To train a model under this use case, you will need to create feature groups of the following type(s):
Feature Group Type | API Configuration Name | Required | Description |
---|---|---|---|
User Attributes | TABLE | True | This dataset corresponds to the user attributes relevant to predict if the customer/user is going to churn out or not. |
Note: Once you upload the datasets under each Feature Group Type that comply with their respective required schemas, you will need to create Machine learning (ML) features that would be used to train your ML model(s). We use the term, "Feature Group" for a group of ML features (dataset columns) under a specific Feature Group Type. Our system support extensible schemas that enables you to provide any number of additional columns/features that you think are relevant to that Feature Group Type.
This dataset corresponds to the user attributes relevant to predict if the customer/user is going to churn out or not.
Feature Mapping | Feature Type | Required | Description |
---|---|---|---|
CHURNED_YN | categorical | Y | Specifies whether a user has churned out or not. You need some examples of users that have churned out, for our AI engine to create a predictive model. |
USER_ID | categorical | Y | The unique identifier for the user. |
[RELEVANT ATTRIBUTE] | Y | Any relevant attribute that could influence customer churn, for e.g., user's attributes like age, location, service subsribed, average monthly bill, etc and service/product attributes like popularity, cost to user, etc. The more data you have, the better the model at prediction. |