Have you ever wondered what’s happening behind the scenes when you see Einstein AI recommendations, predictions, or automated actions? This post unpacks what’s happening under the hood—no complex mathematics, just clear, intuitive insights into the algorithms at play that help businesses make smarter, data-informed decisions.
What Is Salesforce Einstein AI?
Think of Einstein AI as a digital assistant that’s always learning from your data. Just like a great salesperson who has learned to recognize which leads are most likely to convert, Einstein AI learns from the patterns it sees in your data and uses those patterns to make predictions and recommendations. Einstein accomplishes this by using advanced algorithms to analyze trends in customer behavior, sales activities, and business performance. The result? More accurate predictions and automated insights that help you focus on the right opportunities and serve your customers better.
How Does Einstein AI Work? A Peek Behind the Curtain
While we may not know everything about how Salesforce proprietary algorithms work, understanding these foundational techniques gives us a glimpse into how Einstein AI might be operating behind the curtains. Let’s look at a few of these data science methodologies that make this possible, breaking them down into everyday terms to better understand their role in surfacing insights from data. Here’s a simple breakdown of what is likely going on behind the scenes.
1. Predicting Outcomes: Linear Regression and More
When we want to predict numerical outcomes—like the probability that a lead will convert or the expected revenue from an opportunity—we can use techniques like linear regression.
Metaphor: Imagine trying to draw a straight line through a cloud of points on a graph, where each point represents a past customer interaction. The line tries to get as close as possible to all the points, and from that, you can estimate where new points, i.e. future interactions are likely to fall.
In Practice: Einstein might look at past deals and use this line to predict which new opportunities are likely to close, helping sales reps focus their efforts on the most promising leads.
2. Sorting Decisions with Decision Trees
For making quick, informed, automated decisions or predictions, a common data-driven approach is to use decision trees. These are like flowcharts that ask a series of yes-or-no questions about your data, ultimately leading to a decision.
Metaphor: Think of it as playing the children’s game “Guess Who?” with your data. Is the lead in a high-value industry? Did they attend a recent webinar? Each question helps narrow down the possible outcomes until the decision tree arrives at a prediction, like whether a lead is likely to convert.
In Practice: Einstein Lead Scoring might use this approach to rank leads based on how well they match the profiles of leads that converted in the past.
3. Finding Patterns: Clustering
In some contexts, what we want to do is group similar data points together, so that we can identify natural groupings of customers, products or transactions, for this we use a technique called clustering.
Metaphor: Imagine you’re at a candy store, and you’re sorting M&Ms by color without knowing how many colors there are. You look for natural groupings—the blues, the reds, the greens—and sort them accordingly.
In Practice: Einstein might use clustering to identify customer segments, grouping similar customers together based on their buying behavior, interests, or engagement levels. This can help your marketing team create more targeted campaigns.
4. Understanding Language: Natural Language Processing (NLP)
AI also excels at making sense of unstructured data—like text from emails, support tickets, or social media posts—using Natural Language Processing (NLP).
Metaphor: Think of this as a detective who reads through hundreds of letters and identifies the tone of the writer—whether it’s positive, negative, or neutral.
In Practice: Einstein Sentiment Analysis might be used to scan customer reviews and feedback to gauge how happy or frustrated customers are, allowing your service team to respond more effectively.
What Can Einstein AI Do for Your Business? Real-World Examples
Let’s take a closer look at how Einstein AI helps businesses automate smarter decisions across Sales, Service, and Marketing.
Lead Scoring and Prioritization
Scenario: Imagine your sales team is overwhelmed with leads. They don’t have the time to chase down every single one, and they need to focus on the most promising opportunities.
How Einstein AI Helps: Einstein Lead Scoring analyzes historical data from your CRM—things like past deals, lead interactions, and demographic information—and assigns a score to each lead. The higher the score, the more likely it is that the lead will convert into a paying customer.
Result: Sales reps can prioritize their time on high-scoring leads, leading to a higher conversion rate and shorter sales cycles.
Predicting Churn
Scenario: Your customer service team wants to reduce customer churn but doesn’t know which customers are at risk of leaving.
How Einstein AI Helps: Einstein Prediction Builder can analyze customer behavior and engagement history to predict which accounts are at risk of churning.
Result: The service team can reach out proactively to these customers, offer personalized support, and retain more customers.
Recommending Next Best Actions
Scenario: Your marketing team wants to make sure they’re sending the right offers to the right customers at the right time.
How Einstein AI Helps: Einstein Next Best Action looks at customer data and recommends personalized offers or actions that are most likely to resonate with each customer.
Result: The marketing team can deliver tailored messages that drive engagement and increase conversion rates.
Why Should You Care About Einstein AI?
At its core, Einstein AI is about bringing the power of AI to your everyday business processes. It’s not just about fancy algorithms—it’s about automating tasks, predicting outcomes, and helping you make decisions that drive growth. Whether you’re a sales leader looking to boost win rates, a marketer aiming for more personalized campaigns, or a customer service manager trying to keep customers happy, Einstein AI helps you work smarter by turning data into actionable insights.
So the next time you see an Einstein prediction or a recommended action pop up in Salesforce, it’s likely that one of the methodologies we discussed is happening behind the scenes. Now, you hopefully will now have a bit more of a mathematical intuition, rather than everything seeming like a completely mysterious black box.
Want to know more about Einstein AI? Reach out to us at CloudKettle for a consultation today, and discover how Einstein AI can transform your team’s ability to work smarter and unlock more value from your Salesforce data.
