The data used to generate the numerical and categorical features is based on the public dataset KDD Cup 2009: Customer relationship prediction. The dataset we use is synthetically generated and available under the CC BY 4.0 license.
We create a SageMaker endpoint to make real-time predictions using the model and provide more insight to customer service agents as they handle customer phone calls.
We can use this historical information to train an ML classifier model, which we can then use to predict the probability of customer churn based on the customer’s profile information and the content of the phone call transcription. The data also includes transcriptions of the latest phone call conversations between the customer and the agent (which could also be the streaming transcription as the call is happening). In this post, we use a mobile operator’s historical records of which customers ended up churning and which continued using the service. The following figure illustrates the architecture for the solution. We use Amazon Simple Storage Service (Amazon S3) to store the training data and model artifacts, and Amazon CloudWatch to log training and endpoint outputs. We use SageMaker training jobs to train the churn prediction model and a SageMaker endpoint to deploy the model. In this solution, we focus on SageMaker components. The JumpStart solution launch creates the resources properly set up and configured to successfully run the solution. When your Studio instance is ready, use the instructions in JumpStart to launch the solution. To run this JumpStart 1P solution and have the infrastructure deploy to your AWS account, you must create an active Amazon SageMaker Studio instance (see Onboard to Amazon SageMaker Studio). The solution outlined in the post is part of Amazon SageMaker JumpStart.If you don’t have an account, you can sign up for one. To try out the solution in your own account, make sure that you have the following in place: Build, tune, and deploy an end-to-end churn prediction model using Amazon SageMaker Pipelines.Preventing customer churn by optimizing incentive programs using stochastic programming.Predicting Customer Churn with Amazon Machine Learning.Interested in learning more about customer churn models? These posts might interest you:
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In addition to textual inputs, we show you how to incorporate other types of data, such as numerical and categorical features in order to predict customer churn. In this post, we walk you through the process of training and deploying a churn prediction model on Amazon SageMaker that uses Hugging Face Transformers to find useful signals in customer-agent call transcriptions. Customer churn prediction using machine learning (ML) techniques can be a powerful tool for customer service and care. Therefore, it’s crucial for businesses to understand why and when a customer might stop using their services or switch to a competitor, so they can take proactive measures by providing incentives or offering upgrades for new packages that could encourage the customer to stay with the business.Ĭustomer service interactions provide invaluable insight into the customer’s opinion about the business and its services, and can be used, in addition to other quantitative factors, to enable the business to better understand the sentiment and trends of customer conversations and to identify crucial company and product feedback. Given the marketing and operational costs of customer acquisition and satisfaction, and how costly losing a customer to a competitor can be, generally it’s less costly to retain new customers.
These are all key factors in a sustainable and long-term business growth strategy. Customer satisfaction links directly to revenue growth, business credibility, and reputation. Businesses go to great lengths to acquire and more importantly retain their existing customers. Regardless of the industry or product, customers are the most important component in a business’s success and growth.
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