What is Predictive analytics?
Predictive analytics uses a customer's past behaviour to estimate what they are likely to do next, such as when they will order again or whether they are likely to stop buying.
In Klaviyo, predictive fields on a profile can include expected date of next order, predicted lifetime value and churn risk. They are estimates based on patterns in your own order history, not guarantees.
They are most useful for segmentation. You might target people expected to reorder soon, or nudge customers whose risk of lapsing has risen.
Predictions need enough order history to be meaningful. A new store with few orders will get thinner results, and any prediction should be checked against what you actually see before building a strategy on it.
A worked example.
A hypothetical coffee shop builds a segment of customers whose expected next order date falls in the coming week and who have not ordered yet. They receive a reminder, while customers flagged as high churn risk get a different, gentler message.
Common mistakes.
- Treating predictions as certain
- Using them with very little order history
- Never validating predictions against real outcomes
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