What is Cohort Analysis and How does It Works?

In the fast-paced world of data analytics, extracting actionable insights is crucial for informed decision-making. Cohort Analysis stands as a powerful tool in this realm, providing a nuanced understanding of user behavior over time.

This article aims to demystify Cohort Analysis, elucidating its significance and demonstrating how it effectively groups data by specific characteristics.

What is Cohort Analysis?

Cohort Analysis is a method of grouping and analyzing data based on specific characteristics shared by a set of individuals. These characteristics could include the time of acquisition, geographic location, or any other defining attribute. This method is widely used in various fields, including business, marketing, and healthcare.

Why use Cohort Analysis?

• Understand the user behavior over time: By tracking a particular set of users over time, cohort analysis gives the user valuable insights into their long-term engagement and retention trends as per the process requirement.
• Assessing the Client Retention: To measure the client retention is very much beneficial when using cohort analysis. This aids the required companies to determine the elements that lead to either client churn or loyalty in the required process.
• Optimize the marketing strategies: Cohort analysis basically offers an insightful information that can be used to improve the required marketing tactics. Through user acquisition channel grouping, marketers may evaluate the efficacy of different campaigns and channels over an extended period of time in this process.
• Determine the Feature impact: Cohort analysis lets the user to evaluate how the required new additions or modifications to the product affect user behavior.

Types of Cohort Analysis

• Time-Based Cohort Analysis
  This kind of analysis puts people into the groups according to when they initially signed up as clients or users as per requirement. This can be much helpful in spotting trends in the spending habits or customer retention over time to time. To monitor the purchasing patterns of customers who made their first purchase in November vs those who made their first purchase in December, for instance, a business may employ time-based cohort analysis to overcome the initial process.
• Behavior-Based Cohort Analysis
  These user groups are made up of people who have accomplished and determined a specific goal or demonstrated a certain behavior, such as the formula based signing up for a newsletter, finishing a product, or making a repeat purchase. Cohort analysis is basically based on behavior can be used to spot patterns and trends in user loyalty, retention, and engagement as per requirement.
• Demographic-Based Cohort Analysis
  The users in these cohorts have much comparable age, gender, geography, and income levels, among other demographic traits in the overall process. Businesses may better target distinct audience segments with their marketing messaging, product features, and user experiences or stages by analyzing cohorts based on demographics.
• Size-Based Cohort Analysis
  This kind of analysis basically puts people in groups according to the amount of money they invested or bought initially in the stages. Finding trends in consumer behavior or spending patterns across the various customer categories may benefit from this analysis. To monitor the regular purchasing habits of clients who made tiny initial purchases vs those who made large initial purchases as per requirement, for instance, a business may employ size-based cohort analysis to carry the information.
• Funnel-Based Cohort Analysis
  This kind of analysis puts people into the individual groups according to a funnel’s stages. Finding trends in the engagement or behavior of various user segments may benefit from this type of cohort analysis. A business may, for instance, utilize the funnel-based cohort analysis to monitor the overall required actions of customers who abandoned their carts during checkout as opposed to those who finished their purchases for the particular circumstances.

How does cohort analysis work?

• Extract the required raw data: By using MySQL, raw data is extracted from a database and imported into spreadsheet software as per requirement for further segmentation and joining of user information in the system.
• To create Cohort identifiers: Sort the required user information into distinct categories, including the date of registration, the date of the first transaction, the year of graduation, all mobile devices at a specific location and time to create the basic cohort identifiers.
• Calculating the lifestyle stages: After the customers are grouped into cohorts, required lifecycle stages are computed by measuring the intervals between events assigned to each client in the process.
• Creating required tables and graphs: The required pivot tables and graphs assist in computing the aggregation of various user data dimensions and produce the actual visual representations of comparisons of user data in the process.

Importance of Cohort Analysis

Cohort analysis is very much important and crucial because it helps to identify the required patterns, trends, and changes in user behavior over time. Cohort analysis is used by most of the businesses for the reasons listed below, which highlights its significance to overcome the overall processes:

• To understand the importance of user behavior analysis and understanding.
• To identify the reasons behind client attrition.
• To increase the conversion funnel’s effectiveness as per requirement.
• To calculate or find the customer’s lifetime value.
• To increase the efficiency of client interaction.

Examples of Cohort Analysis

• Initially eCommerce: A retailer may monitor the actual shifts in the average order value or frequency of purchases made by their customers over time by using cohort analysis to process the actual activity. For instance, the merchant may classify all the consumers according to the month of their initial purchase and see how they behave in the ensuing months. So, all the initial types of process are done by this.
• Subscription based Businesses: Customer acquisition cost (CAC) and lifetime value (LTV) can be examined and implemented with the help of cohort analysis.
• Digital marketing process: To monitor the efficacy of various types of campaigns or marketing platforms, a team in charge of digital marketing could do cohort analysis as per requirement. For instance, they may follow a user’s behavior over time by classifying them according to the month of their initial interaction with the brand and carry the overall process.

Steps to Conduct Cohort Analysis

Step 1: Define Cohorts:

Start by determining the criteria for cohort creation. This could be the month of user acquisition, the source of acquisition, or any other relevant characteristic.

Step 2: Data Collection:

Gather comprehensive data on user behavior, ensuring it aligns with the chosen cohort criteria. This data may encompass metrics like user engagement, retention, or conversion rates.

Step 3: Create Cohort Grid:

Organize the data into a cohort grid, where each row represents a cohort and each column signifies a specific time period (e.g., weeks or months).

Step 4: Calculate Metrics:

Compute key metrics for each cohort over time. Common metrics include retention rates, conversion rates, and average revenue per user.

Step 5: Visualize Results:

Utilize charts and graphs to visualize cohort trends. This aids in easily identifying patterns, such as whether certain cohorts exhibit higher or lower engagement over time.

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 datasetT

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=".2%")
plt.title('Cohort Analysis on Titanic Dataset')
plt.xlabel('Age Cohorts')
plt.ylabel('Embarked Cohorts')
plt.show()

Output:

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.

Benefits of Cohort Analysis

• Cohort analysis helps group customers for targeted strategies.
• Reveals patterns in user behavior over time.
• Informs improvements based on cohort responses.
• Evaluates campaign effectiveness for optimized resource allocation.
• Guides strategies for improved customer loyalty and retent.

Challenges in Cohort Analysis

• Reliable analysis requires accurate and consistent data.
• Balancing cohort size and granularity can be challenging.
• Requires longitudinal data, making real-time insights difficult.
• Economic shifts or events can influence cohort behavior.
• Understanding causation vs. correlation requires careful consideration.

Conclusion

In the dynamic landscape of data analytics, Cohort Analysis emerges as a pivotal tool for unraveling actionable insights crucial for informed decision-making.

Cohort Analysis- FAQs

Can Cohort Analysis be applied to any type of business?

Yes, Cohort Analysis is versatile and can be applied across various industries, including e-commerce, SaaS, and mobile apps.

Are there tools available to facilitate Cohort Analysis?

Absolutely. Many analytics platforms and tools, such as Google Analytics and Mixpanel, offer features specifically designed for Cohort Analysis.

How often should Cohort Analysis be conducted?

The frequency depends on the business objectives. It can be done weekly, monthly, or based on specific milestones. Regular analysis helps in tracking changes and optimizing strategies accordingly.