Kaplan-Meier Survival Analysis Explained

Why timing needs its own method

Ordinary averages break when some cases have not had the event yet. A customer who has stayed eight months so far is not a customer who left at eight months, but both look the same in a spreadsheet. Kaplan-Meier handles these incomplete records instead of discarding them.

Reading a survival curve

The curve shows the share of cases still event-free at each point in time. Steep sections mark the periods where losses concentrate — often the first weeks after onboarding or hire — which is where intervention has the most leverage.

Comparing groups

Two curves can be compared to see whether one group leaves faster, and a test reports whether the difference is larger than chance. Averisys states that result in plain English rather than leaving a p-value to interpret.

When several factors matter at once

Kaplan-Meier compares groups one split at a time. When plan, region, tenure, and price all matter together, a Cox model estimates each factor's effect on timing while holding the others constant. The platform proposes whichever method fits the question and explains why.

From curve to decision

Findings arrive as an interpretation, a risk list of the groups losing cases fastest, a what-if estimate of an intervention's effect, and a recommended action. A free trial is available without a credit card.

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