Applied Customer Churn and Cohort Analysis

A clinical data analyst needed to consolidate multi-row patient records into a validated, one-row-per-patient dataset for a quarterly leadership report. By automating the data pivoting and categorical encoding, the analyst successfully produced a reliable cohort visualization. The resulting dashboard highlights a 303-patient cohort with a median age of 56.0 and an overall disease presence of 45.9%. It also compares clinical metrics, such as average cholesterol (251.5 vs 242.6 mg/dL) and maximum heart rate (139.3 vs 158.4 bpm). Visualizations include a bar chart showing age distribution peaking in the 55-59 band, and a donut chart illustrating the disease presence share.

What it shows:

Automating data deduplication and pivoting ensures accurate cohort reporting for leadership.

A marketing analyst needed to move beyond flat CRM exports and blended averages to identify which customer clusters actually drove campaign conversions. This customer cohort analysis isolates high-value repeat converters from the unresponsive majority. The summary reveals that while non-responders dominate the base at 79.2%, the multi-responder segment holds the strongest commercial value with an average income of $78.9K. Furthermore, the top spend quartile converts at 30.2%, and within this tier, the multi-responder rate jumps to 20.8%. These segmented insights enable highly targeted budget allocation for future campaigns.

What it shows:

Segmenting CRM data by response frequency isolates high-value clusters for targeted marketing.