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

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

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

What Data Is Shown

When you select a non-time category:

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:

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 - Time-based analysis
• How to Create a Chart - Chart creation basics
• Interactive Charts - Drilling and filtering