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Wexmoor Telecom has a model that identifies customers likely to cancel. The data team reports that customers who contacted the call centre three times in a month cancel at twice the average rate. A manager proposes cutting call-centre capacity because 'calls cause cancellations'. Which response is MOST appropriate?

The proposal should be rejected because correlation does not prove causation. Customers who call repeatedly probably have an underlying service problem that drives both the calls and the cancellations, so cutting call-centre capacity could increase churn rather than reduce it.

  1. AAccept the proposal, since the correlation proves the calls cause cancellations
  2. BReject the proposal because correlation alone does not establish causation; the repeated calls may reflect an underlying service problem that drives bothCorrect
  3. CReject the proposal because analytics on historical data can never identify relationships
  4. DAccept the proposal, provided the model is rerun every day

Explanation

A correlation between calls and cancellations does not show that calls cause cancellations. A third factor, such as poor service, may lead customers both to call and to leave, so cutting capacity could worsen churn. The data is still useful for identifying at-risk customers, so option three is too extreme, and rerunning the model more often does not address causation.

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