Package Size Distribution: What It Tells You About Your Business

Most e-commerce operators know their top-selling products and average order value. But few know their package size distribution—which boxes ship most often, how utilization varies across sizes, and where the opportunities hide.

Package size distribution is one of the most underutilized analytics in e-commerce operations. When you understand it, you unlock insights about inventory optimization, box purchasing, shipping cost reduction, and operational efficiency.

This guide explains how to analyze your package size distribution and what to do with what you find.

Why Package Size Distribution Matters

The Hidden Cost Center

What happens without distribution analysis:

What Good Data Reveals

When you analyze package size distribution, you discover:

1. Which boxes actually ship (vs which you thought would)
2. How well products fit (utilization by box size)
3. Where packers default (behavioral patterns)
4. What's missing (size gaps causing oversizing)
5. Seasonal variation (Q4 multi-item orders vs Q1 single-item)

Key Metrics for Package Size Analysis

1. Box Size Frequency

What it measures: How often each box size is used

How to calculate: ` Frequency = Orders using box size ÷ Total orders `

Example distribution:

What it tells you: Your packing patterns, inventory needs, and potential concentration risk.

2. Box Utilization

What it measures: How well products fill each box size

How to calculate: ` Utilization = Product volume ÷ Box internal volume `

Example analysis:

What it tells you: Which boxes are well-matched to your products vs which waste space.

3. DIM Weight Efficiency

What it measures: How much you pay vs actual product weight

How to calculate: ` DIM Efficiency = Actual Weight ÷ Billable Weight (max of actual or DIM) `

Example analysis:

What it tells you: Where DIM weight is costing you the most.

4. Cost Per Cubic Inch

What it measures: True shipping efficiency

How to calculate: ` Cost per cu in = (Shipping cost + Box cost + Void fill) ÷ Box volume `

Example comparison:

Insight: Larger boxes have lower cost per cubic inch BUT only if you're filling them. An empty large box costs more than a full small box.

How to Collect Package Size Data

Method 1: Manual Tracking

For stores with <200 orders/month:

1. Create a spreadsheet with columns: Date, Order ID, Box Used, Products Shipped
2. Have packers record each order for 2-4 weeks
3. Calculate frequencies and patterns

Pros: Low cost, immediate start

Cons: Labor-intensive, prone to errors, temporary

Method 2: Shipping Software Export

For stores using ShipStation, Shippo, or similar:

1. Export shipment data (include package dimensions)
2. Cross-reference with order data
3. Calculate size distributions

Pros: Automated, historical data available

Cons: May not track actual box used (just entered dimensions)

Method 3: Barcode Scanning

For stores with 500+ orders/month:

1. Assign barcodes to each box size
2. Scan box during packing
3. System records automatically

Pros: Accurate, real-time, minimal packer effort

Cons: Setup cost, requires system integration

Method 4: Dedicated Analytics Tools

Box recommendation software like BoxBuddy tracks automatically:

• Recommended vs actual box used
• Utilization by order
• Trends over time
• Optimization opportunities

Analyzing Your Distribution: A Framework

Step 1: Generate Frequency Report

Sample output:

What to look for:

• Top 3 sizes should handle 60-70% of orders
• Long tail (sizes under 5% each) indicates potential consolidation
• Missing common sizes (no 7×5×3?) indicates gaps

Step 2: Calculate Utilization by Size

Sample output:

What to look for:

• Sizes averaging <50% utilization need attention
• High standard deviation indicates inconsistent usage
• Very high utilization (>85%) might indicate underfitting risk

Step 3: Identify Patterns

Look for correlations:

Step 4: Segment by Order Type

Single-item vs multi-item:

Insight: Multi-item orders typically show worse utilization—they're harder to right-size without bin-packing logic.

Step 5: Calculate Financial Impact

For each underperforming size, calculate:

` Monthly Waste = Orders × (Oversized shipping cost - Optimal shipping cost) `

Example:

Common Patterns and What They Mean

Pattern 1: The Default Box Problem

What it looks like:

• One size used 35-50% of the time
• That size has 40-50% utilization
• Other sizes have 60-75% utilization

Diagnosis: Packers defaulting to a "safe" choice rather than right-sizing.

Solution:

• Make right-sized recommendations visible at packing stations
• Change physical box placement (optimal sizes most accessible)
• Track and incentivize utilization, not just speed

Pattern 2: The Missing Middle

What it looks like:

• Jump from 8×6×4 to 12×10×8 with nothing between
• 10×8×6 or similar is chronically oversized
• Products in the 300-600 cu in range are always loose

Diagnosis: Box lineup has a gap.

Solution: Add intermediate size (e.g., 10×8×5 or 9×7×5) to fill the gap.

Pattern 3: The Redundant Sizes

What it looks like:

• 10×8×6 and 11×9×6 both exist
• Both have <40% utilization
• Combined usage is 15-20%

Diagnosis: Overlapping sizes that don't add value.

Solution: Eliminate one, standardize on the other, train packers.

Pattern 4: The Seasonal Shift

What it looks like:

• Q4 distribution shifts heavily toward larger sizes
• Multi-item order frequency doubles
• Utilization drops 10-15% across the board

Diagnosis: Gift-giving season changes order patterns.

Solution:

• Adjust inventory mix seasonally
• Optimize multi-item packing before peak
• Consider seasonal box sizes

Pattern 5: The Product Outlier

What it looks like:

• One product category always uses oversized boxes
• That product has 25-35% utilization
• It represents 10-20% of orders

Diagnosis: Product dimensions don't fit standard lineup.

Solution: Add custom size for that product or redesign product packaging.

Building the Optimal Box Lineup

The Ideal Distribution

Target state for most e-commerce operations:

Framework for Optimization

Step 1: Keep sizes with:

• >10% of orders AND >60% utilization
• Unique role (smallest, mailer, etc.)

Step 2: Consolidate sizes with:

• <5% of orders
• Similar dimensions to another size
• Consistently low utilization

Step 3: Add sizes where:

• Gap exists between popular sizes
• Product analysis shows consistent oversizing
• DIM efficiency is particularly poor

Example Optimization

Before (10 sizes):

After (7 sizes):

Result: 3 fewer sizes, better utilization, simpler operations.

Using Data for Purchasing Decisions

Forecasting Box Needs

Basic formula: ` Monthly Need = Monthly Orders × Box Size % × (1 + Safety Stock %) `

Example:

• 2,000 orders/month
• 10×8×6 = 22% of orders
• Safety stock = 20%

` Need = 2,000 × 0.22 × 1.2 = 528 boxes/month `

Optimizing Inventory Investment

Analyze by value:

Insight: Reducing to optimal sizes reduces inventory value by 20-30% while improving availability.

Advanced Analysis: Multi-Item Order Optimization

The Multi-Item Challenge

Single-item orders are easy—match product to box. Multi-item orders are where utilization falls apart:

Bin-Packing Analysis

Track multi-item orders separately:

Insight: If multi-item orders are >20% of your business and utilization is <50%, bin-packing software pays for itself quickly.

Reporting and Dashboards

Key Reports to Generate

Weekly:

• Size frequency (any unusual spikes?)
• Low utilization alerts (orders <40%)
• Stockout risk (sizes running low)

Monthly:

• Full distribution analysis
• Utilization trends by size
• Cost impact calculations
• Recommendations

Quarterly:

• Box lineup optimization review
• Seasonal pattern analysis
• Purchasing forecast

Dashboard Metrics