CompTIA Data+ (DA0-002)Data AnalysisHard
A data analytics team is preparing to present findings on customer churn to stakeholders. They have identified several factors that contribute to churn, including customer tenure, average monthly spend, and number of support tickets. To communicate the relative importance of these factors in predicting churn, which analysis technique would be most effective?
- ACluster Analysis
- BSurvival Analysis
- CDecision Tree Analysis
- DFactor Analysis
Show answer & explanationAnswer & explanation
Correct answer: C. Decision Tree Analysis
Decision Tree Analysis is highly effective for explaining the relative importance of factors in predicting an outcome like churn. It generates a tree-like model that clearly illustrates the decision rules and the impact of each variable, making it easy for stakeholders to understand.
Why the other options are wrong
- A. Cluster Analysis groups similar data points but does not explain factor importance for a target variable.
- B. Survival Analysis models the time until an event occurs (e.g., churn), but doesn't primarily focus on explaining relative factor importance in an easily interpretable way for stakeholders.
- D. Factor Analysis reduces the number of variables by grouping correlated ones, not for explaining individual factor importance to a target.
Decision Tree Analysis
A supervised learning method used for classification and regression that builds a model in the form of a tree structure, where internal nodes represent tests on an attribute, branches represent outcomes of the test, and leaf nodes represent class labels or predicted values.
- Visually interpretable for explaining predictive factors.
- Handles both numerical and categorical data.
- Identifies the most influential variables and their thresholds.
Memory trick: A Decision Tree helps managers see which branches lead to churn, making it easy to decide what to do.