The life of a customer service agent is getting tougher. Typically, agents field a variety of requests and answer a range of questions with agility. They de-escalate interactions with frustrated customers, find compromises when customers make unreasonable requests, and always meet or exceed customer expectations. But now, they face the additional challenge of working on teams where fewer agents handle larger call volumes.
Conversational AI for customer service can ease those pressures and help teams enhance customer experiences.
Why does customer service need a revamp?
The COVID-19 pandemic sparked a global shift in the workforce and how people work. Remote work opened up more opportunities for people, and once The Great Resignation began, employees saw even more chances to make a change.
Salesforce reports that in early 2022, 71% of customer service agents considered leaving their jobs, and 69% entertained the thought of leaving the field entirely. People leaving customer service jobs is taking its toll. Bloomberg reports that the result is longer wait times, up to triple pre-pandemic stats.
At the same time, customer expectations heightened. During the pandemic, more people began interacting with brands digitally, accessing information with just a few clicks. They have the same expectations when they reach out to customer service.
Organizations must find new ways to deliver information and respond to their customers.
Why is it time to consider conversational AI?
Customer service teams are turning to technology to close gaps. Some have successfully implemented messaging or rules-based chatbots to give customers a way to find information or connect with resources independently. However, many customers still pick up the phone when they need assistance.
Conversational AI powering a chatbot can help it understand a customer when they ask questions or request information in natural language and respond with the answers they need. Deploying voice-based conversational AI for customer service allows teams to address increasing call volumes better.
How are companies benefitting from conversational AI for customer service?
Conversational AI for customer service undoubtedly can expand call center capabilities. In effect, it’s self-service that doesn’t seem like self-service, enabling customers to ask questions naturally, receive friendly assistance, and access information quickly – no human interaction required.
Of course, person-to-person interaction is sometimes necessary to resolve complex issues. A platform designed for conversational AI for customer service can also assist agents. The platform can collect information for a smooth handoff to an agent and reduce response time. With conversational AI as part of the process, customers spend minimal time on hold and resolve their issues more quickly.
But the value of conversational AI for customer service doesn’t end there. Additional use cases for this technology include:
Identify verification: Conversational AI for customer service can strengthen processes to authenticate a customer’s identity. Furthermore, when customer information is only shared with the platform and not with human agents, it adds an extra layer of security and privacy.
Reservations: Solutions leveraging conversational AI can quickly access booking systems, reserve time for appointments, hotel stays, dining reservations, record special customers requests, and send confirmation texts or emails.
Automated sales: Conversational AI can handle some sales, guiding prospects through the customer journey and even managing payments.
Call routing: Customer service teams that use interactive voice response (IVR) to route calls to the right departments can enhance customer experiences by allowing them to ask to speak to specific people or departments in natural language versus listening to a list of options and choosing a number based on their inquiry.
Personalize customer experiences: When conversational AI has access to customer data, it can tailor interactions to each customer. For example, the platform can answer questions about customer’s orders, track them in shipment, upgrade accounts, and accept payments. During these interactions, the conversational AI platform can also greet the customer by name and upsell based on past purchases.
What do you need to build conversational AI products?
Conversational AI for customer service can create natural – and easy – customer experiences. However, achieving such intuitive interactions takes sophisticated technology that works in the background. Elements of a conversational AI for customer service include:
- Voice interface (for speech-based interactions): The platform needs to enable a customer to ask questions or request information in natural language. The platform needs speech recognition and the ability to convert voice data to text.
- Natural language understanding: NLU uses various techniques that allow a machine to understand human language not only to recognize words but also to understand intent.
- Dialog state tracking: The platform needs to determine the customer’s goal to help them achieve it.
- Natural language generation: NLG allows the platform to convert data to natural language.
- Text-to-speech (for speech-based interactions): Finally, the platform must convert text data to an audio output.
Creating conversational AI for customer service presents several challenges. The platform must be designed and trained to consider variations in inputs so it can consistently deliver good customer service experiences. They have different native languages, speak with accents, write with different conventions, use local jargon, and, in the case of speech interaction, they may be calling from a place with a noisy atmosphere. The platform must be designed and trained to consider variations in inputs so it can consistently deliver good customer service experiences.
An effective conversational AI platform is also capable of understanding all the different ways people can say the same thing. This often takes an iterative approach in which many different variations of a phrase are logged and progressively incorporated into the conversational AI knowledge database. Testing and monitoring the platform’s effectiveness are key to conversational AI that increases (not decreases) customer satisfaction.
How do I get started?
Although the conversational AI platform developer will do much of the heavy lifting to create a platform that benefits your customer service team, input from all stakeholders is vital to a conversational AI solution that provides the best experiences for your customers.
Some decisions your team needs to make include:
- The platform’s persona: Choose pleasant voice characteristics for the platform that will appeal to your customers and put them at ease. Consider options, such as phrasing that expresses empathy, to help build trust.
- Data to initialize the system: Collect or record data that you can use to train the conversational AI system on interaction variations. The key is to put the system into production with enough information that allows it to record more data and continuously improve.
- Data to fuel the platform: Give the platform access to a knowledge base, customer data, and other information that will help it answer questions accurately.
- Options to contact a human agent: Design a conversational flow that encourages the customer to use the platform to find answers but eliminates the frustration of being unable to reach an agent for a complex problem when needed.
- Address privacy concerns: If your business must comply with privacy regulations, ensure the conversational AI platform’s operation aligns with requirements.
What is the impact of deploying conversational AI?
Organizations implementing conversational AI for customer service see immediate key performance indicator (KPI) improvements. Deloitte reports that 90% of companies that deployed these platforms saw faster issue resolution, and 80% said that more customers used conversational AI rather than deferring to a human agent.
Those time savings also translate to cost savings. Gartner predicts conversational AI will decrease customer service labor costs by $80 billion by 2026.
Lowering the demand for labor in customer service and saving time and money are all benefits. However, the most significant advantage may be enhancing customer experiences. Customers can immediately provide information or explain their issue at any time, day or night. And in many instances, the conversational AI platform can effectively handle the issue from start to finish.
Additionally, conversational AI can tailor responses, ensuring responses are relevant and the customer is satisfied with your organization’s service. Moreover, the platform can also discern when a customer needs to speak to an agent and seamlessly hand off the call with the information the customer provided.
In an era when experience is everything, brands can see increased loyalty when an effective conversational AI platform enhances customer service.
Exceed expectations
Customer service teams worldwide are turning to conversational AI to usher in a new era of engagement. Although AI can’t replace human interactions in situations that require empathy or complex problem-solving, it can quickly and efficiently handle routine tasks, such as helping a customer track a package or providing information on store locations and hours.
Additionally, it meets consumer demand for self-service and exceeds expectations by allowing people to ask questions or make requests naturally – as if they were talking to a human. Conversational AI can also decrease the pressure on customer service managers to hire during the labor shortage, allowing them to fill gaps with technology when people aren’t available. It’s capable of booking reservations, routing calls, and even closing sales, and accepting payments. However, choosing the right platform that leverages advanced technologies that create smooth, conversational interactions, consistently deliver accurate information, and support your team is pivotal.
Sigma.ai helps some of the world’s biggest brands create conversational AI for customer service. Sigma’s data collection and quality data annotation services create structured datasets that are the foundation of great conversational AI. To learn more about implementing conversational AI for customer service, contact us.