Einstein Discovery in 2024 | CRM Analytics

The Einstein Discovery model (CRM Analytics model is the same if we come across such a name) is a set of tools and data that helps businesses quickly analyze information and predict which outcomes can be improved. This model can handle millions of rows and multiple columns of data, find statistical patterns of past results, and help to cover important insights about a business.

Salesforce Einstein Discovery complements business analytics with statistical modeling and supervised machine learning in an environment with fast iteration, requiring no code writing. It can identify which columns are most related to the outcome we want to improve, visualize valuable information, and suggest ways to improve business processes. In addition, developers can use the Einstein Prediction Service to programmatically retrieve predictions and write predictions to custom fields. Data specialists can predict outcomes within recipes and dataflows. Tableau users can get Einstein Discovery predictions and improvements for their Tableau data.

Dataset

A CRM Analytics dataset is a collection of related data that can be viewed in a tabular format. The data can be used from many sources, including both Salesforce objects and external data sources.

Einstein Discovery requires at least 400 rows with result values and up to 20 million rows as a maximum. Ideally, a dataset should meet the following conditions:

• Includes all the relevant factors associated with the business outcome we want to investigate and improve,
• Omits extraneous columns that add complexity but no analytical value,
• Contains high-quality data that is representative of the operational reality of the outcome we focus on.

Use Cases

In common case every business can start by choosing a business task that needs to be improved, usually some KPIs. Einstein Discovery-based solutions are designed for the following use cases:

• Regressions for numerical results presented any quantity as a measure. Examples:
  • Predicted time-to-close of an opportunity,
  • Predicted employee productivity index,
  • Predicted user satisfaction from using the product,
  • Predicted house price in real estate.
• Binary classification of text results with only two possible results. These are usually questions like "yes" or "no", which are expressed in business terms. Examples:
  • Predict the probability to win an opportunity,
  • Predict the probability a lead will convert,
  • Predict if a customer will renew a subscription,
  • Predict the probability for a contact to buy a specific product.
• Multiclass classification of textual results with possible results from 3 to 10. It’s the likelihood of a record being associated with anywhere from three to ten classes. Examples:
  • Predict the most likely next phase in a business pipeline,
  • Predict the most likely travel destination for a customer (e.g., beach resort, mountain retreat, city tour),
  • Predict the most likely customer segment for a marketing campaign (e.g., high-spenders, budget shoppers, middle tier),
  • Predict the leading factor influencing the choice of the product for the buyer (e.g., quality, price, delivery time).

Don't forget to check out: Getting Started With Salesforce Einstein Discovery

Usage

After deployment the Einstein Discovery model, obtained predictions and proposed improvements can be used in:

• Lightning record pages
• Predict function used in process automation formulas
• Salesforce flows (with Flow Builder)
• Experience Cloud site pages
• Tableau flows, dashboards, and calculated fields
• CRM Analytics Data Prep recipes and dataflows

Setup

• Set up permissions for the user CRM Analytics Admin and enable CRM Analytics.

• Prepare a dataset for Analytics Studio. In the example below a csv file with data about multiple opportunities will be used.
• From the App Launcher find and select Analytics Studio.

• Choose create CRM analytics dataset -> CSV file.

• Set a name for the dataset, by default it's the name of the file.

• Column label and field type can be changed. Click Upload File.

• Creating a model in which we select data and specify which indicators should be improved.

• After clicking Create Model (by default Model is created in automated mode) AI starts to build the predictive model.

• When building is completed, AI will output data about the model and warnings with possible analysis problems (usually it is related with data quality, f.e. detected duplicates or frequent absence of important values).

Check out another amazing blog here by Vimera here: Difference between a Salesforce Administrator and a Salesforce Developer

To continue reading and read about Model deployment, Adding the Einstein Discovery component, and Licenses, please visit our website.

This article is prepared by our Salesforce Administrator Dzmitry Pimenau.