How Einstein for Service Unlocks Service Value

How Einstein for Service Unlocks Service Value

einstein for service

Written by Dave McCall, Service Practice Senior Director at PolSource

Einstein for Service unlocks a variety of improvements for service organizations. If you have a mature service organization, this functionality can be used to provide a whole new set of benefits.

For years, Salesforce has been releasing a variety of Einstein options. With Einstein for Service, several of these functions have been bundled together. In this post, I’ll discuss how each capability can be used to improve your operation.

That said, not every Einstein implementation is made equal. You will need to implement each correctly in order to get the benefits. An experienced partner will guide you toward use cases that will provide the desired benefits. Below you will find just some of the advantages we’ve been able to help customers achieve using this technology.

Case Classification

Case Classification uses machine learning to predict the values of case fields based on past choices. This works by presenting the agent with possible values for a variety of case fields based on the values of other case fields.

For example, let’s assume that your customers submit cases via your website. If the vast majority of cases in a certain category are classified by the agents as high priority, Case Classification will recommend that priority to agents. This is a simple example. In reality, Case Classification will take into account multiple field values to make its predictions.

The following are some of the benefits of using this technology.

Average Handle Time Agents spend less time classifying cases, simply verifying the system’s choices
Training Time & Time-to-Proficiency Agents will learn from the collective group of agents seeing the predictions based on the whole body of case data
Data Quality Removing some friction from correctly classifying cases will improve the amount and quality of data collected

Your implementation partner should help guide you to identify what data to inspect and which fields to include in your predictions. There is also an important consideration for your organization around how this should be used and your partner should guide you on how to instruct agents to use this functionality.

Case Routing

Case Routing is very similar to Case Classification except that in this case the machine learning looks at incoming digital channel cases and routes them based on how cases with similar classification have been routed.

The following are some of the benefits of using this technology.

Average Handle Time /
Customer Satisfaction
Routing a case to the right agent the first time both keeps the full handle time down and improves the customer satisfaction

Your implementation partner should help guide you to identify what data to inspect and which fields to include in your predictions. There is also an important consideration for your organization around how this should be used and your partner should guide you on how to instruct agents to use this functionality.

Reply Recommendations

Reply Recommendations use machine learning to provide an agent with text to be used within a chat conversation. This works like Quick Text that you don’t have to set up and which is automatically surfaced for the agent without them looking for it. Reply Recommendations look at previous conversations for context and learn from snippets of text that have been previously used. If you’ve been offering chat for a while now, you likely have enough data already to take advantage of this capability.

While this is not currently generally available, we anticipate the following benefits.

Average Handle Time Employ Reply Recommendations to shorten customer conversations
Costs Employ Reply Recommendations to allow agents to handle more chats at one time

Your implementation partner should provide guidance to you around how to instruct agents to use this functionality, as every interaction is another opportunity to train the artificial intelligence.

Article Recommendations

Article Recommendations use machine learning to provide an agent with relevant knowledge based on what has been used in past cases. Rather than require the agent to search or provide static data categories, Article Recommendations update based on what articles have been provided on similar cases in the past.

While this is not currently generally available, we anticipate the following benefits.

Average Handle Time Employ Article Recommendations to shorten customer conversations
First Contact Resolution /
Customer Satisfaction
Providing the right answer the first time should be a primary measure for customer service organizations

Your implementation partner should help guide you to identify what data to inspect to begin using this functionality. For organizations that are not in the habit of including articles in case responses, this may be a change to procedure and will require some training and encouragement.

Next Best Action

Next Best Action provides context-based agent prompts for the next steps in the customer interaction. It is used to surface specific offers based on a variety of measures. Coupling this with Prediction Builder helps identify opportunities that a human may not immediately see.

The following are some example use cases and their benefits.

Customer Loyalty Offer your loyalty program to customers who are not already enrolled, but have a high likelihood to join.
Customer Attrition Offer appropriate appeasements based on the customer’s value and attrition propensity
Customer Attrition Offer customer save flows for customers who are calling to cancel
Cart Values
CSAT
Offer appropriate promotions to customers who are likely to accept
CSAT Provide value-added information to customers (e.g., event information, or partner promotions)
Agent Training Time & Time-to-Proficiency Guide agents through complicated processes (e.g., RMA submission, price calculation, etc.)

This is the trickiest of the capabilities in Einstein for Service to implement for value. This requires some careful consideration of each use case and the values desired. Your implementation partner should be able to provide consulting based on your specific business drivers and help guide you to offers that work for you.

Service Analytics

Included in Einstein for Service is this subset of the Tableau CRM (formerly Einstein Analytics) capabilities. This application provides dashboards and reports built for service businesses. It is able to use both on-platform and off-platform data to surface hidden insights and opportunities for your business by allowing users to dig deeper into data and analyze your business from a variety of dimensions.

Predicting the outcome of this analysis is impossible, but the insights are always valuable.

Your implementation partner should help you to understand how to use this capability to look at the dimensions that are most important to you. If given the opportunity, we would be happy to help you interpret the data and make some recommendations for operational improvements.

At PolSource, we have built an amazing team of professionals to help deliver excellent implementations for Service. In our organization over one-third of all of our employees have a Salesforce Service Consultant certification. We have achieved 7 of 9 of the Salesforce Navigator specializations in Service, earning their “Expert” designation in Service. Our average consultant has more than 3 Salesforce certifications. Almost half of our employees have an advanced degree, including numerous master’s degrees and PhDs in computer science including many specializations in artificial intelligence.

We have also worked with customer service organizations at companies of a variety of sizes and in a variety of verticals. I would love to share our expertise with you and bring some of our experiences to your implementation. Whether you already have dipped your toes into what Einstein can do for you or whether you’d like to talk about a fresh implementation, I’d be happy to talk through what makes sense for you.

Have more questions about Einstein for Service? Contact us today to learn more.