By Tal Frankfurt

The Salesforce Einstein Analytics tool provides organizations with the ability to visualize the activity taking place within their unique Salesforce environment, offering invaluable insights and health checks on their data.  Designed as a tool to help grant you an ease of access across your organization’s opportunities, Einstein Analytics provides a system conducive to progress, and recent updates and upgrades are doubling down on that fact.  Let’s consider the benefits of Einstein Analytics in relation to the ability of a higher education institution to build their constituent base and engage their alumni.  

As a higher education institution looking to convert as many alumnae to sustainers as possible, you want to learn two things from your data: 1) How likely is an alum to become a sustainer? 2) What is the best way to engage with a graduate, given their likelihood of becoming (or not becoming) a sustainer? 

Enter Einstein Prediction Builder and Next Best Action.

Einstein Prediction Builder can create predictions based on almost every field in the Education (or Nonprofit) Cloud, allowing you to utilize a number of intelligent predictions, such as: 

  • How many days until we can make the next ask? 
  • How many people are going to attend our next gala? 
  • How much revenue can this program generate for our institution? 
  • What is the likelihood of this student to drop off? 
  • And of course: Will this donor become a sustainer? 

Understanding the likelihood is useful, but where Einstein Prediction Builder really adds value is when it is combined with Einstein Next Best Action.  Next Best Action uses predictions to automate the process of choosing the right action to take for a contact (or any other Salesforce object).  Additionally, it eliminates the need to look at each individual prediction because it allows you to create custom recommendations or actions, such as offering a welcome gift to encourage membership.  Working in tandem, the Einstein Prediction Builder and Einstein Next Best Action opens up possibilities for any proactive higher education institution or nonprofit organization to better understand their data and more effectively utilize that data to inform action. 

I created the example below centered around the sustainer program.  For this example, I used fields such as First Gift Date, Events Attended, Graduation Year, and Income Range to predict the likelihood of the recurring giving.  Einstein will evaluate all the data we have in Salesforce based on the parameters that I created in order to make sure that we have enough data to support the model.  It even shows the prediction quality and my top predictors.

Next, I created a Next Best Action Strategy.  The strategy that I create will go through all the recommendations and assign them to donors based on predictions that were made for them.

My strategies can be very simple or include multiple elements.  The above example only has a couple of elements.  When I apply the strategy to our Sustainer donors, Einstein Next Best Action assigns recommendations to them based on their predicted behavior.  These recommendations are the next best action for each donor, effectively building a roadmap for your higher education institution or nonprofit organization designed to intelligently inform your constituent engagement strategy.  Here is an example of what it would look like on the contact record:

Both the Einstein Prediction Builder and Einstein Next Best Action tools have the potential to fast-track your predictions, create effective offers and actions for users, and enable your business processes with future behaviors in mind.  Capitalize on the predictive potential within Salesforce’s AI capabilities and provide your fundraisers with the intelligent tools they need to progress your organization’s effectiveness.

Tal Frankfurt

Tal Frankfurt

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