Rethinking the Chatbot in Customer Service

Rethinking the Chatbot in Customer Service

9 min read

When is a customer service chatbot more than a chatbot? When it communicates naturally with customer service AI.

Chatbots in customer service—the two seem to go hand in hand. Yet despite their prevalence, today’s customer service chatbot has changed little over the past couple decades. While effective at answering basic queries, such as FAQs, these bots fall short when it comes to fielding complex customer requests. And with call complexity on the rise, that’s bad news for companies entrenched in legacy chatbot software.

However, while customer service chatbots may seemingly be stuck in a time capsule, the technology around them certainly isn’t. Over the past several years, customer service AI has made significant inroads in speech recognition, natural language understanding and processing. Today, AI-powered intelligent virtual agents (also called intelligent virtual assistants) can not only analyze and interpret complex customer queries; they can also respond dynamically using natural language. These intelligent programs are light years ahead of the traditional customer service chatbots of yore. And, unsurprisingly, they’re gaining wider appeal as companies scramble to deliver competitive customer experience in an increasingly virtual world. 

How Chatbots are Transforming Customer Service—with Some Serious Help from AI

The ubiquity of chatbots in the CX space can be put down to the simple fact that they provide fast, easy and human-like customer service at first contact. While the standard view of a chatbot is one that operates as an interactive virtual agent and is capable of assisting customers with simple inquiries, thanks to its chat-based interface, some solutions are able to handle more complex tasks.  

Uniphore’s U-Self Serve, for example, can go far beyond a simple question-and-answer format. Built on the conversational leader’s premier AI and automation framework, the X-Platform, U-Self Serve cannot only capture and analyze spoken and written input with unparalleled accuracy; it can also interpret a customer’s intent, sentiment and even emotional state. This forms a more comprehensive understanding of a customer’s need(s) and creates a richer, more seamless experience—without the typical friction points found in traditional customer service chatbots. 

Due to its ability to deliver on more complex interactions with consumers, U-Self Serve is perfectly suited to deliver a number of vital benefits to the contact center, including: 

Improved efficiency and speed

The most obvious benefit of chatbots is the efficiency and speed of service. By resolving customer queries through self-service, chatbots eliminate the frustration of waiting for an agent. After all, they never get tired, they never get angry, they never require breaks and they can simultaneously have conversations with thousands of people.

24/7 availability

Because self-service chatbots can resolve customer issues without involving an agent, they can provide customer service well outside of regular business hours. No matter what time of the day it is or how many people are contacting you, every single one of them will be answered immediately.

Increased accuracy and consistency

Today’s customer service AI—which includes conversational AI, emotion AI and behavioral science—can understand more conversational nuances than ever before. As a result, it can capture customer data and initiate relevant tasks with greater accuracy and consistency than yesterday’s customer service chatbots.

Personalized experience

Using AI, customer service can now collect and analyze data from a wide range of sources, including previous support interactions. When fed into an intelligent self-service solution, this data can deliver a more personalized experience (and spare customers from having to repeat themselves multiple times, whether within a chatbot or with a live agent).

Increased customer satisfaction

According to multiple sources, more than half of customers prefer interacting with a chatbot in customer service to a live agent. By deploying the next generation of chatbots with AI, customer service providers can simultaneously drive customer satisfaction up and operational costs down (more on that below).

Cost savings

For companies with a lot of volume, increasing automation rates for routine customer requests enables companies to essentially improve customer service levels with fewer agents needed to operate the call center, which also means less space is required. Together, these factors can result in millions of dollars in operational savings.

Real-Life Examples of Chatbots in Customer Service

Today, nearly every major company utilizes a chatbot in customer service. While most can handle basic customer queries and answer simple questions, a growing number of industry leaders are leveraging customer service AI to handle complex customer issues and create more personalized experiences. These include some of the leading names in:


Banking & Finance

Key Features of an Effective Chatbot for Customer Service

What makes an effective chatbot for customer service? When it acts more like a human agent than an emotionless chatbot. Here are six key features that are helping today’s AI-powered intelligent virtual agents stand out from—and outperform—yesterday’s customer service chatbots.

Natural language processing (NLP)

Natural language processing and natural language understanding (NLP and NLU) are components of conversational customer service AI that help computers understand and interpret human language. NLP and NLU create more natural, dynamic interactions and help IVAs sound more human (and less like a soul-less machine).

Integration with CX software

The best chatbots are only as good as their ability to integrate with a company’s existing customer service software. Pairing low-code/no-code automation with conversational AI, customer service providers can easily layer next-generation CX capabilities on top of their legacy platforms.

Customization and branding options

In addition to integrating seamlessly with legacy programs, low-code/no-code conversational automation software makes it easy to customize and brand the customer experience. Additionally, with a platform that allows open data access, providers can own and leverage all of their data as they see fit—instead of going through vendor gatekeepers.

Automated responses and self-service options

To be truly effective, customer service chatbots must be able to interpret and respond to a range of customer inputs. This has traditionally been a challenge for chatbots with a limited range of scripted responses. However, with conversational automation, today’s intelligent self-service solutions can “learn” from conversational data and provide a wider—and more accurate—range of responses.

Ability to escalate to a human agent

There are some queries that a chatbot simply can’t handle alone, and a human agent is needed. Escalating queries from self-service to live assistance has traditionally been a pain point for chatbots, requiring customers to repeat the same information (including authentication) in both channels. However, with conversational AI, information fed into the self-service channel is transferred seamlessly—along with context—to live channels.

Data analysis and reporting

Another benefit of intelligent self-service over traditional customer service chatbots is access to deeper data points. With conversational AI, customer service providers can uncover previous hidden customer insights—including sentiment, intent and even emotional data—that can help improve overall customer experience.

Best Practices for Implementing a Chatbot in Customer Service

Thinking of deploying a customer service chatbot? There are some important things you should consider before you get started. Our conversational design e-book, Conversation AI+, can help. To ensure your implementation delivers the results you want, here are four of the top best practices for implementing a chatbot in customer service.

Thinking of Building an Intelligent Virtual Assistant?

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The Future of Chatbots in Customer Service

As customer service AI continues to evolve so too will the face of the chatbot in customer service. Some of these advancements are technical, such as natural language processing innovations that better understand how we speak. (Uniphore’s platform, for example, continues to fine-tune its NLP and NLU capabilities, regularly adding new languages and dialects to its growing database.) Others are cultural, like the drive for data sovereignty and the rise of open data enterprise solutions.


One thing remains certain: tomorrow’s customer service chatbot won’t look anything like the chatbots of the past 20 years. With the ability to comprehend intent, sentiment and emotion and to communicate naturally across multiple channels, these intelligent solutions are more than just the next generation of chatbots; they’re an evolutionary leap forward. For customer service, AI is the future. And the future looks brighter than ever.


What is a chatbot in customer service?
Customer service chatbots are programs that interact with customers to answer questions, resolve queries and automate routine tasks.  

How do chatbots improve customer service?
Customer service chatbots allow customers to easily resolve customer service issues on their own (without having to engage with a live agent).  

What are the key features of an effective chatbot for customer service? 
To be effective, a customer service chatbot must be able to understand a customer’s query, execute the appropriate corresponding task(s) and escalate the call, if needed, to a live agent.  

How do I implement a chatbot in customer service? 
Start by defining the basic chatbot use cases that will have the greatest impact on your customer service operations. Then, train it on the data variations for each use case. Use any new to continually improve and fine tune the chatbot over time.  

What is the future of chatbots in customer service? 
Customer service AI is driving chatbots to evolve beyond simple question-and-answer tools toward more dynamic solutions that leverage sentiment, emotion and behavioral data to create a more holistic, personalized and engaging customer experience.

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