Start tracking your progress
Trailhead Home
Trailhead Home

Learn About Einstein Case Classification

Learning Objectives

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

  • Explain what case classification is.
  • Describe the benefits of case classification.
  • Begin planning for case classification.

What Is Einstein Case Classification?

Ursa Major Solar’s business is booming. Solar panel sales have skyrocketed. The business has expanded beyond the Southwest, which means making sure thousands more customers remain happy and loyal. The CEO of Ursa Major Solar, Sita Nagappan-Alvarez, spotted a statistic from Salesforce Research that intrigued her: 84% of customers say that the experience a company provides is as important as its products and services.

Sita uses Service Cloud to help her company create exceptional customer experiences. The service platform:

  • Unifies case management across many engagement channels, such as email, phone, and the web.
  • Helps customers and support agents access, capture, and share useful information with a knowledge base.
  • Uses bots and artificial intelligence (AI) to quickly answer customers’ frequently asked questions online.

Sita wonders what else she can do to improve service. She asks her rock-star admin, Maria Jimenez, for advice.

Sita and Maria standing alongside the Ursa Major Solar logo.

Maria explains that according to Salesforce Research, 62% of customers are open to companies using AI to improve the experience they receive. AI makes your daily experiences smarter by embedding predictive intelligence into your everyday apps.

Enter Einstein Case Classification, which uses predictive intelligence to recommend, populate, or save field values on new cases based on past data. It uses machine learning—AI technology that improves prediction accuracy over time—to remove the guesswork involved in completing case fields, freeing up time for support agents. More time for support agents means more time to help customers.

When a support agent has a case open in the Service Console, the agent sees an alert that Einstein recommendations are available. To view the recommendations, the agent clicks the alert. In Lightning Experience, these recommendations show up in the Einstein Field Recommendations component.

Case Classification field recommendations shown on a case in the Lightning Service Console

Case classification also gives you full control of how each field’s predictions are applied. Einstein estimates the accuracy of each field prediction, so you can choose a minimum prediction confidence level for each action that Einstein takes. Einstein can:

  • Just recommend the field value
  • Populate the field, but let the agent save the change
  • Populate the field and auto-save the change
You can apply assignment rules and routing to auto-updated cases so they’re routed to the right agent and resolved more quickly. You can even choose a user to attribute the updates to.

Case Classification and Artificial Intelligence

Case classification uses machine learning, which is only as good as the data it’s provided. Machine learning runs on data. The accuracy of data on closed cases, along with how many closed cases are used to build the learning model, helps Einstein Case Classification correctly predict field values. Most machine learning relies on human beings to identify and describe features in a data set—in this case, cases in a specific Salesforce org.

A typical machine learning solution can have thousands or even millions of hand-designed features. Once humans have done all of this identification work by hand, the machine uses a learning algorithm to adjust the weighting of each feature and make more accurate predictions. When Maria implemented Einstein Bots, she leveraged Natural Language Understanding (NLU), a different AI technology that relies on human-created content.

Benefits of Case Classification

As CEO, Sita sees that Einstein Case Classification supports her vision of delivering exceptional service experiences to customers, leading to more sales.

  Benefit   Description
Saved time for agents As support agents work on cases, they spend less time scrolling and searching for the right field values. Automatic case routing lets agents focus on high-order tasks.
Improved data quality The predictive model improves data accuracy on cases because there’s less likelihood for human error.
Faster case resolution Since cases are automatically classified based on user histories and trends, cases can route to the right support agents for quicker resolution.
Better customer service More time for agents, improved data accuracy, and faster case resolution lead to more focus on building strong customer relationships and increasing customer satisfaction (CSAT) scores.

Plan for Case Classification

Adding AI is the fourth stage of the general setup process for Service Cloud. (See the Service Cloud for Lightning Experience module for a refresher.)

Service Cloud’s implementation process represented by concentric circles with an arrow pointed at the last circle, which is AI and Bots.

Maria also set up case management features for Ursa Major Solar, and understands why it’s best if an admin doesn’t set up AI first. If the right case fields, case notifications, case assignment rules, and case routing processes aren’t implemented, it doesn’t matter how much time Einstein Case Classification saves support agents; they’ll be too busy figuring out how to capture the right information and determining who should work on each case. Nobody wants customers or agents to get lost in an unclear case management process.

Maria knows that Ursa Major Solar is ready for AI. Before she clicks anything in Setup for case classification, she meets with Ursa Major Solar’s service team to learn some details about how they operate.

  Question   Answer
Which picklist, checkbox, or lookup fields on cases are best suited to predictive intelligence?
Some useful fields to predict are Case Reason, Language, Escalated, and Priority . Einstein can also predict the values of custom picklist, checkbox, and lookup fields.

To predict Language, you don't need to have a historic case data set in that language.
For each field that you want to predict for your agents, are there at least 400 closed cases with a value in that field?
Hmm… Someone will have to look into that. Yes, we more than likely have 400 closed cases that use each field we want to predict.
Who on the team can review closed case data to ensure its accuracy before we use it to build a predictive model?
Maybe Ryan De Lyon? He’s a customer service manager and knows about cases.
Do we want case classification to automatically populate field values, or should support agents review recommendations first?
For now, let’s have our agents review recommendations first. Later, let’s consider automating field values that Einstein predicts with high confidence.
Have we identified specific support agents who should have access to case classification?
Yes. Our Tier 1 support team should have access, so we can assign them the Einstein Case Classification User permission set during implementation.

With this bit of planning done, Maria is ready to take the next step to implement case classification for Ursa Major Solar—preparing data to build a predictive model.

Resources

Rights of ALBERT EINSTEIN are used with permission of The Hebrew University of Jerusalem. Represented exclusively by Greenlight.