Einstein for Nonprofits Architecture

Learn about Einstein for Nonprofits architecture.

Einstein for Nonprofits helps you to obtain actionable insights using the Contact, Opportunity, and Recurring Donation objects. The Nonprofit Success Pack (NPSP) app is a prerequisite for Einstein for Nonprofits. Einstein for Nonprofits analyzes records in the NPSP Contacts object, and uses custom fields to map and store generated metrics. Then the donor score is loaded back in NPSP. Einstein for Nonprofits interacts with and modifies objects and fields in NPSP version 3.210 or later.

Einstein for Nonprofits aims to ensure the benefits of AI are accessible and inclusive for employees working at nonprofits, including admins and those involved in donor outreach or business development. The product satisfies rigorous privacy and compliance standards. Einstein for Nonprofits harnesses the power of machine learning to make predictions about future donor behavior. Einstein for Nonprofits is:

• Responsible for safeguarding and protecting NPSP data on which predictions are based.

• Accountable to users by providing admins with the “Don’t Profile” option for consent management.

• Transparent by striving for model explainability and clear usage terms.

• Empowering nonprofits by promoting growth, and benefiting society as a whole.

• Inclusive and respectful of the values of our users by testing models with diverse data sets.

To start a workflow, a Salesforce admin invokes Insights Prediction in Einstein for Nonprofits. Configuration options allow various parameters to be set, including:

• Generating predictive insights for individual donor behavior and contribution patterns.

• Deploying Backup Models to generate predictive metrics while the org is bolstering its dataset to reach the minimum number of records required by Einstein Prediction Builder (EPB).

• Setting up notifications by Einstein by Nonprofits when your org reaches the required data volume to successfully obtain EPB output. You can also opt to switch back to using metrics generated by Einstein Predictions Builder.

• Displaying predictive metrics data on Contact cards.

The Einstein for Nonprofits AI model is trained periodically. Internal calculations are used to produce metrics that are dependent on the data in your org and subsequently produce custom metrics for your constituents. For more information, see Predictive Insights using Backup Models.