Churn & Reorder-Gap Prediction

Find the accounts drifting away from their normal buying pattern while there's still time to keep them.

The problem it solves

Repeat accounts rarely cancel outright, they just slow down: a monthly order becomes quarterly, then stops. Aggregated reporting smooths this drift out, and by the time an account is flagged as lost in a quarterly review, the buying relationship has usually already moved elsewhere.

What it does for your business

Scores accounts against their own history

Order cadence, spend trend, and product mix are compared to that account's own pattern, not a company-wide average.

Flags the specific gap

Shows which products an account stopped reordering and how far past its normal cycle it is, so the follow-up conversation is specific, not a generic check-in.

Prioritizes the call list

Ranks at-risk accounts by revenue exposure, so a rep's week starts with the accounts most worth saving.

Tells normal apart from seasonal

A weekly buyer going quiet for three weeks is a warning sign; a seasonal buyer doing the same is expected. The model is trained to know the difference per account.

Where this moves the needle

Earlier risk detection Higher account save rate More repeat revenue protected Faster time from warning sign to outreach
How it works
A classification model trained on account-level order histories, built from cadence, spend trend, and product-mix features, and validated against accounts that genuinely went quiet in prior periods.
What we need from you
Roughly the last two years of order history and your customer master. Support ticket history is optional but improves the signal. One ERP export is enough to start.

See Churn & Reorder-Gap Prediction on data like yours

A short walkthrough on sample data, including how accounts are scored and how alerts get routed to the right owner.

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