The COVID-19 pandemic has prompted many enterprises to re-think digital transformation and their approach to customer service.
The customer journey shifted abruptly from a combination of in-person and digital to a digital-first (and often digital-only) path, increasing the load on digital infrastructures and suddenly remote workforce. The race was on for organizations to either create or accelerate their digital plans in order to scale up and meet new customer expectations.
As a result, over the past year we have seen the adoption of artificial intelligence (AI)-based systems in general expand across the market. However, contact centers still have several challenges to navigate as customer expectations evolve.
- Excessively long wait times—30 minutes, 60 minutes, sometimes hours—to get to a human agent.
- Customers getting transferred between agents and having to repeat information.
- Agents who aren’t experts in their brands/products.
- Limited time to resolve issues once customers connect with the right agents for what’s needed.
These are a few of the biggest customer pain points that continue to keep contact center teams up at night. Yet the AI-using systems that many contact centers have adopted, like chatbots, appear to be inadequate by themselves to handle them.
So, for organizations looking at technologies that can alleviate the extreme levels of stress that agents have experienced over the past two years, AI-based conversational automation is a natural fit. In fact, Gartner predicts that in 2022 20% of customer service interactions will be handled by conversational AI agents.
As organizations revisit their customer service approach this year, here are three ways that “super agents” and their conversational AI sidekicks are transforming the contact center of the future today: scaling customer support, empowering human agents, and pacing with customer service expectations.
1. Triaging Customer Support
When it comes to customer service, the goal is to help customers quickly and competently at any time and on any channel. Effective conversations with customers—whether virtual or voice calls with live agents—lead to quicker response times and higher customer satisfaction.
However, scaling contact centers and maintaining sufficient capacity at all times is very expensive, and most enterprises are not staffed to levels that would support live agent calls 24/7 with little to no wait times.
Enter conversational AI. Conversational AI does more than help eliminate dull and repetitive tasks like authenticating customers. The partnership of human agents and conversational AI provides flexible customer service that can scale up or down as needed.
Conversational AI automates conversations between computers and humans. It is built on natural language processing (NLP) and machine learning, which gives the technology the ability to interpret customer conversations and automatically respond with relevant, contextual answers.
Now on a level where it can mimic human dialog, which makes interactions more natural and yes conversational, conversational AI enables organizations to create the most personalized experience possible between humans and machines. This can drastically improve business processes and communication experiences for the end-users.
Because it’s communication-centric, conversational AI is leveraged across the enterprise in several areas—including customer service, HR, marketing, IT help desk, and sales—in the form of messaging apps, chatbots, and voice-based virtual agents.
Chatbots and other AI-based self-service agents, combined with conversational AI, provide a first line of always-on support that allows human agents to invest more time and effort in resolving complex customer issues.
In addition, when more specialized support is needed, conversational AI can automatically and directly route a customer’s interaction to an agent who is experienced, empowered, and available to solve the specific problem. It also provides historical and real-time data that allows agents to view the larger customer journey pictures.
Agents combine these insights with the nuanced emotional understanding and topic expertise needed to resolve complex customer issues. This combination provides the flexibility needed to ensure that contact centers run smoothly.
2. Equipping the Super Agent’s Toolbelt
While more and more consumers are becoming accustomed to working with virtual support, live voice engagement is still the gold standard when it comes to complex questions or complicated requests.
When the human touch is needed, conversational AI steps in for a technology tag team that elevates the customer experience (CX). It not only offloads agents to focus on the more complex customer items, but also empowers agents with the tools they need to engage smarter and more efficiently.
Through data insights, these super agents can access customers’ steps to date faster than a speeding bullet and leap across multiple system silos in a single bound to quickly capture comprehensive views of the customers’ experience paths, with less need for them to repeat information.
This human-machine combination enables agent teams to become customer and subject matter experts quickly, allowing them to focus the interaction time on building relationships with customers in order to ultimately be more engaged and helpful – two key elements leading to positive CXs.
As organizations integrate their contact center more closely with other systems, conversational AI provides super agents with real-time, accurate, and up-to-date data for a clear view of the customer’s journey to that point, enabling them to address the customer request quickly and efficiently.
3. Keeping Pace with Evolving Customer Expectations
Spending a lot of time at home during quarantines and social distancing, consumers have been in touch with customer service more often than ever before.
Faced with contact centers that have had to shift to working from home, and from staffing issues that have had to direct more contacts into self-service, consumers have become more comfortable using chatbots and the quicker resolutions that such AI-based self-service tools offer. Particularly for more common support issues.
Enterprises need to keep up with those expectations. For example, consumers are expecting to be able to contact enterprises on the channel that they feel most comfortable with, whether that is via phone, text, chatbots, or IMs like WhatsApp or Facebook Messenger.
When these virtual tools don’t work as expected, can’t resolve their issue as fast as they would like, or if the issue is more complex than what the AI system is designed to support, consumers get frustrated. Which often translates into negative experiences and less consumer confidence in using these AI-based self-service approaches in the future.
Likewise, if internal conversational AI-based systems aren’t delivering the right information at the right time to the agent teams, it is more difficult for agents to provide customers the guidance they need to resolve their issues, which also often results in customer dissatisfaction. When this happens, it can be an even more frustrating experience for customers than waiting on a long hold.
With recent advancements in speech-to-text, NLP, natural language understanding, and sentiment analysis, AI-powered virtual agents can not only manage back-and-forth dialog but they can also understand the context and nuances of customers’ individual conversations.
These tools can identify and avoid trigger terms that alter customers’ attitudes, usually negatively. Chatbots trained with sentiment analysis can also pick up on customers’ inflections, intensity, and tone to adjust their responses with the appropriate levels of empathy or escalate the interactions up to human agents if needed.
A better understanding of what the customers want to achieve from the interactions helps to achieve that goal more quickly and completely. That could mean resolving the issues directly or creating an information base that allows human agents to tag team and resolve more challenging customer situations with less time and frustration.
Powering for the Modern Contact Center
The future of the contact center is a human +AI hybrid, leveraging advancements in chatbot and voicebot technologies to enhance the human agent workforce.
And the future is already in motion. According to CCW Digital’s recent “AI Operations” report, 51% of companies view the advancement of chatbot technology as critical for enhancing digital experiences.
Conversational AI brings the power of automation to the contact center, partnering with human agents to transform customer service and enhance the CX.
Agents combine these insights with the nuanced emotional understanding and topic expertise needed to resolve complex customer issues.