Question 552 of 941
How to Enable the Churn Prediction Model in Dynamics 365 Customer Insights
A financial services company uses Dynamics 365 Customer Insights to generate a 360-degree view of customers. They want to use AI to predict which customers are likely to churn in the next 30 days. What should they configure?
Quick Answer
The correct answer is to enable the churn prediction model in the AI insights module. This is the right configuration because Dynamics 365 Customer Insights includes prebuilt AI models that analyze customer data—such as interactions, transactions, and engagement history—to predict which customers are likely to churn within a specified time frame, like 30 days. The model uses machine learning to identify behavioral patterns and assign a churn score, allowing the financial services company to proactively target at-risk customers with retention campaigns. On the MB-910 exam, this question tests your understanding of the AI insights module’s capabilities within Customer Insights, often appearing as a scenario where you must distinguish between prebuilt AI models and custom analytics features. A common trap is confusing the churn model with the product recommendation model, which serves a different purpose. Memory tip: think “Churn = Customer Health,” and remember that enabling the prebuilt model in AI insights is the only way to get that predictive score without custom development.
⚠ Common exam trap
Many candidates confuse static segmentation or manual measures with AI-powered predictive models, assuming that historical data alone can predict future behavior without machine learning.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable the churn prediction model in the AI insights module
The churn prediction model in the AI insights module is the correct configuration because Dynamics 365 Customer Insights provides prebuilt AI models, including a churn prediction model, that analyze customer data (e.g., interactions, transactions, and engagement) to predict which customers are likely to churn within a specified time frame, such as 30 days. This model uses machine learning to identify patterns and assign a churn score, enabling proactive retention efforts.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable the churn prediction model in the AI insights module
Why this is correct
The AI insights module includes prebuilt models for churn prediction.
- ✗
Define a measure to calculate average transaction amount
Why it's wrong here
Measures calculate historical metrics, not predictions.
- ✗
Create a static segment based on past churn behavior
Why it's wrong here
Segments are not predictive; they group existing data.
- ✗
Add enrichment data from external demographics
Why it's wrong here
Enrichment adds data but does not predict future behavior.
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Same concept, more angles
1 more way this is tested on MB-910
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A manufacturing company uses Dynamics 365 Sales and Customer Insights. They want to identify customers who are likely to churn based on support ticket volume and purchase recency. Which Customer Insights feature should they use to build this analysis?
medium- ✓ A.Predictive models
- B.Measures
- C.Segments
- D.Enrichment
Why A: A is correct because Predictive models in Dynamics 365 Customer Insights use built-in machine learning to analyze historical data—such as support ticket volume and purchase recency—and generate a churn probability score for each customer. This allows the company to proactively identify customers at risk of churning without requiring custom data science work.
Last reviewed: Jun 24, 2026
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