Abstract: 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.

Abstract: Methods and systems of using reinforcement learning for promotions. A first promotion is offered to a customer for a product and/or service. A first reward or penalty is determined, via a reinforcement machine learning model, based on the customer's reaction to the first promotion, wherein the reinforcement machine learning model is at a first state. Feedback to the reinforcement machine learning model is provided based on the first reward or penalty. A state of the reinforcement machine learning model is changed, based on the feedback, from the first state to a second state.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A processor circuit may perform processing associated with storing in a memory a record for each of a plurality of transactions, each record comprising an indication identifying at least one item, of a set of items, involved in the transaction and an indication of a time elapsed between the transaction and a previous transaction. The processor circuit may perform processing associated with analyzing a sequential plurality of the transactions using at least a long short term memory (LSTM) network to produce a probability that the at least one item will be involved in a next transaction for each at least one item, and a time estimate until the next transaction occurs. The processor circuit may perform processing associated with storing each probability and the time estimate in the memory as a prediction.

Abstract: 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.