The Boston Consulting Group, Inc.

Vincent Francois Faber

Arun Karthik Ravindran

Jack Chua

Aaron Dean Arnoldsen

Victor Kostyuk

Daniele Gaudenzio Wolfgang Parenti

Methods and systems for predicting are disclosed. Records for transactions can be stored, each record comprising an indication identifying items involved in a transaction and an indication of a time elapsed between the transaction and a previous transaction. The records for transactions can be analyzed to produce a probability that the items will be involved in a next transaction.

Methods and systems for analyzing and predicting transactions

Methods, systems, and computer storage media for providing a data analytics index associated with a crisis-recovery data analytics engine in a data analytics system. The data analytics index is a consolidated single index representation of a set of variables associated with a set of consumer behaviors. The crisis-recovery data analytics engine supports generating the data analytics index associated with a pre-crisis period and a crisis-recovery period. In operation, a crisis-recovery dataset—associated with a set of variables of a set of consumer behaviors that support quantifying recovery from a crisis event—is accessed. The set of consumer behaviors are selected based on a crisis-recovery machine learning model that is trained on a pre-crisis dataset and a crisis dataset for selecting the set of consumer behaviors. A data analytics index is generated based on the crisis-recovery dataset. A data visualization comprising crisis-recovery data indicating recovery from the crisis event is generated.

Crisis-recovery data analytics engine in a data analytics system

Methods and systems for using reinforcement learning to optimizing promotions. A promotion for a product and/or service is offered to a customer using a reinforcement learning model. The customer's reaction is observed. A reward or a penalty is collected based on the customer's reaction. The reinforcement learning model is adapted based on the reward or penalty.

Systems and methods of using reinforcement learning for promotions

Systems and methods for predicting are described herein. A record for each of a plurality of events associated with user transactions can be stored. A sequential plurality of the events can be analyzed using a unidirectional long short term memory (LSTM) and first and second dense neural network layers configured to receive output from the LSTM network.

Systems and methods for learning and predicting transactions

Systems and methods for predicting transactions. A record is stored for each of a plurality of transactions, each record comprising an indication identifying each item from a set of items involved in the transaction and an indication of a time elapsed between the transaction and a previous transaction. A sequential plurality of the transactions is analyzed using a unidirectional long short term memory (LSTM) network to produce a probability that each item from the set of items will be involved in a next transaction, and a time estimate value for a future time when the next transaction will occur. The probability for each item and the time estimate value is stored in the memory as a prediction.

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Boston, MA, United States of America

Vincent Francois Faber