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?

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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 Use template option and then choose Customer Churn and click Use this template.

 

App builder pipeline

  • Transactional Churn

  • Subscription Churn

Step 1: Select the transactional churn template

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

Transactional 1

Step 2: Define business requirements

Next, appropriately define your unique business requirements. This includes lead time, churn definition and prediction frequency.

Transactional 2

Step 3: Review data requirements and import your data

The data the transactional churn template requires is:

  • Transaction data (required data)

  • Customer information data (optional data)

For each dataset, you will need to provide a label to specify the type of events it contains.

Transactional 3.1

Step 4: Define contributing factors

Next, define contributing factors that are usually predictive to the likelihood of churn. This includes time-based factors and attributes from the optional customer information dataset.

Transactional 4

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.

Transactional 5

 

Step 1: Select the subscription churn template

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

Subscription 1

 

Step 2: Define business requirements

Next, appropriately define your unique business requirements. This includes prediction frequency and churn lead time, which results in three categories of predictions:

  1. Too late: These are customers who have a high risk to churn in the immediate future.
  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.

Subscription 2

Step 3: Review data requirements and import your data

The data the template requires is:

  • Subscription start and churn dates (required data)

  • One or more event logs (required data)

  • Customer info (optional data)

For each dataset, you will need to provide a label to specify the type of events it contains.

Subscription 3.1

Step 4: Define contributing factors

Next, define contributing factors that are usually predictive to the likelihood of churn. This includes time-based factors and attributes from the optional customer information dataset.

Subscription 4(1)

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.

Subscription 5

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.

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Customer Churn Prediction Webinar