# Visualizing Data not by Time

Analyze data by categories, segments, and distributions

While Scoop excels at time-series analysis, many business questions don't involve time as the primary dimension. You may want to compare regions, analyze product mix, or understand customer segments. Scoop's category-based visualization handles these scenarios.

# When to Use Category Analysis

| Analysis Type | Example Question |
|---------------|------------------|
| **Distribution** | How is revenue distributed across regions? |
| **Comparison** | Which products perform best? |
| **Segmentation** | What's the breakdown by customer type? |
| **Ranking** | Who are our top performers? |
| **Composition** | What's our product mix? |

# Switching to Category Mode

By default, Scoop analyzes data over time. To switch to category-based analysis:

1. Enable **Advanced Mode** using the slider in the upper right

<Image align="center" width="300px" src="https://docs.scoopanalytics.com/assets/legacy/3c16ff2-image-6939be9707.png" />

2. Select a category column for your primary axis (instead of Time)

## What Data Is Shown

When you select a non-time category:

| Dataset Type | Data Shown |
|--------------|------------|
| **Transactional** | All records in the dataset |
| **Snapshot** | Most recent snapshot only |

This ensures you're analyzing current state when time isn't the focus.

# Building Category Visualizations

## Step 1: Choose Your Category Axis

Select the dimension you want to analyze by:

![](https://docs.scoopanalytics.com/assets/legacy/ec379f9-image-b186f855cc.png)

**Common category axes:**
- Region, Territory
- Product, Product Line, Category
- Sales Channel, Source
- Customer Segment, Account Type
- Rep, Team, Manager

## Step 2: Add Grouping (Optional)

Add a second dimension to break down each category:

![](https://docs.scoopanalytics.com/assets/legacy/b6b18da-image-90842729fa.png)

**Example combinations:**
- Revenue by Region, grouped by Product
- Deals by Stage, grouped by Sales Rep
- Customers by Segment, grouped by Industry

## Step 3: Select Chart Type

Choose a visualization that fits your analysis:

| Chart Type | Best For |
|------------|----------|
| **Bar Chart** | Comparing values across categories |
| **Pie/Donut** | Showing composition (parts of whole) |
| **Stacked Bar** | Comparing with breakdown by group |
| **Table** | Detailed category data with multiple metrics |

# Common Category Analysis Patterns

## Regional Performance

```
Category Axis: Region
Metrics: Revenue, Deal Count, Win Rate
Grouping: Product Line

Shows: How each region performs across products
```

## Sales Pipeline by Stage

```
Category Axis: Stage
Metrics: Pipeline Value, Deal Count
Grouping: Sales Rep

Shows: Pipeline distribution and rep focus areas
```

## Product Mix Analysis

```
Category Axis: Product Category
Metrics: Units Sold, Revenue, Margin
Grouping: Customer Segment

Shows: Which products appeal to which customers
```

## Customer Segmentation

```
Category Axis: Customer Tier
Metrics: Avg Deal Size, Lifetime Value
Grouping: Industry

Shows: Customer value across segments and industries
```

# Multiple Dimensions

You can layer multiple dimensions for deeper analysis:

1. **Primary category**: Main grouping (e.g., Region)
2. **Secondary grouping**: Breakdown within each category (e.g., Product)
3. **Filters**: Focus on specific subsets (e.g., only Q4 data)

## Example: Multi-Dimensional Analysis

```
Primary: Sales Channel (Direct, Partner, Online)
Group by: Deal Size Bucket (Small, Medium, Large)
Filter: This Quarter Only
Metric: Revenue, Count

Result: See which channels excel at which deal sizes
```

# Combining Time and Category

You can use category analysis alongside time:

## Time-Filtered Category Analysis

1. Set category as your axis (e.g., Product)
2. Use time as a filter (e.g., Last Quarter)
3. See category breakdown for that time period

## Comparing Periods by Category

Create multiple visualizations:
- Category breakdown for Current Period
- Same breakdown for Prior Period
- Place side-by-side on a canvas for comparison

# Best Practices

## Choosing Categories

- Select categories with meaningful, distinct values
- Avoid categories with too many values (>20 gets hard to read)
- Consider grouping small categories into "Other"

## Effective Groupings

- Pair complementary dimensions (Region + Product, not Region + Territory)
- Use grouping to answer "what's driving this?" questions
- Limit to 2 dimensions to keep visualizations readable

## Chart Selection

- Use bar charts for comparisons (rankings, performance)
- Use pie charts sparingly (only for composition with fewer than 6 categories)
- Use tables when you need multiple metrics per category

# Drilling Down

Category visualizations support drilling:

1. Click any bar, slice, or row
2. Select a dimension to drill by
3. See the breakdown of that specific category

**Example drill path:**
- Start: Revenue by Region
- Click: "West" bar
- Drill by: Sales Rep
- Result: West region revenue by each rep

# Troubleshooting

## No Data Showing

- Verify you're not filtering out all data
- Check if snapshot dataset has data for today
- Ensure the category column has non-null values

## Too Many Categories

- Add a filter to limit to relevant categories
- Create a calculated field to group small categories
- Use Top N filtering to show only largest

## Unexpected Totals

- Check if you're looking at snapshot vs. full history
- Verify filters aren't excluding data
- Confirm you're aggregating correctly (sum vs. count vs. average)

# Related Topics

- [Charting Time Series Data](charting-time-series-data) - Time-based analysis
- [How to Create a Chart](chart-types/how-to-create-a-chart) - Chart creation basics
- [Interactive Charts](../Canvases/canvas-objects/interactive-charts-and-tables) - Drilling and filtering