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How to Use Product Variation Performance in Etsy: Step-by-Step Tutorial

By MCP Analytics Team | Published , updated

Count how many orders, units and dollars each variation option brings in, so you can see which options sell most and which sell least.

By MCP Analytics Team |

Introduction to Product Variation Performance

If you're running an Etsy shop with multiple product variations (different colors, sizes, materials, or styles) you've likely wondered: which variations are actually selling? Should you keep offering that rarely-purchased option, or double down on your best performers?

Product variation performance analysis answers these critical questions by showing which options customers actually bought. Buying is not the same as preferring: an option may sell less because it was offered for less time or shown less. Read the counts with that in mind. They can still help you plan inventory and spot slow stock.

In this tutorial you will count orders, units and revenue for each variation option, run the steps on a small made-up file, and learn what the results can and cannot tell you.

Prerequisites and Data Requirements

Before diving into variation analysis, ensure you have the following in place:

Required Access and Tools

Data Collection Steps

Follow these steps to gather the necessary data from your Etsy shop:

Step 1: Access Your Etsy Shop Manager

Sign in to Etsy.com and go to Shop Manager. Select Settings, then Options, then the Download Data tab, then Orders. This is the path in Etsy's help article "How to Download a Spreadsheet of Your Sold Transactions" (linked under Sources). Menu names can change, so follow Etsy's page if yours differs.

Step 2: Export Order Data

Select the date range for your analysis. For variation performance, we recommend:

  • Minimum: Last 90 days for seasonal products
  • Recommended: Last 6-12 months for comprehensive insights
  • Consider: Full year data to account for seasonal variations

Etsy's help article lets you pick the CSV type, month and year (leave Month blank for a full year) and then select "Download CSV". Pick the item-level file if your menu offers one, because variations belong to items, not to whole orders. Then open it and check that it holds variation details for each purchase.

Step 3: Verify Data Completeness

Open your downloaded CSV. Etsy's help page lists only a few fields (payment method, item price, item title and SKU if you added one) and does not list the column names, so look at your own file. You need to find:

  • an order number
  • the item name
  • the variation text
  • the quantity
  • the money for that line

In the example below we call these order_id, item_name, variations, quantity and item_total. Those are names we chose, not Etsy's. If your variation text looks like this, the steps work as written:

"Size: Large | Color: Navy Blue"
"Material: Sterling Silver | Length: 18 inches"
"Style: Modern | Finish: Matte"

Understanding Variation Data Structure

This tutorial assumes each variation appears as "Property: Value" pairs separated by pipes (|). We could not confirm from Etsy's help pages that your export looks like this, so open your own file and change the separators in the script to match what it actually contains:

Example 1: Single Property
Variations: "Size: Medium"

Example 2: Multiple Properties
Variations: "Size: Large | Color: Forest Green | Material: Organic Cotton"

Example 3: No Variations
Variations: (empty field)

Pro Tip: Products without variations will have empty variation fields. These should be analyzed separately as standalone products rather than variation options.

Step-by-Step Variation Performance Analysis

Step 1: Count Orders by Variation Option

Split each line's variation text into its Property: Value pairs, then count distinct orders, units and revenue for each pair. Here is a tiny example you can check by hand. It is a made-up file, not real shop data. Save it as etsy_orders.csv. Each row is one item line. item_total is the money for that line (price times quantity). Order 3003 has two lines, and one of them has no variations.

order_id,item_name,variations,quantity,item_total
3001,Ceramic Mug,Size: Large | Color: Navy,1,24.00
3002,Ceramic Mug,Size: Small | Color: Navy,2,36.00
3003,Ceramic Mug,Size: Large | Color: Sage,1,24.00
3003,Mug Gift Card,,1,10.00
3004,Ceramic Mug,Size: Small | Color: Coral,1,18.00
3005,Ceramic Mug,Size: Large | Color: Navy,3,72.00
3006,Ceramic Mug,Size: Small | Color: Sage,1,18.00
3007,Ceramic Mug,Size: Large | Color: Navy,1,24.00
3008,Ceramic Mug,Size: Large | Color: Coral,1,24.00

Save this script as variations.py next to the file and run python3 variations.py. It needs pandas.

import pandas as pd

df = pd.read_csv("etsy_orders.csv")
df["variations"] = df["variations"].fillna("")
print(len(df), "rows,", df["order_id"].nunique(), "orders,",
      int((df["variations"] == "").sum()), "row without variations")

# one row per option: split on "|", then trim spaces
df["option"] = df["variations"].str.split("|")
opts = df.explode("option")
opts = opts[opts["option"].fillna("").str.strip() != ""].copy()
opts["option"] = opts["option"].str.strip()
opts["property"] = opts["option"].str.split(":").str[0]

summary = opts.groupby(["property", "option"]).agg(
    orders=("order_id", "nunique"),
    units=("quantity", "sum"),
    revenue=("item_total", "sum"),
)
summary["share_%"] = (100 * summary["revenue"]
                      / summary.groupby("property")["revenue"].transform("sum")).round(1)
summary = summary.sort_values(["property", "revenue", "orders"], ascending=[True, False, False])
print(summary)

The script above prints this (we ran it with Python 3 and pandas):

9 rows, 8 orders, 1 row without variations
                       orders  units  revenue  share_%
property option                                       
Color    Color: Navy        4      7    156.0     65.0
         Color: Coral       2      2     42.0     17.5
         Color: Sage        2      2     42.0     17.5
Size     Size: Large        5      7    168.0     70.0
         Size: Small        3      4     72.0     30.0

Check one by hand. Navy is in orders 3001, 3002, 3005 and 3007, so 4 orders. Its units are 1 + 2 + 3 + 1 = 7. Its revenue is 24 + 36 + 72 + 24 = 156. The Color revenue total is 156 + 42 + 42 = 240, and 156 / 240 = 65.0%. The gift card line has no variations, so it is left out of every option row. If an order had two lines with the same option, "orders" would count that order once, because the script counts distinct order numbers. Coral and Sage tie on every number, so this file cannot tell them apart. With 8 orders, none of this says anything about a real shop. It only shows the method. In a spreadsheet you can do the same with one row per option and a pivot table, counting distinct order numbers.

If your own export has no separate variation column, or the text uses other separators, change the split in the script. Etsy does not list these columns in its help page, so check your file first.

Step 2: Review Variation Performance Metrics

Once you have the counts, look at these metrics for each variation. The Step 1 output above comes from the 8-order file on this page. All other figures in the examples below are invented to show the idea; they are not from a real shop and are not output of the Step 1 script.

Core Performance Metrics Explained

1. Order Volume

The total number of orders containing this specific variation option. This is observed sales volume. It reflects customer preference only if every option was available for the same time, at the same price and shown equally.

Example Output:
Color: Navy Blue - 847 orders
Color: Forest Green - 312 orders
Color: Burgundy - 156 orders
Color: Coral - 89 orders

Interpretation: Of the 1,404 color orders shown, Navy Blue accounts for 847 (60.3%) and Coral for 89 (6.3%). Navy Blue sells the most; whether Coral underperforms depends on how long and how prominently it was offered.

2. Revenue Contribution

Total revenue generated by each variation, accounting for both price and volume. Some variations may sell less frequently but at higher prices.

Example Output:
Size: Small - $12,450 (425 orders @ avg $29.29)
Size: Medium - $18,920 (680 orders @ avg $27.82)
Size: Large - $15,680 (534 orders @ avg $29.36)
Size: X-Large - $8,340 (220 orders @ avg $37.91)

Interpretation: While Medium has the highest order volume, X-Large has the highest average order value, suggesting premium pricing opportunities.

3. Percentage of Total Sales

Each variation's contribution to your overall sales volume, helping you understand relative importance.

Example Output:
Material: Sterling Silver - 62.3% of jewelry orders
Material: Gold Filled - 28.7% of jewelry orders
Material: Rose Gold - 9.0% of jewelry orders

Interpretation: Sterling Silver dominates, but Rose Gold's lower share doesn't necessarily mean poor performance: consider inventory costs and profit margins.

4. Trend Analysis

Month-over-month and quarter-over-quarter performance changes reveal seasonal patterns and emerging preferences.

Example Output:
Style: Minimalist - +23% growth (Q4 vs Q3)
Style: Bohemian - -8% decline (Q4 vs Q3)
Style: Vintage - +2% stable (Q4 vs Q3)

Interpretation: Minimalist style is gaining momentum while Bohemian may be losing appeal or facing increased competition.

Step 3: Identify Top and Bottom Performers

You can segment your variations into performance tiers like these. This classification helps prioritize your next actions.

Performance Tier Definitions

🌟 Top Performers (Top 20%)
- High order volume AND strong revenue contribution
- Growing or stable trend patterns
- Action: Maintain inventory, consider expansion

⭐ Solid Performers (20-60%)
- Consistent sales with moderate volume
- Stable performance without major declines
- Action: Maintain current approach, monitor trends

⚠️ Underperformers (60-90%)
- Below-average order volume
- Limited revenue contribution
- Action: Evaluate for improvement or discontinuation

🚫 Poor Performers (Bottom 10%)
- Minimal orders over analysis period
- Declining trends or stagnant sales
- Action: Strong candidate for discontinuation

These tiers are rank cutoffs on order volume and revenue. They do not adjust for seasonality, price or how long an option has been offered, so check those yourself before acting on a tier. Rank within one property (Size against Size), not across properties. If an option ranks high on orders but low on revenue, or the reverse, do not average the two ranks. Look at why, for example a low price or many multi-unit orders. In the Step 1 output, Navy ranks first on both orders and revenue, while Coral and Sage tie on both, so a rank cutoff would put them in the same tier.

Step 4: Conduct Deeper Statistical Analysis

For variations near decision thresholds, apply statistical rigor to avoid premature conclusions, using a test similar to those in A/B testing.

When to Apply Statistical Testing

Example: Comparing Two Similar Variations

Suppose you're deciding between two colors with similar performance:

Color: Sage Green
- Orders: 124
- Revenue: $3,596
- Trend: +5% growth

Color: Dusty Rose
- Orders: 118
- Revenue: $3,422
- Trend: +3% growth

Rather than discontinuing Dusty Rose based on slightly lower numbers, run a simple test. For counts like these, an exact binomial test asks whether 124 versus 118 orders could plausibly come from an even split:

Orders: 124 vs 118 (51.2% and 48.8% of 242)
Exact binomial test against an even split: p = 0.75

This assumes both colors were offered for the same time and shown to the same buyers. The p-value is the probability of data at least this extreme if there were no real difference; a small p-value is evidence against no difference, not proof of an effect, and a large one is not proof of no effect. Here the data show no evidence of a difference in demand, which is not evidence that the two are equal, so decide on cost and context too.

Step 5: Consider Context Beyond Raw Numbers

Quantitative data tells most of the story, but contextual factors matter for final decisions:

Inventory and Supply Chain

  • Minimum order quantities from suppliers
  • Cost differences between variation materials
  • Storage and handling complexity
  • Lead times for restocking

Customer Experience

  • Offering variety as a competitive advantage
  • Meeting diverse customer preferences
  • Bundle and upsell opportunities
  • Customer feedback and reviews

Operational Efficiency

  • Production time differences between variations
  • Error rates and quality control challenges
  • Packaging and shipping complexity
  • Photography and listing maintenance burden

Interpreting Your Results

Making Discontinuation Decisions

Discontinuing a variation is a significant decision that requires careful consideration. Use this decision framework:

Definite Discontinuation Candidates

Consider removing variations that meet multiple criteria:

  • Fewer than 10 orders in the past 6 months
  • Represents less than 2% of category sales
  • Declining trend over multiple quarters
  • High production complexity or cost
  • No strategic value (e.g., doesn't complete a collection)

Investigate Before Discontinuing

Variations requiring deeper analysis before removal:

  • Recently launched (less than 6 months available)
  • Seasonal products outside peak season
  • Part of a coordinated product collection
  • Receives positive customer feedback despite lower sales
  • Fills a specific niche or customer segment

Retain and Monitor

Variations worth keeping despite moderate performance:

  • Enables bundle purchases with top performers
  • Low incremental cost to maintain
  • Differentiates your shop from competitors
  • Shows stable (not declining) performance
  • Represents your brand identity or aesthetic

Identifying Expansion Opportunities

Top-performing variations often reveal untapped potential. Look for these expansion signals:

1. Capacity Constrained Winners

Indicator: Variation frequently sells out
Example: "Size: Large" shows 15 stockout periods
Action: Raise inventory levels, sized to your supplier lead times

2. Adjacent Variation Opportunities

Indicator: Strong performance in related options
Example: "Color: Navy" and "Color: Forest Green" both top performers
Action: Test additional dark, muted tones like "Charcoal" or "Burgundy"

3. Price Point Optimization

Indicator: Premium variations outperform expectations
Example: "Material: Gold Filled" at +$15 shows strong conversion
Action: Test additional premium options or higher base pricing

4. Trend Acceleration

Indicator: Rapid growth in specific variation
Example: "Style: Minimalist" shows +45% quarterly growth
Action: Expand minimalist variations across other product lines

Advanced Analysis: Variation Combinations

The most powerful insights come from analyzing how variations perform together. Counting orders by combination of options shows which combinations sell most:

Top Combination Analysis:
1. Size: Medium + Color: Navy + Material: Organic Cotton - 156 orders
2. Size: Large + Color: Black + Material: Bamboo - 142 orders
3. Size: Small + Color: White + Material: Linen - 98 orders

Underperforming Combinations:
1. Size: X-Large + Color: Yellow + Material: Polyester - 3 orders
2. Size: Small + Color: Neon Green + Material: Synthetic - 5 orders

In this synthetic example, Medium + Navy + Organic Cotton is the top-selling combination. Order counts alone do not show whether buyers seek that combination out or whether it simply combines popular options; to test that, compare each combination's count with the count expected from the individual options' popularity.

Implementing Your Insights

Creating an Action Plan

Transform your analysis into a structured implementation plan with clear priorities and timelines.

Phase 1: Quick Wins (Week 1-2)

  1. Discontinue clear underperformers: Remove variations with fewer than 5 orders in 6 months and no strategic value
  2. Update inventory levels: Increase stock for top performers showing stockout patterns
  3. Adjust listing prominence: Move top variations to first position in listing photos and descriptions

Phase 2: Strategic Adjustments (Week 3-6)

  1. Test new variations: Launch 1-2 new options based on top performer patterns
  2. Price optimization: Adjust pricing for premium variations showing strong demand
  3. Bundle creation: Develop product bundles featuring top-performing combinations

Phase 3: Continuous Monitoring (Ongoing)

  1. Monthly reviews: Track variation performance metrics monthly
  2. Quarterly deep dives: Comprehensive analysis every quarter
  3. A/B testing: Systematically test new variation hypotheses

Tracking Implementation Success

Establish clear metrics to measure the impact of your variation optimization:

Key Performance Indicators:
- Overall Revenue: set your own target
- Inventory Turnover: reduce slow-moving stock
- Average Order Value: Increase through better variation mix
- Customer Satisfaction: Monitor reviews for discontinued items
- Operational Efficiency: Reduce SKU complexity overhead

Re-run the analysis on a fresh export each month to track progress against your own targets.

Run It on Your Own Etsy Export

If you would rather not run the script, you can upload your Etsy order CSV at MCP Analytics and ask which variation options bring in the most orders and revenue. Check the method named in the report and compare a few numbers with a hand count like the one in Step 1. Nothing connects to your Etsy account.

Try it on your data

Common Issues and Solutions

Problem 1: Insufficient Data for Analysis

Symptom: Analysis shows "insufficient data" warnings for most variations.

Cause: Fewer than 30-50 orders per variation, making statistical patterns unreliable.

Solution:

  1. Extend your analysis period to 12+ months
  2. Combine data across similar product listings
  3. Focus on property-level analysis (all "Size" variations together) rather than specific combinations
  4. Wait until you accumulate more sales data before making major decisions

Problem 2: Variations Not Parsing Correctly

Symptom: Variation data appears as raw text instead of separated properties.

Cause: Non-standard formatting in your Etsy variation setup or CSV export issues.

Solution:

  1. Verify your CSV uses UTF-8 encoding (re-export if necessary)
  2. Check how your file writes variations. This tutorial assumes "Property: Value | Property: Value", which we could not confirm from Etsy's help
  3. Contact support if custom variation formats require special parsing
  4. Manually standardize variation naming in your Etsy listings for future consistency

Problem 3: Seasonal Products Show Misleading Trends

Symptom: Variations show strong negative trends but are actually seasonal.

Cause: Analysis period doesn't account for natural seasonal fluctuations.

Solution:

  1. Analyze full 12-month periods to capture complete seasonal cycles
  2. Compare year-over-year (YoY) rather than quarter-over-quarter
  3. Segment analysis by season: compare this summer to last summer
  4. Note seasonal context in your decision-making process

Problem 4: Recently Added Variations Appear as Underperformers

Symptom: New variations rank in bottom tier despite positive early signals.

Cause: Insufficient time in market compared to established variations.

Solution:

  1. Filter analysis to include only variations available for the full analysis period
  2. Create separate "new variation" tracking with appropriate benchmarks
  3. Use daily or weekly performance rates instead of absolute totals
  4. Wait at least 90 days before making discontinuation decisions on new options

Problem 5: Multiple Properties Create Analysis Complexity

Symptom: Products with 3+ variation properties (Size, Color, Material, Style) create hundreds of combinations.

Cause: Combinatorial explosion makes it difficult to identify clear patterns.

Solution:

  1. Analyze each property dimension independently first
  2. Focus on two-way combinations for the most important properties
  3. Consider simplifying your variation structure by reducing property count
  4. Group similar combinations by hand before counting

Problem 6: Conflicting Signals Between Metrics

Symptom: A variation shows high order volume but low revenue, or vice versa.

Cause: Price differences, discounting patterns, or bundling affects metrics inconsistently.

Solution:

  1. Calculate and compare profit margins, not just revenue
  2. Consider strategic value: volume drivers vs. profit maximizers both have roles
  3. Review your pricing strategy for the conflicting variation
  4. Examine whether the variation is frequently purchased with others (bundle effect)

Next Steps with Etsy Analytics

Product variation performance is just one dimension of Etsy shop optimization. Expand your data-driven approach with these related analyses:

Advanced Analytics Techniques

Complementary Etsy Analyses

Continuous Improvement Process

Establish a regular cadence for variation analysis and optimization:

Monthly: Quick review of top and bottom performers
Quarterly: Comprehensive analysis with discontinuation decisions
Biannually: Strategic planning for new variation launches
Annually: Complete product line review and restructuring

This systematic approach ensures your product offerings continuously evolve based on real customer preferences rather than assumptions or guesswork.

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