AI Propensity Score - gettectonic.com
Salesforce AI Propensity Scores
08Jun

Salesforce AI Propensity Scores

AI-driven propensity scores take an existing data model and improve its predictions, speed, and analysis with AI. Salesforce AI Propensity Scores in CRM: In CRM, a propensity score is the model’s probabilistic estimate of a customer performing a specific action.  A propensity model is a mathematical formula that takes into account all of the known factors that are associated with conversion. The model then uses this information to estimate the likelihood that a given lead will convert to a customer. In super geeky terms, The propensity score is the probability of a unit (e.g., person, classroom, school) being assigned to a particular treatment given a set of observed covariates. How do you calculate propensity score? Propensity scores are generally calculated using one of two methods: a) Logistic regression or b) Classification and Regression Tree Analysis. a) Logistic regression: This is the most used method for estimating propensity scores. It is a model used to predict the probability that an event occurs. Why do we need propensity score? Propensity score analysis (PSA) arose as a way to achieve exchangeability between exposed and unexposed groups in observational studies without relying on traditional model building. Exchangeability is critical to our causal inference. Get Accurate Predictions by Defining the Target Variable Defining the target variable is crucial for accurate model predictions. Your model needs a primary focus for analysis and predictions. Scoring models uncover relationships between features and the target variable, providing insights on how to maximize or minimize this variable. For example, to predict the likelihood of opportunities converting into accounts, define a target variable that indicates this conversion. You can also apply custom logic to refine this target variable further. Make Informed Business Decisions Based on Historical Trends Generate predictions for specific periods to make informed business decisions and maximize revenue based on historical trends. For example, to determine which accounts sales representatives should focus on in the next 30 days, select a 30-day prediction duration. Then, use CRM Analytics datasets with historical data to identify revenue trends from accounts in the past 30 days. Effortlessly Build and Deploy Propensity Models Utilize the Scoring Framework to build and deploy generic propensity models for various industries without coding. Configure and deploy Einstein Discovery models through the AI Accelerator, which displays predictions and Einstein Next Best Action recommendations on record pages using the AI Accelerator—Einstein Predictions & Recommendations component. Scoring Framework Features AI Accelerator and Scoring Framework Integration Get real-time predictions across multiple industries by integrating AI Accelerator. Build generic propensity models without writing code using the Scoring Framework. Configure and deploy Einstein Discovery models, and showcase predictions and Next Best Action recommendations on record pages. AI Accelerator Functionality Salesforce Einstein’s Role Salesforce Einstein integrates robust AI technologies within the Lightning Platform, offering administrators and developers a comprehensive set of platform services to build smarter apps and customize AI for their businesses. Scoring Framework and CRM Analytics Use the Scoring Framework, based on CRM Analytics, to quickly build and deploy propensity models for various industries. Define template configurations, create CRM Analytics apps, and develop Einstein Discovery models and recipes effortlessly. Validating Input Features and Prediction Accuracy Train your model to validate input features and prediction accuracy. Then, deploy the model based on these predictions. Predictions Based on Standard or Custom Objects Build predictive models using standard or custom objects, enhancing business processes with smarter and more predictive capabilities. Making Business Decisions from Historical Trends Generate predictions based on historical trends to help make informed business decisions aimed at maximizing revenue. Enhancing Analysis with Additional Input Features Improve data analysis by incorporating features from CRM Analytics datasets along with object data. Focused Predictions with Defined Target Variables Improve prediction accuracy by defining the primary focus variable for your model. Customize input features to ensure valuable and accurate predictions for your use case. Targeted Predictions with Data Subsets Enhance prediction relevance by focusing on specific data subsets using filter conditions. Contextual Predictions by Storing in Records Store predictions in records to view them within the context of your use case, facilitating informed decision-making. Real-Time Predictions with AI Accelerator Integrate the Scoring Framework with AI Accelerator for real-time predictions, suggestions, and insights. Template Configuration and Data Requirements Ensure data requirements are met while configuring templates, confirming that there are enough records in the dataset. Quick Access to CRM Analytics and AI Accelerator Easily access CRM Analytics apps or AI Accelerator use cases by clicking the relevant button on the template configuration card. Handling Template Configuration and AI Accelerator Issues Retry template configuration activation or deactivation if unsuccessful. Similarly, retry creating or deleting AI Accelerator use cases as needed. Exploring New Metadata Types and Tooling API Objects Explore new metadata types and use the Tooling API to work with Scoring Framework setup objects, enhancing your capabilities within the Scoring Framework. Content updated April 2024. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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June 8, 2022in Salesforce Einstein
AI-driven propensity scores
20May

AI-Driven Propensity Scores

AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables through machine learning, without explicit programming. This insight has gone through numerous updates as the information and use of AI-driven propensity scores evolved. In many cases, writers give a brief overview of the what of a tool. Today, we are going way beyond “what the sausage tastes like” to “how the sausage is made” Tectonic hopes you will enjoy learning how propensity models and AI driven propensity scores improve your data. Propensity Model in Artificial Intelligence: Propensity modeling generates a propensity score, representing the probability that a visitor, lead, or customer will take a specific action. For instance, a propensity model, using data science or machine learning, can help predict the likelihood of a lead converting to a customer. AI-driven propensity scores take an existing data model and improve its predictions, speed, and analysis with AI. Propensity Score in CRM: In CRM, a propensity score is the model’s probabilistic estimate of a customer performing a specific action. Grouping customers by score ranges allows for effective comparison and analysis within each bucket. Enhancing Propensity Modeling with AI: Traditional statistical propensity models might lack accuracy, but integrating machine learning technologies, as demonstrated by Alphonso, can significantly optimize ad spend and increase prediction accuracy from 8% to 80%. That’s a whopping 72% improvement. Propensity Modeling Overview: Propensity modeling involves predictive models analyzing past behaviors to forecast the future actions of a target audience. It identifies the likelihood of specific actions, aiding in personalized marketing. Role of Machine Learning in Propensity Models: Propensity models rely on machine learning algorithms, acting as binary classifiers predicting whether a certain event or behavior will occur. Logistic regression and Classification and Regression Tree Analysis are common methods for calculating propensity scores. Characteristics of Effective Propensity Models: For robust predictions, propensity models should be dynamic, scalable, and adaptive. Dynamic models adapt to trends, scalable for diverse predictions, and adaptive with regular data updates. Propensity Modeling Applications: Propensity models find applications in predicting customer behavior, such as purchasing, converting, churning, or engaging. Real-time predictions, data analysis, and AI integration contribute to successful implementations. AI-driven propensity scores are extremely useful in that they can be coupled with many other models to give additional insights to your data. Types of Propensity Score Models: Various models include propensity to purchase/convert, customer lifetime value (CLV), propensity to churn, and propensity to engage. Combining models can enhance the effectiveness of marketing campaigns. When to Use Propensity Scores: Propensity scores are beneficial when random assignment of treatments is impractical. They help estimate treatment effects in observational studies, providing an alternative to traditional model-building methods. Limitations of Propensity Score Methods: While propensity scores help achieve exchangeability between exposed and unexposed groups, they do not claim to eliminate confounding due to unmeasured covariates. Findings from observational studies must be interpreted cautiously due to potential residual confounding. Content updated October 2021. Content updated February 2024. Like3 Related Posts Salesforce Artificial Intelligence Is artificial intelligence integrated into Salesforce? Salesforce Einstein stands as an intelligent layer embedded within the Lightning Platform, bringing robust Read more Salesforce’s Quest for AI for the Masses The software engine, Optimus Prime (not to be confused with the Autobot leader), originated in a basement beneath a West Read more How Travel Companies Are Using Big Data and Analytics In today’s hyper-competitive business world, travel and hospitality consumers have more choices than ever before. With hundreds of hotel chains Read more Sales Cloud Einstein Forecasting Salesforce, the global leader in CRM, recently unveiled the next generation of Sales Cloud Einstein, Sales Cloud Einstein Forecasting, incorporating Read more

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May 20, 2019in Salesforce Einstein

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