Salesforce Announces Einstein for Service and Quip for Service

Salesforce today announced two new solutions that aim to empower agents to focus more on the human aspect of service. The new solutions—Einstein for Service and Quip for Service—provide agents with AI-powered recommendations, automated routing, and embedded productivity and collaboration capabilities.

Einstein for Service has three key features: Einstein Article and Reply Recommendations, Einstein Next Best action, and Einstein Case Routing. Einstein Article Recommendations automatically recommend the best knowledge article to agents, with an eye on providing them with the information they need to quickly resolve cases. Einstein Reply Recommendations use natural language processing to suggest best responses to agents over chat and messaging, with the goal of saving time and improving the accuracy of response to customers’ inquiries.

“Agents today spend a lot of time searching across different systems or copying and pasting answers or responses out of notepads and relying on their experience to figure out how to help solve customer questions. That’s really taking a lot of time that agents just don’t have,” says Martha Walchuk, senior director of service cloud product marketing at Salesforce. “Now, with Einstein Article and Reply Recommendations, we’re using machine learning from past cases to identify how articles were used successfully and to look at chat conversations and surface the best responses to an agent right in line, right in the console, so that they can save time in searching for that information and really focus on the customer.”

By leveraging business rules and predictive intelligence, Einstein Next Best Action suggests the best course of action to agents at the point of maximum impact in interactions with customers. In so doing, it aims to increase customer satisfaction and provide cross-sell and upsell opportunities.

“Today, a lot of agents have a lack of clarity about the next step that they should take when they’re working through a case with a customer,” Walchuk says. “That could be a next action that they need to perform to resolve the case or it could be what’s the right offer, the right cross-sell or upsell to deliver to a customer. We’re bringing together a powerful combination of business process with predictive models and machine learning so that we are surfacing the right action for an agent to take at the right time so that it will have the most impact on customer satisfaction.”

Einstein Case Routing automates the routing process by using machine learning that sends cases to the right queue or agent based on preset criteria such as expertise and past outcomes. “As cases come into the contact center, with Einstein Case Routing what we’re doing is making sure that every case gets in the hands of the right agent to solve it even faster. A lot of times there’s time spent looking at the case, reading through context, figuring out what the case is about or what priority or status it is and then somebody has to go in and manually fill in those fields. We’re advancing how we’re using machine learning to apply the context to the case automatically and route it directly to the right person who can help solve the case faster,” Walchuk says.

Quip for Service provides agents with a collaboration tool that is embedded in the agent console. It allows agents to co-author documents, bring in subject matter experts from across the business, and carry on live collaborative conversations directly within the case record. Additionally, admins can build and publish flexible Quip templates in the agent console as well as customize them based on use cases and organizational needs.

“With Quip for Service, we’re bringing this collaboration right into the heart of the console so agents don’t have to leave that experience if they need to [for example] pull in an expert or swarm with their team around solving an issue for a customer—they can do all of this right in the heart of the console,” Walchuk says.

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