case study analyzing customer churn in power bi

Case Study: Analyzing Customer Churn in Power BI

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Are you ready to apply your Power BI skills to a real-world dataset? For subscription-based businesses, reducing customer churn is a top priority. In this Power BI case study, you'll investigate a dataset from an example telecom company called Databel and analyze their churn rates. Analyzing churn doesn’t just mean knowing what the churn rate is: it’s also about figuring out why customers are churning at the rate they are, and how to reduce churn. You'll answer these questions by creating measures and calculated columns, while simultaneously creating eye-catching report pages. Read more.

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Posted on Jan 4, 2023

Case Study: Analyzing Customer Churn in Power BI

Summary of case study.

This case study is for the purpose of applying skills developed through the Power Bi course offered by Data Camp using a real-world problem.

The Problem:

The task is to solve customer churn for a Telecom provider called Databel where I will be using a fictitious churn dataset. I will be analyzing why customers are churning and the churn rate.

Defining churn

A good definition is the one from Investopedia : “The churn rate, also known as the rate of attrition or customer churn, is the rate at which customers stop doing business with an entity.”

Calculating churn

The simplified formula for churn is to divide customers lost by the total number of customers.

Churn rate = customers lost/total number of customers

Example: Churn rate = 10/100 = 10%

There are multiple methods to calculate churn, and depending on the industry. A traditional e-commerce platform might consider a certain customer a churner if he or she hasn’t made a purchase in the last 12 months.

Key Characteristics:

• One big table with 29 different columns with one row per customer.
• Snapshot of the database at a specific moment in time, meaning there is no time dimension.
• The dataset contains more than just dimensions. Here is a view of the metadata

The first step in any analysis is doing a data check. I will create two measures to check if the count of customer ids is equal to the count of unique customer ids. This check is particularly important to prevent double-count costs later incase of duplicates.

Both values are the same.

Calculating Churn Before getting further into the analysis, there is a churn label column that indicates “Yes” (if customer has churned) or “No” (If customer has not churned.) Because working with this column is difficult, it is best to convert it to a binomial column (1/0) that indicates whether or not the client churned.

Creating a measure for the churned customers using a conditional IF expression based on the Churn Label

Creating a measure to calculate the churn rate

The total churn rate for “Databel” is * 26.86% *

Investigating Churn reasons

Next, I will investigate the different reasons why customers churned.

The top three reasons are:

• Competitors made better offers
• Competitors had better devices
• Attitude of support person

Digging deeper into churn categories

Churn Reasons are grouped together in the Churn Category column. The “Extra data charges”, “Price too high” and other price related reasons are grouped together in the “Price” category. I will be displaying all churn categories in one visualization.

The largest proportion of churned customers churning is related to the competitor category.

Databel wants to know if the aggressive promotions launched by competitors in different states has had an impact on their customers. The task is to create a map that will allow me to look at the churn rate by state.

Insights discovered so far

• The Churn rate is for Databel is on average 27%
• 45% of the reasons why customers churn is related to competitors
• The Churn rate in California is abnormally high

The next stage is to analyze more columns, starting with the demographics of Databel.

Analyzing Demographics

The IF() method will be used to create a column with three age demography categories:

• “Under 30,”
• and “Other.”

The churn rate for senior citizens is higher than the average.

Analyzing Age groups

From the above visualization, the senior citizens churn more often. This suggests the need t have a more detailed look at the ages. Next, I will create combo chart visualizing the number of customers per bracket and their respective churn rates.

In general, the churn rate has an increasing trend through the age brackets. As the age increases the average churn rate for age brackets also increases.

Multiple field investigation

For this task, I will use the function SWITCH() which allows creation of a new column by assigning new results to the values in a column. We will group 3 different contract types into two for easy observation of yearly and monthly contracts. We will further analyze the churn rate based on gender.

Monthly contract customers churn more than yearly contract customers and the larger churning gender is females.

Group Consumption and Unlimited Plan

Databel has a hypothesis that people who are not on an unlimited data plan are more likely to churn. The task is to investigate this theory and prove if its accurate or not. I will also create a new column Grouped Consumption that categorizes the average monthly GB download into the following groups to determine whether it’s related to the amount of mobile data (GB) used:

• Less than 5 GB.
• Between 5 and 10 GB.
• 10 or more GB.

It appears that the hypothesis is incorrect and instead, customers who are on an unlimited plan are more likely to churn.

International Calls and Contract Types

Databel has a request to analyze the international activity of customers and its relationship to churn. They would like to know if paying for an international plan influences customer loyalty. This will also help us find out if customers without international plans are making international calls and vice versa.

The findings were that customers who pay for an international plan but do not make international calls had a very high churn rate. The recommendation to Databel would be to contact customers who have an international plan but have not made any international calls and suggest that they downgrade their plan.

Contract Type

It seems the churn rate decreases over time.

Summary Overview

• The Churn rate in California is abnormally high and this is due to campaigns launched by competitors inthe region
• There is a higher churn rate from senior citizens
• There is a higher churn rate from individual who have an international plan but have not made any international calls
• Customers with a monthly contract type have a higher churn rate

The next phase would be to identify reasons for the churn rates inthe different categories and recommend solutions for Databel.

Thank you for reading

Author: Gcinithemba Sherilyn Maphosa Data Wired

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How to Create a Churn Rate Report in Power BI

In today’s data-driven business landscape, understanding and measuring customer churn rate is crucial for organizations looking to make informed decisions and improve customer retention. Power BI, Microsoft’s powerful business intelligence tool, provides businesses with the capabilities to create comprehensive churn rate reports that enable them to analyze and gain insights into customer attrition. In this article, we will explore the step-by-step process of creating a churn rate report in Power BI, from understanding the concept of churn rate to utilizing advanced features for predictive analysis and optimizing report performance.

Table of Contents

Understanding Churn Rate and its Importance in Business

Churn rate, also known as attrition rate or customer turnover, refers to the percentage of customers who stop using a product or service within a given time period. A high churn rate indicates that a significant number of customers are leaving, which can be detrimental to a business’s growth and profitability. By understanding and monitoring churn rate, organizations can identify potential issues and take proactive measures to reduce customer attrition.

Churn rate is an essential metric for businesses across various industries. It helps companies gauge customer satisfaction, identify areas for improvement, and make data-driven decisions to retain existing customers. Creating a churn rate report in Power BI allows businesses to visualize this data effectively and gain valuable insights into customer behavior.

One of the key factors that contribute to a high churn rate is poor customer experience. When customers have a negative experience with a product or service, they are more likely to switch to a competitor. Therefore, businesses should focus on improving customer satisfaction by providing excellent customer service, addressing customer concerns promptly, and continuously enhancing the quality of their offerings.

In addition to customer experience, pricing and value proposition also play a significant role in churn rate. If customers perceive that a product or service is overpriced or does not provide enough value for the cost, they may choose to discontinue their subscription or switch to a more affordable alternative. Businesses should regularly evaluate their pricing strategy and ensure that they are offering competitive prices and delivering sufficient value to their customers.

Introduction to Power BI and its Data Visualization Capabilities

Power BI is a powerful business intelligence tool that enables users to visualize data and gain insights through interactive reports and dashboards. With its user-friendly interface and robust capabilities, Power BI empowers businesses to transform raw data into meaningful visualizations that drive informed decision-making.

Power BI offers a range of data visualization options, including charts, graphs, maps, and tables, allowing users to present information in a visually appealing and easy-to-understand format. These visualizations can be customized and interactive, providing users with the ability to explore data and uncover hidden patterns or trends.

In addition to its data visualization capabilities, Power BI also offers advanced analytics features that enable users to perform complex calculations and statistical analysis on their data. With built-in functions and formulas, users can create calculated columns and measures to derive new insights from their data. Power BI also supports the integration of R and Python scripts, allowing users to leverage the power of these programming languages for advanced analytics tasks.

Gathering and Preparing Data for the Churn Rate Report

Before creating a churn rate report in Power BI, it is essential to gather and prepare the required data. Start by identifying the data sources that contain relevant customer information, such as a customer relationship management (CRM) system, sales database, or customer feedback platform.

Once the data sources are identified, extract the necessary data and ensure that it is in a clean and standardized format. This may involve cleaning up missing or inaccurate data, merging datasets, and transforming data into a consistent structure. Power BI provides various data preparation tools and capabilities that make this process efficient and straightforward.

After the data has been cleaned and standardized, it is important to validate the accuracy and completeness of the data. This can be done by performing data quality checks, such as checking for duplicate records, outliers, or inconsistencies. Additionally, it is crucial to ensure that the data aligns with the defined business rules and requirements for calculating churn rate.

Exploring Different Churn Rate Metrics and Formulas

When creating a churn rate report, it is crucial to select the appropriate churn rate metric and formula that aligns with your business goals and objectives. Several churn rate calculations exist, each providing a slightly different perspective on customer attrition.

Common churn rate metrics include customer churn rate (number of customers lost over a given period), revenue churn rate (revenue lost from churned customers), and user churn rate (number of active users lost over a given period). Power BI enables users to calculate these churn rate metrics using DAX (Data Analysis Expressions) formulas and perform comparisons between different time periods or customer segments.

Step-by-Step Guide: Building a Churn Rate Dashboard in Power BI

Now that the data is prepared, it’s time to build a churn rate dashboard in Power BI. Start by opening Power BI Desktop, the authoring tool for creating reports and dashboards. Connect to the data sources containing the churn rate data, and import the necessary datasets.

Once the data is imported, users can start building visualizations to analyze churn rate. Drag and drop the desired fields onto the canvas to create charts, graphs, or tables. Power BI provides a wide range of visualization options, including line charts, bar charts, and pie charts, which can be customized to suit specific needs.

Customizing Visualizations to Analyze Churn Rate Trends

Power BI offers a range of customization options that allow users to enhance and refine their churn rate visualizations. Users can adjust formatting, colors, and axis settings to create visually appealing and insightful charts. Additionally, Power BI provides interactive functionality, such as drill-through and slicers, which enable users to explore the data further and analyze churn rate trends by different dimensions or filters.

Utilizing Power BI’s Advanced Features for Predictive Analysis

In addition to analyzing historical churn rate data, businesses can leverage Power BI’s advanced features for predictive analysis. Power BI offers capabilities such as forecasting, trend analysis, and machine learning integration, empowering organizations to predict and anticipate customer churn. By incorporating predictive analytics into the churn rate report, businesses can take proactive measures to retain customers and mitigate churn.

Interpreting the Churn Rate Report: Key Insights and Takeaways

Once the churn rate report is built, it’s essential to interpret the findings and extract actionable insights. Analyze the visualizations and identify patterns, trends, or anomalies that may influence customer churn. Power BI allows users to add data-driven insights and annotations to the report, facilitating effective communication and collaboration among stakeholders.

Key insights from the churn rate report may include identifying factors contributing to high churn, recognizing segments or regions with higher attrition rates, and evaluating the effectiveness of customer retention strategies. These insights can inform decision-making and steer businesses towards targeted actions to reduce churn and improve customer retention.

Tips and Best Practices for Creating an Effective Churn Rate Report in Power BI

When creating a churn rate report in Power BI, it is essential to keep in mind several tips and best practices to ensure its effectiveness and usability. Firstly, clearly define the report’s objective and target audience to tailor the visualizations and insights accordingly. Consider the level of detail required and select appropriate visualizations that effectively communicate the intended message.

It is also crucial to periodically update and refresh the churn rate report to ensure it reflects the most recent data. Power BI provides features for scheduling data refreshes, allowing users to automate this process. Regularly reviewing and updating the report helps businesses stay up-to-date and make informed decisions based on the latest churn rate information.

Troubleshooting Common Issues and Errors in Power BI Report Creation

While creating a churn rate report in Power BI, users may encounter common issues or errors that hinder the report’s creation or functionality. These can range from data connection problems to formatting issues or performance bottlenecks. Power BI provides a comprehensive knowledge base and community forums where users can seek assistance and find solutions to common problems.

Integrating External Data Sources to Enhance the Churn Rate Report

In some cases, integrating external data sources can enhance the churn rate report and provide additional insights. By connecting Power BI to other systems or data sources, such as social media platforms or customer feedback surveys, businesses can enrich the report with contextual information. This integration allows organizations to gain a comprehensive view of customer behavior and preferences, enabling more targeted strategies for customer retention.

Automating the Churn Rate Report: Scheduling Refreshes and Sharing with Stakeholders

To ensure the churn rate report remains up-to-date and accessible to stakeholders, businesses can automate the report’s refresh and share it through various channels. Power BI allows users to schedule data refreshes, ensuring that the report reflects real-time or periodic updates. Additionally, users can share the report through Power BI’s collaboration features, such as embedding it in a website or sharing it with specific stakeholders via email or organizational dashboards.

Comparing Different Churn Rate Analysis Techniques in Power BI

Power BI offers various techniques for churn rate analysis, each providing unique insights into customer attrition. As part of the churn rate report, businesses can explore and compare different techniques to identify the most suitable approach for their specific needs. This may involve comparing customer segments, analyzing churn over different time periods, or evaluating the impact of specific factors on attrition rates.

Case Study: Real-Life Examples of Successful Churn Rate Reports in Power BI

Real-life examples of successful churn rate reports in Power BI can provide valuable insights and inspiration for businesses looking to create their reports. These case studies showcase how organizations have leveraged Power BI’s capabilities to analyze churn rate, uncover insights, and implement successful customer retention strategies. By studying these examples, businesses can learn from proven approaches and adapt them to their own churn rate reporting processes.

Optimizing Performance and Efficiency of the Churn Rate Report in Power BI

As the churn rate report grows in complexity and size, it is crucial to optimize its performance and efficiency to ensure a smooth user experience. Power BI offers features and best practices for improving report performance, such as optimizing data models, utilizing query folding, and implementing efficient data loading techniques. By following these optimization strategies, businesses can ensure that the churn rate report remains responsive and delivers insights in a timely manner.

In conclusion, creating a churn rate report in Power BI enables organizations to understand and analyze customer attrition, identify trends, and make data-driven decisions to improve customer retention. By following the step-by-step process outlined in this article, businesses can leverage Power BI’s capabilities to transform raw data into meaningful visualizations and gain actionable insights. Whether it’s customizing visualizations, utilizing advanced features for predictive analysis, or troubleshooting common issues, Power BI empowers businesses to create comprehensive churn rate reports that drive growth and profitability.

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In this Power BI case study, we'll investigate a dataset from an example telecom company called Databel and analyze their churn rates.

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For subscription-based businesses, reducing customer churn is a top priority. In this Power BI case study, we'll investigate a dataset from an example telecom company called Databel and analyze their churn rates. Analyzing churn doesn’t just mean knowing what the churn rate is: it’s also about figuring out why customers are churning at the rate they are, and how to reduce churn. We'll answer these questions by creating measures and calculated columns, while simultaneously creating eye-catching report pages.

Step One: Exploratory Analysis

We start exploring the new dataset and revisit creating measures in Power BI to get a better understanding of why customers are churning.

• Explor Data
• Analyze and Visualise Data
• Dashboarding
• Communicate Insights

Step Two: Investigating Churn Patterns

We'll continue by leveraging the Power BI knowledge we have gained to further investigate why customers are leaving Databel.

Step Three: Visualizing the Analysis

We create dashboard-style pages and arrange them into stories, so that we can share the results with stakeholders.