ds@mcpanalytics.aiEmail your data and your question and get an answer about your own data, with the method shown.Email your data and question. Get an answer.

How to Use Product Mix in Etsy: Step-by-Step Tutorial

By MCP Analytics Team | Published , updated

Discover which product categories sell best and optimize your Etsy shop for maximum profitability

Introduction to Product Mix Analysis

Understanding your product mix is one of the most powerful ways to optimize your Etsy shop's performance. Your product mix reveals which items drive revenue, which variations customers prefer, and where your highest profit margins lie. Without this view, inventory decisions rest on impressions rather than on your order history.

To run a product mix analysis on your own Etsy export, upload your orders CSV, or follow the steps below by hand.

As an Etsy seller, you've probably asked yourself: "Should I focus on physical products or digital downloads?" "Which variation of my bestselling item should I promote?" "Am I pricing my products optimally?" A product mix analysis gives you order, revenue and margin figures to bring to each of these questions. It does not decide them: whether a price is optimal or a variation deserves promotion is something you test, for example by changing one thing and comparing against a baseline.

In this comprehensive tutorial, you'll learn how to perform a complete product mix analysis for your Etsy shop. We'll walk through analyzing your physical versus digital sales split, identifying your top-performing variations, mapping price tiers, and calculating profit margins. You will have category, variation, and margin summaries to help you choose changes to test.

Steps: Prerequisites, then 1. Physical vs digital split, 2. Popular variations, 3. Price tiers, 4. Highest margins. A small runnable worked example follows.

Related: to see which of your own items sell together, try the free market basket tool.

Worked Example: Physical and Digital Items

Question: In this small sample, how do physical and digital items compare by order count, revenue and margin?

Inputs: Simulated data, seed 17. The script makes 80 orders with one item each. It gives each item a type, a price and an assumed cost. Physical cost is set to 55% of price; digital cost is set to $3 per item. These costs are assumptions for the example. The larger example tables in Steps 1 to 4 below are separate made-up illustrations and do not come from this script.

Method: Group the orders by item type. Add the item prices and costs. Divide each type's orders and revenue by the totals to get its shares. Compute margin as revenue less assumed cost, divided by revenue. The margin omits any other costs.

import random
from collections import defaultdict

# Simulated data, seed 17. Each row is one order with one item.
random.seed(17)
items = []
for order_id in range(1, 81):
    kind = random.choices(["Physical", "Digital"],
                          weights=[3, 2])[0]
    if kind == "Physical":
        price = random.choice([12, 20, 30, 40])
        cost = price * 0.55
    else:
        price = 20
        cost = 3
    items.append({"order_id": order_id, "type": kind,
                  "price": price, "cost": cost})

by_type = defaultdict(
    lambda: {"orders": 0, "revenue": 0.0, "cost": 0.0})
for item in items:
    row = by_type[item["type"]]
    row["orders"] += 1
    row["revenue"] += item["price"]
    row["cost"] += item["cost"]

total_revenue = sum(r["revenue"] for r in by_type.values())
total_cost = sum(r["cost"] for r in by_type.values())
n = len(items)
print("Simulated data, seed 17")
print(f"Input: {n} one-item orders; cost assumptions: "
      "physical = 55% of item price, digital = $3 per item")
print("Type | Orders | Revenue | Share of orders | "
      "Share of revenue | Margin before other costs")
for kind in ["Physical", "Digital"]:
    row = by_type[kind]
    margin = (row["revenue"] - row["cost"]) / row["revenue"] * 100
    print(f"{kind} | {row['orders']} | ${row['revenue']:.2f} | "
          f"{row['orders']/n*100:.1f}% | "
          f"{row['revenue']/total_revenue*100:.1f}% | "
          f"{margin:.1f}%")
total_margin = (total_revenue - total_cost) / total_revenue * 100
print(f"Total | {n} | ${total_revenue:.2f} | 100.0% | 100.0% | "
      f"{total_margin:.1f}%")

Output of this script:

Simulated data, seed 17
Input: 80 one-item orders; cost assumptions: physical = 55% of item price, digital = $3 per item
Type | Orders | Revenue | Share of orders | Share of revenue | Margin before other costs
Physical | 49 | $1322.00 | 61.3% | 68.1% | 45.0%
Digital | 31 | $620.00 | 38.8% | 31.9% | 85.0%
Total | 80 | $1942.00 | 100.0% | 100.0% | 57.8%

Result: The simulated physical items account for 49 orders and $1322.00 in revenue, with a 45.0% margin before other costs. The digital items account for 31 orders and $620.00 in revenue, with an 85.0% margin before other costs. The margins are set by the stated cost rules (1 minus 55%, and 1 minus 3/20 at a $20 price), and the 3 to 2 split of orders is set in the draw, so the table shows how to read this layout, not a finding about shops.

Limit: These results show only this simulated sample. The cost assumptions omit fees, labor, shipping and other costs, so the margin is not a shop's full profit margin.

Sources

Prerequisites and Data Requirements

Before diving into product mix analysis, you'll need to gather the right data and ensure you have the necessary access. Here's what you'll need:

Required Data

Access Requirements

Exporting Your Etsy Data

Etsy changes Shop Manager, so check the steps and the file's columns against Etsy's current help (linked under Sources above) and your own download. Choose an order or sold-transactions download that covers your date range.

Check that your file has item names, variation text, quantity and price. Which columns exist depends on the export you choose, so the column names used below are examples to map onto your own headers. Keep this file handy as we work through each analytical step.

What You'll Accomplish

By following this tutorial, you will:

Step 1: What's My Physical vs Digital Sales Split?

Understanding the balance between physical and digital product sales helps you allocate resources effectively and identify growth opportunities.

Why This Matters

Physical and digital products have vastly different economics. Physical products require inventory management, shipping, and materials, while digital products have a low marginal delivery cost per extra sale, though support, marketing and platform costs still grow with volume. Knowing your split helps you:

Analysis Process

To calculate your physical vs digital split:

1. Open your Etsy order export CSV file
2. Add a new column called "Product_Type"
3. For each product, categorize as either "Physical" or "Digital"
4. Create a pivot table with:
   - Rows: Product_Type
   - Values: DISTINCT COUNT of Order_ID (each order counted once per row)
   - Values: SUM of Item_Total (for revenue)
   A plain COUNT of Order_ID counts item rows, so an order with three
   items would be counted three times. Excel offers Distinct Count when the
   data is added to the Data Model; Google Sheets has COUNTUNIQUE.
   An order holding both a physical and a digital item is counted in both
   rows, so the order percentages can then add to more than 100%.
   Decide up front how you treat mixed orders.

Example Result (synthetic data, one item per order):
Product_Type | Orders | Revenue  | % of Orders | % of Revenue
Physical     | 503    | $10,639  | 80.2%       | 81.1%
Digital      | 124    | $2,480   | 19.8%       | 18.9%
Total        | 627    | $13,119  | 100.0%      | 100.0%

All figures in the example tables of Steps 1 to 4 come from one synthetic data set generated for the tutorial (not the script in the worked example above); they are not from a real shop. The tables reconcile with each other: the physical row is the five print sizes of Step 2, and the digital row is the Digital Template of Step 4.

Interpreting the Results

In the example above, physical products represent 80.2% of orders and 81.1% of revenue, so average order value is slightly higher for physical items ($21.15 against $20.00). Here's how to interpret your results:

Expected Output

You should now have clear percentages showing your revenue and order distribution between physical and digital products, plus insights into average order values for each type.

Step 2: Which Product Variations Are Most Popular?

Product variations (such as size, color, material, or design) often make the difference between a bestseller and a slow mover. This step reveals which variations resonate most with your customers.

Understanding Variation Performance

Most Etsy sellers offer variations of their core products. For example, a print seller might offer the same design in multiple sizes, or a jewelry maker might offer the same necklace in silver, gold, and rose gold. Knowing which variations sell best allows you to:

Analysis Process

To identify your top-performing variations:

1. In your CSV, identify the "Variations" column
2. Parse variations into separate attributes (Size, Color, etc.).
   Example: if a cell reads   Size: 8x10; Color: Sepia   then
   =MID(A2, FIND("Size:",A2)+6, FIND(";",A2&";",FIND("Size:",A2))-FIND("Size:",A2)-6)
   returns 8x10 (the &";" lets it work when Size is the last attribute).
   Check how your own export formats this column before using it.
3. Count orders and sum revenue per value (a pivot table does this).

Example for Size Variations (the five print listings of the synthetic data set):
Size     | Orders | Revenue  | Avg Price | % of Total Orders
8x10"    | 145    | $2,175   | $15.00    | 28.8%
11x14"   | 122    | $2,440   | $20.00    | 24.3%
16x20"   | 98     | $2,940   | $30.00    | 19.5%
5x7"     | 87     | $1,044   | $12.00    | 17.3%
24x36"   | 51     | $2,040   | $40.00    | 10.1%
Total    | 503    | $10,639  |           | 100.0%

Example for Color Variations (same 503 print orders):
Color        | Orders | Revenue  | % of Total Revenue
Black/White  | 178    | $3,204   | 30.1%
Sepia        | 124    | $2,232   | 21.0%
Full Color   | 201    | $5,203   | 48.9%
Total        | 503    | $10,639  | 100.0%

Key Insights to Extract

Look for these patterns in your variation data:

In the example above, 8x10" prints lead in volume, but 16x20" prints generate more total revenue despite fewer orders. Whether promoting larger sizes would raise revenue is a question to test, in the way A/B testing compares variants; the counts alone do not establish it.

Expected Output

You should have detailed tables showing performance metrics for each product variation type, with clear identification of your top performers and underperformers.

Step 3: What Are My Price Tiers?

Price tier analysis helps you understand how your products are distributed across different price points and which tiers drive the most revenue. This is crucial for strategic pricing and product development.

Why Price Tiers Matter

Price bands used in this tutorial, illustrative and not Etsy categories: Entry, item price below $25; Mid, $25 up to but not including $75; Premium, $75 up to but not including $150; Luxury, $150 or more. Assign each item to the one band its price meets, then count orders, revenue, and margin in each band.

Creating Your Price Tier Map

To analyze your price tier distribution:

1. Define your price tiers based on your product range
2. Categorize each product into a tier
3. Calculate metrics for each tier:

Example Price Tier Analysis (same synthetic data set; tier = item price):
Price Tier    | Listings | Orders | Revenue   | % Orders | % Revenue
$0-$25        | 4        | 478    | $8,139    | 76.2%    | 62.0%
$25-$75       | 2        | 149    | $4,980    | 23.8%    | 38.0%
$75-$150      | 0        | 0      | $0        | 0.0%     | 0.0%
$150+         | 0        | 0      | $0        | 0.0%     | 0.0%
Total         | 6        | 627    | $13,119   | 100.0%   | 100.0%
(8x10, 11x14, 5x7 and the Digital Template are in the first tier;
16x20 and 24x36 in the second. The totals equal Step 1's totals.)

Volume by Tier:
Entry ($0-$25):     ██████████████████████ 76.2%
Mid ($25-$75):      ███████ 23.8%
Premium ($75-$150): (no orders)
Luxury ($150+):     (no orders)

Strategic Insights

Use your price tier analysis to identify strategic opportunities:

In the example above, 76.2% of orders and 62.0% of revenue sit in the lowest tier, and nothing is priced above $40. That shows the shop has no premium or luxury listings; it does not show that adding one would sell. To find out, list one such item and compare it with what you have now.

Expected Output

You should have a clear picture of how your revenue, orders, and margins distribute across price tiers, with identified opportunities for portfolio optimization.

Step 4: Which Products Have the Highest Margins?

Profit margin analysis is the most critical component of product mix optimization. High-revenue products aren't valuable if they don't generate profit. This step helps you identify your most profitable products.

Understanding Product Margins

Your profit margin is the percentage of revenue remaining after subtracting costs. For Etsy sellers, costs typically include:

Calculating Margins

To calculate profit margins for your products:

Margin Formula:
Profit Margin % = ((Revenue - Total Costs) / Revenue) × 100

Example Calculation for a $40 Print (assumed fee rates, synthetic):
Revenue:              $40.00
Materials (COGS):     -$8.00
Etsy Transaction:     -$2.60  (6.5% assumed)
Payment Processing:   -$1.45  (3% + $0.25 assumed)
Listing fee:          -$0.20  (assumed, one listing charge)
Shipping (absorbed):  -$5.00
Labor (15 min @ $30/hr): -$7.50
------------------------
Net Profit:           $15.25
Margin:               38.1%

Product Margin Comparison (same synthetic data set, selected listings):
Product           | Revenue | Net Profit | Margin % | Orders
Digital Template  | $2,480  | $2,110     | 85.1%    | 124
Art Print 8x10    | $2,175  | $1,088     | 50.0%    | 145
Art Print 5x7     | $1,044  | $627       | 60.1%    | 87
Art Print 16x20   | $2,940  | $764       | 26.0%    | 98

Identifying Margin Optimization Opportunities

Once you've calculated margins for all products, look for these patterns:

In the example above, the digital template has the highest margin (85.1%) on the second-highest revenue of the four, so it is a candidate for more promotion; the 16x20 print has the lowest margin (26.0%) on the highest revenue of the four, so it is a candidate for a cost or price review. Both are hypotheses to test, since the table says nothing about how demand would respond.

Expected Output

You should now have a comprehensive margin analysis showing which products drive the most profit, which need optimization, and where to focus your growth efforts.

Interpreting Your Results

Now that you've completed all four analytical steps, it's time to synthesize your findings into actionable strategies. Here's how to interpret your complete product mix analysis:

Creating Your Product Mix Matrix

Combine your insights into a strategic framework:

Product Mix Matrix:
                    High Volume          Low Volume
High Margin     | STARS               | HIDDEN GEMS
                | - Promote heavily   | - Increase visibility
                | - Protect pricing   | - Test price increases
                | - Expand variations | - Develop similar products
                |                     |
Low Margin      | WORKHORSES          | QUESTION MARKS
                | - Optimize costs    | - Discontinue or
                | - Test price increases| - Reposition as premium
                | - Cross-sell stars  | - Reduce complexity

Strategic Actions by Product Type

Stars (High Margin, High Volume)

These are your best performers. Actions to take:

Hidden Gems (High Margin, Low Volume)

These products are profitable but underselling. Actions to take:

Workhorses (Low Margin, High Volume)

These drive traffic but not profit. Actions to take:

Question Marks (Low Margin, Low Volume)

These products may not deserve space in your catalog. Actions to take:

Portfolio-Level Insights

Look at your entire product mix holistically:

Run It on Your Own Etsy Export

Export your orders from Etsy as a CSV and upload it at MCP Analytics.

Try it on your data

Next Steps with Etsy Analytics

Now that you understand your product mix, consider these advanced analytical techniques to further optimize your Etsy shop:

1. Time-Series Analysis

Track how your product mix evolves over time. Are certain products seasonal? Are margins improving or degrading? Time-series analysis reveals trends that snapshot analysis misses.

2. Customer Segmentation

Different customers prefer different products. Segment your buyers by purchase behavior to tailor marketing messages and product recommendations.

3. Cohort Analysis

Track groups of customers acquired in the same period to understand which products drive repeat purchases and customer lifetime value.

4. Attribution Modeling

Understand which marketing channels drive sales for different product categories. Your physical products might acquire customers differently than digital products.

5. Predictive Analytics

Use historical product mix data to forecast future demand, similar to how accelerated failure time models predict event timing in other contexts.

Recommended Reading

Continue Optimizing

Product mix analysis isn't a one-time exercise. Markets change, customer preferences evolve, and new products get added. Schedule regular product mix reviews:

Troubleshooting Common Issues

Here are solutions to common problems you might encounter during product mix analysis:

Issue: Inconsistent Product Names

Problem: Your Etsy data shows the same product with multiple names or spellings, making aggregation difficult.

Solution: Create a product mapping table that standardizes names. Use Excel's VLOOKUP or spreadsheet formulas to replace variations with canonical names before analysis.

Example Mapping:
"Botanical Print - Rose" → "Botanical Print"
"botanical print rose" → "Botanical Print"
"Rose Botanical" → "Botanical Print"

Issue: Missing Cost Data

Problem: You don't have accurate COGS for all products, making margin calculation impossible.

Solution: Start tracking costs immediately for new products. For historical products, estimate conservatively:

Issue: Variations Data is Messy

Problem: Variations may appear in a single cell, for example (an assumed layout) "Primary color: Blue; Size: Large", making it hard to analyze each attribute separately.

Solution: Use spreadsheet text functions to parse variations:

In Excel/Google Sheets:
1. Use Text to Columns to split on semicolons
2. Use FIND and MID functions to extract specific attributes
3. Or use the formula:
   =MID(A1, FIND("Size:", A1)+6, FIND(";", A1&";", FIND("Size:", A1))-FIND("Size:", A1)-6)

Issue: Seasonal Products Skew Results

Problem: Holiday or seasonal products appear as top sellers, but only for part of the year.

Solution: Run separate analyses for different time periods:

Issue: Low Sample Sizes

Problem: You're a new seller with limited historical data, making statistical analysis unreliable.

Solution: Focus on directional insights rather than precise percentages:

Issue: Negative Margins on Some Products

Problem: Analysis reveals some products lose money after all costs are included.

Solution: This is actually a valuable finding! Take immediate action:

Issue: Analysis Results Don't Match Intuition

Problem: The data shows results that contradict your gut feeling about which products perform best.

Solution: Trust the data, but verify your analysis:

Explore more: Etsy Shop Analytics: all tools, tutorials, and guides →

Not sure which plan? Compare plans →

Your turn

Bring your own data and the question you actually need answered.

MCP AnalyticsData science by email Send us your data and your question, and we will reply. ds@mcpanalytics.ai