Read the first article in this series on Sales Use Cases, which can improve productivity of sales teams and help achieve targets faster 🎯. Read this article to understand how to tackle below models: Note: A ready-reference of this blog is available as a pdf in our revamped Help article section. Here is the direct […]
This will be a series of posts detailing Predictive Use Cases that can be built with Data cloud’s Model Builder. The first article in this series is aimed at Sales Use Cases, which can improve productivity of sales teams and help achieve their targets faster 🎯. Bonus: A ready-reference of this blog is available as
This guide will walk you through the process of building a predictive model for a forecasting use case using the Model builder in Data Cloud. If you’re new to building predictive models and exploring the No-code model builder, make sure to check out the excellent Salesforce Blog Build an AI Model With Clicks In Data
With this feature, Einstein Studio now gives you full visibility into how the deployed models are used and helps assess Data quality issues on incoming data to ensure accuracy and reliability of the models live in production. 🔥 Explore what’s new: In this article, get a deep-dive on these brand new feature: Model Monitoring post
This article rounds up the latest enhancements in Einstein Studio’s Model Builder, designed to simplify key stages of the machine learning lifecycle, from training data preparation and feature engineering, to model retraining and agentic flow integration. Note: Read the previous blog on Using Model Predictions in Data Cloud One (DC1) Orgs 🔥 Explore what’s new:
This blog talks about how predictive models built in the Home org of a DC1 setup using the No-code Model builder is surfaced across all of its companion orgs basis data access Basics of Data Cloud One Salesforce Data Cloud One is a Data Cloud feature which facilitates businesses to seamlessly manage multi-org environments. In
The all new Einstein Studio tab in Data Cloud is now GA and will allow users with access to Data Cloud to build, connect, and manage their predictive and generative models. Models which can be seamlessly integrated into any Salesforce Customer 360 application for intelligent decision making. Previously Einstein Studio allowed users to connect to
Einstein Discovery drives business value for companies by eliminating friction in using machine learning, and maximizing its time-to-value. It is designed to facilitate every step of the journey towards operationalizing Machine Learning in the workspace, in a safe, ethical and most of all practical and easy way. This applies training the model and interpreting the Story, but it
This blog uses Channel optimization as a use case to explain and contrast the 2 approaches to solving a classification problem: Multiclass model and Multiple binary models Channel optimization, a very popular marketing use case, is essentially choosing the best channel to contact each customer from options like Email, Phone, Social Media, SMS and more.
Have you ever tried to build a segment, an AI model, or a set of match rules and felt like your data was in a ‘black box’? You know the data is there, but you can’t quite tell if there are any missing values or other silent data gaps, which can be costly in downstream