Gratitude prediction machine learning models

One or more non-transitory computer storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations comprising:

• obtaining a plurality of training examples, wherein each training example corresponds to a respective person that has interacted with an organization, and each training example comprises:
• (i) a training input comprising history data characterizing previous interactions of the corresponding person with the organization and third party data characterizing aspects of the corresponding person outside of their previous interactions with the organization, and (ii) a target gratitude score that characterizes whether the corresponding person has taken a specified action on behalf of the organization;
• training a gratitude prediction machine learning model having a plurality of model parameters on the plurality of training examples, wherein:
• the gratitude prediction machine learning model is configured to process history data and third party data for an input person in accordance with values of the plurality of model parameters of the gratitude prediction machine learning model to generate a gratitude score for the input person;
• the gratitude score for the input person characterizes a likelihood that the input person will take the specified action on behalf of the organization in the future;
• training the gratitude prediction machine learning model comprises determining trained values of the model parameters of the gratitude prediction machine learning model from initial values of the model parameters of the gratitude prediction machine learning model by a machine learning training technique;
• and
• training the gratitude prediction machine learning model on the plurality of training examples comprises, for each training example, training the gratitude prediction machine learning model to process the training input of the training example to generate an output gratitude score that matches the target gratitude score for the training example;
• after training the gratitude prediction machine learning model:
• generating a respective gratitude score for each of a plurality of people using the trained machine learning model;
• determining, for each of the plurality of people, a respective composite score for the person based on:
• (i) the gratitude score for the person, and (ii) an amount of resources associated with the person;
• determining a ranking of the plurality of people based on their respective composite scores;
• and
• taking an action based on the ranking of the plurality of people, comprising:
• processing the ranking of the plurality of people based on their respective composite scores to automatically generate a communication list that designates a subset of the plurality of people;
• and
• automatically transmitting communications using the subset of the plurality of people that are designated by the communication list.

A system comprising:

• one or more computers;
• and
• one or more storage devices communicatively coupled to the one or more computers, wherein the one or more storage devices store instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising:
• obtaining a plurality of training examples, wherein each training example corresponds to a respective person that has interacted with an organization, and each training example comprises:
• (i) a training input comprising history data characterizing previous interactions of the corresponding person with the organization and third party data characterizing aspects of the corresponding person outside of their previous interactions with the organization, and (ii) a target gratitude score that characterizes whether the corresponding person has taken a specified action on behalf of the organization;
• training a gratitude prediction machine learning model having a plurality of model parameters on the plurality of training examples, wherein:
• the gratitude prediction machine learning model is configured to process history data and third party data for an input person in accordance with values of the plurality of model parameters of the gratitude prediction machine learning model to generate a gratitude score for the input person;
• the gratitude score for the input person characterizes a likelihood that the input person will take the specified action on behalf of the organization in the future;
• training the gratitude prediction machine learning model comprises determining trained values of the model parameters of the gratitude prediction machine learning model from initial values of the model parameters of the gratitude prediction machine learning model by a machine learning training technique;
• and
• training the gratitude prediction machine learning model on the plurality of training examples comprises, for each training example, training the gratitude prediction machine learning model to process the training input of the training example to generate an output gratitude score that matches the target gratitude score for the training example;
• after training the gratitude prediction machine learning model:
• generating a respective gratitude score for each of a plurality of people using the trained machine learning model;
• determining, for each of the plurality of people, a respective composite score for the person based on:
• (i) the gratitude score for the person, and (ii) an amount of resources associated with the person;
• determining a ranking of the plurality of people based on their respective composite scores;
• and
• taking an action based on the ranking of the plurality of people, comprising:
• processing the ranking of the plurality of people based on their respective composite scores to automatically generate a communication list that designates a subset of the plurality of people;
• and
• automatically transmitting communications using the subset of the plurality of people that are designated by the communication list.