What is Cohort Analysis and How does It Works?

Last Updated : 19 Nov, 2025

Cohort Analysis is a method of grouping and examining data based on shared characteristics of a specific set of individuals. By segmenting users for example, by acquisition date, location, or behavior this technique helps teams understand trends, retention, and engagement over time.

  • Provides a clear view of how different groups of users behave over a period.
  • Widely used in business, marketing, product management, and healthcare.
  • Helps identify patterns, optimize strategies, and make data-driven decisions.

Why use Cohort Analysis?

Understand User Behavior Over Time: Track a specific group of users to gain insights into long-term engagement and retention trends.

Assess Client Retention: Identify factors driving customer loyalty or churn, helping improve overall retention strategies.

Optimize Marketing Strategies: Evaluate the effectiveness of campaigns and acquisition channels over time, enabling better allocation of marketing resources.

Determine Feature Impact: Analyze how new features or product changes influence user behavior and adoption.

When to Use Cohort Analysis

Cohort analysis is a weekly effective tool to look at how user in a certain time period react on our product and services. Here are some key scenarios where it shines:

Understanding User Acquisition:

  • Build cohorts based on acquisition channels (e.g., paid ads, SEO) to see which channels bring long-term users.
  • Compare retention patterns across cohorts to evaluate marketing effectiveness.

Optimizing User Retention:

  • Identify causes of churn by analyzing retention over time for different cohorts.
  • Spot moments when users disengage and improve the experience or launch re-engagement campaigns.

Segmenting Your User Base:

  • Group users based on age, behavior, preferences, or other shared characteristics.
  • Tailor marketing messages, product features, and support strategies for specific segments.

Measuring the Impact of Changes:

  • Track how product updates, pricing changes, or marketing strategies affect different cohorts.
  • Evaluate effectiveness and identify unintended side effects.

Predicting Future Behavior:

  • Use historical cohort data to model and forecast user behavior.
  • Assess customer lifetime value, identify high-risk or high-potential users, and personalize experiences.

Types of Cohort Analysis

Some of the common types of Cohort Analysis are discussed below:

  • Groups users based on when they first signed up or made a purchase.
  • Useful for spotting trends in retention, spending habits, or engagement over time.
  • Example: Comparing customers who joined in November vs. December.

Behavior-Based Cohort Analysis

  • Groups users according to specific actions or behaviors, like signing up for a newsletter or making repeat purchases.
  • Helps identify patterns in loyalty, retention, and engagement.

Demographic-Based Cohort Analysis

  • Groups users by demographic traits such as age, gender, location, or income.
  • Allows businesses to tailor marketing, product features, and experiences to specific audience segments.

Size-Based Cohort Analysis

  • Groups users based on the amount they initially spent or purchased.
  • Useful for analyzing spending behavior and purchasing patterns across different customer categories.
  • Example: Comparing small initial purchasers vs. large initial purchasers.

Funnel-Based Cohort Analysis

  • Groups users based on stages in a funnel (e.g., onboarding, checkout, feature adoption).
  • Helps track engagement and behavior at different funnel stages.
  • Example: Comparing users who abandoned carts vs. those who completed purchases.

How does cohort analysis work?


Extract Raw Data:

  • Collect user data from databases (e.g., MySQL) and import it into spreadsheet or analytics tools.
  • Include details needed for segmentation and further analysis, such as registration dates, transactions, or user activity.

Create Cohort Identifiers:

  • Group users into distinct categories based on shared attributes or events, such as:
    • Sign-up date or first purchase date
    • Year of graduation or account creation
    • Device type or location
  • These identifiers form the basis of each cohort.

Calculating the lifestyle stages:

  • Measure intervals between key events for each user within the cohort.
  • Examples: Time from registration to first purchase, repeat engagement periods, or subscription renewal intervals.

Creating required tables and graphs:

  • Use pivot tables or analytics software to aggregate and visualize cohort data.
  • Graphs show comparisons over time, highlighting trends in retention, engagement, or behavior across cohorts.

Importance of Cohort Analysis

Cohort analysis is a crucial tool for understanding user behavior and optimizing business processes. Its significance lies in the following points:

  • Understand User Behavior: Reveals how different groups of users interact with a product over time, helping to uncover patterns and trends.
  • Identify Causes of Attrition: Highlights why customers leave or disengage, allowing businesses to address churn proactively.
  • Optimize Conversion Funnels: Shows where users drop off in the funnel, enabling improvements to increase conversions.
  • Calculate Customer Lifetime Value (CLV): Helps estimate the long-term value of different user segments, guiding investment and retention strategies.
  • Enhance Customer Engagement: Provides insights to improve user interactions, personalize experiences, and strengthen loyalty.

Steps to Conduct Cohort Analysis

There are 6 Steps that are involved in Cohort Analysis, Lets understand all the steps using an example. Imagine you're running a subscription box service for dog lovers. You deliver curated boxes of treats, toys, and accessories to pamper pups every month. Business is booming, but you want to understand your customer base better. Here's how cohort analysis can help:

1. Define Goals and Questions

  • Goal: Increase customer retention for your dog box subscription service.
  • Research Question: Are customers who sign up for a longer subscription plan (e.g., 6 months) more likely to stay subscribed compared to those who choose a shorter plan (e.g., monthly)?

2. Choose Cohort Definition

  • We'll define cohorts based on the subscription plan chosen at signup (monthly vs. 6-month plan). This allows us to compare the retention rates of these two customer groups.

3. Identify Relevant Metrics

  • Retention Rate: Percentage of customers who remain subscribed after a specific period (e.g., 3 months, 1 year).
  • Churn Rate: Percentage of customers who cancel their subscription within a given timeframe.

4. Gather Your Data

  • We'll need customer data on signup date, chosen subscription plan, and cancellation history. This data can be retrieved from your customer relationship management (CRM) system.

5. Analyze the Cohorts

  • Create a cohort table showing customer retention and churn rates for both monthly and 6-month plan subscribers over time (e.g., months).
  • You might see that after 6 months, 70% of customers who signed up for the 6-month plan are still subscribed, whereas only 40% of monthly subscribers remain.

6. Take Action

  • These findings suggest that customers who commit to a longer plan upfront have a higher retention rate.
  • You can use this insight to develop targeted marketing campaigns that incentivize longer subscriptions, perhaps offering discounts or bonus treats for 6-month plans.
  • Additionally, you could analyze the churn rate for monthly subscribers and see if there's a specific point where they tend to cancel. This might reveal areas for improvement in your service, such as offering more customization options or providing additional value to keep them engaged.

Examples of Cohort Analysis

The dog box subscription example showcases a common marketing scenario. However, cohort analysis is a powerful tool applicable across various industries. Here are some additional examples:

E-commerce Platform

  • Goal: Increase Customer Lifetime Value (CLTV).
  • Cohorts: Group customers based on their first purchase amount (e.g., <$25, $25–$50, $51–$100). Track metrics like purchase frequency (5–10 orders) and average order value for each cohort over time.
  • Use: Identify high-value customer segments to target with promotions or loyalty programs to drive repeat purchases.

Saas Company

  • Goal: Convert free trial users into paying customers and expand revenue.
  • Cohorts: Segment users by trial membership and feature usage (e.g., basic vs. advanced features).
  • Use: Determine which features drive higher conversion rates, optimize the free trial experience, and design a seamless customer journey.

Mobile Gaming App

  • Goal: Improve user engagement.
  • Cohorts: Group users based on in-app purchase history (spending vs. non-spending users). Track login frequency, quest completion, and social feature usage.
  • Use: Identify low-engagement segments and implement strategies to enhance their experience, boosting retention and revenue.

Streaming Service

  • Goal: Personalize content recommendations.
  • Cohorts: Group users by viewing habits or genre preferences (e.g., action vs. comedy viewers).
  • Use: Deliver tailored content to each cohort, increasing satisfaction and platform stickiness.

Python Implementation - Cohort Analysis

Import the necessary Libraries

At first we will import the libraries that we will be using.

Python 
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

Load the dataset

he next step is to load inbuilt dataset. Use load_dataset from seaborn library to load the dataset.

Python 
# Load the Titanic dataset
titanic_df = sns.load_dataset('titanic')
titanic_df.head()

Output:

    survived    pclass    sex    age    sibsp    parch    fare    embarked    class    who    adult_male    deck    embark_town    alive    alone
0    0    3    male    22.0    1    0    7.2500    S    Third    man    True    NaN    Southampton    no    False
1    1    1    female    38.0    1    0    71.2833    C    First    woman    False    C    Cherbourg    yes    False
2    1    3    female    26.0    0    0    7.9250    S    Third    woman    False    NaN    Southampton    yes    True
3    1    1    female    35.0    1    0    53.1000    S    First    woman    False    C    Southampton    yes    False
4    0    3    male    35.0    0    0    8.0500    S    Third    man    True    NaN    Southampton    no    True

Data Cleaning

From the dataset we can see that there are some missing values, so we are dropping the missing values from specific columns.

Python 
titanic_df.isna().sum()
# Drop missing values
titanic_df = titanic_df.dropna(subset=['embarked', 'age','deck'])
titanic_df.isna().sum()

Output:

survived       0
pclass         0
sex            0
age            0
sibsp          0
parch          0
fare           0
embarked       0
class          0
who            0
adult_male     0
deck           0
embark_town    0
alive          0
alone          0
dtype: int64

Since, cohort analysis involves grouping individuals based on shared characteristics or experiences over time. In this case, the "embarked" values are being used to create cohorts, which are subsets of the data based on a common attribute. Before moving forward let's change the data type of age into integer making it easier to categorize and analyze cohorts.

Python 
titanic_df['age'] = titanic_df['age'].astype(int)

Cohort Analysis

Now, let's define bins and labels:

  • Labels to enhance understanding for analysts and stakeholders, improving communication and insight interpretation. The line assigns labels to each age range defined in the bins.
  • Binning converts a continuous variable (like age) into categories, facilitating comparisons and analyses of different population segments. The line defines the boundaries for age ranges.
  • Categorization with pd.cut(): Utilizes pd.cut() to categorize passenger ages into specified ranges (bins).

The code is creating a new column in the DataFrame called age_cohorts based on the values in the existing age column. This step is performed for the purpose of binning or categorizing age values into specific ranges or cohorts.

Python 
# Create cohorts based on age ranges
bins = [0, 10, 20, 30, 40, 50, 60, 70, 80]
labels = ['0-10', '11-20', '21-30', '31-40',
          '41-50', '51-60', '61-70', '71-80']
titanic_df['age_cohorts'] = pd.cut(
    titanic_df['age'], bins=bins, labels=labels, right=False)
titanic_df['age_cohorts'].head()

Output:

1     31-40
3     31-40
6     51-60
10     0-10
11    51-60
Name: age_cohorts, dtype: category
Categories (8, object): ['0-10' < '11-20' < '21-30' < '31-40' < '41-50' < '51-60' < '61-70' < '71-80']

Next step is Grouping and Aggregation:

Grouping the data in the DataFrame titanic_df by two columns: 'cohorts' and 'age_cohorts'. Then, Selection of 'survived' column within each group. At last, calculation of mean survival for each group.

The next line pivots the data for better visualization. It takes the DataFrame cohort_survival and creates a pivot table where the rows correspond to the unique values of 'cohorts', the columns correspond to the unique values of 'age_cohorts', and the values within the table are the survival rates.

Python 
# Calculate survival rates within each cohort
cohort_survival = titanic_df.groupby(['embarked', 'age_cohorts'])[
    'survived'].mean().reset_index()
# Pivot the data for better visualization
cohort_survival_pivot = cohort_survival.pivot(
    'embarked', 'age_cohorts', 'survived')
cohort_survival_mean_imputed = cohort_survival_pivot.fillna(
    cohort_survival_pivot.mean())
print(cohort_survival_mean_imputed)

Output:

age_cohorts  0-10     11-20    21-30     31-40     41-50     51-60     61-70  \
embarked                                                                       
C             0.8  0.857143  0.81250  0.733333  0.833333  0.545455  0.666667   
Q             0.8  0.803571  0.75625  1.000000  0.000000  0.541958  0.416667   
S             0.8  0.750000  0.70000  0.757576  0.473684  0.538462  0.166667   

age_cohorts  71-80  
embarked            
C              0.0  
Q              0.0  
S              0.0

Visualize the Results

The final visualization code is here.

Python 
# Plot the cohort analysis
plt.figure(figsize=(12, 8))
sns.heatmap(cohort_survival_mean_imputed, annot=True, cmap='Blues', fmt=&quot;.2%&quot;)
plt.title('Cohort Analysis on Titanic Dataset')
plt.xlabel('Age Cohorts')
plt.ylabel('Embarked Cohorts')
plt.show()

Output:

cohort
Cohort Analysis
  • The heatmap shows the survival rate of passengers on the Titanic, broken down by age and embarkation cohort. The embarkation cohorts are C (Cherbourg), Q (Queenstown), and S (Southampton).
  • Children under 10 had the highest survival rate, at 80%. This is likely because they were given priority in evacuation efforts.
  • Passengers between 21 and 30 had the lowest survival rate, at around 50%. This may be because they were more likely to be men, who were not given priority in evacuation efforts.
  • Passengers who embarked from Southampton had the highest survival rate, at around 60%. This may be because they were generally wealthier and had better access to lifeboats.
  • Passengers who embarked from Queenstown had the lowest survival rate, at around 40%. This may be because they were generally poorer and had less access to lifeboats.
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