Learn how clustering using RFM (Recency, Frequency, Monetary) values can segment a customer base in this Azure AI Fundamentals certification exam sample question. Boost your AI-900 exam prep with our expert explanations.
Table of Contents
Question
Using RFM (Recency, Frequency, Monetary) values to identify segments of a customer base is an example of _____. Select the answer that correctly completes the sentence.
A. Regularization.
B. Classification.
C. Regression.
D. Clustering.
Answer
D. Clustering.
Explanation
Using RFM (Recency, Frequency, Monetary) values to identify segments of a customer base is an example of clustering.
Clustering is an unsupervised machine learning technique that involves grouping similar data points together based on their features or attributes. The goal is to create clusters where data points within each cluster are more similar to each other than to points in other clusters.
In the context of customer segmentation, RFM analysis considers three key attributes for each customer:
- Recency: How recently did the customer make a purchase?
- Frequency: How often do they purchase?
- Monetary: How much do they spend?
By clustering customers along these three dimensions, distinct customer segments can be identified, such as:
- High-value customers (high recency, frequency, monetary values)
- Churned customers (low recency)
- Infrequent big spenders (low frequency, high monetary)
- Frequent small spenders (high frequency, low monetary)
This is a clustering problem rather than the other listed options because:
- Classification predicts a category for each data point, but the categories/segments are not predefined here.
- Regression predicts a numeric value, not a customer segment.
- Regularization prevents model overfitting but doesn’t segment customers.
So in summary, using RFM values to group similar customers and discover customer segments is a quintessential example of clustering in a customer analytics context. Understanding clustering and its common business applications is key to acing the AI-900 Azure AI Fundamentals certification exam.
Using RFM (Recency, Frequency, Monetary) values to identify segments of a customer base is an example of clustering. Clustering techniques are used to group customers based on their behavior and characteristics, allowing businesses to tailor their marketing strategies to different segments effectively.
Microsoft Azure AI Fundamentals AI-900 certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Microsoft Azure AI Fundamentals AI-900 exam and earn Microsoft Azure AI Fundamentals AI-900 certification.