Understand your customers with Machine Learning
Customer relationship is a central business part. Companies whose main revenue come from monthly payment like streaming services, for example, need to be one step ahed from their customers to keep them using their services and products.
Researches point that acquiring new customers is more expensive than keeping the old ones. According to Harvard Business Review publication: "acquiring a new customer is anywhere from five to 25 times more expensive than retaining an existing one". If we think about telecom companies, for instance, we may realize that to get new customers it is common that they offer discount on their services or subsidize a smartphone.
Another point that contributes to customer churn is business growth, since it is more difficult to preserve an individual and custom service with your clients.
This relationship is important to predict when a customer is about to churn. CRM systems have data and tools to help customer loyalty, but in order to know the right action and moment to act, it is necessary to analyze the data you have.
It is here where we can have a great potential for Machine Learning (ML) applications.
Machine Learning is one of Artificial Intelligence (AI) areas that uses algorithms and statistics to produce applications that perform certain tasks. This tasks are not explicitly programmed, instead the ML system objective is learning the task that it should perform based on the data provided.
These data are used during a training phase, where it enables the ML application to learn how to do its job. During this phase it is important to have a dataset as complete as possible, that way the model will learn properly and with lower overfitting probability (the model learns very well how to represent training data, but do not generalizes).
After training step is completed the application is ready to perform inferences, that is, to execute the task it was trained for. Furthermore, it is common that during the application life cycle it is retrained with new data to improve its accuracy.
Since these models are able to do tasks that are not based on rules, we can use them to predict customer churn and, moreover, to create a custom experience to each client.
Using Machine Learning it is possible to identify clients that have risk to churn and promote better actions in order to avoid it. For example, PayPal uses Machine Learning algorithms to predict if and when a customer could churn, so they can come with a new marketing campaign to retain those users.
Besides churn prediction, ML applications are also applied to improve customers experience, another important retention aspect. Amazon uses AI to suggest products based on user's preferences, creating a personalized experience in its services. Further, Spotify is able to deliver songs suggestions according users preferences. These use cases show how it is possible to use a big user dataset for your business benefit, once consumers behavior may be modeled into groups or clusters.
These are just a few exemples of applications that can be created applying Machine Learning to your customers data. The more important here is understanding your company needs and how ML can bring more value to your customers and business.