How to use the templated ML solution for transactional churn?

This article demonstrates how to use the templated machine learning solution to predict the possibility of customer churn, for a transaction-based business.

Customer churn is a major concern for many businesses, as it directly impacts revenue and business strategy. To identify which customers are likely to churn, a comprehensive solution is required that involves complex and labor-intensive data processing steps, selecting the relevant factors that affect churn and applying the suitable machine learning algorithm.

This is not a trivial task and it varies depending on the nature of the business. The AI & Analytics Engine offers you an effective solution to this problem with the Customer Churn Prediction ML Solution Template.

You just need to enter some quantitative information about your business nature and objectives, and the entire process will be automated, enabling you to predict customer churn with speed and accuracy.

There are two options in the Customer Churn Prediction ML Solution Template:

1. Predicting whether a transactional activity will decline in volume, or frequency for each customer who is currently active

2. Predicting the likelihood of each customer terminating their ongoing paid subscription service

This article will explore the Customer Churn Prediction template for transactional businesses. This refers to businesses with customers that make transactions or individual purchases.

Use case: Predicting customer churn for banking service

Suppose you're a bank employee who wants to utilize your customer data to initiate a successful customer retention campaign. You aim to identify customers who are likely to leave your service in the near future, so you can target them with retention efforts, resulting in significant cost savings as retaining existing customers is far cheaper than acquiring new ones.

To accomplish this, you use the Engine's transaction-based Customer Churn Prediction template to build a machine learning powered app that identifies customers that are likely to churn.

Data

The input data required for the template consists of the following datasets:

Customer transaction dataset (required)

A required dataset that records the details of customer transactions, with three key attributes that will be used:

• Account ID (who made the transaction)

• Date (when the customer made the transaction)

• Amount

In this specific example, each transaction represents a record of spending, which can occur either through direct card usage or cash withdrawal.

Preview of the customer transactions dataset

Customer information dataset (optional)

An optional dataset that contains customer biographic or demographic profile data, for example:

• District ID

• Sex

• Date of birth

• Etc.

Preview of the customer information dataset

Creating a Customer Churn Prediction app

In this section, you can follow some simple steps and provide the required information to create an application that uses machine-learning models to predict customer churn.

You’ll observe how this templated solution shortens the time between feeding raw data to getting insightful predictions.

To access the app builder, on the Project Homepage select Customer Churn under Machine Learning Solution Templates.

Select Customer Churn from the Project Home page

 

Then create the application by clicking Use this template and naming the application. This will take you to the App builder pipeline.

Select Use this template to create the application

 

Step 1: Select a template that matches your business type

Within the App builder pipeline, for transaction-based businesses, choose the option “Predict if transactional activity of my currently active customers will go down in volume or frequency ”.

Choose the second option for transaction-based businesses

 

Step 2: Define business requirements

You need to define the business requirements including defining churn and the prediction frequency. This step requires an understanding of what churn means to your business.

For this example, define churn as: Predicting if a customer will churn after the next 30 days, and that your customer is considered churned if their total number of transactions in 60 days is less than 2.

Select a prediction frequency of 1 month.

Appropriately define churn and prediction frequency based on business goals

 

Step 3: Import the transaction data and the optional customer-information data

Review the data requirements to build the models. Based on the parameters provided when defining the business requirements, the transaction history data required must span at least 330 days.

The data you need is:

• Transaction data (required data)

• Customer info (optional data)

Review data requirements

 

Select the appropriate columns in the transaction dataset

 

Requirements for the transaction dataset. The transactions should only be spending or refund.

 

Select the identifier column in the customer-information dataset

 

Step 4: Determine the time frames in the past that influence the probability of churn

It is crucial to identify the time frames in which customer transaction patterns may indicate their likelihood of churn. Initially, three time frames have been pre-selected for you, as depicted in the figure below. These can be altered or supplemented with any additional time frames that meet your requirements.

Furthermore, the potential influence of demographic information should be taken into account. You can choose any demographic factors that you believe might impact the customer's probability of churn.

By default, three time-frames are provided. You can customize them or add more.

 

Select appropriate attributes from the customer-information dataset

 

 

Step 5: Configure model training

The final stage involves selecting the configuration for your model's training. You have the option to allow the Engine to automatically identify the best models according to your specified criteria, or you can choose to manually select the algorithms.

For this example select the top 3 algorithms with estimated prediction quality of at least 70% 

Automatically train top algorithms based on given criteria

The app-building process will start as soon as you complete this step, and the resulting app will allow us to make predictions and explore the model insights.

You will be directed to the App summary page with the label Processing. You can see the progress of the app in each panel.

App summary once the app is ready

Analysing churn in historical data

Once the training dataset is complete, you can click View analysis under Data preparation in the App Summary page to see the charts analysing churn in historical data.

Analysis charts of historical Churn

Inspecting the model performance

Once the app building process is complete, the models begin training. When the training is complete, on the right side of the App Summary page there is a short overview of the best trained model, which shows the prediction quality of that model.

Recommended model and its prediction quality

 

Performance

For more detailed information about the model performance, you can click on the model name, and navigate to the Insights tab, which will display the Performance page, which provides insights on the accuracy of the models predictions.

Included is multiple evaluation metrics, a confusion matrix, and charts of the ROC Curve and Precision Recall Curve.

Performance page under Insights tab

 

Feature Importance

The Insights tab also contains the Feature Importance page, which can provide insight into the most important behavioural predictors for customer churn, and the factors that affect the likelihood of customer churn at a given snapshot in time.

Feature importance page under Insights tab

Prediction Explanation

The next page under the Insights tab is Prediction Explanation. This feature allows you to examine the prediction of a given customer, providing what factors increase or decrease their likelihood of churning, and to what extent.

Prediction explanation page under Insights tab

Customer details window, accessible by clicking view details for any customer in Prediction explanation.

Making predictions

Predictions from the Customer Churn Prediction ML Solution Template provide following:

• A list of currently active customers.

• The predicted probability of churn for each of them.

To initiate a prediction, click the Make prediction button on the App overview page. You have the option to select either a One-time prediction to quickly make a single prediction, or Periodically scheduled predictions, which generates predictions at regular intervals.

With both options, you’ll need to provide the same data sources you provided when creating the application. In this example, both:

• Transaction data
• Customer information

Provide the necessary inputs for prediction

 

For the scheduled periodic prediction option, you also need to select the frequency that the Engine will perform the prediction.

Specify the schedule in the periodic prediction option

 

Once these steps are completed, click Run prediction to start the prediction process which will direct you to the Prediction details page. You can see the prediction status in this prediction details page or in the App details page.

Processing Status on the App details page

Once the prediction status changes to Ready, you can consume the predictions in three ways.

1. Preview a sample of the output

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

3. Export output to a dataset within the Engine or to an external database

A preview of the predicted churn probability for each account ID

 

Conclusion

In this article, a walkthrough is provided on how the AI & Analytics Engine can assist you in predicting customer churn for a transactional business with an efficient and effective solution. To create a customer churn prediction model, you only need to upload your raw datasets and define a few configuration features, which was illustrated using a banking scenario. This enables you to understand your customers' behaviors and develop the appropriate retention strategies to prevent them from churning.