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.
This MB-910 practice question tests your understanding of describe dynamics 365 customer insights. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company wants to use Customer Insights to improve customer retention. Which TWO features should they use?
Data profiling
Why wrong: Profiling is for data quality, not retention.
Segments (based on churn risk)
Segments can be used to target retention campaigns.
Data sources configuration
Why wrong: Data sources are foundational but not directly for retention.
Predictive models (churn model)
Churn models help identify at-risk customers.
Data exports to external systems
Why wrong: Exports are for integration, not directly for retention.
Answer choices
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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.
Related concept
Read the scenario before looking for a memorised answer.
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.
Related concept
Read the scenario before looking for a memorised answer.
Data exports to external systems
Why it's wrong here
Exports are for integration, not directly for retention.
Common exam traps
The trap here is that candidates 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.
Detailed technical explanation
The churn model in Customer Insights is a built-in predictive model that uses a gradient boosting machine (LightGBM) algorithm trained on your customer activities (e.g., purchase frequency, support tickets, last interaction date) to output a churn score between 0 and 1. This score is then used to create dynamic segments that update automatically as new data flows in, allowing marketers to trigger real-time actions like sending a discount offer to customers whose churn probability exceeds 0.8. A subtle behavior is that the model requires at least 12 months of historical data and a minimum of 100 churned customers in the training set to produce reliable predictions.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Describe Dynamics 365 Customer Insights — This question tests Describe Dynamics 365 Customer Insights — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Same concept, more angles
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Variation 1. A company wants to use Dynamics 365 Customer Insights to predict which customers are likely to churn. Which feature should they use?
easyWhy D: Option D is correct because 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.
Last reviewed: Jun 11, 2026
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