Use Churn Analytics to Improve Retention

Use Churn Analytics to Improve Retention

19 May 2022
Use Churn Analytics To Improve Retention 47Billion

Churn analysis uses historical data to understand why customers have stopped using your product or service. Human Resources Department can also understand employee attrition using churn analysis. It helps to understand:

  • Which customers are leaving and why? 
  • What remedies may help to Reduce Churn?

Churn rate tells you what portion of customers drops over some timeFor example, your mobile application had 10,000 customers on Jan. 1, 2020, but on Jan 2021, just 7000 have renewed their subscription. Then the churn rate would be 30%.

Benefits of Churn Analysis

  • Improve retention.
  • Expose the strengths and weaknesses of a business. 
  • Improve user experience. 
  • Help in the optimization of products and services.  
  • Unearth new opportunities. 
  • Predict and reduce Churn. 

Different Types of Churn

Customers may cancel their plans or subscriptions for several reasons and in multiple ways. 

Below are the types of churns:

Involuntary or Passive Churn

This Churn occurs due to leakage in revenue generation models, such as

  • Payment failure or card declined by a bank.
  • Hard declines due to a stolen or lost card.
  • Using an expired card.

Voluntary Active Churn

This Churn happens when customers cancel a service or product. It could be due to poor customer service, poor onboarding, or switching over to a competitor. This type of Churn is a significant portion of lost revenue.

Downgrade Churn

This Churn occurs when a customer opts for a lower plan than the existing plan, leading to Downgrade MRR (Monthly Recurring Revenue). Such a Churn could happen for the following reasons:

  • Misalignment of value propositions.
  • Price sensitivity.

Good Churn

Generally, customer churn filters out the customers that were a bad match for a service or product. Customers may leave after their product’s short-term need is fulfilled, adding to this kind of Churn. 

How to perform Churn Analysis?

Having data is good, but too much data can lead to analysis paralysis. So instead of diving headfirst into the hurricane of data and hoping to get lucky with actionable insights, it’s always necessary to thoroughly analyze data.

Leverage data analysis to understand the business flow

Deep diving into data allows us to keep track of the performance of KPIs and other metrics. This step lets you dig into how people use and engage with your product and services.

Analyze customers by segments

Customer segmentation is the process of grouping customers with similar traits. Segmentation enables us to analyze Churn by revenue, business type, or demographics. We can also investigate Churn across various customer segments:

  • Churn Analysis by Demographics
  • Churn Analysis by Revenue
  • Churn Analysis by Industry

Determine when and why Churn is occurring

Only when you are aware of the reason for the Churn will you be able to decide the course of action to curb it.

Exploring customer workflows and drop stages helps define and set up prevention strategies. Analyzing the timing of the Churn adds depth to Churn Analysis. A good start could be analyzing Churn by subscription activation dates. It tells how sooner or later (or not), the customer churned after activating the service. Visualization of MRR tells about addition, growth, and Churn behavior based on when you acquired the customer and what happened in a particular month.

Proactive retention using predictive analytics

To retain customers who would have abandoned the service, we can predict which customers may churn in the future. Predictive modeling techniques for churn prediction help to understand the exact customer behaviors and attributes that indicate the risk of Churn. Such an analysis helps formulate and roll out retention strategies before it is too late. 

How to perform Churn Analytics? 

Let’s understand the steps involved in churn analytics using a sample dataset. The pipeline to perform churn analysis consists of the following steps

1. Define Objective
2. Collect Data
3. Perform Exploratory Data Analysis
4. Feature Engineering
5. Train-Test split
6. Train and evaluate Model
7. Improve Model

Define Objective

Defining an objective is the very first step in any analysis. For a subscription-based service, the aim can be to predict if an individual customer will churn or not.

Collect Data

Once we define the objective, data collection is crucial for achieving the end goal. 

Perform Exploratory Data Analysis

After data collection, this step aims to understand the data structure, conduct initial preprocessing, clean the data, and identify patterns and inconsistencies in the data (outliers, missing values).

The example data below is related to the telephonic usage of customers of a phone service provider. The data consists of a total of 20 attributes and one target variable. 

The following features are in the dataset:

  • Churn — Whether the customer churned or not (Yes, No)
  • Phone Service — Whether the customer has a phone service (Yes, No)
  • Multiple Lines — Whether the customer has multiple lines (Yes, No, No phone service)
  • Internet Service — Customer’s internet service provider (DSL, Fiber optic, No)
  • Online Security — Whether the customer has online security (Yes, No, No internet service)
  • Online Backup — Whether the customer has online backup (Yes, No, No internet service)
  • Device Protection — Whether the customer has device protection (Yes, No, No internet service)
  • Tech Support — Whether the customer has tech support (Yes, No, No internet service)
  • Streaming TV — Whether the customer has streaming TV (Yes, No, No internet service)
  • Streaming Movies — Whether the customer has streaming movies (Yes, No, No internet service)
  • Tenure — Number of months the customer has stayed with the company
  • Contract — The contract term of the customer (Month-to-month, One year, Two years)
  • Paperless Billing — Whether the customer has paperless billing (Yes, No)
  • Payment Method — The customer’s payment method (Electronic check, mailed check, Bank transfer (automatic), Credit card (automatic))
  • Monthly Charges — The amount charged to the customer monthly
  • Total Charges — The total amount charged to the customer
  • Customer ID — Customer ID
  • Gender — Whether the customer is a male or a female
  • Senior Citizen — Whether the customer is a senior citizen or not (1, 0)
  • Partner — Whether the customer has a partner or not (Yes, No)
  • Dependents — Whether the customer has dependents or not (Yes, No)

Key Patterns and Insights generated after the exploratory analytics:

  • Total number of rows and columns, missing values, type of variables
  • Class imbalance
  • Tenure of churning customers vs. non-churners
  • Total Charges and MonthlyCharges
  • Senior citizens churn rate vs. non-senior churn rate.
  • Customers with fiber optic have a considerably higher churn rate
  • Electronic checks’ payment method shows a much higher churn rate than other payment methods
  • No outliers were detected in the numerical features

Feature Engineering

Since the most number of the features are categorical label encoding technique is used to convert yes/no into 0/1. Min-Max scaling is used to scale numeric features. Since there is a class imbalance in the dataset, a stratified sampling technique and up-sampling techniques were used to balance the dataset.

Train-Test Split

For model training and testing, the dataset is split in the ratio of 80:20. The Churn column is defined as the predictor class and the remaining columns as the features.

Train and evaluate the Model

Since it’s a classification problem, supervised ML algorithms such as logistic regression, random forest, SVM, and KNN are used in model training, and results are observed. The accuracy of each Model is measured with precision-recall numbers. If the model performance on the test set is low compared to the train set, the hyperparameter tuning is done.

Improve the Model

Different parameters of machine learning models like estimators, number of N, cross-validation techniques, etc., are used to tune the models. Cross-validation, along with other hyperparameters, is used during hyperparameter tuning. After hyperparameter tuning, the random forest had better performance and results.

Challenges in Churn Analysis

  • One of the main challenges faced while designing a Churn model is a suitable Churn Modelling approach. Machine learning techniques are used because of their efficiency, categorization, and manipulation of complex data sets. One needs to compare the performance of several models and identify the most effective method for achieving business goals.
  • Exploratory Analysis can help identify irregularities, outliers, and the relationships between different functions. A deep and careful execution of exploratory Analysis is necessary to build an efficient churn prediction model.
  • Choosing the right metrics is a crucial step in churn prediction. The precision of a selected model impacts the decision-making process and performance. Therefore, it is necessary to measure model performance before its implementation.


It is essential to regularly calculate and track churn metrics with automated analytics to spot and resolve problems. A prescriptive churn analytics solution helps make informed business decisions about customer and employee retention based on data insights.

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