How to Use Product Performance Analysis in WooCommerce: Step-by-Step Tutorial

Step-by-Step Guide to Product Performance Analysis

Step 1: Export Your Order Items

Export order line items from WooCommerce as a CSV (from the orders list, an export plugin, or a database query; the menus and options differ by version and plugin). Include the columns listed above and save the file as woocommerce_order_items.csv.

Expected Outcome: A CSV with one row per product on each order, with quantities, line totals and dates.

Step 2: Choose the Period and Filters

Defining the right period and filters ensures your analysis answers a specific business question. Load the file, keep paid orders, and set the date range:

import pandas as pd

items = pd.read_csv('woocommerce_order_items.csv', parse_dates=['Order Date'])

# Keep paid orders only: drop cancelled and failed
items = items[~items['Order Status'].isin(['cancelled', 'failed'])]

# Optional filters: period and product subset
items = items[(items['Order Date'] >= '2024-07-01') & (items['Order Date'] <= '2024-12-31')]

print(f"Line items: {len(items)}, orders: {items['Order ID'].nunique()}, products: {items['Product SKU'].nunique()}")
print(f"Date range: {items['Order Date'].min().date()} to {items['Order Date'].max().date()}")

Expected Output:

Line items: 7017, orders: 3969, products: 20
Date range: 2024-07-01 to 2024-12-31

Choose a period that reflects your business cycle: a recent month or quarter for fast-moving inventory, a full year to capture seasonality, or custom periods to compare campaigns and holiday seasons. You can narrow to a category, price range or tag with another filter line, such as items[items['Product Name'].isin([...])].

Performance Metrics

Decide which metrics matter for your objective:

• Total Revenue: Sum of line totals per product
• Units Sold: Total quantity ordered
• Average Selling Price: Revenue divided by units
• Conversion Rate: Views to purchases (needs page-view data from outside WooCommerce's order tables)
• Profit Margin: (revenue - costs) / revenue x 100 (needs cost data)
• Customer Retention: Percentage of repeat purchasers for each product

Expected Outcome: Your data is scoped to answer a specific question such as "Which products generated the most revenue last quarter?"

Step 3: Compute Revenue, Units and Share per Product

Group the line items by product:

perf = items.groupby('Product Name').agg(revenue=('Line Total', 'sum'), units=('Quantity', 'sum'))
perf['avg_price'] = (perf['revenue'] / perf['units']).round(2)       # revenue per unit
perf['pct_of_total'] = (100 * perf['revenue'] / perf['revenue'].sum()).round(1)
perf = perf.sort_values('revenue', ascending=False)

print(perf.head(5).to_string())
print(f"Total store revenue: ${perf['revenue'].sum():,.2f}, units: {perf['units'].sum()}")

Expected Output:

                       revenue  units  avg_price  pct_of_total
Product Name                                                  
Premium Headphones   153004.00   1061     144.21          37.1
Wireless Mouse        50937.92   1605      31.74          12.4
Mechanical Keyboard   27420.90    310      88.45           6.6
USB-C Cable (3pk)     24609.42   2498       9.85           6.0
Bluetooth Speaker     17682.50    323      54.74           4.3
Total store revenue: $412,416.29, units: 10513

The average price is revenue divided by units, so it can sit slightly below list price when some orders were discounted (the headphones' list price in this file is $145.00). In this file one product, Premium Headphones, earns 37.1% of revenue, which is critical information for inventory and marketing decisions.

Expected Outcome: You can see which products contribute most to revenue and where the store depends on a single item.

Step 4: Interpret Performance Metrics in Context

Raw numbers tell only part of the story. Here is how to read the key metrics:

Revenue vs. Volume Analysis

Compare revenue with units. Premium Headphones earned $153,004 from 1,061 units (about $144 per unit), while USB-C Cable (3pk) earned $24,609 from 2,498 units (about $10 per unit). The first is a high-priced item and the second sells in volume; a high average price alone does not show pricing success, which needs costs, demand and a comparison with alternatives.

Performance Distribution

Measure how concentrated your revenue is instead of assuming an 80/20 split:

share = perf['revenue'].cumsum() / perf['revenue'].sum()
n = len(perf)
top20 = max(1, round(0.2 * n))
print(f"Products: {n}")
print(f"Top 20% of products ({top20}) earn {100 * share.iloc[top20 - 1]:.1f}% of revenue")
print(f"Products needed for 80% of revenue: {int((share < 0.8).sum() + 1)}")

Expected Output:

Products: 20
Top 20% of products (4) earn 62.1% of revenue
Products needed for 80% of revenue: 9

Here the top 20% of products earn 62.1% of revenue and it takes 9 of 20 products to reach 80%. Your catalog will have its own concentration, which is the number to use for planning inventory and marketing.

Trend Analysis

Snapshots are valuable, but trends reveal trajectory. Compare the current period with the previous one:

items['quarter'] = items['Order Date'].dt.to_period('Q')
q = items.pivot_table(index='Product Name', columns='quarter', values='Line Total', aggfunc='sum', fill_value=0)
q.columns = [str(c) for c in q.columns]
q['growth_%'] = (100 * (q['2024Q4'] - q['2024Q3']) / q['2024Q3']).round(1)
print(q.loc[perf.head(5).index].round({'2024Q3': 0, '2024Q4': 0, 'growth_%': 1}).to_string())

Expected Output:

                      2024Q3   2024Q4  growth_%
Product Name                                   
Premium Headphones   71572.0  81432.0      13.8
Wireless Mouse       27721.0  23217.0     -16.2
Mechanical Keyboard  14756.0  12665.0     -14.2
USB-C Cable (3pk)    12373.0  12236.0      -1.1
Bluetooth Speaker     8954.0   8728.0      -2.5

Premium Headphones grew 13.8% quarter over quarter while Wireless Mouse fell 16.2% and Mechanical Keyboard 14.2%. A decline of that size is a prompt to investigate (stock, price, competition, seasonality), but a one-quarter change can also be ordinary variation, so look at more periods before acting.

For background on telling real changes from random variation, see A/B Testing and Statistical Significance.

Expected Outcome: You can identify not just top performers, but products on the rise, products in decline, and patterns that inform inventory planning.

Step 5: Identify Top Performers and Underperformers

With your data analyzed and contextualized, group products into performance tiers. The tier boundaries are your choice; a simple start is revenue rank combined with the quarter-over-quarter trend:

Star Performers (Top Revenue Contributors)

These products show:

• High revenue contribution
• Strong or growing sales trends
• Healthy profit margins (if cost data is available)
• Low return rates

Action Items: Check that stock keeps pace with demand, consider product bundles, and consider targeted advertising or product line extensions

Rising Stars (High Growth Potential)

These products show:

• Fast growth from a modest revenue base
• Strong conversion or engagement metrics, if you have them

Action Items: Increase visibility through homepage features, email campaigns, and social media promotion

Steady Contributors (Middle Performers)

These products provide:

• Consistent but unremarkable performance
• Stable demand without significant growth or decline
• Catalog completeness and variety

Action Items: Maintain current inventory and marketing levels, test price changes, explore cross-sell opportunities

Underperformers (Low Revenue Contributors)

These products show:

• Low revenue despite adequate inventory and visibility
• Declining or stagnant trends
• Poor conversion or high return rates, if you have them
• Low or negative profit margins, if you have cost data

Action Items: Investigate causes (pricing, product-market fit, descriptions, images), test discount promotions, consider discontinuation or clearance

Expected Outcome: You have a categorization of your product portfolio with candidate actions for each tier.

Step 6: Deep Dive into Underperformer Causes

Identifying underperformers is just the beginning. Understanding why they underperform enables targeted fixes:

Common Underperformance Causes

1. Visibility Issues: Product isn't featured in navigation, search results, or recommendations
   Test: Compare product page views with category averages (needs page-view data)
2. Pricing Misalignment: Price is too high relative to perceived value or competitor offerings
   Test: Compare price points to similar products and review cart abandonment (needs checkout data)
3. Poor Product Presentation: Low-quality images, inadequate descriptions, missing specifications
   Test: Review bounce rates and time-on-page for product listings (needs page analytics)
4. Product-Market Fit: Product doesn't match customer needs or expectations
   Test: Analyze customer reviews, return reasons, and support tickets
5. Inventory or Fulfillment Issues: Frequent stockouts or slow shipping
   Test: Review stock history and delivery times

Several of these tests need data that is not in the order export, such as page views, checkout steps, ratings and returns. Combine the sources you have, compare a weak product with products in the same category, and treat each test as a hypothesis about the cause rather than a diagnosis.

Expected Outcome: You have a short list of hypotheses for each underperformer and know which data would test them.

Step 7: Take Action and Measure Results

Product performance analysis is only useful if it changes something. Implement changes one product at a time where you can, and measure their impact:

Optimization Playbook

For Top Performers:

1. Stock: base reorder quantities on demand variability and supplier lead time, so top sellers do not run out
2. Bundles: "Frequently Bought Together" featuring your stars
3. Variants: new colors, sizes, or configurations
4. Retargeting: visitors who viewed but did not buy
5. Placement: homepage features, category headers, email spotlights

For Underperformers:

1. Price test: try a lower price on a subset of traffic and compare units and margin
2. Listings: professional photography, detailed descriptions, videos
3. Limited-time promotion: boost visibility and gather data
4. Bundle with stars: pair with top performers to increase exposure
5. Clearance decision: if there is no improvement after a period you set in advance, move to clearance

Measurement Framework

After implementing a change, recompute the same metrics from Step 3 for the same product over a comparable period and compare them with the period before the change. Revenue before and after a change also reflects seasonality, promotions and stock, so compare against similar products you did not change, and watch margin as well as revenue: a clearance can raise revenue while lowering margin.

Expected Outcome: You have a repeatable process of changing one thing, measuring it, and keeping what works.