MB-910 Describe Dynamics 365 Customer Insights Practice Question
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?
⚠ Common exam trap
Many candidates confuse Segments (which can be dynamic and based on conditions) with Predictive models, not realizing that Segments only filter existing data while Predictive models generate new probabilistic insights.
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
✓
Predictive models
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.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Predictive models
Why this is correct
Predictive models, such as churn models, use historical data to predict future behavior.
- ✗
Measures
Why it's wrong here
Measures are aggregations, not predictive models.
- ✗
Segments
Why it's wrong here
Segments group customers but do not predict churn.
- ✗
Enrichment
Why it's wrong here
Enrichment adds external data, but does not predict churn.
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