Walmart Apollo, LLC

Wal-mart Stores, Inc.

Vahid Jalalibarsari

Wei Shen

Lu Wang

Chenxi Liu

Zhao Zhao

Tao Zhu

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: calculating a first user propensity score to take first actions and a second user propensity score to take second actions based on at least one feature vector of historical data of a user; using the first user propensity score to place the user into a first segment; using the second user propensity score to place the user into a second segment different than the first segment; and facilitating a display of one or more selectable elements of a GUI for the user based on the first segment and the second segment. Other embodiments are disclosed.

Systems and methods for behavior based messaging

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 receiving, from an extraction, transform, load (ETL) application, a matrix, which can comprise raw feature data; storing the matrix comprising the raw feature data in a standard format in the one or more non-transitory computer readable storage devices; receiving a configuration file over a computer network; storing the configuration file in a standard format in the one or more non-transitory computer readable storage devices; instantiating one or more unifier applications based upon the configuration file; identifying relevant feature data of the raw feature data; storing the relevant feature data in a standardized format in an output file in the one or more non-transitory computer-readable storage devices; and transmitting, over the computer network in real time to a model building system, the output file comprising the relevant feature data from the one or more non-transitory computer-readable storage devices, so that the machine learning model building applications have immediate access to up-to-date data. Other embodiments are disclosed herein.

Systems and methods for a machine learning framework

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 functions comprising: calculating a first user propensity score and a second user propensity score for a user based on at least one feature vector of historical data of the user; using the first user propensity score to place the user into a first segment; using the second user propensity score to place the user into a second segment different than the first segment; and facilitating delivery of a message to an electronic device of the user based on the first segment and the second segment. Other embodiments are disclosed.

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 collecting historical data of a user; converting the historical data of the user into at least one feature vector; calculating a first user propensity score for the user using the at least one feature vector; calculating a second user propensity score for the user using the at least one feature vector, the second user propensity score representing a different user propensity than the first user propensity score; normalizing the first user propensity score; normalizing the second user propensity score; using the first user propensity score, as normalized, to place the user into a first segment; using the second user propensity score, as normalized, to place the user into a second segment different than the first segment; and facilitating delivery of a message to the user based on the first segment and the second segment.

Disclosed herein are systems and methods for managing machine learning algorithms. Some embodiments comprise one or more transformer applications, one or more streamliner applications, a configuration file; accessing the configuration file, and an output file. In various embodiments, the streamliner application can be configured to read a configuration file and identify one or more attributes defining a machine learning algorithm. The streamliner application can also run each transformer application in an order determined by an ordered list in the configuration file. An output file can then be created for running one or more machine learning algorithms. 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 determining a lifetime value (LTV) for customers of a retailer, segmenting the customers into customer states based upon one or more purchases made by each customer at the retailer within a predetermined period of time, determining a first average LTV for customers in a first customer state and a second average LTV for customers in a second customer state lower than the first average LTV, coordinating a first display of a first online advertisement for customers in the first LTV to transition the customers from the first customer state to the second customer state, and coordinating a second display of a second online advertisement for customers in the second state.

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Sunnyvale, CA, United States of America

Vahid Jalalibarsari