Churn Prediction and Customer Retention in Dynamics 365 Customer Insights
A company wants to use Customer Insights to improve customer retention. Which TWO features should they use?
Quick Answer
The answer is Predictive models (churn model) and Segments based on churn risk. These two features work together in Dynamics 365 Customer Insights to enable effective churn prediction and customer retention: the predictive churn model uses historical data and machine learning to assign a churn probability score to each customer, while segments based on that risk allow you to group high-risk individuals for targeted retention campaigns. On the MB-910 exam, this tests your understanding of how Customer Insights transforms raw data into actionable intelligence—a common trap is choosing only one feature, but the question requires both the analytical engine (the model) and the operational output (the segment). Remember the pairing: the model calculates the risk, the segment acts on it. A simple memory tip is “Model then Mold”—first build the predictive model, then mold your audience into a churn-risk segment.
⚠ Common exam trap
Test-takers frequently confuse foundational setup tasks (data profiling, data sources, exports) with the actual analytical features (segments and predictive models) that directly solve the business problem of improving retention.
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
✓
Segments (based on churn risk)
Segments based on churn risk (Option B) allow you to group customers who are likely to leave, enabling targeted retention campaigns. Predictive models, specifically the churn model (Option D), use historical data and machine learning to calculate a churn probability score for each customer, which directly feeds into those segments. Together, they provide the actionable intelligence needed to proactively intervene and improve retention.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Data profiling
Why it's wrong here
Profiling is for data quality, not retention.
- ✓
Segments (based on churn risk)
Why this is correct
Segments can be used to target retention campaigns.
- ✗
Data sources configuration
Why it's wrong here
Data sources are foundational but not directly for retention.
- ✓
Predictive models (churn model)
Why this is correct
Churn models help identify at-risk customers.
- ✗
Data exports to external systems
Why it's wrong here
Exports are for integration, not directly for retention.
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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 company wants to use Dynamics 365 Customer Insights to predict which customers are likely to churn. Which feature should they use?
easy- A.Unification
- B.Measures
- C.Segments
- ✓ D.Predictive models
Why D: Dynamics 365 Customer Insights includes a dedicated predictive model for churn. This feature uses historical customer data and machine learning to calculate a churn score for each customer, enabling proactive retention efforts. The other options focus on data preparation or basic segmentation, not predictive analytics.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MB-910 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MB-910 exam.