Square, Inc.

Block, Inc.

Wael Al-Sallami

Drew Richards

Xuanhua Ren

Neil Straghalis

Christine Loh

Vitaly Odemchuk

Katherine Swett

Medum Choe

Techniques described herein are directed to centralized brand asset management. In an example, a first computing device associated with a first point-of-sale (POS) service can receive an instruction to update a web page of a merchant, wherein the instruction to update the web page of the merchant changes a brand asset of the merchant. The first computing device can send a request to update the brand asset of the merchant to a centralized data storage storing one or more brand assets of one or more merchants. The centralized data storage can update the brand asset to an updated brand asset, which can be available to at least a second computing device associated with a second POS service. The second POS service can update a respective POS service feature based on the updated brand asset.

Centralized brand asset management

Using combined eCommerce and brick-and-mortar data to produce intelligent recommendations for web page operation is described. In an example, a request to access an eCommerce GUI presented on behalf of a merchant can be received from a device operable by a viewer. Brick-and-mortar point-of-sale (POS) transaction data associated with one or more customers that transact with the merchant or one or more other merchants that utilize the payment processing service to process brick-and-mortar POS transactions can be used to determine a variation of the eCommerce GUI presented on behalf of the merchant to present to the viewer. Instructions for rendering the variation of eCommerce GUI can be sent to the device operable by the viewer.

Using combined ecommerce and brick-and-mortar data to produce intelligent recommendations for web page operation

Using combined eCommerce and brick-and-mortar data to produce intelligent recommendations for website editing is described. In an example, brick-and-mortar transaction data associated with a first merchant and second merchant(s) can be compared to identify a subset of the second merchant(s) that are similar to the first merchant. Performance metrics associated with eCommerce graphical user interfaces (GUIs) presented on behalf of the subset of the second merchant(s) can be determined based on aggregated transaction data associated with eCommerce point-of-sale (POS) transactions associated with the subset of the second merchant(s) and a recommendation based on content, a layout, and/or a design of at least a portion of the eCommerce GUIs can be generated. The recommendation can be sent to a computing device operable by the first merchant for use in creating or editing an eCommerce GUI to be presented on behalf of the first merchant.

Using combined eCommerce and brick-and-mortar data to produce intelligent recommendations for web page editing

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San Francisco, CA, United States of America

Wael Al-Sallami