Learn Cohort Assignment Techniques | Cohort Data Structuring and Preparation

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Cohort analysis is a powerful technique in analytics that allows you to group users based on shared characteristics or experiences within a defined timeframe. The most common method is to assign users to cohorts according to the date of their first transaction or interaction. This approach enables you to track how different groups behave over time, revealing trends such as retention, engagement, and churn that would be hidden in aggregate data.

Assigning users to cohorts is a foundational step in cohort analysis. By defining clear rules for cohort assignment - such as grouping by the month or week of a user's first purchase - you can create meaningful segments for deeper analysis. This process not only helps you identify changes in user behavior but also supports more targeted business decisions, such as evaluating the impact of product changes or marketing campaigns on specific user groups.

Understanding and implementing proper cohort assignment ensures that your analysis reflects true user journeys and provides actionable insights. The following code sample demonstrates how to assign users to cohorts using their first transaction date in Python with pandas.


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import pandas as pd

# Sample transaction data
data = {
    "user_id": [1, 2, 1, 3, 2, 4],
    "transaction_date": [
        "2024-01-15", "2024-01-20", "2024-02-10",
        "2024-03-05", "2024-03-10", "2024-03-15"
    ],
    "amount": [100, 150, 200, 120, 80, 90]
}

df = pd.DataFrame(data)
df["transaction_date"] = pd.to_datetime(df["transaction_date"])

# Assigning each user to a cohort based on their first transaction month
df["cohort_month"] = df.groupby("user_id")["transaction_date"].transform("min").dt.to_period("M")

print(df[["user_id", "transaction_date", "cohort_month"]])

The logic behind cohort assignment is to identify a unique event or characteristic - most often the user's first transaction date - and use it to define the cohort for each user. In the code sample above, you use pandas to group the data by user_id and find the minimum transaction_date for each user. This date is then converted to a monthly period, creating a cohort_month that represents the user's cohort.

When implementing cohort assignment, consider edge cases such as users with multiple transactions on the same day, missing transaction dates, or users who may re-enter the system after a long absence. It is best practice to ensure that the cohort assignment logic is robust to these situations by handling missing values and validating that each user is assigned to exactly one cohort based on their true first interaction.

Following these principles helps maintain the integrity of your cohorts, ensuring that subsequent analysis accurately reflects user behavior and supports sound business decisions.

Task

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You are given a DataFrame df with columns user_id and signup_date representing user signups. Complete the following steps:

Convert the signup_date column to datetime format.
For each user, identify their earliest signup_date.
Create a new column cohort_week that contains the weekly period (YYYY-MM-DD with weekly frequency) of each user's first signup date, using pandas' period functionality with 'W' frequency.
Print the resulting DataFrame, which should include the new cohort_week column.

Do not modify any other columns or the structure of the DataFrame except to add the required cohort_week column.

Solution

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