Digitata's Churn Prediction allows you to instantaneously predict, with high accuracy, which of your subscribers are going to churn.

Quickly and easily create your own version from the AWS Marketplace or contact us for more details.

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82.8%

Of Churners Identified

94.3%

Correct Churn Predictions

9.7%

Actual Churners

500+

Model Attributes

It costs between 5 times and 25 times as much to find a new customer than to retain an existing one.*

*According to Harvard Business Review

Quick Predict

Voice Active Days 4weeks

Recharge Count 4weeks

Voice OOB Spend 4weeks

Rechage Spend 4weeks

Voice Active Days 4weeks 2

Change in Recharge Count

Change in Recharge Amount

Change in Voice Active Days

Total Recharges 4weeks

Out of Bundle Call Count

Select calculate to start

Planet

Don't be startled by churn.

Predict it.

Planet

Churn prediction for marketing.

Plan with it.

Planet

Churn prediction to increase.

Customer loyality.

Planet

Churn has too often been approached with scattershot, manual methods. By using machine learning algorithms this enables a scientific, accurate and automated method of managing churn.

Here are some interesting churn related resources: AWS Machine Learning Blog - Preventing customer churn by optimizing incentive programs using stochastic programming

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Follow the steps below:

1. Start

Click on this link to open the AWS marketplace.

2. Region and Setup

Follow the steps on the AWS marketplace to deploy and setup the churn model in your AWS account. You will choose your AWS region and click Continue to subscribe.

3. Subscribe

You will be presented with a summary of what will be deployed. Review the listed product and license agreement :wand select Accept Offer.

4. Configure

After subscribing, select Continue to configuration. You can choose to launch either using the AWS CLI (Command Line Interface) or using the Sagemaker Console. If you are not familair with the AWS Command Line Tools, it is suggested that you select Sagemaker Console. Keep the selection at Create real-time inference endpoint under Sagemaker Options.

5. Install

Select View in Amazon Sagemaker or enter the commands if you are launching through the CLI. If launching through the CLI, skip to step 8. Otherwise continue onto the next step.

6. Create Model

On the Create Endpoint/Model page, enter a name for the model that will be deployed. Something like churn-prediction for example. Select Next at the bottom, unless you have specific networking requirements we don't require Container or VPC setup at this point in time.

7. Create Endpoint

On the Create Endpoint/Endpoint page, enter a name for the endpoint that will be deployed. This name is what we will use when we are doing real-time inferences against the model. Select Create endpoint configuration. Wait for the Console to update your Endpoint Configuration, then select Submit.

8. Run

Recall the Endpoint name you selected in the previous step. Using your favorite language of choice, you can now use the AWS SDK to perform real-time inferences against the model. Such as the following: Use the following commands to run an inference against your endpoint:

const AWS = require('aws-sdk');
const sagemaker = new AWS.SageMakerRuntime();
const Params = {
  EndpointName: 'model_name_from_setup',
  Body:'data in csv format',
  ContentType: 'csv',
}
sagemaker.invokeEndpoint(Params, function (err, data) {
  if (err) { console.log('error', err); return }
  console.log('probability to churn', data.Body.toString());
});
              

9. Data Preparation

Our model uses over 500 parameters from our 360 Customer View solution to determine churn for a user. To help you get started, we provide some code here to map common attributes to our list of expanded attributes. Contact us if you require help gathering more attributes and making your predictions even better.