From reviews to ranked gaps
Signal Vector helps you move from raw App Store feedback to product opportunities you can inspect, compare, save, and monitor.
Step 1
Choose a market
Start with a focused category or competitor set instead of chasing broad trends.
Step 2
Read the reviews
Collect public App Store reviews and ratings from the apps that define that space.
Step 3
Find the patterns
Group repeated complaints, requests, and workflow friction into clear themes.
Step 4
Score the gaps
Rank each gap by frequency, intensity, rating pressure, demand, and build complexity.
Step 5
Check the evidence
Open each signal to inspect the reviews, category context, and scoring rationale.
Step 6
Monitor changes
Save promising gaps and keep watching the category as new review pain appears.
What you get
Evidence first, ideas second.
The workflow is designed to keep you close to the source material: real reviews, repeated pain, and transparent scoring. It should help you decide what is worth deeper research, not hand you a vague app idea.
Research operating loop
A compact view of how raw review data becomes a scored, reviewable opportunity signal.
| Phase | Input | Output | Action |
|---|---|---|---|
| Scope | Category or app set | Focused review corpus | Pick the categories and competitors worth tracking. |
| Cluster | Recent App Store reviews | Complaint clusters | Group similar complaints and feature requests. |
| Score | Clusters and app metadata | Opportunity score | Compare frequency, rating pressure, intensity, and build effort. |
| Review | Ranked opportunities | Research decision | Inspect review evidence before saving or ignoring a signal. |
| Monitor | Saved opportunities | Monitoring queue | Watch saved markets as new review pain appears. |