Walmart Apollo, LLC

Tao Zhu

Patrick Harrington

Wei Shen

Madhusudan Mathihalli

Vahid Jalalibarsari

Lei Tang

Bin Li

Fnu Manasi

Thanh Ngo

A system and method for optimizing marketing campaigns is presented. Two marketing campaigns are received. Each is presented to a subset of users. The conversion rates of both marketing campaign are used to determine weighting of the two marketing campaigns. The weighting is determined using a range of conversion rates and maximizing the minimum expected value through the range of conversion rates. The process can be iteratively performed to converge upon an optimum weighting of the first and second conversion rates. More than two marketing campaigns can be used. The marketing campaign can be an email marketing campaign, a web marketing campaign, or an advertising keyword campaign. Other embodiments also are disclosed.

System for providing a robust marketing optimization algorithm and method therefor

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.

Systems and methods for determining customer state transitions for growth of customer lifetime values

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 receiving, from a plurality of entities, objectives and budgets associated with a plurality of keywords, determining cost-per-click (CPC) bids for each of the entities for some of the plurality of keywords using the objectives and the budgets, determining internal bids by the plurality of entities for the plurality of keywords by multiplying the CPC bid and a pacing multiplier, performing one or more iterative internal auctions, determining a highest bidder for each keyword of the plurality of keywords, and coordinating bidding by only the highest bidding entity for each of the keywords on a third-party external auction.

Systems and methods for a search engine marketing internal auction system

Providing personalized item recommendations using scalable matrix factorization with randomness. Various embodiments involve ecommerce websites, an item interaction matrix, item interaction weightings, factorizing into a user feature matrix and an item feature matrix, computing a thin matrix, generating a random Gaussian matrix, decomposing a matrix Y by a QR factorization, performing a singular value decomposition, performing alternative least squares, computing item interaction scores, selecting at least one item for recommendation to an Internet user based on the item interaction scores, or a combination thereof.

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Millbrae, CA, United States of America

Tao Zhu