TEMPLATED SOLUTIONS

Customer Churn Prediction

Why wait until your customers leave to do something about it, when you can use the AI & Analytics Engine to quickly, easily, and affordably predict churn with machine learning?

Use churn template
Get a demo

What is customer churn?

Customer churn, also known as customer attrition, is when a regular or subscribed customer stops purchasing a business' products or services in a given period of time.

 

Why customer churn matters

Customer churn is a strategically important concern for any business, as it directly impacts revenue and business growth.

Customer churn has an astonishingly significant impact on a business's bottom line, in fact, increasing customer retention by just 5% can increase profits by between 25% to 95%, according to a Harvard Business School report.

This dramatic increase is due to the fact that acquiring a new customer is far greater than retaining an existing one, as much as 5x greater, according to Forbes.

So from a business efficiency standpoint, it pays to ensure your existing customers stay.

 

What can you do about it?

Predicting customer churn ahead of time and taking actions impacts a business's growth immensely. 

Customer churn prediction allows a business to strategically target at-risk targets with retention strategies, resulting in an increased likelihood of them continuing business, and stopping them from switching to an alternative.

 

How to predict customer churn?

Traditional statistical methods of churn analysis are costly, often requiring a team of analysts and a heavy investment of both time and money. Furthermore, the complexity and quantity of factors result in underwhelming accuracy.

Machine learning (ML) is a subset of AI, that uses algorithms to build data-driven predictive models, making it ideal to analyze complex customer and transactional data.

Predicting Churn with Machine Learning

To achieve a high quality result, a comprehensive solution is required that involves:

  • Complex and labor-intensive data processing steps.
  • Selecting the relevant factors that affect churn.
  • Applying the suitable machine learning algorithm.

However, this is not a trivial task and it varies depending on the nature of the business.

 

The Engine's Templated Churn Solution

The Engine’s Customer Churn Prediction Template provides a complete ML solution for predicting churn before it happens.

The AI & Analytics Engine (The Engine) provides an easy-to-use template to predict customer churn based on the historical data of customers and their activity, using machine learning.

This template currently has two options:

  • Transactional Churn: Predicting whether transactional activity will decline in volume or frequency for each customer who is currently active.
  • Subscription Churn: Predicting for each customer the likelihood that they will terminate their ongoing paid subscription service.

Creating a customer churn prediction app walkthrough

Selecting the customer churn template

First things first, sign up for The AI & Analytics Engine free trial. Then create a project, and on the project overview page, select the “Use template” option and then choose “Customer Churn”.

  • Transactional Churn

  • Subscription Churn

Video guide:

 

Step 1: Select the transactional churn template 

To start, select the type of churn that matches your business type, in this case, "predict if the transactional activity of my current active customers will go down in volume or frequency".

step 1 transactional: select template

Step 2: Import transaction and customer-information data

The second step is uploading customer transaction and customer information (optional) data, and selecting the appropriate columns. This can be done either through a direct file upload or connecting to a database.

step 2 transactional: import data

Step 3: Define churn

Next, appropriately define the conditions which constitute a "churned customer", based on knowledge of business behavior and goals.

step 3 transactional: define churn

Step 4: Define contributing factors

Next is to define contributing factors that influence the likelihood of a customer's churn. There are two categories:

  • Time-based factors from the transactional data: These are the time frames that influence the probability of churn, and help the Engine generate additional features that improve the speed and quality of your prediction. By default, three time frames are provided, but you can customize or add more.
  • Attribute from the customer information data: Select appropriate attributes from the customer-information dataset.

step 4 transactional: define factors

Step 5: Build models

The final step is to configure model training. This can be done by placing restrictions on training time or prediction quality, or by manually selecting the algorithm.

step 5 transactional: build models

Video guide:

 

Step 1: Select the subscription churn template

To start, select the type of churn that matches your business type, in this case, "predict if my customers will terminate their ongoing paid subscription".

step 1 subscription: select template

Step 2: Import subscription, event log and customer-information data

The second step is uploading customer subscription, event logs and customer information (optional) data, and selecting the appropriate columns for each. This can be done either through a direct file upload or connecting to a database.

step 2 subscription - import data

Step 3: Define churn period

Next, appropriately define the churn period, specifying the business's definition of churn .  There are two parameters needed, resulting in 3 distinct time periods.

  1. Too late: These are customers who have a hight risk to churn in the immediate future, and it is too late for retention efforts.
  2. About time: These are customers who are at risk of churning, but there is sufficient time to intervene with retention efforts.
  3. Safe: These are customers who are not at risk of churning in the near future, and no retention efforts are required.

step 3 subscription - define churn period

Step 4: Define contributing factors

Next is to define contributing factors that influence the likelihood of a customer's churn. There are two categories:

  • Time based factors from the subscription and event-log data: These are the the time frames that influence the probability of churn, and help the Engine generate additional features that improve the speed and quality of your prediction. By default, three time-frames are provided, but you can customize or add more.
  • Attribute from the customer information data: Select appropriate attributes from the customer-information dataset.

step 4 subscription - define factors

Step 5: Build models

The final step is to configure model training. This can be done by placing restrictions on training time or prediction quality, or by manually selecting the algorithm.

step 5 subscription - build models

What's next?

Congratulations, you have now built your, customer churn prediction app, and your machine learning models are training - All without a single line of code.

app ready

Once the app is ready and the models have trained, there are two options to start getting value:

Model Performance and Insight

Having a model is great, but it’s important to evaluate it to determine how well it’s working.

The model leader board ranks the performance of all trained models, in terms of prediction quality, prediction time, and training time.

More in-depth details on a model’s are available on the individual model details page, displaying , a multiclass confusion matrix, and visualizations of

Equally valuable to the churn predictions are the insights on your data, which the Engine provides within the model insights tab.
These are displayed in three tools:

01. Feature Importance 

The feature importance tool tells you exactly how impactful each factor is in contributing to a churned customer in general.

02. Prediction Explanation

The prediction explanation tells you how much impact each factor for any given prediction.

03. What-if

What-if analysis allows you to change inputs for factors for any given prediction, allowing you to see the new prediction and compare results.

Generating predictions

Once your model has finished training, you can start generating predictions from the app’s summary page by clicking "make prediction". The two options:

  • Make a one-off prediction: An option if for testing the model quickly or make a single prediction.

  • Schedule periodic predictions: This option enables the use of the ML prediction pipeline built by the template in production, to automatically generate updated predictions periodically, requiring no manual intervention. To schedule churn predictions this way, live customer data must be connected to your database, to periodically ingest new data available and generate the latest predictions.

Scheduled predictions require a connection to a database such as SQL, Oracle, or Snowflake, in order to input new transaction data.

Consuming your results

Once you’ve generated your predictions and gathered contextual insights, it's time to consume your predictions. Predictions can be consumed in three ways:

  • Download the output as a csv, json lines or parquet file.

  • Export output to a dataset within the engine or to an external database.

Now - Go proactively retain your customers

Customer churn is important, and accurately predicting who is likely to churn and understanding why, is the first step in creating a retention strategy that will spike your profitability.

PI.EXCHANGE’s customer churn template allows you to harness the power of machine learning in order to do this in the most efficient way possible.

So why wait until your customers leave to do something about it, when you can use the AI & Analytics Engine to quickly, easily, and affordably predict churn with machine learning? Get started with our 2 week free trial.

Use churn template
Get a demo
REGISTER FOR THE ON-DEMAND WEBINAR

See the Customer Churn Template in action. 

Register for our on-demand webinar today with Data Scientist Dr. Kathy Sarafrazi and Content Coordinator Fyona Endy as they demonstrate how easy it is to use the churn prediction template.

REGISTER FOR THE WEBINAR
Customer Churn Prediction Webinar