Walmart Apollo, LLC

Divya Chaganti

Kannan Achan

Sushant Kumar

Hyun Duk Cho

Aysenur Inan

Ashish Ranjan

Sooraj Mangalath Subrahmannian

Evren Korpeoglu

Rahul Iyer

Yokila Arora

Jiwen You

Sneha Gupta

Niousha Bolandzadeh Fasaie

Kamilia Ahmadi

Keerthi Gopalakrishnan

In some examples, a system may be configured to obtain a set of features of a set of users including one or more features of transactional data of the set of users and one or more features of engagement data of the set of users. Additionally, the system may be configured to implement a first set of operations that generate output data including a plurality of trial membership scores, based on the set of features. In some examples, each trial membership score of the plurality of trial membership scores are associated with a particular user of the set of users and characterize a likelihood of an acquisition event of the corresponding user changing from a non-member status to a trial member status.

Methods and apparatus for predicting a user acquisition event

In some examples, a system may be configured to obtain a set of features of a set of users including one or more features of transaction of the set of users and one or more features of engagement data of the set of users. Additionally, the system may be configured to implement a first set of operations that generate output data including a plurality of churn scores, based on the set of features. In some examples, each churn score of the plurality of churn scores being associated with a particular user of the set of users and characterize a likelihood of a churn event of the corresponding user.

Methods and apparatus for predicting a user churn event

Systems and methods utilizing a classification model and a ranking model are disclosed. A user identifier is received and a user profile is generated. The user profile includes a plurality of user features. The user profile is classified into a classification using a trained classification model. The trained classification model receives a first subset of the plurality of user features. A set of communication elements is ranked using a trained ranking model. The trained ranking model receives a second subset of the plurality of user features. An electronic communication including a plurality of interface elements is generated. The plurality of interface elements includes at least one communication element selected from the ranked set of communication elements in descending ranked order. A type of the electronic communication is selected based on the classification of the user profile.

Systems and methods for benefit affinity using trained affinity models

An approach is disclosed for recommending complementary items based on customer shopping routines. The approach receives anchor item data. The approach identifies a routine that corresponds to the anchor item data. The routine is based on an item purchasing behavior of a customer. The approach determines categorical data within the identified routine by applying a ranking algorithm to the categorical data of the categories and the anchor item data. The categorical data is relevant to the anchor item data. The approach generates relevant item data from the categorical data by applying an item recommendation model to item data that corresponds to the categorical data.

Routine item recommendations

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Dublin, CA, United States of America

Divya Chaganti