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PAIGE & Predicted Churn Alerts
PAIGE & Predicted Churn Alerts

Learn how our A.I. driven Predicted Churn Alerts pro-actively identify customers you're at risk of losing - even before you've lost them.

Jessica Nash avatar
Written by Jessica Nash
Updated over a week ago

What is PAIGE?

The Prospect A.I. Growth Engine (P.A.I.G.E.) allows you to see who you're missing orders from to increase order frequency and customer retention. With the Prospect Growth Engine, your Sales team are empowered to chase up missing orders - without weeks of traditional reporting.

Our A.I. driven Predicted Churn Alerts pro-actively identify customers that you're at risk of losing - even before you've lost them.

In trials, the Growth Engine identified customer churn several weeks before Account Managers did, and about 8 weeks before traditional reporting methods highlighted the churn.

How does it work?

The Growth Engine can process huge amounts of data in a few like how many days it's been since a customer last ordered with you, and the average number of days between their orders, combined with their standard deviation, and then comparing these with similar customers. All this is fed into the A.I. engine, which spits out critical insights - allowing your teams to act quicker and more decisively when there is an alert, but also allows them to relax, and focus elsewhere when a customer doesn't have an alert - knowing that the Growth Engine is monitoring every single customer, every single day, so that you don't have to.

How do I use PAIGE in Prospect?

You can find your customer churn alerts via the Account Manager Dashboard, located in the top right-hand corner. Click the 'See More' button (or click here) to get the complete list of customers who should have ordered by now.

The 'Customers who should have ordered by now' Report gives you a full list of all customers you're missing orders from, based on their buying pattern. Alongside each customer name, you'll see the Account Manager assigned to this customer, as well as other information like the number of days since they last ordered (by default, this is sorted by fewest number of days first), the average number of days between orders, and the order days warning level. For example, a customer with 31 order days warning level who hasn't ordered in 38 days should be a red flag and prioritised to be contacted. Plus, the RFM Categorisation labels highlight which segment the customer falls into based on their buying behaviour since they started ordering with you.

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