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:

Enable Advanced Mode using the slider in the upper right

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:

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:

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:

Primary category: Main grouping (e.g., Region)
Secondary grouping: Breakdown within each category (e.g., Product)
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

Set category as your axis (e.g., Product)
Use time as a filter (e.g., Last Quarter)
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 <6 categories)
Use tables when you need multiple metrics per category

Drilling Down

Category visualizations support drilling:

Click any bar, slice, or row
Select a dimension to drill by
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)

Visualizing Data not by Time