How to Use Sales Channel Performance in Shopify: Step-by-Step Tutorial

Step 1: Which Channel Generates the Most Revenue?

The first and most fundamental question in channel analysis is identifying your revenue leaders. This metric shows you where the bulk of your sales dollars are coming from and helps prioritize resource allocation.

Exporting Your Shopify Order Data

Begin by accessing your Shopify admin panel and navigating to the Orders section:

1. Log into your Shopify admin dashboard
2. Navigate to Orders in the left sidebar
3. Click the Export button in the top right corner
4. Select your date range (we recommend at least 90 days for meaningful insights)
5. Choose "All columns" to ensure you capture channel information
6. Export as CSV format

Analyzing Revenue by Channel

Once you have your export, you'll need to aggregate revenue by sales channel. Here's how to approach this analysis:

# Example Python code to analyze channel revenue
import pandas as pd

# Load your exported Shopify data
df = pd.read_csv('shopify_orders.csv')

# Convert financial_status to filter only paid orders
df_paid = df[df['financial_status'].isin(['paid', 'partially_refunded'])]

# Group by sales channel and sum total price
channel_revenue = df_paid.groupby('sales_channel').agg({
    'total_price': 'sum',
    'id': 'count'
}).round(2)

channel_revenue.columns = ['Total Revenue', 'Order Count']
channel_revenue = channel_revenue.sort_values('Total Revenue', ascending=False)

print(channel_revenue)

Expected Output

Your analysis should produce a table similar to this:

Sales Channel          Total Revenue    Order Count
----------------------------------------------------
Online Store           $245,830.50      1,247
Instagram              $89,245.20       623
Facebook Shop          $56,234.80       412
Point of Sale          $34,567.90       289
Amazon                 $28,945.60       156

Key Insights to Extract

• Dominant Channel: Which single channel contributes the most to your total revenue?
• Revenue Concentration: What percentage of total revenue comes from your top channel? High concentration (>70%) suggests dependency risk
• Long Tail Channels: Are there channels generating minimal revenue that might not justify their maintenance costs?
• Growth Opportunities: Which channels show promise but are currently underutilized?

For a more sophisticated analysis using machine learning techniques, consider exploring AdaBoost for predictive modeling to forecast which channels will perform best in future periods.

Step 2: How Does AOV Vary by Channel?

While total revenue tells you where the money comes from, Average Order Value (AOV) reveals customer spending behavior across channels. A channel with lower total revenue but higher AOV might represent a more valuable customer segment worth cultivating.

Calculating AOV by Channel

Average Order Value is calculated by dividing total revenue by the number of orders. However, for accurate insights, you should exclude cancelled and unpaid orders:

# Calculate AOV by channel
channel_aov = df_paid.groupby('sales_channel').agg({
    'total_price': ['sum', 'mean', 'count'],
    'subtotal_price': 'mean',
    'total_discounts': 'mean'
}).round(2)

# Flatten column names
channel_aov.columns = ['_'.join(col).strip() for col in channel_aov.columns.values]
channel_aov = channel_aov.rename(columns={
    'total_price_sum': 'Total Revenue',
    'total_price_mean': 'AOV',
    'total_price_count': 'Orders',
    'subtotal_price_mean': 'Avg Subtotal',
    'total_discounts_mean': 'Avg Discount'
})

channel_aov = channel_aov.sort_values('AOV', ascending=False)
print(channel_aov)

Expected Output

Sales Channel     AOV        Orders    Avg Subtotal    Avg Discount
-------------------------------------------------------------------
Amazon            $185.55    156       $198.20         $12.65
Online Store      $197.05    1,247     $215.30         $18.25
Point of Sale     $119.58    289       $125.40         $5.82
Facebook Shop     $136.49    412       $148.90         $12.41
Instagram         $143.26    623       $156.70         $13.44

Interpreting AOV Differences

Several factors influence why AOV varies across channels:

• Customer Intent: Customers on your main online store may browse more extensively, adding multiple items to cart, while social media buyers often purchase single items they discovered
• Product Mix: Different channels may naturally attract customers interested in different product categories or price points
• Promotional Activity: Channels with heavy discounting will show lower AOV but might compensate with higher volume
• Checkout Friction: Channels with streamlined checkout (like Shop Pay) may have higher completion rates but lower cart sizes

Strategic Actions Based on AOV

1. High AOV Channels: Consider increasing ad spend or promotional efforts on channels where customers naturally spend more
2. Low AOV Channels: Implement upselling, cross-selling, or bundle strategies to increase basket size
3. Discount Analysis: If a channel has high discounts but low AOV, your promotions may be too aggressive

To validate whether differences in AOV are statistically significant rather than random variation, apply A/B testing statistical significance methods to your channel comparisons.

Step 3: Which Channels Have the Best Conversion?

Conversion efficiency measures how effectively a channel turns traffic or engagement into actual sales. This is crucial for understanding return on investment, especially for paid channels where you're buying traffic or visibility.

Understanding Channel Conversion Metrics

Unlike website conversion rates (visitors to buyers), channel conversion in Shopify often requires combining order data with traffic data from analytics platforms. However, you can still derive valuable efficiency metrics from order data alone:

# Analyze order completion and refund rates by channel
channel_conversion = df.groupby('sales_channel').agg({
    'id': 'count',
    'financial_status': lambda x: (x == 'paid').sum(),
    'cancelled_at': lambda x: x.notna().sum(),
    'total_price': 'sum'
})

channel_conversion.columns = ['Total Orders', 'Paid Orders', 'Cancelled Orders', 'Revenue']

# Calculate metrics
channel_conversion['Payment Rate'] = (
    channel_conversion['Paid Orders'] / channel_conversion['Total Orders'] * 100
).round(2)

channel_conversion['Cancellation Rate'] = (
    channel_conversion['Cancelled Orders'] / channel_conversion['Total Orders'] * 100
).round(2)

channel_conversion['Avg Revenue per Order'] = (
    channel_conversion['Revenue'] / channel_conversion['Paid Orders']
).round(2)

print(channel_conversion.sort_values('Payment Rate', ascending=False))

Expected Output

Sales Channel      Total Orders  Paid Orders  Payment Rate  Cancel Rate  Avg Rev/Order
-------------------------------------------------------------------------------------
Online Store       1,289         1,247        96.74%        2.87%        $197.05
Amazon             162           156          96.30%        3.70%        $185.55
Point of Sale      295           289          97.97%        1.02%        $119.58
Instagram          645           623          96.59%        3.26%        $143.26
Facebook Shop      429           412          96.04%        3.96%        $136.49

Advanced Conversion Analysis

For channels where you have traffic data, calculate true conversion rates:

# If you have traffic data (from Google Analytics or Shopify Analytics)
traffic_data = {
    'Online Store': {'sessions': 45230, 'orders': 1247},
    'Instagram': {'sessions': 18940, 'orders': 623},
    'Facebook Shop': {'sessions': 12680, 'orders': 412}
}

for channel, data in traffic_data.items():
    conversion_rate = (data['orders'] / data['sessions']) * 100
    print(f"{channel}: {conversion_rate:.2f}% conversion rate")

Expected Output with Traffic Data

Online Store: 2.76% conversion rate
Instagram: 3.29% conversion rate
Facebook Shop: 3.25% conversion rate

What Conversion Metrics Reveal

• High Payment Rates: Indicate smooth checkout processes and serious buyer intent
• High Cancellation Rates: May suggest product quality issues, shipping concerns, or buyer's remorse specific to that channel
• Conversion Rate Variations: Reflect differences in traffic quality, user intent, and checkout experience across channels

For stores looking to predict future conversion patterns and optimize channel mix, explore Accelerated Failure Time (AFT) models to understand time-to-conversion dynamics across channels.