MB-910 Describe Dynamics 365 Customer Insights Practice Question
A marketing team wants to send a promotional email to customers who have a high likelihood of purchasing a new product. Which Dynamics 365 Customer Insights feature should they use?
⚠ Common exam trap
A common mix-up: candidates confuse descriptive analytics (like measures or segments based on past data) with predictive analytics, assuming that historical behavior alone is sufficient for targeting future purchases, when in fact a prediction model is required for likelihood scoring.
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
✓
Use a prediction model for purchase propensity
A prediction model for purchase propensity uses machine learning to analyze customer signals (e.g., browsing history, past purchases, demographics) and assign a probability score indicating how likely each customer is to buy a new product. This directly meets the marketing team's need to target high-likelihood customers, as it goes beyond simple historical data to predict future behavior.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use a prediction model for purchase propensity
Why this is correct
Propensity models predict likelihood of purchase.
- ✗
Define a measure of average spend
Why it's wrong here
Average spend is not predictive.
- ✗
Create a segment based on past purchases
Why it's wrong here
Past purchases don't indicate future likelihood.
- ✗
Run data unification
Why it's wrong here
Unification prepares data but does not predict.
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