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.

Shopify Cohort Analysis: Customer Retention Guide

By MCP Analytics Team | Published , updated

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.

To see how many of your own customers come back, cohort by cohort, try the free cohort retention tool.

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 questions like "What percentage of customers from January ordered again in June?" This is a Python tutorial (pandas), with a CSV upload route described near the end if you would rather not write code.

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 ground revenue forecasts in observed retention.

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 a Shopify Admin Export (For Smaller Stores)

Export your orders from the Shopify admin as a CSV, with a date range that covers your full history. Per Shopify's help centre, an order with several items shows the extra line items on separate rows with many order-level fields blank, and the export identifies the customer by email (there is no customer ID column). The script below keeps one row per order and uses the lower-cased email as the customer key. Its column names (Id, Name, Email, Total, Created at) are the headers Shopify documents; if your file differs, adjust them. If your export also lists cancelled or test orders, remove them before you continue.

import pandas as pd

# The Admin export puts each extra line item of an order on its own row, with the order-level
# fields (including Created at) blank, so keep one row per order
raw = pd.read_csv('your-orders-export.csv')
export = raw.dropna(subset=['Created at']).drop_duplicates(subset=['Name'])

# Shopify's order export has no customer ID column, so the email identifies the customer
orders = pd.DataFrame({
    'id': export['Id'],
    'email': export['Email'].str.lower().str.strip(),
    'created_at': pd.to_datetime(export['Created at'], utc=True),
    'total_price': export['Total'],
})
orders['customer_id'] = orders['email']

orders.to_csv('shopify_orders_export.csv', index=False)
print(f"Prepared {len(orders)} orders from {orders['customer_id'].nunique()} customers")
print(f"Earliest order: {orders['created_at'].min():%Y-%m-%d}   Latest order: {orders['created_at'].max():%Y-%m-%d}")

Expected Output:

Prepared 2856 orders from 1721 customers
Earliest order: 2024-01-01   Latest order: 2024-10-10

The figures in every expected output on this page come from running this code on a synthetic set of orders generated for the tutorial (about 1,700 customers, January to 10 October 2024), not from a real store, so your numbers will differ.

Option B: Using the Shopify API (For Larger Stores)

For stores with extensive order histories, the Shopify API provides more flexibility. Three things to get right:

import requests
import pandas as pd

# Shopify API credentials
SHOP_NAME = 'your-store-name'
API_VERSION = '2026-10'  # use a version Shopify currently supports
ACCESS_TOKEN = 'your-access-token'  # needs read_orders, plus read_all_orders for orders older than 60 days

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

headers = {'X-Shopify-Access-Token': ACCESS_TOKEN}

# Parameters for the first request only
params = {
    'status': 'any',
    'limit': 250,
    'fields': 'id,email,customer,created_at,cancelled_at,test,total_price',
}

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

    # The Link header can hold both a previous and a next page; ask for the next one by name
    url = response.links.get('next', {}).get('url')
    params = None  # the next-page URL already carries the cursor, limit and fields

# Convert to DataFrame, then keep real orders only
orders = pd.json_normalize(all_orders)
orders = orders[orders['cancelled_at'].isna() & ~orders['test']].copy()

# One customer key: the Shopify customer ID, or the email for guest checkouts
orders['email'] = orders['email'].str.lower().str.strip()
orders['customer_id'] = orders['customer.id'].astype('Int64').astype('string').fillna(orders['email'])
orders['created_at'] = pd.to_datetime(orders['created_at'], utc=True)

orders[['id', 'email', 'customer_id', 'created_at', 'total_price']].to_csv('shopify_orders_export.csv', index=False)
print(f"Fetched {len(all_orders)} orders, kept {len(orders)} after dropping cancelled and test orders")
print(f"Earliest order: {orders['created_at'].min():%Y-%m-%d}   Latest order: {orders['created_at'].max():%Y-%m-%d}")

Expected Output:

Fetched 2856 orders, kept 2759 after dropping cancelled and test orders
Earliest order: 2024-01-01   Latest order: 2024-10-10

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 from the shopify_orders_export.csv file that either option above writes:

import pandas as pd

# Load the orders prepared in Step 1
df = pd.read_csv('shopify_orders_export.csv')

# Convert dates to datetime (UTC) and create the month column
df['created_at'] = pd.to_datetime(df['created_at'], utc=True)
df['order_month'] = df['created_at'].dt.tz_localize(None).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.tz_localize(None).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:

Prepared 2572 cohort records from 1697 unique customers

This is the Option B file, which also dropped cancelled and test orders and used the Shopify customer ID where there was one. The step writes cohort_analysis_ready.csv.

Step 3: Run Cohort Analysis

Now that your data is properly structured, you can calculate the cohort retention matrix.

Calculating the Retention Matrix

A customer counts as retained in a month if they placed at least one order in that month. The code keeps two kinds of empty cell apart: a month that has finished with no repeat orders is 0, and a month that has not happened yet for that cohort is NaN. If both became NaN, the average would skip the worst months and overstate retention. Set last_complete_month to the last month your export covers in full.

import pandas as pd
import numpy as np

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

# The last month your export covers in full (a month still in progress would look like lost customers)
last_complete_month = pd.Period('2024-09', 'M')
df = df[df['order_month'] <= last_complete_month]

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

# Customers who ordered in each cohort-period
cohort_counts = df.groupby(['cohort_month', 'cohort_period'])['customer_id'].nunique()
cohort_pivot = cohort_counts.unstack('cohort_period')

# A month that has finished with no repeat orders is 0, not missing...
max_age = (last_complete_month - cohort_pivot.index.min()).n
cohort_pivot = cohort_pivot.reindex(columns=range(max_age + 1)).fillna(0)

# ...and only months that have not happened yet for that cohort are blanked out
for cohort in cohort_pivot.index:
    cohort_pivot.loc[cohort, cohort_pivot.columns > (last_complete_month - cohort).n] = np.nan

# Calculate retention percentages (every customer ordered in month 0, so column 0 is the cohort size)
cohort_size = cohort_pivot[0]
retention_matrix = cohort_pivot.divide(cohort_size, axis=0) * 100

print("Retention Cohort Matrix (%), first 7 months:")
print(retention_matrix.iloc[:, :7].round(1))
print("\nCustomers per cohort:")
print(cohort_size.astype(int).to_string())

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

Expected Output:

Retention Cohort Matrix (%), first 7 months:
cohort_period      0     1     2     3     4     5    6
cohort_month                                           
2024-01        100.0  18.2  12.8  17.2  11.8  12.3  6.9
2024-02        100.0  19.3  16.5  10.6  11.9   7.3  9.2
2024-03        100.0  18.5  11.9   9.6   5.9  11.1  4.4
2024-04        100.0  19.4  14.1   9.2   4.9  10.2  NaN
2024-05        100.0  18.7  15.2   9.4   7.6   NaN  NaN
2024-06        100.0  17.2  15.7  10.6   NaN   NaN  NaN
2024-07        100.0  25.2  10.8   NaN   NaN   NaN  NaN
2024-08        100.0  18.5   NaN   NaN   NaN   NaN  NaN
2024-09        100.0   NaN   NaN   NaN   NaN   NaN  NaN

Customers per cohort:
cohort_month
2024-01    203
2024-02    218
2024-03    135
2024-04    206
2024-05    171
2024-06    198
2024-07    139
2024-08    189
2024-09    179
Freq: M

Month 0 is the month of the first order. The January 2024 cohort has 203 customers, and 18.2% of them ordered again in February (month 1). NaN marks months that have not finished yet.

Step 4: Calculate Key Retention Metrics

Beyond the basic retention matrix, several derived metrics provide deeper insights. The score below is the mean of month 1 and month 3 retention, so only cohorts that have reached month 3 get one; the others are NaN. Differences of a few points between cohorts of 135 to 218 customers are within the noise described in the prerequisites.

# 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 (only cohorts old enough to have both months)
cohort_metrics['retention_score'] = cohort_metrics[['month_1_retention', 'month_3_retention']].mean(axis=1, skipna=False)
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}%)")
print(cohort_metrics['retention_score'].round(1).to_string())

Expected Output:

Best Performing Cohort: 2024-01 (Score: 17.7%)
Worst Performing Cohort: 2024-06 (Score: 13.9%)
cohort_month
2024-01    17.7
2024-02    14.9
2024-03    14.1
2024-04    14.3
2024-05    14.0
2024-06    13.9
2024-07     NaN
2024-08     NaN
2024-09     NaN
Freq: M

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

Compare Against Your Own History

Repeat-purchase rates vary a lot with product type, price and how often people need to reorder, and we do not publish industry benchmark ranges here. The comparison that means something is your own history: the same product mix, cohort by cohort, at the same age.

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:

Each of these segments is the same calculation as above on a subset of customers; keep the cohort-size caution in mind, because segments are smaller than cohorts.

Taking Action on Your Insights

Improving Low-Retention Cohorts

Once you've identified retention issues, these are interventions to test:

1. Post-Purchase Engagement Campaigns

For cohorts showing steep month 1 drop-off, test a post-purchase email sequence such as:

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['retention_score'].nlargest(3).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")

Expected Output:

Identified 500 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.

Run It on Your Own Shopify Export

Export your orders from Shopify as a CSV and upload it at MCP Analytics. The analysis runs a cohort retention method on your file and shows the result with the method used. You choose the columns; nothing connects to your Shopify account.

Try it on your data

Next Steps and Advanced Techniques

Review Regularly

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, large swings in retention rates from one cohort to the next, 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:

# Use the lower-cased email as the one customer key, then rebuild the cohort files
orders = pd.read_csv('shopify_orders_export.csv')
orders['customer_id'] = orders['email'].str.lower().str.strip()
print(orders['customer_id'].nunique(), 'customers by email')

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: Retention that looks very low to you at month 1 or month 6, and concern about the business.

Solutions:

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.

If you would rather not write the code, you can upload a Shopify orders export to MCP Analytics instead.

Explore more: Shopify 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