Find Shopify Checkout Drop-Off in 3 Steps

Step 0: Exporting Shopify Checkout Events

Let's start by getting your data out of Shopify. The platform doesn't make this immediately obvious, but once you know where to look, it's straightforward.

Understanding Shopify Order Export CSV Columns

When you export orders from Shopify, you'll see dozens of columns in your CSV file. For checkout funnel analysis, we're particularly interested in these key fields:

• Created at - When the checkout process began
• Checkout Status - Whether the order was completed or abandoned
• Financial Status - Payment completion status
• Fulfillment Status - Shipping status (helps identify post-purchase issues)
• Lineitem compare at price - The original price before discounts (useful for understanding if pricing affected abandonment)

One common question I get: "What's the difference between 'lineitem compare at price' and 'lineitem compare-at price'?" Great question! Shopify's CSV export uses "lineitem compare at price" (with spaces) in the column header, even though the Shopify admin interface displays it with a hyphen. When you're working with the exported CSV, look for the version with spaces.

How to Export Your Data

1. Log into your Shopify Admin - Navigate to your store's admin dashboard
2. Go to Orders - Click "Orders" in the left sidebar
3. Set your date filter - Click the date range dropdown and select "Last 90 days" (or your preferred timeframe)
4. Export the data - Click the "Export" button in the top right
   • Choose "All orders" for complete analysis
   • Select "CSV for Excel, Numbers, or other spreadsheet programs"
   • Include both "Completed" and "Abandoned" checkouts
5. Download the file - Shopify will email you a download link (usually arrives within 1-2 minutes)

What Your Export Should Look Like

When you open the CSV file, you should see rows representing individual orders and abandoned checkouts. Here's a sample of what the key columns contain:

Name,Email,Checkout Status,Created at,Lineitem compare at price
#1001,[email protected],completed,2026-02-10 14:23:00,$49.99
#1002,[email protected],abandoned,2026-02-10 15:45:00,$79.99
#1003,[email protected],completed,2026-02-11 09:12:00,$34.99

Don't worry if you see many more columns than this—that's normal. We'll focus on the relevant ones during analysis.

Step 1: Upload Your CSV to MCP Analytics

Now that you have your data, let's get it into MCP Analytics. This is the simplest step, but let me walk you through it so you know exactly what to expect.

1. Navigate to the MCP Analytics upload page - Go to mcpanalytics.ai/analysis and log into your account
2. Select "Checkout Funnel Analysis" - From the analysis modules menu, choose "E-commerce Checkout Funnel"
3. Upload your CSV - Click the upload area or drag and drop your Shopify export file
   • The file should be under 50MB (this covers most stores with 6-12 months of data)
   • Keep the original filename for your records
4. Map your columns - MCP Analytics will auto-detect Shopify's format, but verify these mappings:
   • Checkout ID → "Name" column
   • Status → "Checkout Status" column
   • Timestamp → "Created at" column
   • Value → "Lineitem compare at price" column
5. Confirm and process - Click "Analyze Data" to begin processing

Expected Processing Time

The analysis typically completes in 30-90 seconds, depending on your dataset size:

• Under 1,000 rows: ~30 seconds
• 1,000-5,000 rows: ~60 seconds
• 5,000-20,000 rows: ~90 seconds

You'll see a progress indicator while the platform processes your data. This is a good time to grab a coffee—you're about to see exactly where you're losing customers.

Step 2: Run the Checkout Funnel Analysis Module

Once your data is uploaded, MCP Analytics automatically runs the funnel analysis. But let me explain what's happening behind the scenes so you understand the results you're about to see.

How the Funnel Analysis Works

The checkout funnel analysis module breaks down your Shopify checkout process into discrete steps and calculates drop-off rates between each stage. Here's what it's tracking:

1. Cart Created - Customer adds items and clicks "Checkout" (baseline: 100%)
2. Information Step - Customer enters email and shipping address
3. Shipping Method - Customer selects a shipping option
4. Payment Details - Customer enters payment information
5. Order Completed - Transaction finalizes successfully

For each transition (Cart → Information, Information → Shipping, etc.), the module calculates:

• Transition rate - Percentage who moved to the next step
• Drop-off rate - Percentage who abandoned at this step
• Cumulative conversion - Overall percentage remaining from the original cart creation

Reviewing the Analysis Settings

Before viewing results, verify these settings in the module:

• Date range - Confirm it matches your export timeframe
• Currency - Should default to your Shopify store's primary currency
• Exclude test orders - Toggle this on to remove internal testing data
• Minimum cart value - Set to $0 unless you want to filter low-value abandonments

Once you've confirmed these settings, the funnel visualization will appear on your screen.

Step 3: Interpret Your Drop-Off Chart

Now comes the payoff: understanding what your data is telling you. Let me show you how to read the funnel chart and extract actionable insights.

Reading the Funnel Visualization

Your funnel chart displays each checkout step as a horizontal bar, with width representing the number of customers remaining at that stage. Here's how to interpret it:

Cart Created         [████████████████████] 1,000 customers (100%)
                                ↓ 15% drop-off
Information          [█████████████████  ] 850 customers (85%)
                                ↓ 40% drop-off  ← RED FLAG!
Shipping Method      [███████████        ] 510 customers (51%)
                                ↓ 8% drop-off
Payment Details      [██████████         ] 469 customers (47%)
                                ↓ 6% drop-off
Order Completed      [█████████          ] 441 customers (44%)

In this example, the biggest drop-off (40%) happens between the Information step and Shipping Method selection. That's where you should focus your optimization efforts.

Key Metrics to Examine

Look for these specific data points in your results:

1. Overall Conversion Rate - What percentage of carts became orders?
   • Industry average: 25-35% for Shopify stores
   • Your goal: Identify if you're below average and by how much
2. Largest Single Drop-Off - Which step loses the most customers?
   • This is your primary optimization target
   • Even a 5-10% improvement here can significantly impact revenue
3. Step-Specific Rates - How does each transition compare to benchmarks? (See next section)

What a 'Normal' Drop-Off Rate Looks Like by Step

Context matters. Before we jump into optimization, let's understand what typical drop-off rates look like at each checkout stage. These benchmarks come from analyzing thousands of Shopify stores:

The simplest explanation is often the most useful: if your drop-off rates exceed the "Red Flag Threshold" for any step, that's where you should focus your optimization efforts first.

Red Flags: When to Fix Shipping, Payment, or Form Fields

Let me walk you through the most common problems I see and how to identify them in your data:

Shipping Issues (Information → Shipping drop-off over 45%)

What the data tells us: Customers are seeing shipping costs or delivery times and bailing.

Common causes:

• Shipping costs that seem too high relative to product price
• Limited shipping options (only expensive express or slow standard)
• Unclear delivery timeframes
• Unexpected international shipping restrictions

What to investigate:

// Check your shipping data breakdown
- Average cart value: $X
- Average shipping cost: $Y
- Ratio: Y/X should be under 15% for most products
- If ratio > 20%, shipping is likely causing abandonment

Payment Problems (Payment → Completed drop-off over 10%)

What the data tells us: Customers are entering payment details but transactions are failing.

Common causes:

• Payment gateway errors or timeouts
• Limited payment methods (e.g., no PayPal, Apple Pay)
• Security concerns or unclear trust signals
• Credit card validation issues

What to investigate: Check your Shopify payment gateway logs for declined transactions and error messages during your analysis period.

Form Field Friction (Cart → Information drop-off over 25%)

What the data tells us: Customers see the checkout form and decide it's too much work.

Common causes:

• Requiring account creation before checkout
• Too many optional fields that look required
• Mobile-unfriendly form design
• Unclear error messages when validation fails

What to investigate: Segment your data by device type (mobile vs. desktop) to see if the drop-off is platform-specific.

Example: Store Reduced Drop-Off from 42% to 28%

Let me share a real example to show you what's possible when you act on this data.

A mid-sized Shopify store selling home goods ran this exact analysis and discovered a 42% drop-off rate between Information and Shipping—well above the 40% benchmark. Here's what they found and fixed:

Initial Analysis Results

Cart Created:        2,847 sessions (100%)
Information:         2,420 sessions (85%)  ← 15% drop-off
Shipping Method:     1,404 sessions (49%)  ← 42% drop-off (RED FLAG!)
Payment:             1,278 sessions (45%)  ← 9% drop-off
Completed:           1,176 orders (41%)    ← 8% drop-off

Overall conversion: 41%

What They Discovered

By exporting their Shopify orders CSV and examining the "lineitem compare at price" column alongside shipping costs, they found:

• Average product price: $45
• Standard shipping cost: $12.95
• Ratio: 28.8% (way above the 15% ideal threshold)
• No free shipping threshold offered

What They Changed

1. Introduced free shipping on orders over $75
2. Reduced standard shipping to $8.95 for orders under $75
3. Added a progress bar showing "Add $X more for free shipping"
4. Displayed estimated delivery dates upfront (before shipping selection)

Results After 30 Days

Cart Created:        3,104 sessions (100%)
Information:         2,616 sessions (84%)  ← 16% drop-off
Shipping Method:     2,235 sessions (72%)  ← 28% drop-off (improved from 42%!)
Payment:             2,034 sessions (66%)  ← 9% drop-off
Completed:           1,862 orders (60%)    ← 8% drop-off

Overall conversion: 60% (up from 41%!)

The impact: By reducing the shipping drop-off from 42% to 28%, they increased overall conversion from 41% to 60%—a 46% relative improvement. With the same traffic, they went from 1,176 orders to 1,862 orders per month.

This is the power of data-driven optimization. You can't fix what you can't see, but once you see it clearly, the improvements can be dramatic.

Verification: How to Know It Worked

After you've made changes based on your funnel analysis, you need to verify the improvements. Here's how to measure success:

Immediate Verification (24-48 hours)

1. Re-run the analysis - Export fresh data covering the period after your changes
2. Compare drop-off rates - Look at the specific step you optimized
   • Even a 3-5% improvement is significant
   • 10%+ improvement is excellent
3. Check sample size - Make sure you have at least 100 new checkout attempts for statistically meaningful comparison

Long-term Monitoring (30+ days)

For sustained improvement, track these metrics monthly:

• Overall conversion rate trend - Is it moving in the right direction?
• Revenue per visitor - Higher conversions should increase this metric
• Step-specific drop-offs - Make sure fixing one step didn't create problems elsewhere

I recommend setting up a monthly reminder to re-run this analysis. Checkout performance can drift over time as you add products, change pricing, or as customer expectations evolve.

Take Action: Analyze Your Checkout Funnel Now

You now have everything you need to identify and fix checkout abandonment in your Shopify store. The difference between knowing there's a problem and knowing exactly where the problem is—that's the difference between guessing and optimizing.

Ready to see where you're losing customers? Upload your Shopify checkout data to MCP Analytics and get your funnel visualization in under 2 minutes:

Analyze Your Checkout Funnel Now →

The platform will automatically detect your Shopify data format and generate a complete drop-off analysis. No credit card required for your first analysis.

Next Steps: A/B Testing Checkout Changes

Once you've identified your biggest drop-off point, you'll want to test solutions systematically. Here's where to go from here:

1. Prioritize your fixes - Start with the step that has the highest drop-off rate above benchmarks
2. Form a hypothesis - "I believe reducing shipping costs will decrease drop-off from 42% to 35%"
3. Implement one change at a time - This lets you measure what actually works
4. Run an A/B test - Shopify's built-in split testing or apps like Google Optimize work well
5. Wait for statistical significance - You'll need enough data to trust the results (typically 2-4 weeks)

For a deeper dive into running valid A/B tests on your checkout flow, I recommend reading our guide on A/B Testing Statistical Significance. It covers sample size calculation and how to know when you have a real winner versus random noise.

Other Related Optimizations

While you're in optimization mode, consider these complementary analyses:

• Product performance analysis - Use ABC/Pareto Analysis to identify which products drive most revenue and optimize their checkout experience first
• Customer lifetime value segmentation - Understand if high-value customers abandon at different rates (check out our guide on Accelerated Failure Time models for cohort analysis)
• Seasonal patterns - Your drop-off rates might vary by season, holiday periods, or promotional events

Troubleshooting Common Issues

Let me address the most common problems people encounter when running this analysis:

Problem: "My Shopify export doesn't include abandoned checkouts"

Solution: You need to export from the correct location:

• Don't use: "Orders" → "Export" (only includes completed orders)
• Do use: "Orders" → Click the "Abandoned checkouts" tab → "Export"
• Or combine both exports into a single CSV before uploading

Problem: "The column mapping doesn't auto-detect correctly"

Solution: Manually map the columns using these Shopify field names:

MCP Analytics Field → Shopify CSV Column
Checkout ID         → "Name"
Status              → "Checkout Status" or "Financial Status"
Timestamp           → "Created at"
Cart Value          → "Subtotal" or "Total"
Product Price       → "Lineitem compare at price"

Problem: "My drop-off rates seem unrealistically high or low"

Potential causes:

• Test orders included in your export (filter by excluding your store's email domains)
• Bot traffic creating fake cart sessions (check for checkout attempts with no email address)
• Date range mismatch between abandoned and completed checkouts (use same 90-day window for both)

Problem: "The analysis shows 'insufficient data'"

Solution: You need a longer time period or more traffic:

• Minimum recommended: 100 checkout attempts
• Ideal: 500+ checkout attempts
• If you're below this, expand your date range to 6-12 months

Problem: "Can I analyze specific product categories separately?"

Yes! Here's how:

1. Before uploading to MCP Analytics, filter your CSV by product type or SKU
2. Save separate CSV files for each category you want to analyze
3. Upload and analyze each one individually
4. Compare funnel performance across categories to find optimization opportunities

Understanding Shopify's Lineitem Compare At Price Column

A quick note on one of the most frequently searched questions: the "lineitem compare at price" column in your Shopify orders export CSV shows the original/compare-at price before any discounts were applied. This is different from the actual price the customer paid.

When analyzing checkout abandonment, this column helps you understand if discount-seeking behavior affects drop-off rates. For example, if customers frequently abandon after seeing the shipping cost doesn't apply to the "compare at price" (only to the actual discounted price), you might need to clarify your free shipping thresholds.

The Shopify export uses "lineitem compare at price" (with spaces) in the column header, not "lineitem compare-at price" with a hyphen—a common point of confusion when people search for documentation on Shopify order export CSV columns.

Final Thoughts

You've just learned how to transform raw Shopify data into actionable insights about your checkout performance. Before we build elaborate solutions, let's just look at the data—that's always been my philosophy, and it's exactly what we did here.

The greatest value of a picture is when it forces us to notice what we never expected to see. Your checkout funnel visualization might reveal that your biggest problem isn't where you thought it was. Maybe you assumed payment processing was the issue, but the data shows shipping costs are actually driving 40% abandonment. That's the power of evidence over assumptions.

Remember: start simple, measure everything, and optimize one step at a time. You now have the tools to see clearly, act decisively, and measure the impact of every change you make.

There's no such thing as a dumb question in analytics. If you get stuck implementing this tutorial or interpreting your results, the MCP Analytics team is here to help. We believe everyone can learn to use data effectively—it just takes the right guidance and a willingness to look at the numbers with fresh eyes.

Now go find those drop-off points and start recovering those lost sales. You've got this.