Part 1: Assessing the Need for AI in Salesforce Einstein Implementation

Are you viewing AI as a silver bullet for every potential business issue? 

It must be checked whether AI is truly needed for the service operations of your business before beginning with AI implementation. For instance, Gartner predicts that by 2027, 40% of large organizations will implement AI across all business processes, whereas McKinsey discovered that only about 20% of businesses use it at scale. In this first part of our 3-part series on how to implement Salesforce Einstein AI, we will discuss step one with a use case, which is determining whether there is a need for artificial intelligence. 

Read More: The 3A’s of Right Salesforce Einstein AI Implementation: An Overview

An Overview of a Leading Retail Enterprise 

A leading retail enterprise manages its service operations utilizing Salesforce Service Cloud. Their service teams have been struggling with many operational problems, like inconsistent customer service, frequent equipment downtime, and customer retention issues, despite having a strong system. As a solution to these challenges, the company decided to implement Salesforce Einstein AI on top of the Salesforce Service Cloud. 

How a Leading Retail Enterprise Determined Their Business Needs AI? 

Here we’ll delve into the challenges faced by the company that led them to recognize the necessity of integrating AI into their operations. We will look at how initial challenges in customer service, frequent equipment downtime, and customer retention have necessitated a more sophisticated and AI-based approach. 

3 Operational Challenges Faced by a Leading Retail Enterprise and Their Solutions  

The company serves both commercial and residential customers. It faced several operational problems, even with a dedicated staff and a sizable clientele. Faced with these challenges, the company’s leadership recognized the need for a more advanced solution that could leverage their existing data to provide deeper insights and predictive capabilities. They decided to enhance their Salesforce Service Cloud implementation with Salesforce Einstein AI to address these issues. 

1. Inefficient Customer Service 

Problem: The company’s customer service team struggled to provide consistent and personalized support. Customer inquiries often went unresolved for extended periods, leading to frustration and dissatisfaction. 

Current Solution: The company used Salesforce Service Cloud to manage customer interactions, but the system lacked the advanced capabilities needed to analyze and predict customer needs. 

How did AI Resolve the Inefficient Customer Service Issues? 

The company board members recognized that providing exceptional customer service was key to staying competitive. They faced challenges in delivering personalized support and timely responses to customer inquiries. By implementing Salesforce Einstein AI, the company was able to: 

• Proactive Support for Billing: If a customer frequently calls customer support about billing, the rep will get served with proactive solutions based on that specific customer history directly in Einstein AI, reducing unnecessary calls and increasing satisfaction. 

• AI-driven support recommendations: By analyzing customer interactions, the AI suggests tried-and-true responses. This helps your agents focus their efforts better, closing out cases more quickly to create great service experiences for customers. 

2. Frequent Equipment Downtime 

Problem: The company experienced frequent equipment failures, leading to unplanned downtime. This not only disrupted services but also resulted in significant financial losses and customer complaints. 

Current Solution: Maintenance schedules were based on fixed intervals rather than actual equipment conditions, making it difficult to predict and prevent failures. 

How did AI resolve the equipment downtime issues? 

Equipment downtime is a major operational challenge for the company, which results in customer dissatisfaction and revenue loss. To address this, they used Salesforce Einstein AI to:  

• Detect potential issues in advance: Einstein AI analyzed data from sensors and maintenance logs to identify patterns indicating potential equipment failures. This allowed the company to schedule maintenance before failures occurred, minimizing downtime.  

• Predict maintenance needs on-time: The AI predicts when specific equipment would require maintenance based on historical data and usage patterns, ensuring maintenance was performed just in time and reducing the risk of unexpected failures. 

3. Customer Retention Issues 

Problem: The company had difficulty identifying customers at risk of leaving. This lack of insight led to a reactive rather than proactive approach to customer retention. 

Current Solution: Customer data was collected but not effectively analyzed, limiting the company’s ability to identify patterns and trends indicative of churn risk. 

How did AI resolve the customer retention issues? 

Customer retention was crucial for the company, especially in a competitive market. Losing valuable customers to competitors could significantly impact the bottom line. By implementing Salesforce Einstein AI, the company could: 

• Identify At-Risk Customers: Einstein AI analyzed customer behavior, interaction history, and other factors to predict which customers were at risk of leaving. This allowed the company to take proactive steps to retain these customers. 

• Personalized Next-Best Actions: Salesforce Einstein AI suggests personalized actions to engage at-risk customers. For instance, when a customer showed signs of dissatisfaction, Einstein AI recommended a special offer or tailored communication to re-engage them. 

Conclusion 

In this first part of our blog series, we’ve explored how a leading retail enterprise assessed the need for AI in enhancing customer service, minimizing downtime, and retaining valuable customers. By recognizing these needs, the company set the foundation for implementing Salesforce Einstein AI effectively. 

Stay tuned for Part 2, where we will delve into analyzing historical data to make accurate predictions and drive informed decisions. 

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