How to Use Customer Retention Cohort Analysis in Shopify: Step-by-Step Tutorial

Category: Shopify Analytics | MCP Analytics

What You'll Learn

Customer retention cohort analysis is one of the most powerful analytics techniques for understanding how well your Shopify store keeps customers coming back over time. Unlike simple retention rates that lump all customers together, cohort analysis groups customers by their first purchase date and tracks their behavior over subsequent periods. This reveals patterns that aggregate metrics miss—like whether customers acquired during your holiday sale retain better than those from other campaigns, or if retention has improved as you've refined your product and service.

In this comprehensive tutorial, you'll learn exactly how to perform customer retention cohort analysis for your Shopify store, from data preparation through interpretation and action. By the end, you'll be able to answer critical questions like "What percentage of customers from January are still purchasing in June?" and "Which acquisition channels produce customers with the best long-term value?"

This analysis is essential for data-driven e-commerce operations because it directly impacts your customer lifetime value (CLV), marketing ROI calculations, and inventory planning. Understanding retention patterns helps you allocate marketing budgets more effectively, identify at-risk customer segments, and predict future revenue with greater accuracy.

Prerequisites and Data Requirements

What You Need Before Starting

Before diving into cohort analysis, ensure you have the following:

Required Data Fields

Your cohort analysis will require these essential data points from Shopify:

Technical Requirements

You'll need basic familiarity with:

Step-by-Step Implementation

Step 1: Export Customer and Order Data from Shopify

The first step is extracting your customer purchase data from Shopify. You have several options depending on your store size and technical capabilities:

Option A: Using Shopify Admin (For Smaller Stores)

  1. Log into your Shopify admin panel
  2. Navigate to Customers section
  3. Click Export in the top right
  4. Select "All customers" and CSV format
  5. Download the export file

Then export order data:

  1. Navigate to Orders section
  2. Click Export
  3. Select your date range (recommend all historical data)
  4. Choose CSV format and download

Option B: Using Shopify API (For Larger Stores)

For stores with extensive customer bases, the Shopify API provides more flexibility:

import requests
import pandas as pd
from datetime import datetime

# Shopify API credentials
SHOP_NAME = 'your-store-name'
API_VERSION = '2024-01'
ACCESS_TOKEN = 'your-access-token'

# API endpoint for orders
url = f'https://{SHOP_NAME}.myshopify.com/admin/api/{API_VERSION}/orders.json'

headers = {
    'X-Shopify-Access-Token': ACCESS_TOKEN,
    'Content-Type': 'application/json'
}

# Parameters for fetching orders
params = {
    'status': 'any',
    'limit': 250,
    'fields': 'id,customer,created_at,total_price'
}

# Fetch orders with pagination
all_orders = []
while url:
    response = requests.get(url, headers=headers, params=params)
    data = response.json()
    all_orders.extend(data.get('orders', []))

    # Check for next page
    link_header = response.headers.get('Link', '')
    url = None
    if 'rel="next"' in link_header:
        url = link_header.split(';')[0].strip('<>')
    params = None  # Params are in the URL for next page

# Convert to DataFrame
orders_df = pd.json_normalize(all_orders)
orders_df.to_csv('shopify_orders_export.csv', index=False)
print(f"Exported {len(orders_df)} orders successfully")

Expected Output: A CSV file containing all customer orders with timestamps, customer IDs, and order values.

Step 2: Structure Data for Cohort Analysis

Once you have your raw Shopify data, you need to transform it into the format required for cohort analysis. The key is identifying each customer's first purchase date (their cohort) and tracking their activity in subsequent periods.

Required Data Structure

Your final dataset should have these columns:

customer_id,cohort_month,order_month,order_count,revenue
12345,2024-01,2024-01,1,89.99
12345,2024-01,2024-03,1,124.50
12346,2024-01,2024-01,1,45.00
12347,2024-02,2024-02,1,199.99
12347,2024-02,2024-04,1,89.99

Data Transformation Script

Here's how to prepare your data:

import pandas as pd
from datetime import datetime

# Load exported Shopify orders
df = pd.read_csv('shopify_orders_export.csv')

# Convert dates to datetime
df['created_at'] = pd.to_datetime(df['created_at'])

# Extract customer ID (handle nested structure if from API)
if 'customer.id' in df.columns:
    df['customer_id'] = df['customer.id']
else:
    df['customer_id'] = df['customer_id']

# Create month columns
df['order_month'] = df['created_at'].dt.to_period('M')

# Find first purchase for each customer (cohort assignment)
cohort_data = df.groupby('customer_id')['created_at'].min().reset_index()
cohort_data.columns = ['customer_id', 'first_purchase']
cohort_data['cohort_month'] = cohort_data['first_purchase'].dt.to_period('M')

# Merge cohort back to orders
df = df.merge(cohort_data[['customer_id', 'cohort_month']], on='customer_id')

# Aggregate by customer, cohort, and order month
cohort_analysis = df.groupby(['customer_id', 'cohort_month', 'order_month']).agg({
    'id': 'count',
    'total_price': 'sum'
}).reset_index()

cohort_analysis.columns = ['customer_id', 'cohort_month', 'order_month', 'order_count', 'revenue']

# Convert periods to strings for CSV export
cohort_analysis['cohort_month'] = cohort_analysis['cohort_month'].astype(str)
cohort_analysis['order_month'] = cohort_analysis['order_month'].astype(str)

# Save prepared data
cohort_analysis.to_csv('cohort_analysis_ready.csv', index=False)
print(f"Prepared {len(cohort_analysis)} cohort records from {df['customer_id'].nunique()} unique customers")

Expected Output: A message like "Prepared 8,432 cohort records from 2,156 unique customers" and a CSV file ready for analysis.

Step 3: Run Cohort Analysis

Now that your data is properly structured, you can perform the actual cohort retention analysis. The most efficient approach is using MCP Analytics' Shopify Customer Retention Cohort tool, which automatically generates retention matrices and visualizations.

Using MCP Analytics Platform

  1. Navigate to the Customer Retention Cohort Analysis tool
  2. Upload your cohort_analysis_ready.csv file
  3. Select your cohort granularity (monthly, quarterly, or weekly)
  4. Choose your retention metric (customer count or revenue)
  5. Click "Generate Analysis"

Manual Calculation (Alternative Approach)

If you prefer to calculate cohorts manually or need custom logic, here's the core algorithm:

import pandas as pd
import numpy as np

# Load prepared data
df = pd.read_csv('cohort_analysis_ready.csv')
df['cohort_month'] = pd.to_period(df['cohort_month'])
df['order_month'] = pd.to_period(df['order_month'])

# Calculate cohort period (months since first purchase)
df['cohort_period'] = (df['order_month'] - df['cohort_month']).apply(lambda x: x.n)

# Create cohort retention matrix
cohort_counts = df.groupby(['cohort_month', 'cohort_period'])['customer_id'].nunique().reset_index()
cohort_pivot = cohort_counts.pivot(index='cohort_month', columns='cohort_period', values='customer_id')

# Calculate retention percentages
cohort_size = cohort_pivot.iloc[:, 0]
retention_matrix = cohort_pivot.divide(cohort_size, axis=0) * 100

print("Retention Cohort Matrix (%):")
print(retention_matrix.round(1))

# Save results
retention_matrix.to_csv('retention_cohort_matrix.csv')

Expected Output: A retention matrix showing what percentage of each cohort remained active in subsequent months:

Retention Cohort Matrix (%):
cohort_period    0     1     2     3     4     5     6
cohort_month
2024-01       100.0  32.5  28.3  25.1  22.8  20.5  18.9
2024-02       100.0  35.2  30.1  26.8  24.3  21.7    —
2024-03       100.0  33.8  29.5  25.9  23.1    —     —
2024-04       100.0  31.2  27.4  24.0    —     —     —

Step 4: Calculate Key Retention Metrics

Beyond the basic retention matrix, several derived metrics provide deeper insights:

# Calculate average retention by period across all cohorts
avg_retention_by_period = retention_matrix.mean()

# Calculate cohort-specific metrics
cohort_metrics = pd.DataFrame({
    'cohort_size': cohort_size,
    'month_1_retention': retention_matrix[1],
    'month_3_retention': retention_matrix[3],
    'month_6_retention': retention_matrix[6] if 6 in retention_matrix.columns else np.nan
})

# Identify best and worst performing cohorts
cohort_metrics['retention_score'] = cohort_metrics[['month_1_retention', 'month_3_retention']].mean(axis=1)
best_cohort = cohort_metrics['retention_score'].idxmax()
worst_cohort = cohort_metrics['retention_score'].idxmin()

print(f"\nBest Performing Cohort: {best_cohort} (Score: {cohort_metrics.loc[best_cohort, 'retention_score']:.1f}%)")
print(f"Worst Performing Cohort: {worst_cohort} (Score: {cohort_metrics.loc[worst_cohort, 'retention_score']:.1f}%)")

Expected Output:

Best Performing Cohort: 2024-02 (Score: 32.6%)
Worst Performing Cohort: 2024-04 (Score: 27.8%)

Interpreting Your Retention Results

Understanding the Retention Matrix

Your cohort retention matrix reveals critical patterns about customer behavior. Here's how to read and interpret the data:

Key Patterns to Look For

Benchmark Retention Rates

While retention varies by industry and business model, here are general Shopify benchmarks:

For more sophisticated statistical analysis of whether retention differences are significant, consider applying confidence intervals to your cohort comparisons.

Common Red Flags

Watch for these warning signs in your retention data:

Segmenting Cohorts for Deeper Insights

Don't stop at simple date-based cohorts. Create segments by:

The MCP Analytics retention cohort service can automatically generate these segmented analyses for you.

Taking Action on Your Insights

Improving Low-Retention Cohorts

Once you've identified retention issues, implement these evidence-based interventions:

1. Post-Purchase Engagement Campaigns

For cohorts showing steep month 1 drop-off, create automated email sequences:

2. Win-Back Campaigns for Lapsed Customers

Target customers from high-retention cohorts who haven't purchased in their expected cycle:

# Identify at-risk customers from good cohorts
good_cohorts = cohort_metrics.nlargest(3, 'retention_score').index

# Find customers from these cohorts who haven't purchased recently
recent_orders = df[df['order_month'] >= df['order_month'].max() - 2]
at_risk = df[
    (df['cohort_month'].isin(good_cohorts)) &
    (~df['customer_id'].isin(recent_orders['customer_id']))
]['customer_id'].unique()

print(f"Identified {len(at_risk)} at-risk customers from high-performing cohorts for win-back campaign")

3. Adjust Customer Acquisition Strategy

If certain acquisition channels produce low-retention customers, reallocate budget:

Predictive Applications

Use your cohort retention patterns for forecasting and planning:

Advanced practitioners can combine cohort analysis with survival analysis techniques to model time-to-churn probabilities.

Automate Your Cohort Analysis

While the manual approach outlined above provides complete control, running cohort analysis regularly can be time-consuming. The MCP Analytics Customer Retention Cohort Analysis tool automates the entire process—from data ingestion through visualization and insight generation.

Key benefits of the automated tool:

Try Customer Retention Cohort Analysis Now →

Next Steps and Advanced Techniques

Continuous Monitoring

Cohort analysis isn't a one-time exercise. Establish a regular cadence:

Related Analyses to Explore

Complement your cohort analysis with these related techniques:

Building a Data-Driven Retention Culture

Share cohort insights across your organization:

For organizations looking to implement AI-powered analytics workflows, cohort analysis serves as an excellent foundation for more sophisticated predictive models.

Troubleshooting Common Issues

Issue 1: Insufficient Data for Meaningful Cohorts

Symptoms: Very small cohort sizes (fewer than 30 customers per cohort), high variance in retention rates, or cohorts with zero activity in later periods.

Solutions:

Issue 2: Inconsistent Customer IDs

Symptoms: Customers appearing in multiple cohorts, artificially low retention rates, duplicate customer records.

Solutions:

# Deduplicate by email address
df['email'] = df['email'].str.lower().str.strip()
df = df.sort_values('created_at').drop_duplicates(subset=['email', 'order_month'], keep='first')

Issue 3: Seasonal Effects Distorting Cohorts

Symptoms: Holiday cohorts (November/December) showing dramatically different patterns, making year-over-year comparisons difficult.

Solutions:

Issue 4: Data Export Limitations

Symptoms: Shopify admin exports timing out, incomplete data downloads, or API rate limiting.

Solutions:

Issue 5: Unclear Retention Definitions

Symptoms: Confusion about whether "retained" means any purchase activity, minimum purchase amount, or specific product category purchases.

Solutions:

Issue 6: Interpreting Low Overall Retention

Symptoms: Month 1 retention below 15%, month 6 retention below 5%, concerns about business viability.

Solutions:

Getting Help

If you encounter issues not covered here, the MCP Analytics team can assist with:

Visit our Shopify Customer Retention Cohort service page for personalized support options.

Conclusion

Customer retention cohort analysis transforms abstract customer behavior into actionable insights. By following this step-by-step tutorial, you now have the complete framework to:

Remember that cohort analysis is most powerful when done regularly and combined with experimentation. As you identify retention gaps, test interventions (improved onboarding, win-back campaigns, loyalty programs) and measure their impact on subsequent cohorts. This creates a continuous improvement cycle that compounds over time.

The difference between a 25% and 35% month-3 retention rate might seem small, but compounded across hundreds or thousands of customers, it represents substantial revenue impact and business sustainability. Start analyzing your cohorts today, and let the data guide your retention strategy.

Ready to automate your cohort analysis? Get started with MCP Analytics' Customer Retention Cohort tool and unlock deeper insights in minutes instead of hours.