Explore Machine Learning Predictions

Learning Objectives

After completing this unit, you’ll be able to:

• Understand what machine learning can do for your CRM data.
  
• Identify ways to use machine learning predictions.
  

Unlock the Power of Your Data 

Today, trillions of gigabytes of data exist. And as a company in the age of artificial intelligence (AI), the data you possess can predict future outcomes and drive decision-making. But according to the Salesforce Untapped Data Research, most companies aren’t harnessing the power of their data’s potential even though four out of five (80%) business leaders say data is critical to decision-making. 

Using a type of AI called machine learning (ML), you can transform historical data into predictive insights. This is known as predictive AI, which is different from generative AI. 

• Predictive AI informs, using existing data to discover something new about the data. For example, predictive AI estimates the likelihood of attrition or fraud.
  
• Generative AI assists, using existing data to create new data similar to what already exists. For example, generative AI creates new text and images.
  

To learn more about predictive vs generative AI, check out the Discover AI Techniques and Applications unit in Data Fundamentals for AI. 

Inject Machine Learning into Your Business

Predictive insights from machine learning enable business users to make better decisions, faster. Use predictive AI from machine learning to drive innovation, efficiency, and strategic decision-making across your organization. Here are some of the top use cases for predictive insights (but there are plenty more).

Benefit from Machine Learning, No Data Science Experience Required

You don’t need to be a data scientist to leverage machine learning. While data scientists are often involved in training and refining ML models, the insights from the model are meant for the business users. According to the Harvard Business Review, data scientists struggle to communicate the value of ML insights and cite “results not used by decision makers” as one of the top challenges. 

Adding ML to Salesforce bridges the gap between data scientists and the business. In Salesforce, users can make decisions and take actions on the ML insights, without having to read a data science report.

You can transform historical data into meaningful insights for your organization using Einstein.

1. Einstein Studio: Use the ML-powered insights in Data Cloud with Connected Models data science teams already built by connecting to it and bringing the model’s insights into Salesforce. You can leave the model where it is (like AWS SageMaker), and leverage its ML-powered insights within Salesforce.
   
2. Einstein Discovery: Build your own ML model from the ground up in CRM Analytics, with Einstein’s guidance. Rely on Einstein to walk you through building, training, evaluating, and activating an ML model in Data Cloud.
   

Now you understand what machine learning can do for your data, and can identify ways your organization can benefit from ML-powered predictions. 

Resources

• Trailhead: Data + AI + CRM: Quick Look
• Trailhead: Data Fundamentals for AI
• Trailhead: Artificial Intelligence Fundamentals
• Trailhead: Einstein Discovery Basics