Is Generative AI the Holy Grail of Customer Experience?

I read an article written by Charlie Mitchell titled “Generative AI in customer experience: The 11 most implemented use cases.”

The use cases mentioned include:

• Auto-generating customer replies for agents (50.2 percent have done so)
• Automating quality assurance (QA) processes (45.1 percent)
• Auto-generating knowledge articles (39 percent)
• Automating after-call work (38 percent)
• Automating lead generation initiatives (42.3 percent)
• Auto-generating personalized customer communication (41.3 percent)
• Auto-summarizing sales meetings (35.2 percent)
• Automating customer onboarding (35.2 percent)
• Auto-generating ad copy (45.1 percent)
• Auto-generating content for blogs, web posts, and images (44.6 percent)
• Automating social media management processes (39.9 percent)

More than a third of the businesses have implemented each of these use cases. In 45.5 percent of companies, contact centers have received more GenAI investment than commerce, marketing, and sales.

I wanted to delve deep into this use case, Auto-generating customer replies for agents, in this article.

Auto-Generating Customer Replies for Agents

This is how it works.

Step 1

GenAI uses natural language processing (NLP) to analyze customer inputs from different channels, like chat, email, or social media.

Step 2

It references internal knowledge bases, past customer interactions, and real-time contextual data. This allows GenAI to understand customer intent.

Step 3

It suggests contextual and accurate responses that agents can review and edit before sending.

This allows you to have the best of both worlds: the efficiency of AI and the empathy of human agents.

Let us look at some scenarios to understand how the combination of GenAI and human agents would work with customer responses.

Scenario 1: Refund Inquiry

Customer: I returned an item last Thursday, but I haven’t received my refund yet.

GenAI suggestion: Hi Customer Name, I understand your concern about the delay. Refunds take 5-7 business days. I’ll confirm the exact status and keep you updated.

Agent edit: Adds a personal touch. Thanks for your patience, CustomerName. I’ll prioritize this for you.

Scenario 2: Technical Support

Customer: My Internet connection is not working.

GenAI suggestion: Hi CustomerName, I’m sorry to hear about the issue. Have you tried restarting your modem? If that doesn’t resolve it, I can schedule a visit by our technician to resolve the issue.

Agent edit: Replaces “I can schedule a visit by our technician to resolve the issue” with “I will take you through specific troubleshooting steps that would resolve your issue. Otherwise, I will schedule a visit by our technician.”

Scenario 3: Billing Discrepancy

Customer: Why is my bill higher this month?

GenAI suggestion: Hi CustomerName, I see there’s a charge for a specific reason. Would you like me to explain it?

Agent edit: Add empathy to the response. I know unexpected charges can be frustrating. Let’s resolve this quickly.

What Are the Benefits of Using GenAI in Composing Responses?

• Agents no longer have to compose responses from scratch
• GenAI suggestions expedite customer interactions
• Allows agents to dedicate more time to complex or sensitive cases
• Higher customer satisfaction due to faster responses
• It ensures consistency, accuracy, and uniformity in responses
• Human intervention ensures personalization and understanding

GenAI-Powered Auto Replies – Success Stories

Here are some success stories for you to consider:

• A leading telecom provider reduced its average handling time (AHT) by 20%, leading to significant operational cost savings by implementing GenAI-powered auto-replies. GenAI helped the provider instantly retrieve and suggest solutions for common technical issues. This allowed their agents to handle more queries in less time while maintaining high service quality.
• An e-commerce giant saw a 15% improvement in customer satisfaction scores (CSAT) by integrating GenAI into its customer service operations. GenAI helped draft personalized, order-specific replies for queries about delivery delays or refunds, reduced frustration, and enhanced customer trust.
• A multinational bank used GenAI to reduce overall query resolution time by 25% and boost agent productivity by 30%. GenAI helped address high volumes of routine customer inquiries, such as account balances and transaction statuses, thereby allowing their agents to spend time addressing complex queries.
• A global hotel chain saw a 40% reduction in response times, ensuring seamless customer experiences even during high-demand periods. GenAI helped handle booking modifications and refund requests during peak travel seasons using auto-replies.

What Is the Biggest Fear for Businesses?

When it comes to implementing GenAI, businesses fear that they may lose the human touch. However, in the model that is proposed here, human agents are the final reviewers, which balances AI efficiency with human empathy.

Besides, GenAI continuously learns from the agent edits and improves its suggestions over time. It is a highly dynamic tool that becomes accurate with use.

We only looked at one of the use cases, which is agent auto-reply using GenAI. There are a lot of possibilities, such as task automation itself. For instance, it can automate actions such as issuing refunds or scheduling appointments without any human interference.

GenAI can help predict customer preferences and behavior patterns, which can act as input for your marketing, promotions, and product development/offerings.

By combining AI’s intelligence with human creativity, organizations can unlock new levels of customer satisfaction and operational efficiency.