The only thing most of us will tolerate less than an inefficient customer service agent is not being able to reach one at all . We long for efficiency but at the same time we ’ re wary of technology taking over too much of how we experience customer service .
In fact , automation technology is already deeply embedded in the process - from IVR systems that distribute calls - to automated customer service follow-up surveys that register our level of satisfaction .
Automation of these and other processes has streamlined contact center workflow efficiency , but it hasn ’ t improved agents ’ ability to satisfy customers once they ’ re connected .
That ’ s because the agent ’ s job entails handling calls directly and also processing and interpreting the data needed to resolve the caller ’ s issue . That takes time and stretches agents thin , and as expanding processing capabilities make data more accessible , the limits of human processing efficiency have become more glaring .
In a fraction of the time required by a human agent , AI can process massive amounts of data to identify relevant patterns and predict the best path to solving whatever issue is at hand .
By removing a major source of inefficiency in the process , automation produces a dual benefit . One , faster problem resolution by rapidly placing critical information at the agent ’ s disposal . Two , agents will have more capacity to bring empathy and nuanced judgment to customer interactions because they no longer need to process as much background information .
BY IDENTIFYING NEGATIVE SENTIMENTS AND POTENTIAL ISSUES EARLY ON , COMPANIES CAN TAKE PREEMPTIVE ACTION TO ADDRESS AND PREVENT CUSTOM- ER COMPLAINTS .
TACKLING PROBLEMS AT THE SOURCE
One of the benefits of today ’ s new generation of automation technology is its unique ability to identify and prevent many of the back-office inefficiencies that add complexity to customer service inquiries . This is true for contact centers serving retailers as well as financial institutions , healthcare or insurance groups , government service providers , and other organizations with large customer service centers .
By leveraging AI technologies such as machine learning ( ML ), natural language processing ( NLP ), and predictive analytics , organizations can glean valuable insights from customer data . They can then implement proactive measures to bypass traditional sources of confusion and frustration for both agents and customers .
By analyzing customer interactions , transaction history , and feedback , AI algorithms can detect and solve recurring sources of dissatisfaction . For example , if AI algorithms detect a significant number of customers asking the same question or experiencing the same technical issue , they can alert the organization to investigate and address the problem before it escalates further .
In addition , advanced NLP capabilities enable chatbots to decipher and respond to common transactional customer inquiries and resolve them without the need for human intervention .
These chatbots can ensure consistent and accurate responses across various channels , including websites , social media , and other messaging platforms . This accelerates the pace of problem solving and relieves some of the pressure on contact center managers to allocate precious human resources to staff the expanding list of customer service access channels .
AUTOMATION
New AI-powered technologies can also leverage predictive analytics to anticipate the needs and preferences of retail customers , for example , and enable businesses to take proactive measures to offer better post-purchase customer service .
By analyzing historical data , AI algorithms can identify patterns and make predictions about future customer behavior . For instance , the technology can anticipate when a customer is likely to require assistance based on their browsing patterns , previous transactions , or engagement history .
By reaching out to customers before they encounter problems , businesses can offer personalized support , recommend relevant products or services , or provide preemptive solutions . All of which can reduce the need for or at least the complexity of customer service intervention . Sentiment analysis , another AI-powered technique , can monitor and analyze customer feedback as well as social media posts and online reviews to gauge customer satisfaction levels .
By identifying negative sentiments and potential issues early on , companies can take preemptive action to address and prevent customer complaints . This proactive approach not only helps in preventing problems but also enhances overall customer experience ( CX ) and loyalty . Embracing AI in customer service can help create more efficient and effective support systems and provide a more seamless CX .
LEARN FROM THE PAST …
The emergence of ChatGPT and other new AI tools represents an exciting new phase in the capacity of technology to reshape the world we live in . Things are moving quickly , and businesses need to think now about how they can harness the power of AI within their own customer service operations to improve performance beyond what basic process automation has long made possible .
CONTINUED ON PAGE 36
NOVEMBER 2023 33