This article explains what the customer churn prediction template provides in the AI & Analytics Engine.
For more information about what a template is, read this article.
The customer churn prediction template provides a complete machine learning (ML) solution from start to finish, for businesses seeking to reduce customer attrition by predicting their customers' likelihood to churn.
The solution is designed to predict the risk of churn for active customers in advance, according to the desired period configured to suit the business' need. For example, if a business requires 30 days time to mitigate churn, the prediction can be made for currently active customers churning after 30 days.
The template supports two different types of churn use cases, based on two common business models.
Note: For more information about the types of churn supported within the customer churn template, read this article.
Similar to all business templates, it is highly accessible to business users without needing any technical expertise in data engineering, ETL, ML pipelines, and etc. Once the models are built, users can examine whether their performance is satisfactory and also understand how they predict.
Predictions generated by the customer churn template:
Using the customer churn template, users simply need to provide a connection to their live database, and obtain periodic predictions sent to chosen destinations on a set desired schedule, in the form of a tabular dataset containing:
- The current active customers (who have not churned)
- Their predicted likelihoods of churn within or after on pre-configured periods
- The features (variables) that were used to make the churn prediction.
Prediction output from an app built using the transactional churn use case within the customer churn prediction template: who are the currently active customers and what their churn probabilities are
The Engine also provides all insights for models trained using the churn template, as with any other classification & regression model on the Engine trained with the “build from scratch” option.
Users can see how well the model performs on test data consisting of the last 30 days of all data with known outcomes:
Performance insights: Leaderboard summary
Performance insights: Metrics
Performance insights: Precision-recall curve
Users can also obtain further insights like feature importance and prediction explanation:
Feature importance values: provides insights such as the most important factors that contribute to churn
Prediction explanation: provides how different feature values impact a particular prediction
They can also explore how feature values impact the models' predictions using the what-if tool:
The template also provides a way to get a complete ML pipeline by entering only business-specific objectives and domain-knowledge inputs, which does not require any deep technical knowledge of data science/machine learning concepts.
Identification of specific columns: which column stores customers' unique identifiers (ID), which column stores the transaction timestamp, and which column stores the transaction amount.
- Definition of churn: how churn is assessed in their business based on activity threshold (e.g. 30-day period with < 100 dollars in total monetary value)
- Domain-knowledge input: where signals that foretell impending churn can potentially be found, in other words the parameters of the time windows relative to the prediction date
Choice of model training: Either let the Engine automatically identify the top models and train them straightaway, or pick them manually.
No other input or configuration is needed from the user. The template automatically does the heavy lifting by building the right data science & machine learning solution based on the user’s business objectives – without requiring the need for any technical concepts.
Automation provided by the template
The churn template completely automates the process of aggregating and joining the various input datasets in a suitable manner, by:
- Providing full automation of feature engineering and data wrangling process.
- It then immediately proceeds pick the right ML problem type (classification) and the target column
- It then creates and processes the necessary feature sets, and trains the models according to the selection in the template
- At the end, it will allow the user to run the entire pipeline in prediction mode using the chosen trained model, and automatically obtain periodic predictions. One-time predictions can also be triggered with a simple point-and-click UI interface
Once the data and the business-problem definitions are provided, the Engine automatically processes everything and provides a trained model to predict churn.The full behind-the-scenes ML pipeline built by the Engine to suit the user’s business objectives. The Engine takes care of the tech expertise required to build ML solutions