Predicting which customers plan to close their accounts, before it’s too late

Project Case Study: ”If only we knew which customers were planning to close their accounts, we could try to convince them to stay…”

Client: Confidential

Challenge: Large customers were closing their accounts at a mid-size financial services firm on a weekly basis, but the company couldn’t be proactive to convince people to stay, because there was no way to predict who was going to leave next.

Solution: Conducted RFM analysis (recency, frequency, monetary value) to identify patterns in failed customer-retention behavior. Identified key predictors. Performed back-testing to confirm hypotheses. Shared list of major at-risk customers with client, so they could attempt to talk people into staying.

Result: Successfully identified $150 million in at-risk accounts, for retention activity by client’s sales and customer service team.

My Role: Took the initiative to conduct the analysis, found the key predictors, flagged the $150 million in at-risk accounts, and shared the findings with client’s management team for followup activity.

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