Fellow Salesforce Treasure Hunters, on my hunt for AI treasure from Salesforce I was intrigued with Einstein Next Best Action. The promise of automating decisions for agents, end users, or helping customers, sounded like a great new tool. And Einstein Next Best Action can help you determine the best course of action for
each customer or user. However, Salesforce admins and business owners still need to take time to set up business rules and identify the data that you want to analyze to help users or customers.
What Is Einstein Next Best Action?
Einstein Next Best Action is an AI-powered platform that uses predictive analytics and machine learning to analyze customer data and provide insights that can help users in Salesforce make informed decisions. But the backbone of Einstein Next Best Action is many of the same tools that Salesforce already had; flows, strategies, and the Recommendation object.
The real advantage is helping to take away indecision from your end users or customers. And Einstein Next Best Action serves as a guide for what is the best course of action on a customer record or site based on previously defined logic.
For example, customer service agents using Service Cloud with Next Best Action can have a Recommendation built in Salesforce to help them with routing a case. Next Best Action will be able to look at the chosen fields on a record and quickly make a decision on where to route a case based on the information already gathered in the case fields. But this is using predefined data/logic and a decision strategy that business owners have already created.
Business owners need to take time to setup the right decisions for Recommendations and Flows to direct users.
Measuring Success and Impact
Also, Next Best Action would need to be constantly reviewed to determine if suggestions are having the right impact.
Here are some key metrics you should track:
- Recommendation Acceptance Rate: This metric measures the percentage of times that a Next Best Action recommendation is accepted by a customer. A high acceptance rate indicates that the recommendations are relevant and helpful to customers.
- Conversion Rate: This metric measures the percentage of customers who take the desired action after receiving a Next Best Action recommendation. For example, if the recommendation is to purchase a product, the conversion rate measures the percentage of customers who actually make a purchase.
- Customer Satisfaction: This metric measures how satisfied customers are with the Next Best Action recommendations. You can measure customer satisfaction through surveys, feedback forms, or other methods.
By tracking these metrics, you can identify areas for improvement and optimize your Next Best Action implementation for maximum impact.
Overall, Einstein Next Best Action is a great tool for helping to make faster decisions, present the right options to users and help streamline some of your business logic. However it requires guidance from business owners and a constant analysis of data and feedback to verify it is having the right impact.
If you want to test out Einstein Next Best Action or other AI in Salesforce follow the steps here to get a temporary Einstein AI org: sfdctreasures.com/salesforce-einstein-ai-demo-org
This Trailhead module gave me a better understanding of the steps needed to setup and use Einstein Next Best Action: https://trailhead.salesforce.com/content/learn/modules/einstein-next-best-action/understand-how-einstein-next-best-action-works