Keep Track of eCommerce Sales
Accept returns for online sales regardless of the in-store POS. When customers wish to make returns, they can bring their online-shopped merchandise directly to any nearby store instead of dealing with the hassle of mailing them back.
With BORIS, they can refrain from the multifaceted returns process, which includes repackaging the item, requesting and printing a shipping label, making a trip to the post office, and waiting for the merchant to process the return and issue a refund.
Automate Returns Flow
When customers step in the store to return items, store associates can easily search eCommerce orders to locate the item a customer wants to return.
They can directly look-up the order number or search by customer name and apply filters based on the desired date range to quickly pinpoint the required order.
Automate Returns Flow
Based on a customer's request, store associates can either select the entire order or choose specific items to be returned and create an RMA to process returned items.
Automate Returns Flow
Ensuring compliance with the brand’s return policy and quality check is crucial in a return process. Once store associates have carefully inspected the merchandise being returned, they can accept and authorize the return with confidence.
Automate Returns Flow
The store which is receiving the returned merchandise is automatically selected for restocking the inventory.
Returns handling approaches vary for every merchant, so immediate restocking of returned items is configurable. If returned merchandise is ready to resell right away, then restocking it will publish it online as well.
Automate Returns Flow
Once store associates confirm that the return has been fully processed and initiate a refund, a request is automatically sent to eCommerce to complete it and issue a refund to the customer.
Reduce Returns with In-Depth Analysis
Merchandisers have access to multiple comprehensive returns dashboards that provide insights on online purchases that have been returned, return rates across product styles and geographical regions where the most-or least-returns happened.
The detailed dataset can be used for further analysis, such as identifying return reasoning, evaluating product performance, and understanding customer behavior.
