What Data Do You Need to Predict Customer Reorders?

The short answer

You already have the data. Predicting when a recurring customer will reorder needs nothing more than the order history a distributor records as a matter of course. The useful fields are the account, the product, the quantity, and the date of each order.

This surprises people who assume prediction takes some exotic feed. It does not. The whole signal sits in the transactions a distributor has been logging for years, which is why a distributor can start from records they already own.

What each field contributes

Each piece of the record plays a part in finding the reorder pattern.

• Account: ties the orders together so each customer's rhythm is read on its own.
• Product: a customer reorders different items on different cycles, so the pattern is per product.
• Quantity: how much they buy, compared with how long it lasts, sets the expected reorder interval.
• Date: the spacing between order dates is the raw signal for cadence and for spotting a slip.

What you do not need

You do not need the customer's own usage logs, you do not need them to tell you their schedule, and you do not need a new system of record. The order history in an Epicor P21 or Eclipse system, or even a clean export, holds enough to read the pattern.

Turning the data into a prediction

The method is statistical, not magic. Divide what an account bought by how long it tends to last, and you get an expected next order date with a window of slack around it. Do that for every account and product, watch the calendar against each window, and the accounts that are due rise to the top of a ranked list. Because it runs on order history alone, it stays current as new orders land.