insights are actively blocking sales
- Evidence — top 50 verbatim quotes ranked by business impact, with customer names and severity