Salesforce, inc. (20240193466). GENERATING SEGMENTS BASED ON PROPENSITY SCORES CONFIGURED VIA A TEMPLATED MODEL BUILDER EXPERIENCE simplified abstract
Contents
GENERATING SEGMENTS BASED ON PROPENSITY SCORES CONFIGURED VIA A TEMPLATED MODEL BUILDER EXPERIENCE
Organization Name
Inventor(s)
Heather Phillips Stables of Woodstock GA (US)
Paul Joseph Nix of Indianapolis IN (US)
Tejas Sanghavi of Palo Alto CA (US)
Jonathan Daniel Showers Belkowitz of New York NY (US)
Amrutha Krishnan of San Francisco CA (US)
GENERATING SEGMENTS BASED ON PROPENSITY SCORES CONFIGURED VIA A TEMPLATED MODEL BUILDER EXPERIENCE - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240193466 titled 'GENERATING SEGMENTS BASED ON PROPENSITY SCORES CONFIGURED VIA A TEMPLATED MODEL BUILDER EXPERIENCE
The abstract describes a method for training an artificial intelligence model for propensity score prediction, where users can input sets of entities and outcome conditions to generate prediction metrics.
- The data service receives user inputs for training an AI model and defining outcome conditions.
- The AI model generates propensity scores for a set of entities based on the user inputs.
- An indication of the AI model is stored for user review and approval.
- Once approved, the AI model is published, and propensity scores are used to generate entity segments.
Potential Applications: - Customer behavior prediction in marketing campaigns - Personalized recommendations in e-commerce platforms
Problems Solved: - Efficiently predicting customer behavior based on defined outcome conditions - Streamlining the process of training AI models for propensity score prediction
Benefits: - Improved targeting of customers with personalized offers - Enhanced decision-making based on accurate prediction metrics
Commercial Applications: - Marketing analytics software for businesses to optimize customer engagement strategies
Prior Art: Prior research in machine learning algorithms for propensity score prediction and customer behavior analysis.
Frequently Updated Research: Ongoing advancements in AI models for propensity score prediction and customer segmentation techniques.
Questions about the technology: 1. How does this method improve upon traditional customer behavior prediction techniques? 2. What are the key factors influencing the accuracy of the propensity scores generated by the AI model?
Original Abstract Submitted
methods, systems, apparatuses, devices, and computer program products are described. a data service may receive a first user input indicating a first set of entities for training an artificial intelligence (ai) model for propensity score-prediction. the data service may receive a second user input indicating a set of outcome conditions which define what a user would like to predict about a customer (e.g., propensity to purchase). the data service may generate the ai model accordingly, and based on executing the ai model, generate a set of prediction metrics (propensity scores) for a second set of entities. the data service may store an indication of the ai model for review by a user. when the user approves the ai model and publishes the ai model to the data service, the generated propensity scores may be used to generate a segment of entities of the second set of entities.
