Walmart Apollo, LLC

Wal-Mart Stores, Inc.

Zhao Zhao

Wei Shen

Lu Wang

Chenxi Liu

Vahid Jalalibarsari

Yuan Xie

A system including one or more processors, and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations: segmenting each respective user of one or more users into a respective user state of one or more respective user states; predicting, using a machine learning LTV update model, a respective LTV for each respective user of the one or more users; determining a respective transition path from the respective user state of the respective user to a different respective user state; determining a respective online advertisement to transition a respective user of the one or more users from a respective transition state to the different respective user state; and displaying the respective online advertisement for the respective user of the one or more users. Other embodiments are disclosed herein.

Systems and methods for determining customer lifetime value

Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of storing, for each respective customer of one or more customers, respective customer information in a customer database; predicting, using a machine learning lifetime value (LTV) update model, a respective LTV for each respective customer of the one or more customers; determining an online advertisement for each respective customer of the one or more customers using the respective LTV, as predicted, for each respective customer of the one or more customers; and coordinating displaying the online advertisement for at least a portion of the one or more customers. Other embodiments are disclosed herein.

Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of obtaining a uniform resource locator (URL) of a first webpage that is shown on a graphical user interface and that is external to a website of a retailer (where, in some embodiments, the URL is obtained from a referral website or is entered by a user from a chat window or search box), using a web scraper to extract web text displayed on the first webpage on the graphical user interface, processing the web text displayed on the first webpage on the graphical user interface to determine an interest of a user, using a set of rules to determine items related to the web text displayed on the first webpage on the graphical user interface, and coordinating displaying the items on a second webpage to promote the items as related to the interest of the user, where the second webpage is internal or external to the website of the retailer.

Systems and methods for processing or mining visitor interests from graphical user interfaces displaying referral websites

Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of: categorizing a plurality of customers based on whether or not each customer of the plurality of customers had (1) an online store transaction with a retailer, (2) an offline store transaction with the retailer, or (3) an online interaction with the retailer within a predetermined period of time; predicting, using a lifetime value (LTV) update model, a LTV for customers who did have an interaction or transaction with the retailer; predicting, using one or more LTV decay functions, the LTV for customers who did not have an interaction or transaction with the retailer; and determining an online advertisement for each customer based on the LTV for each customer.

Systems and methods including one or more processors and one or more non-transitory computer-readable media having computing instructions that are configured to run on the one or more processors and perform acts of receiving a test set comprising potential candidate items for substitution for a target item, determining association scores for each of the potential candidate items in the test set, determining one or more semantic similarity features of the potential candidate items in the test set, determining one or more image similarity features of the potential candidate items in the test set, and creating a substitutive probability model by determining a relative contribution of each of the association scores, the semantic similarity features and the image similarity features to a substitutive probability for the potential candidate items in the test set, with reference to a baseline set of the potential candidate items. Additional embodiments are disclosed herein.

Growing community of inventors

Sunnyvale, CA, United States of America

Zhao Zhao