Generative AI (also called genAI) will undoubtedly have a massive impact on customer service teams. Thanks to their ability to mimic human-like speech, generative AI solutions can provide relevant and personalized responses to your customers lightning-fast – a welcome boost to a crucial part of the customer journey.
According to a Gartner research study, genAI’s ability to provide rapid, accurate, and personalized responses leads to a 33% increase in customer satisfaction compared to traditional customer service methods.
But you need to be careful about how you implement and manage these solutions. If not done correctly, they can hallucinate and give outright false answers – something you must avoid when dealing with agitated customers.
To help you do that, we are sharing our experience using generative AI solutions for customer service. Here’s what we’ll cover:
Let’s dive in.
Key Benefits of Using Generative AI in Customer Service
Generative AI solutions can bring many benefits to first-level customer service. For example, in the same study we mentioned above, businesses that leveraged generative AI reported a 27% increase in efficiency and a 24% reduction in operational costs.
However, in this article, we will focus on the benefits of generative AI that have the biggest impact on customer service:
- improved customer interactions
- round-the-clock customer support
- scalability
- cost savings
- actionable insights
Improved Customer Interactions
GenAI solutions can provide a more engaging experience for your customers through human-like interactions. Since these solutions “understand” the context, sentiment, and intent behind customer queries, they are able to deliver relevant and personalized responses.
As a result, your customers’ satisfaction with the support they receive increases significantly.
Round-the-clock Customer Support
Generative AI enables companies to provide customer support 24/7. Virtual agents powered by generative AI can handle customer queries at all hours, and unlike human agents, they don’t require breaks.
This ensures continuous availability and immediate response to customers, significantly boosting customer satisfaction and loyalty.
Scalability
Thanks to generative AI, companies can now efficiently manage large volumes of customer queries. These solutions can handle multiple customers simultaneously, ensuring immediate responses and eliminating waiting times.
As a result, you can increase your operational efficiency and scale up the customer support function without additional hiring.
Cost Savings
Implementing GenAI in customer service operations can lead to significant cost savings. Automating routine and repetitive tasks allows customer service teams to focus on more complex issues, optimizing resources.
Moreover, generative AI can help prevent unnecessary costs by reducing the incidence of human error, streamlining workflow inefficiencies, and minimizing the need for high volume staffing during peak service times.
The scalability of generative AI means that in the future, businesses can continue to handle higher customer volumes without increasing their workforce.
Actionable Insights
Generative AI can analyze customer interactions to produce valuable insights – crucial for any data-driven company. It can identify patterns and trends in customer behavior, feedback, and sentiment and use them to provide businesses with crucial data needed to improve customer service, tailor offerings, and make data-driven decisions.
Besides first-level support, generative AI also empowers second-level customer service teams with actionable insights. By analyzing large volumes of customer requests, it can identify frequently occurring issues and help customer service team introduce core enhancements to avoid future problems.
Generative AI in Customer Service: Key Use Cases
Let’s dive deep into how generative AI is used in customer service, showcasing its transformative impact on the industry. We will focus on the following use cases:
- genAI-powered virtual agents
- document summarization and agent assistance
- customer feedback and sentiment analysis
GenAI-powered Virtual Agents
Virtual agents are one of the most exciting use cases of generative AI in customer service. Virtual agents are nothing new, but empowered with generative AI, they can provide more natural and personalized exchanges.
This improvement makes them an invaluable tool for businesses looking to provide round-the-clock support without exhausting their customer service teams.
As a result, AI-based customer service solutions (link to CCAI) are becoming the first point of contact in many customer interactions thanks to their ability to handle high volumes of customer requests.
According to a 2022 BCG survey of global customer service leaders, as many as 95% anticipate AI chatbots will have a role in their customer service within the next three years.
It’s important to mention that virtual agents powered by generative AI are far superior to their standard counterparts. While traditional virtual agents typically respond to customer queries based on a predefined set of rules and scripts, genAI virtual agents leverage machine learning to generate responses.
Instead of being limited to a fixed script, they analyze the context and sentiment behind customer inquiries to provide more relevant and personalized responses.
They can also learn from their past interactions, improving over time, which results in ever-increasing customer satisfaction.
Document Summarization and Agent Assistance
The central role of generative AI is not to replace your people – it is there to augment them and make their work better and faster. And in customer service, this is best highlighted through the genAI document summarization use case.
Here, the solution analyzes internal documents or whole databases to extract crucial information that agents can use to respond quickly and accurately to customer inquiries.
Some of that information can be pulled from customer data platforms, helping customer service teams to provide better, more informed, and personalized responses.
This process saves time for your agents and minimizes human errors that may occur when they manually search through vast amounts of data. With generative AI, your customer service team can solve complex issues in seconds, enhancing the overall customer experience.
For example, consider a call center dealing with queries on a wide range of products. In this case, agents have to sift through detailed product documentation to answer specific customer questions. With document summarization, a genAI solution could parse these long documents and identify and summarize the relevant sections related to the customer’s query – thereby significantly reducing response times.
Generative AI not only enables quicker responses, but it also helps in structuring requests more efficiently and organizing knowledge bases more effectively, aiding first-level support to provide faster and more accurate solutions.
Customer Feedback and Sentiment Analysis
Generative AI models can analyze vast volumes of data and are able to understand the context of a conversation, making them perfect for analyzing customer sentiment and experiences.
And as they also learn from past interactions, they can identify areas for improvement and take proactive measures to address any issues before they escalate.
In the first step, the solution collects first-party data from various sources like social media, customer surveys, and review platforms. This data, usually in unstructured text form, is then preprocessed to remove irrelevant information and standardize the text.
The AI model is then trained on this data, learning to understand the sentiment behind the text based on prior examples.
Once trained, the AI can analyze future customer feedback, identifying the sentiment behind each one. Through machine learning, the AI continually improves its accuracy in sentiment detection, allowing for more sophisticated trend analysis and identification of critical customer pain points over time.
Risks and Considerations for Implementing Generative AI in Customer Service
Every new technology comes with risks, and generative AI is no exception. LLM-based chatbots are trained on vast amounts of data that sometimes contain errors or inconsistencies – causing them to ‘hallucinate’.
These errors can cause not only a distraction in a specific customer engagement but also a brand risk and loss of loyalty in the long run. Therefore, human oversight is still needed to improve these game-changing solutions further.
To prevent the misuse of genAI in customer service, businesses should focus on several key areas:
- Close monitoring: Customer service professionals must closely monitor generative AI systems and regularly test for biases or errors. For the current generation, your agents should check the content AI systems created before it is sent to a customer.
- Ongoing training and fine-tuning: Proper training and monitoring must be in place to ensure the virtual agents are representing the company’s brand accurately and consistently.
- Transparent communication: Businesses must be transparent about using generative AI in customer service and ensure customers are aware when interacting with a virtual agent rather than a human. This transparency will help build trust and prevent any negative brand perception.
Final Thoughts on Generative AI for Customer Service
In conclusion, generative AI is revolutionizing customer service in various ways. From virtual agents providing round-the-clock support to document summarization and sentiment analysis, the technology enhances customer experiences and improves business efficiency.
However, implementing generative AI in customer service requires careful consideration and monitoring to ensure its success and avoid potential risks.
With the right approach, businesses can harness the power of generative AI to transform customer service and stay ahead in this rapidly evolving digital landscape.
