The possible lost sales algorithm is used to identify regularity in purchase periods for customers by item. The validation page explains how this regularity was detected and shows individual purchases.
This module employs time-series modeling techniques to detect the purchasing habits of individual customers. By modeling individual customers, you can detect anomalies–or the possibility of a customer leaving–early on.
Configurable parameters allow you to fine-tune the algorithm’s capability to detect regularity in purchases and make predictions of the next expected sale as accurately as possible. A customer not making a predicted next sale in a certain time window is used as one possible early indication of the risk of that customer leaving and changing the supplier.
For more complex algorithms, our solution offers in-app help for choosing optimal configuration parameters.
SKU development is essentially a collaborative filtering algorithm. Because of the specifics of the wholesale domain, items used for recommendation are grouped based on the Generic Product Identifier (this is configurable on the playbook page of the application). The validation page shows recommended items by customer as well as prior purchases with attached GPI codes.