Block, Inc.

Square, Inc.

Xuanhua Ren

Vitaly Odemchuk

Katherine Swett

Drew Richards

Neil Straghalis

Wael Al-Sallami

Christine Loh

Medum Choe

Lisa Fong

Katherine Ahn

David Pearson

Joseph Boudreau

Bryan Ashley

Jocelyn Fu

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

Techniques described herein are directed to centralized identities for personalization of data presentation. A centralized identity is generated when similar user identifiers as stored across resources are determined. The centralized identity associates the user identifiers with each other and allows for user-related data from the multiple disparate resources to be queried and received. This user-related data can then be utilized to determine one or more personalizations to apply, and an application being used by the user may be modified to present the personalizations.

Centralized identity for personalization of data presentation

Customer-device application sites (e.g., content presented via a progressive web applications) that are accessible via merchant-managed identifiers (e.g., identification codes) are described. An identification of a physical location associated with a merchant can be associated with an identification code. The identification code can be received from a computing device of a customer and, based at least in part on receiving the identification code, merchant data associated with the merchant can be caused to be presented via a web browser of the computing device of the customer. In an example, the merchant data can be presented via the web browser by a progressive web application downloaded onto the computing device of the customer via the web browser. An indication of an input received via the progressive web application can be received by the computing device of the customer and an operation based at least in part on the input can be performed.

Customer-device application sites accessible via merchant-managed identifiers

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

Xuanhua Ren