Favorite Dashboard Cards
Pin your most-used Cluster Dashboards and Saved Searches to the homepage. These appear as interactive cards with trend visualizations that update automatically.What Can Be Favorited?
- Cluster Dashboards
- Saved Searches
Cluster Dashboards group related signals into themed clusters. When favorited, they display as trend cards showing how that cluster’s signals are changing over time.Use cases:
- Track feature request volume trends
- Monitor bug report severity over time
- Watch sentiment shifts for specific topics
You can also favorite Folders, Documents, Recordings, and individual Signals. These appear as quick-access links on your homepage rather than dashboard cards with visualizations.
How to Add a Favorite
1
Navigate to the Dashboard or Saved Search
Go to Explore > Clusters for cluster dashboards, or Explore > Saved Searches for datasets.
2
Open the Item
Click into a specific cluster dashboard or saved search to open it.
3
Click the Star Icon
In the top-right title bar, click the star icon. It turns yellow when favorited. You’ll see a confirmation: “Added to favorites.”
4
View on Homepage
Return to your homepage. Your favorited item now appears as an interactive dashboard card in the Favorites section.
Dashboard Card Options
Each favorite card can be customized with different visualization types and time ranges.Changing the Visualization
Click the three-dot menu in the top-right corner of any favorite card to access options:| Visualization | Description |
|---|---|
| Volume Trend | Sparkline showing signal count over time (default) |
| Sentiment Trend | Sparkline showing average sentiment progression |
| Severity Trend | Sparkline showing average severity levels |
| Impact Distribution | Stacked bar chart showing impact type breakdown |
| Metrics Only | Simple count display without charts |
Selecting a Time Range
From the same three-dot menu, choose your preferred time window:| Time Range | Best For |
|---|---|
| Last 7 days | Real-time monitoring, daily standups |
| Last 30 days | Weekly reviews, sprint planning |
| Last 90 days | Quarterly analysis, trend identification |
| Last 365 days | Annual reviews, long-term patterns |
Time range preferences are saved per card in your browser, so each dashboard can have its own time window.
Understanding Trend Indicators
Each dashboard card shows trend information to help you spot changes:Trend Arrows
- Up arrow (green): Metric increased compared to previous period
- Down arrow (red): Metric decreased compared to previous period
- Flat indicator: No significant change
Percentage Change
The percentage shown indicates how much the metric changed compared to the equivalent previous period. For example, with “Last 7 days” selected, it compares to the 7 days before that.Volume Trend Details
Volume Trend Details
Shows the count of signals matching your filter over time. Useful for tracking:
- Are feature requests increasing or decreasing?
- Is a bug getting more reports?
- How active is feedback from a specific segment?
Sentiment Trend Details
Sentiment Trend Details
Shows average sentiment (-10 to +10) over time. Useful for tracking:
- Is customer satisfaction improving?
- Did a release impact sentiment?
- How do different segments feel about your product?
Severity Trend Details
Severity Trend Details
Shows average severity level over time. Useful for tracking:
- Are issues becoming more critical?
- Is your team addressing high-severity feedback?
- How urgent is feedback in a specific area?
Impact Distribution Details
Impact Distribution Details
Shows breakdown by impact type (Revenue, Adoption, Satisfaction, Retention, Efficiency). Useful for tracking:
- What business areas does this feedback affect?
- Is this feature request about growth or retention?
- How should you prioritize based on impact?
Managing Favorites
Removing a Favorite
To remove a dashboard card from your homepage:- Go to Explore > Clusters or Explore > Saved Searches
- Open the item you want to unfavorite
- Click the yellow star icon in the top-right to unfavorite
- You’ll see a confirmation: “Removed from favorites”
Reordering Favorites
Favorite cards appear in the order they were added. To change the order, remove and re-add items in your preferred sequence.Example Dashboard Setups
Product Manager View
| Favorite | Visualization | Time Range |
|---|---|---|
| ”Feature Requests” cluster | Volume Trend | 30 days |
| ”Bug Reports” cluster | Severity Trend | 7 days |
| ”Enterprise Customers” saved search | Sentiment Trend | 30 days |
Customer Success View
| Favorite | Visualization | Time Range |
|---|---|---|
| ”Churn Risk Signals” saved search | Volume Trend | 7 days |
| ”Support Issues” cluster | Severity Trend | 7 days |
| ”Praise & Wins” cluster | Volume Trend | 30 days |
Leadership View
| Favorite | Visualization | Time Range |
|---|---|---|
| ”All Feedback” saved search | Impact Distribution | 90 days |
| ”Competitor Mentions” cluster | Volume Trend | 30 days |
| ”Strategic Themes” cluster | Sentiment Trend | 90 days |
Best Practices
Pin what matters most: Only favorite dashboards you check regularly. Too many cards can be overwhelming.
Use appropriate time ranges: Match the time range to how you use the data. Daily standup? Use 7 days. Monthly review? Use 30 days.
Mix visualization types: Use volume trends for quantity tracking, sentiment trends for satisfaction monitoring, and impact distribution for prioritization.
Create focused saved searches: The more specific your saved search filter, the more actionable the trend data will be.
Review trends regularly: Dashboard cards are most valuable when checked consistently to spot patterns early.
Workspace Stats
In addition to your favorite dashboard cards, BuildBetter displays aggregate metrics for your workspace:Hours Saved
Estimated time saved through AI automation based on your usage
Documents
Total documents created in your workspace
Recordings
Total calls and meetings recorded
Items Tagged
Number of tags applied to recordings
Signals
Total insights extracted from conversations
Answers
AI assistant responses generated
Workspace stats appear automatically once you have data. No setup required.