Customer Churn Prediction

Why churn matters

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.

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%. This is because acquiring a new customer is 5-10x greater than retaining an existing one, so from an efficiency standpoint, it pays to ensure customers stay.

What can you do

Predicting if a customer will churn is like a superpower, allowing a business to strategically target at-risk targets with retention strategies, keeping them doing business and stopping them from switching to alternatives.

However, 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.

Predicting Churn with Machine Learning

How can you predict customer churn? Machine learning (ML) is a subset of artificial intelligence (AI), that uses algorithms to build data-driven predictive models. This makes it ideal to analyze complex customer and transactional data in order to predict churn.

The AI & Analytics Engine (the Engine) is an ML tool that helps you quickly, easily, and affordably turn data into value - no coding required.

The Solution: 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 template is designed for non-contractual, monetary transactions. That is to say, purchases involve spending money and customers do not inform the business if they will stop purchasing at a date, for example, personal banking, e-commerce or retail.

The business can decide at what activity level threshold (total number of transactions or total transaction amount) a customer is considered churned.

In addition to the prediction value, the Engine provides insights to give a deeper understanding. Feature importance values detail the most impactful factors that contribute to churn. For any particular prediction, the prediction explanation tool decodes the impact of each factor for any particular prediction, and the what-if tool provides a way to see how that prediction would change if the contributing factors change.

Churn Template Walkthrough

Creating your Application

First things first, sign up for the AI & Analytics Engine free trial. Create a project, this will take you to the project overview page, from the project overview page, select “Use template” and then “Customer Churn Template”. From here, there is a simple three-step process to go through to start predicting churn.

Step 01: Data

The first step is uploading customer transaction data, as well as customer information (optional), which can be done either through a direct file upload or connecting to a database.

Step 02: Define Churn 

The next step in the process is defining the conditions, based on your domain knowledge, that constitute a churned customer for you. This can be defined either by: 

1. The quantity of transactions,
2. Or,  the total transaction amount is under a certain threshold.

Step 03: Contributing Factors

Finally, add some contributing factors. Contributing factors are defined as factors that influence the likelihood of a customer's churn. By default, the Engine pre-selects the transaction amount, transaction timestamp columns and define rolling date ranges most recent 30 days  30 days, 30 days ago and  30 days, 60 days ago . 

The date ranges help the Engine generate additional features (that is columns in a dataset) that improve the speed and quality of your prediction. You can modify or remove these default rolling dates, or add new ones. 

After these three easy steps, the Engine will automatically recommend the optimal model for you. Simply 

Generating your predictions

Once your model has finished training, you can start generating predictions from the app’s summary page. The two options are making a one-off prediction if you want it immediately or scheduling periodically if you want them continuously, as new data is entered.

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

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 models built, giving you advanced metrics Which metrics are used to evaluate a binary classification model's performance?, along with visualizations of the precision-recall and ROC curves What are ROC and PR Curves for binary classification.

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.

Consuming your results

Once you’ve generated your predictions and gathered contextual insights, it's time to consume your predictions. Predictions can be previewed, downloaded, or exported, as a dataset to a project within the Engine, or as a table to a connected database.

Now - Go proactively retain your customers

Customer churn is important, and accurately predicting who is likely and understanding why it’s happening, 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.