Charts used in AI-Powered Analytics fit into three main categories:
- Comparison (comparing values).
- Distribution (understanding how data is spread).
- Composition (showing how parts make up a whole).
When customizing your dashboard, you can edit and evolve each chart type based on your requirements.
Comparison Charts
Comparison data shows how different items, categories or groups relate to each other based on one or more specific metrics.
This allows you to identify trends by highlighting differences or similarities between data points.
Examples could include:
- Forecasted rebate compared with actual rebate by dimension or attribute.
- Comparing effective rebate rate by dimension or attribute.
- Comparing total rebate earnings by trading partner or dimension attribute.
Bar, column, line, KPI, pivot table, radar, and pareto are types of visuals that can be used for comparison.
Distribution Charts
Distribution data shows how data points are spread across different categories, often focusing on how values cluster, spread, or vary.
This allows you to identify patterns or outliers in your data.
Examples could include:
- Showing how non-rebated products are distributed by revenue/margin.
- Showing how products/trading partners are distributed by revenue/rebate rate.
- Showing branches that have higher revenue.
Scatter and heatmap are types of visuals that can be used for distribution.
Composition Charts
Composition data shows how individual components contribute to an overall data figure.
This allows you to identify how different subgroups or categories make up the total, helping to visualize the proportions of data.
Examples could include:
- The composition of trading program activation or renewal statuses.
- The composition of payments within the Finance app.
Stacked bar, stacked column, donut, area, and waterfall are types of visuals that can be used for composition.
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