Dynamics 365 Customer Insights comes with a variety of options that leverage AI and machine learning to predict data.
The easiest way to start with predicting data are predefined models, often referred to as out-of-box models. They only require certain data and structure to generate insights quickly. Currently, the following models are available:
- Customer lifetime value: Predicts the potential revenue of a customer throughout the entire interaction with a business.
- Product recommendation: Suggests sets of predictive product recommendations based on purchase behavior and customers with similar purchase patterns.
- Subscription churn: Predicts whether a customer is at risk for no longer using your company’s subscription products or services.
- Transactional churn: Predict if a customer will no longer purchase your products or services in a certain time frame.
- Sentiment analysis: Analyze sentiment of customer feedback and identify business aspects that are frequently mentioned.
We recommend that you regularly refresh out-of-the box models with updated data to ensure they accurately inform your business use case. Data is refreshed ad-hoc when the system ingests new or updated data sources. However, models will only rescore in this case and continue to use the existing training data.
You can configure an Update schedule by setting the model retraining schedule in the configuration experience. The model will retrain and rescore on this schedule, which you can change at any time.
Azure Machine Learning integration
If an organization already uses machine learning scenarios based on Azure Machine Learning experiments, the custom models feature in Customer Insights helps to connect the dots. Create workflows that help you choose the data you want to generate insights from and map the results to your unified customer profiles. For more information, see Custom machine learning models.
AI Builder prediction
Sometimes, data sets are incomplete and some values are missing. Customer Insights can help to predict missing values for the Customer entity and segments. For more information, see Complete your partial data with predictions.
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