Abstract: Embodiments maintain historical activity information for a plurality of entity records. Each historical activity record includes a sequence-number that reflects a temporal position of the represented activity instance relative to other activity instances that targeted the corresponding entity. Based on this historical activity information, embodiments generate marginal value matrices (MVMs) that show the historical effectiveness of activities. Specifically, each entity is assigned to a bucket based on an associated entity score. An MVM charts a marginal value for each combination of bucket index and sequence-number, where the marginal value at each combination of bucket index and sequence-number is the average of the activity result values of the historical activity records, for entities assigned to the bucket index, having the sequence-number. Embodiments use MVMs to predict marginal values for activities targeted to each current entity of the salesperson.

Abstract: Embodiments maintain historical activity information for a plurality of entity records. Each historical activity record includes a sequence-number that reflects a temporal position of the represented activity instance relative to other activity instances that targeted the corresponding entity. Based on this historical activity information, embodiments generate marginal value matrices (MVMs) that show the historical effectiveness of activities. Specifically, each entity is assigned to a bucket based on an associated entity score. An MVM charts a marginal value for each combination of bucket index and sequence-number, where the marginal value at each combination of bucket index and sequence-number is the average of the activity result values of the historical activity records, for entities assigned to the bucket index, having the sequence-number. Embodiments use MVMs to predict marginal values for activities targeted to each current entity of the salesperson.

Abstract: Embodiments maintain historical activity information for a plurality of entity records. Each historical activity record includes a sequence-number that reflects a temporal position of the represented activity instance relative to other activity instances that targeted the corresponding entity. Based on this historical activity information, embodiments generate marginal value matrices (MVMs) that show the historical effectiveness of activities. Specifically, each entity is assigned to a bucket based on an associated entity score. An MVM charts a marginal value for each combination of bucket index and sequence-number, where the marginal value at each combination of bucket index and sequence-number is the average of the activity result values of the historical activity records, for entities assigned to the bucket index, having the sequence-number. Embodiments use MVMs to predict marginal values for activities targeted to each current entity of the salesperson.

Abstract: Techniques are described herein for predicting one or more behaviors by an email recipient and, more specifically, to machine learning techniques for predicting one or more behaviors of an email recipient, changing one or more components in the email to increase the likelihood of a behavior, and determining and/or scheduling an optimal time to send the email. Some advantages of the embodiments disclosed herein may include, without limitation, the ability to predict the behavior of the email recipient and suggest the characteristics of an email which will increase the likelihood of a positive behavior, such as a reading or responding to the email, visiting a website, calling a sales representative, or opening an email attachment.

Abstract: Techniques are described herein for classifying an electronic message with a particular project from among a plurality of projects. In some embodiments, first and second users associated with the electronic message are identified, and one or more first projects associated with the first user and one and more second projects associated with the second user are determined. Projects that are in common between the first projects and the second projects are determined. When only a single project is in common, the electronic message is associated with the single project. When more than a single project is in common, features associated with each of the projects found to be in common are analyzed by a machine learning model to determine the most likely project to associate with the electronic message from among the projects found to be in common.

Abstract: According to various embodiments of the present invention, an automated technique is implemented for resolving and merging fields accurately and reliably, given a set of duplicated records that represents a same entity. In at least one embodiment, a system is implemented that uses a machine learning (ML) method, to train a model from training data, and to learn from users how to efficiently resolve and merge fields. In at least one embodiment, the method of the present invention builds feature vectors as input for its ML method. In at least one embodiment, the system and method of the present invention apply Hierarchical Based Sequencing (HBS) and/or Multiple Output Relaxation (MOR) models in resolving and merging fields. Training data for the ML method can come from any suitable source or combination of sources.

Abstract: Techniques are described herein for classifying an electronic message with a particular project from among a plurality of projects. In some embodiments, first and second users associated with the electronic message are identified, and one or more first projects associated with the first user and one and more second projects associated with the second user are determined. Projects that are in common between the first projects and the second projects are determined. When only a single project is in common, the electronic message is associated with the single project. When more than a single project is in common, features associated with each of the projects found to be in common are analyzed by a machine learning model to determine the most likely project to associate with the electronic message from among the projects found to be in common.

Abstract: Using machine learning to predict behavior based on local conditions. In one example embodiment, a method for using machine learning to predict behavior based on local conditions may include identifying a lead, identifying a target behavior for the lead, identifying a locality associated with the lead, identifying a current local condition of the locality, and employing a machine learning classifier to generate a prediction of a likelihood of the lead exhibiting the target behavior. In this example embodiment, the machine learning classifier may base the prediction on the target behavior, the locality, and the current local condition.

Abstract: Techniques are described herein for predicting one or more behaviors by an email recipient and, more specifically, to machine learning techniques for predicting one or more behaviors of an email recipient, changing one or more components in the email to increase the likelihood of a behavior, and determining and/or scheduling an optimal time to send the email. Some advantages of the embodiments disclosed herein may include, without limitation, the ability to predict the behavior of the email recipient and suggest the characteristics of an email which will increase the likelihood of a positive behavior, such as a reading or responding to the email, visiting a website, calling a sales representative, or opening an email attachment.

Abstract: Techniques are described herein for predicting one or more behaviors by an email recipient and, more specifically, to machine learning techniques for predicting one or more behaviors of an email recipient, changing one or more components in the email to increase the likelihood of a behavior, and determining and/or scheduling an optimal time to send the email. Some advantages of the embodiments disclosed herein may include, without limitation, the ability to predict the behavior of the email recipient and suggest the characteristics of an email which will increase the likelihood of a positive behavior, such as a reading or responding to the email, visiting a website, calling a sales representative, or opening an email attachment.

Abstract: Techniques are described herein for classifying an electronic message with a particular project from among a plurality of projects. In some embodiments, first and second users associated with the electronic message are identified, and one or more first projects associated with the first user and one and more second projects associated with the second user are determined. Projects that are in common between the first projects and the second projects are determined. When only a single project is in common, the electronic message is associated with the single project. When more than a single project is in common, features associated with each of the projects found to be in common are analyzed by a machine learning model to determine the most likely project to associate with the electronic message from among the projects found to be in common.

Abstract: Techniques are described herein for predicting one or more behaviors by an email recipient and, more specifically, to machine learning techniques for predicting one or more behaviors of an email recipient, changing one or more components in the email to increase the likelihood of a behavior, and determining and/or scheduling an optimal time to send the email. Some advantages of the embodiments disclosed herein may include, without limitation, the ability to predict the behavior of the email recipient and suggest the characteristics of an email which will increase the likelihood of a positive behavior, such as a reading or responding to the email, visiting a website, calling a sales representative, or opening an email attachment.

Abstract: Techniques are described herein for predicting one or more behaviors by an email recipient and, more specifically, to machine learning techniques for predicting one or more behaviors of an email recipient, changing one or more components in the email to increase the likelihood of a behavior, and determining and/or scheduling an optimal time to send the email. Some advantages of the embodiments disclosed herein may include, without limitation, the ability to predict the behavior of the email recipient and suggest the characteristics of an email which will increase the likelihood of a positive behavior, such as a reading or responding to the email, visiting a website, calling a sales representative, or opening an email attachment.

Abstract: Techniques are described herein for predicting one or more behaviors by an email recipient and, more specifically, to machine learning techniques for predicting one or more behaviors of an email recipient, changing one or more components in the email to increase the likelihood of a behavior, and determining and/or scheduling an optimal time to send the email. Some advantages of the embodiments disclosed herein may include, without limitation, the ability to predict the behavior of the email recipient and suggest the characteristics of an email which will increase the likelihood of a positive behavior, such as a reading or responding to the email, visiting a website, calling a sales representative, or opening an email attachment.

Abstract: Techniques are described herein for predicting one or more behaviors by an email recipient and, more specifically, to machine learning techniques for predicting one or more behaviors of an email recipient, changing one or more components in the email to increase the likelihood of a behavior, and determining and/or scheduling an optimal time to send the email. Some advantages of the embodiments disclosed herein may include, without limitation, the ability to predict the behavior of the email recipient and suggest the characteristics of an email which will increase the likelihood of a positive behavior, such as a reading or responding to the email, visiting a website, calling a sales representative, or opening an email attachment.

Abstract: Techniques are described herein for predicting one or more behaviors by an email recipient and, more specifically, to machine learning techniques for predicting one or more behaviors of an email recipient, changing one or more components in the email to increase the likelihood of a behavior, and determining and/or scheduling an optimal time to send the email. Some advantages of the embodiments disclosed herein may include, without limitation, the ability to predict the behavior of the email recipient and suggest the characteristics of an email which will increase the likelihood of a positive behavior, such as a reading or responding to the email, visiting a website, calling a sales representative, or opening an email attachment.

Abstract: Using machine learning to predict behavior based on local conditions. In one example embodiment, a method for using machine learning to predict behavior based on local conditions may include identifying a lead, identifying a target behavior for the lead, identifying a locality associated with the lead, identifying a current local condition of the locality, and employing a machine learning classifier to generate a prediction of a likelihood of the lead exhibiting the target behavior. In this example embodiment, the machine learning classifier may base the prediction on the target behavior, the locality, and the current local condition.

Abstract: A method to analyze and determine which source content and user interactions are most popular is provided. The method generates scores for items, e.g., articles, topics, authors, or influencers, on a particular source based on data gathered from both the particular source and social media sources. The scores are used to rank items of the same type, and determine which items are the most popular. The method may also take demographic information as input. Using the demographic information, the system may determine the popularity of a particular item in a particular demographic. The method may also predict which demographic an item may be the most popular in. Furthermore, the method may give a recommendation on which author should write on a particular topic, which topic is most likely to be the most popular for a particular demographic, and which influencers should promote the article.

Abstract: Techniques are described herein for predicting one or more behaviors by an email recipient and, more specifically, to machine learning techniques for predicting one or more behaviors of an email recipient, changing one or more components in the email to increase the likelihood of a behavior, and determining and/or scheduling an optimal time to send the email. Some advantages of the embodiments disclosed herein may include, without limitation, the ability to predict the behavior of the email recipient and suggest the characteristics of an email which will increase the likelihood of a positive behavior, such as a reading or responding to the email, visiting a website, calling a sales representative, or opening an email attachment.

Abstract: Techniques are described herein for predicting one or more behaviors by an email recipient and, more specifically, to machine learning techniques for predicting one or more behaviors of an email recipient, changing one or more components in the email to increase the likelihood of a behavior, and determining and/or scheduling an optimal time to send the email. Some advantages of the embodiments disclosed herein may include, without limitation, the ability to predict the behavior of the email recipient and suggest the characteristics of an email which will increase the likelihood of a positive behavior, such as a reading or responding to the email, visiting a website, calling a sales representative, or opening an email attachment.