Historical Evolution
EYE doesn't just show you a snapshot — it tracks every metric across all analysis runs to reveal trends over time. The evolution view is your window into how your brand's AI presence is changing.
How It Works
Every time you run an analysis, EYE stores a complete snapshot of all your metrics. Over multiple runs, these snapshots build a rich time series that powers the evolution view.
Accessing Evolution Data
Every card in the dashboard has a rotate icon in the header. Click it to flip the card and see its historical data visualized as interactive charts.
Chart Types
Toggle between two chart modes:
- Line charts — Best for continuous trends (GSEO score, sentiment, mentions over time)
- Bar charts — Best for comparative data (share of voice by period, platform breakdown over time)
What Gets Tracked
| Metric | Evolution Shows |
|---|---|
| GSEO Score | Overall score and each component (visibility, position, sentiment, quality) over time |
| Share of Voice | Your brand's mention percentage vs. competitors, run over run |
| Sentiment | Sentiment trajectory — improving, declining, or stable |
| Brand Mentions | Total mention counts and growth rates |
| Competitor Momentum | Which competitors are gaining or losing ground |
| Content Gaps | Whether gaps are closing or widening |
| Source Shifts | Changes in which domains AI platforms cite |
| Journey Performance | Stage-level metrics over time |
Trend Detection
EYE automatically surfaces significant trends:
- Momentum shifts — When a competitor starts gaining ground rapidly
- Score declines — When your GSEO score drops between runs
- Sentiment changes — When sentiment shifts direction
- Emerging competitors — When a new brand starts appearing in AI responses
The Action Plan uses historical evolution data to create trend-aware recommendations. If a competitor's mentions grew +40% over the last month, the plan flags it as urgent and creates defensive actions.
Run-Over-Run Comparison
In the Runs tab, you can select any two analysis runs and compare them directly:
- Which metrics improved?
- Which metrics declined?
- What changed in the competitive landscape?
- Did your optimization efforts have an impact?
Best Practices
- Run analyses regularly — Weekly or bi-weekly runs give you the best trend resolution
- Look for patterns — A single dip isn't a trend; three consecutive drops is
- Correlate with actions — Track whether your optimization efforts are moving the needle
- Use scheduling — Set up recurring runs so you never miss a data point
- Check competitor trends — Your score might be stable, but if competitors are growing, you're effectively declining