Скачать или смотреть How to Calculate Churned Customers in Pandas

Learn how to effectively identify `churned` customers using Pandas with step-by-step instructions and sample code to analyze customer retention.
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Understanding Customer Churn: A Practical Guide to Identifying Churned Customers in Pandas

Customer churn is an important metric for businesses as it quantifies the number of customers who stop engaging with your services over a specific period. Understanding churn is crucial as it can give you insights into customer satisfaction and retention strategies. In this guide, we'll explore how to identify and calculate churned customers using Python's Pandas library.

What Does Customer Churn Mean?

Customer churn can be defined in several ways, including:

Cancellation of a subscription: This is the most common form of churn for subscription-based services.

Closure of an account: Customers might decide to close their account with your service.

Non-renewal of contracts: Failing to renew services or agreements can also signify churn.

Switching to competitors: Customers may simply start using a competitor’s product or service.

The best way to approach churn analysis is to define what constitutes churn for your business context. For this guide, we will focus on calculating the total number of customers lost during a specific period based on their last purchase.

Analyzing Customer Data in Pandas

Let's dive into a sample dataset that resembles customer transaction history. Our data contains the following columns:

Date: The date of the transaction.

Name: The name of the customer.

Subscription: Whether the customer is subscribed (True/False).

Step 1: Prepare Your Data

Start by importing the necessary libraries and creating a DataFrame. Make sure to format the dates properly for analysis.

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Step 2: Determine Last Transaction Date

To identify customers who have churned, we first need to find the date of their most recent transaction.

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Step 3: Calculate Time Since Last Purchase

Next, we will determine how long it's been since each customer made a purchase. We will compute the current date and calculate the difference in days.

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Step 4: Define Churn Criteria

For this example, we will define customers who have not made a purchase in the last 90 days as churned. We can categorize each customer accordingly:

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Final Thoughts

By following the steps outlined above, you can effectively identify churned customers using Pandas. Keeping track of customer retention is vital for improving your business strategies and ensuring a loyal customer base.

Armed with this understanding and the provided code samples, you can begin your data analysis journey to tackle customer churn head-on!