Block, Inc.

Square, Inc.

John Berian James

Rong Yan

Abhay Raj Kumar

Mai Leduc

Gian Perrone

Yongxue Qi

Amrik Kochhar

Dan Hipschman

Jonathan Steven Landy

Mehmet Zahit Guneri

James A Esposito

Matthew R George

Drew A Edmond

Transaction modification based on modeled profiles is described. In an example, transaction data can be received from merchant computing devices associated with merchants associated with a payment processing system. A model can be trained to generate profiles using, as training data, one or more of merchant data, buyer data, or the transaction data. Upon receiving an indication of a particular transaction between a buyer and a merchant, it can be determined that a characteristic of the transaction corresponds to a profile of the generated profiles. Based on the determination that the characteristic corresponds to the profile and the transaction data, a recommendation can be generated for a modification of the transaction to add an item or replace an item.

Transaction modification based on modeled profiles

Techniques and arrangements for concurrent processing of payments to multiple merchants in a shared program are described. A method, performed by a service computing device, can include receiving customer information associated with two transactions of a customer with two respective merchants that are in a shared program. Accrued benefits of the customer for conducting the two transactions in accordance with the program can be determined. The accrued benefits can be communicated to the customer via a customer device. The payments for both transactions can be processed, concurrently, in accordance with the accrued benefits. In examples, the merchants may be associated in the shared program based on locations of the merchants, a type of merchant, and/or goods or services offered for sale by the merchants.

Frictionless entry into combined merchant loyalty program

Transaction modification based on modeled profiles is described. In an example, transaction data can be received by a payment processing system. The payment processing system can generate, using a trained model, profiles. Based at least in part on (i) receiving an indication that a buyer and a merchant are engaged in, or likely to be engaged in, a transaction and (ii) determining that a characteristic of the transaction corresponds to a profile, generating a communication to enable the buyer or the merchant to modify the transaction in association with processing of the transaction.

Techniques and arrangements for generating multi-merchant loyalty programs and dynamically enrolling customers into a multi-merchant programs. The multi-merchant loyalty programs can be generated based on a determination that the merchants in respective programs are complementary, which may be done by determining that items offered for sale are complementary. One merchant can be associated with multiple multi-merchant programs. In some instances, a customer can be automatically enrolled in a multi-merchant loyalty program responsive to conducting a transaction with one of the merchants in the program. After enrollment, the customer can receive incentives to conduct transactions with the merchants in the program.

Techniques and arrangements for generating multi-merchant loyalty programs and dynamically enrolling customers into a multi-merchant programs. The multi-merchant loyalty programs can be generated based on a determination that the merchants in respective programs are complementary, and/or the merchants being located within a threshold distance of one another. In some instances, a customer can be automatically enrolled in a multi-merchant loyalty program responsive to conducting a transaction with one of the merchants in the program. After enrollment, the customer can receive incentives to conduct transactions with the merchants in the program.

Buyer profile management is described herein. In an example, a payment processing service can store, in a database, buyer profiles associated with buyers involved in transactions with one or more merchants of the payment processing service. The payment processing service can receive transaction data associated with a new transaction between a buyer and a merchant and can determine whether the transaction data corresponds to any of the buyer profiles. Based at least in part on determining that the transaction data corresponds to one of the buyer profiles, the payment processing service can update the database storing the buyer profiles, wherein updating the database comprises merging two or more buyer profiles. A recommendation can be made based on the merged buyer profile prior to completion of the new transaction.

Buyer profile management

Techniques and arrangements for industry vertical classification of merchants using merchant signals, based, in part, on comparing the merchant signals with collected business class profiles. The merchant signals can include reported data, collected data, and third-party data associated with the merchant. The techniques can include identifying one or more business class profiles using the data associated with the merchant and classifying and/or reclassifying the merchant within a business class using the one or more identified business class profiles.

Classifying merchants

Techniques for providing items recommendations based on a state of a merchant device are described. For example, a determination of whether or not a merchant device is in an online mode or an offline mode may be made. When it is determined that the merchant device is in the online mode, a recommendation that is received for the current transaction may be presented. When it is determined that the merchant device is in the offline mode, a recommendation that is previously received may be presented. In other examples, other types of recommendations may be presented.

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San Francisco, CA, United States of America

John Berian James