Use Profiles’ predictive features to train machine learning models.
ProfilesML is part of our Early Access Program, where we work with early users and customers to test new features and get feedback before making them generally available. These features are functional but can change as we improve them. We recommend connecting with our team before running them in production.
ProfilesML extends Profiles’ standard feature development functionality and lets you easily create predictive features in your warehouse. You can predict boolean features like:
Is a customer likely to churn in the next 30 days?
Will a user make a purchase in the next 7 days?
Is a lead going to convert?
Finally, you can add the predicted feature to user profiles in your warehouse automatically and deliver ML-based segments and audiences to your marketing, product, and customer success teams.
Use case: Churn prediction
Predicting churn is one of the crucial initiatives across businesses. Without a predicted churn score, your actions are reactive, whereas you can act proactively with a user trait like is_likely_to_churn. Once you have such features, you can activate them with the appropriate outreach programs to prevent user churn.
Definition of the feature that needs to be defined only for a segment of users.
For example, country='US' and is_payer=true
List of columns from the feature table which the model ignores for training.
In your python_model, mention the following parameters:
predict:inputs:- packages/feature_table/models/shopify_user_featuresconfig:data:*model_data_input_configsoutputs:column_names:percentile:&percentile_namepercentile_churn_score_7_daysscore:churn_score_7_daysfeature_meta_data:&feature_meta_datafeatures:- name:*percentile_namedescription:'Percentile of churn score. Higher the percentile, higher the probability of churn'
Path to the base features project.
Column in the output table having the percentile score.
Column in the output table having the probabilistic score.
Schedule your project with the required cadence. Note that this schedule is for prediction.
Trainings are scheduled as per your configuration of the file_validity parameter in the training section of your project.
Final output or the predicted features are pushed to your customer360 table. Use the Explorer tab to check the predicted value for any given user along with the historical values upto last 5 runs.
To check the predicted value for a given user:
In the Preview section, go to the Predictive features tab.
Check the user profile for which the predictive feature has a value.
Search the USER_MAIN_ID of the profile in Profile viewer.
The value of the predictive feature is a probability. You can consider it as true or false based on your threshold.
All your predictive features are listed separately in the Overview tab of your Profiles project. You can check the logs of each run in the artifacts directory (available in the History tab of your Profiles project).
Is there a project to understand ProfilesML further?
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