MB-910 Describe Dynamics 365 Customer Insights Practice Question
A marketing manager wants to automatically send a personalized discount offer to customers who have a high propensity to churn. Which combination of Customer Insights capabilities should they use?
⚠ Common exam trap
Candidates often confuse 'segmentation and AI suggestions' (Option D) with predictive scoring, but segmentation alone cannot predict churn propensity—it only groups customers based on static criteria, whereas predictive scoring uses machine learning to dynamically assess risk.
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 scoring and measures
Predictive scoring in Customer Insights uses machine learning models to calculate a churn probability score for each customer, identifying those with a high propensity to churn. Measures allow the marketing manager to define and compute key business metrics, such as customer lifetime value or churn risk thresholds, which can trigger automated actions like sending personalized discount offers. Together, they enable a data-driven, automated outreach to at-risk customers without manual intervention.
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 scoring and measures
Why this is correct
Predictive scoring models churn propensity; measures can trigger actions like sending a discount offer.
- ✗
Data unification and enrichment
Why it's wrong here
Unification and enrichment prepare data but do not predict churn or trigger actions.
- ✗
Enrichment and Copilot
Why it's wrong here
Enrichment adds external data; Copilot provides insights but not automated actions.
- ✗
Segmentation and AI suggestions
Why it's wrong here
Segmentation groups customers but does not predict churn; AI suggestions are not the same as automated actions.
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