I went with the obvious stuff first, survival curves and cohort retention heatmaps, which felt solid, but then I kind of rambled into scatter plots without really tying them back to churn specifically.
Start by clarifying the churn definition and available data, then propose a mix of visualizations for univariate, bivariate, and multivariate analysis. Focus on how each visualization reveals patterns, trends, or root causes of churn, and tie them to actionable insights.
Pro tip: Emphasize that visualization choices depend on the data type and business question, and mention that you'd validate patterns with statistical tests to avoid drawing false conclusions from noise.
Ask about the definition of churn, the time window, and the available features (e.g., demographics, usage metrics, customer support interactions). This ensures your visualizations are relevant.
Use histograms, box plots, and bar charts to understand the distribution of individual variables and spot anomalies or segments with high churn rates.
Employ scatter plots, line charts, and grouped bar charts to examine relationships between churn and key variables like tenure, usage frequency, or customer service calls.
Leverage heatmaps for correlation matrices, pair plots, and dimensionality reduction techniques (e.g., PCA) visualized in 2D to uncover complex interactions and clusters.
Use time series plots and cohort retention curves to track churn over time and identify when customers are most likely to churn.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.