Understanding who your customers are and how they behave is fundamental to growing your Shopify store. Not all customers are created equal—some spend thousands of dollars annually, while others make a single purchase and never return. The difference between a thriving ecommerce business and one that struggles often comes down to how well you understand and leverage customer segmentation.
Customer segmentation is the process of dividing your customer base into distinct groups based on shared characteristics such as purchase behavior, spending patterns, and engagement levels. By segmenting customers effectively, you can:
This tutorial will walk you through a comprehensive framework for segmenting your Shopify customers by value. You'll learn how to identify VIP customers, analyze one-time buyers, understand revenue distribution across tiers, and detect customers at risk of churning. By the end, you'll have actionable insights to drive targeted marketing campaigns and increase customer lifetime value.
Before you begin this tutorial, ensure you have the following:
Time Required: 30-45 minutes to complete all steps
In this tutorial, you'll create a complete customer segmentation framework that answers four critical questions:
By the end of this tutorial, you'll have clear customer segments that enable data-driven decision making for marketing, retention, and growth strategies.
Your VIP customers are the lifeblood of your business. These are the customers who make frequent purchases, spend significantly more than average, and often act as brand advocates. Identifying them is the first step in building an effective segmentation strategy.
VIP customers typically meet at least two of these three criteria:
If you're using Shopify Analytics, navigate to Analytics > Reports > Customers. Export a customer report that includes:
Here's a simple formula to identify VIP customers using a spreadsheet:
// Assuming your data is in columns A-E
// Column A: Customer ID
// Column B: Email
// Column C: Total Orders
// Column D: Total Spent
// Column E: Average Order Value
// Create a VIP Score column (Column F)
// Formula for cell F2:
=IF(AND(C2>=3, D2>=PERCENTILE($D$2:$D$1000,0.8)), "VIP",
IF(AND(C2>=3, D2>=PERCENTILE($D$2:$D$1000,0.6)), "High Value",
IF(C2>=2, "Repeat Customer", "One-Time Buyer")))
After applying this formula, you should see customers categorized into four tiers:
| Segment | Criteria | Typical % |
|---|---|---|
| VIP | 3+ orders AND top 20% LTV | 5-15% |
| High Value | 3+ orders AND top 40% LTV | 15-25% |
| Repeat Customer | 2+ orders | 20-30% |
| One-Time Buyer | 1 order only | 40-60% |
Count how many customers fall into the VIP category. For a healthy ecommerce business, VIPs typically represent 10-20% of customers but contribute 40-60% of total revenue. If your VIP segment is smaller than 5%, you may need to adjust your criteria or focus on customer retention strategies.
For more advanced segmentation techniques that incorporate statistical significance, check out our guide on A/B testing and statistical significance.
One-time buyers represent a massive opportunity cost for most Shopify stores. These customers made a single purchase but never returned. Understanding this segment is crucial because converting just 10% of one-time buyers into repeat customers can significantly impact revenue.
Using your customer data export, filter for customers where Total Orders = 1. This gives you your one-time buyer cohort.
// One-Time Buyer Percentage Formula
One-Time Buyer % = (Number of Customers with 1 Order / Total Customers) × 100
// Example calculation:
// Total Customers: 2,500
// One-Time Buyers: 1,450
// One-Time Buyer % = (1,450 / 2,500) × 100 = 58%
Segment your one-time buyers further to understand why they didn't return:
// Revenue Opportunity from Converting One-Time Buyers
Potential Revenue = One-Time Buyers × Average Second Order Value × Conversion Rate
// Example:
// One-Time Buyers: 1,450
// Average Second Order Value: $85
// Target Conversion Rate: 15%
// Potential Revenue = 1,450 × $85 × 0.15 = $18,488
This calculation shows the revenue impact of retention campaigns targeting one-time buyers. Use this to justify investment in email marketing, loyalty programs, or retargeting campaigns.
Understanding how revenue is distributed across your customer segments reveals where to focus your resources. This analysis typically follows the Pareto Principle (80/20 rule), where a small percentage of customers drive the majority of revenue.
Organize your customers into five tiers based on lifetime value:
| Tier | Lifetime Value Range | Characteristics |
|---|---|---|
| Tier 1 (VIP) | Top 10% LTV | Highest spenders, most loyal |
| Tier 2 (High Value) | 60th-90th percentile | Regular repeat purchasers |
| Tier 3 (Mid Value) | 30th-60th percentile | Occasional repeat buyers |
| Tier 4 (Low Value) | 10th-30th percentile | Infrequent purchasers |
| Tier 5 (Minimal) | Bottom 10% LTV | Single low-value purchase |
// Revenue Contribution by Tier
// For each tier, calculate:
Tier Revenue % = (Sum of LTV for Tier / Total Revenue) × 100
Tier Customer % = (Number of Customers in Tier / Total Customers) × 100
Revenue per Customer = Sum of LTV for Tier / Number of Customers in Tier
// Example output for Tier 1 (VIP):
// Tier 1 Revenue: $245,000
// Total Revenue: $500,000
// Tier 1 Customers: 150
// Total Customers: 2,500
Tier 1 Revenue % = ($245,000 / $500,000) × 100 = 49%
Tier 1 Customer % = (150 / 2,500) × 100 = 6%
Revenue per Customer = $245,000 / 150 = $1,633
| Tier | Customer % | Revenue % | Strategy |
|---|---|---|---|
| Tier 1 (VIP) | 5-10% | 40-55% | White-glove service, exclusive access |
| Tier 2 | 15-20% | 25-35% | Loyalty rewards, early access |
| Tier 3 | 20-30% | 15-20% | Re-engagement campaigns |
| Tier 4 | 20-25% | 5-10% | Activation campaigns |
| Tier 5 | 25-35% | 2-5% | Minimal investment |
Create a simple visualization to understand your revenue concentration. If your top 10% of customers contribute more than 60% of revenue, you have high concentration risk and should focus on expanding your mid-tier customer base.
For advanced analytical techniques that can help identify revenue patterns, explore our article on AI-first data analysis pipelines.
Customer churn is inevitable, but detecting at-risk customers early allows you to intervene with targeted win-back campaigns. The key metric here is recency—how long has it been since their last purchase?
For each customer, calculate the number of days between today and their last order date:
// Days Since Last Purchase Formula
// Assuming Column G contains "Date of Last Order"
// Formula for cell H2:
=TODAY() - G2
// This gives you the number of days since their last purchase
The churn threshold varies by industry and product type. Calculate your average purchase frequency:
// Average Purchase Frequency (in days)
Average Frequency = Total Days in Analysis Period / Average Orders per Customer
// Example:
// Analysis Period: 365 days
// Average Orders per Customer: 2.5
// Average Frequency = 365 / 2.5 = 146 days
// Churn Threshold = Average Frequency × 1.5
Churn Threshold = 146 × 1.5 = 219 days
Create risk categories based on recency:
| Risk Level | Days Since Purchase | Action Required |
|---|---|---|
| Active | 0-90 days | Continue nurturing |
| At Risk | 91-180 days | Re-engagement email series |
| High Risk | 181-270 days | Special offers, win-back campaign |
| Churned | 270+ days | Last-chance offer or retire from active list |
// Churn Risk Score Formula
// Combines recency with customer value
// Formula for cell I2:
=IF(AND(H2>270, D2>PERCENTILE($D$2:$D$1000,0.8)), "High Value Churned",
IF(AND(H2>180, D2>PERCENTILE($D$2:$D$1000,0.8)), "VIP At Risk",
IF(H2>270, "Churned",
IF(H2>180, "High Risk",
IF(H2>90, "At Risk", "Active")))))
// Win-Back Revenue Potential
Win-Back Revenue = At-Risk Customers × Average LTV × Win-Back Rate
// Example:
// At-Risk Customers (VIP + High Value): 180
// Average LTV for this segment: $650
// Estimated Win-Back Rate: 20%
// Win-Back Revenue = 180 × $650 × 0.20 = $23,400
This calculation justifies investment in retention campaigns and helps set win-back budget allocations.
Now that you've completed all four steps, you should have a comprehensive view of your customer base. Here's how to interpret and act on your findings:
If your VIP segment is small (<5%):
If one-time buyers are high (>65%):
If revenue is highly concentrated (>70% from top 10%):
If churn risk is high (>40% at-risk or churned):
Manual segmentation is time-consuming and prone to errors. MCP Analytics automatically segments your Shopify customers, tracks changes over time, and alerts you when VIP customers are at risk of churning.
Get instant insights into customer lifetime value, purchase frequency, and retention metrics without spreadsheet formulas.
Try Customer Segmentation Tool →Now that you've segmented your customers, here are the next steps to maximize the value of these insights:
Use your segments to create personalized marketing campaigns:
Connect your customer segments to your email marketing platform (Klaviyo, Mailchimp, etc.) and create automated flows:
Analyze which products drive repeat purchases vs. one-time buyers:
Take your segmentation to the next level with predictive analytics:
To dive deeper into predictive modeling techniques, explore our guide on Accelerated Failure Time models for data-driven decisions.
Customer segmentation is not a one-time exercise. Set up a monthly review process:
For a comprehensive solution that handles ongoing segmentation automatically, check out our Shopify Customer Segmentation Service.
Cause: Your VIP criteria are too lenient, or you have unusually high customer retention.
Solution: Increase the VIP threshold to the top 15% of customers by lifetime value instead of top 20%. Alternatively, add a minimum order count requirement (e.g., 5+ orders instead of 3+).
Cause: Your business model may differ from typical ecommerce (e.g., subscription-based, B2B, wholesale).
Solution: Adjust benchmarks for your specific business model. B2B stores often have even higher revenue concentration (80%+ from top 20%), while subscription businesses may have more even distribution.
Cause: Product-market fit issues, poor post-purchase experience, or very long purchase cycles.
Solution:
Cause: Seasonal business or insufficient historical data.
Solution: For seasonal businesses, calculate separate churn thresholds for peak vs. off-peak periods. If you have less than 12 months of data, use industry benchmarks: 90 days for fashion, 120 days for beauty, 180 days for home goods.
Cause: Data formatting issues, blank cells, or inconsistent data types.
Solution:
Cause: Rapid growth, seasonal fluctuations, or one-time promotional events.
Solution: Use rolling percentile-based thresholds rather than fixed dollar amounts for segment boundaries. This ensures segments remain proportional as your business grows. For seasonal businesses, compare year-over-year rather than month-over-month.
Customer segmentation by value is one of the most powerful analytics techniques for growing your Shopify store. By identifying VIP customers, understanding one-time buyer patterns, analyzing revenue distribution, and detecting churn risk, you've built a foundation for data-driven marketing and retention strategies.
The key to success is treating segmentation as an ongoing process rather than a one-time analysis. Customer behavior evolves, and your segments should evolve with it. Set up regular reviews, test different retention strategies, and continuously refine your approach based on results.
Remember that while these manual segmentation techniques are valuable for understanding the fundamentals, automated solutions can save significant time and provide real-time insights. Consider leveraging specialized tools that continuously monitor your segments and alert you to important changes in customer behavior.
Whether you choose to implement this manually or use automated analytics platforms, the important thing is to start using customer segmentation to inform your marketing decisions today. The insights you've gained from this tutorial can immediately improve your marketing ROI, customer retention rates, and overall profitability.