You are advising a subscription service using a churn prediction model. The model gives a customer a churn probability of 0.47. The firm has set its intervention threshold at 0.4. What happens next?

Question

BlackTom AI · Accepted Answer

Consider the scenario: a churn model assigns a probability of 0.47 for a given customer, and the firm uses an intervention threshold of 0.4 to decide when to act.
Option 1: 'The customer is predicted to churn and gets a retention offer.' Since 0.47 is greater than the threshold 0.4, the model flags this customer for intervention, so a retention offer would be appropriate as a proactive measure.
Option 2: 'The customer is not flagged.' This would be true only if the probability were below the threshold. Here, 0.47 exceeds 0.4, so the customer would be flagged rather than ignored.
Option 3: 'The model underperforms due to miscalibration.' There is no information in the scenario that requires assuming miscalibration. A probability of 0.47 being above the threshold simply indicates a decision rule being applied; it does not by itself imply miscalibration or poor performance.
Option 4: 'No decision is made.' With a defined threshold, any probability above that threshold typically triggers a decision to intervene. Therefore, declaring no decision would contradict the stated thresholding rule.

COMM_V 414 103 2025W1 Concept Check 3 (individual)

You are advising a subscription service using a churn prediction model. The model gives a customer a churn probability of 0.47. The firm has set its intervention threshold at 0.4. What happens next?

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