Salesloft, Inc.

Acoustic, L.p.

Scott Hood Mitchell

Jason Burke Brett

Alec Lionel Delany

Sneha Dey Subramanian

Michel Guillet

Rob Forman

Bryan Reid Brown

Carlos Roman Salas

Roi Ceren

Methods and systems for determining cadences are described. Behavior information between a user and a potential customer is stored, where the behavior information comprises actions between the user and the potential customer. The behavior information is analyzed to determine an engagement score to represent time effort the potential customer spends in interacting with the user, where the analyzing utilizes model-free reinforcement learning in a generalized Markovian process. A cadence is determined utilizing the engagement score, where the cadence comprises scheduled action items for the user for future interactions with the potential customer.

Methods and systems for determining cadences

Methods and systems for a cascading model architecture for providing information on a reply email. Training sample data can be created using a user's incoming reply emails from external computer devices to a user's computer device. A receptivity neural network model can be trained using the training sample data of the reply emails, and a trained receptivity neural network model can be used to determine a receptivity classification for whether new reply emails are positive reply emails or non-positive reply emails. Sample data of non-positive reply emails can be augmented, and an objection identification neural network model can be trained on the augmented sample data of the non-positive reply emails. A trained objection identification neural network model can be used to determine a objection classification for new non-positive reply emails. Explainability information for a classified reply email can be determined to provide information key words and/or key phrases that were used by the trained receptivity neural network model and the trained objection identification neural network model to classify new positive reply emails and new non-positive reply emails.

Methods and systems for cascading model architecture for providing information on reply emails

Disclosed are various embodiments for a content module to identify targeted content to a user based on the user's prior interactions with a content providing entity over multiple forms of communication. Information about the user's interaction over the multiple forms of communication, such as, for example, duration of interaction and frequency of interaction, is received by the content module. The content module executes a set of content identification rules to identify the targeted content using the received information about the user's prior interactions. The identified targeted content is provided to the user's device for display on a user interface.

Targeted electronic and networked content delivery

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Smyrna, GA, United States of America

Scott Hood Mitchell