AI-powered contact centers can ease the workload

What is the most basic business proverb? The direct answer is: “The customer is king”. Therefore, companies invest significant energies in designing their products or solutions that please the king. But unfortunately, the royal experience is often interrupted as soon as customers reach out to their favorite brand’s contact center with questions or troubleshooting. There are many reasons for this issue, including overloaded contact center agents, lack of contextual data on previous customer engagements, or simply waiting too long in the queue for the call to be answered. .

Using a pair of optimistic glasses, the challenge is also a significant opportunity to convert customers, aka kings, into true brand ambassadors. All a brand needs to do is fix the broken parts of the contact center experience. Fortunately, technology offers an easy way to do this. As a first step, it is essential to identify appropriate AI-based solutions that automate the iterative parts of the contact center experience. An experienced technology partner can help you with this process by analyzing previous customer voice, email, and chatbot interactions. Recordings or transcripts of these interactions are run through analytics engines capable of natural language processing to identify areas where engagement is falling flat. The exercise helps to understand which parts of customer engagements should be automated for a better result. The result is an optimally automated contact center that delivers a fast, accurate, and emphatic customer experience.

While automation eases the workload of contact center agents, it’s not the end of the game. After all, customer service is all about the warmth, rationality and empathy that only a human can. to offer. When customers arrive at the contact center, they seek reassurance that the issue can be resolved in “X” time and that someone is concerned about their situation as well. Therefore, the next important step is to restructure and retrain human agents to focus on critical incidents that require human intervention. These agents will be aided by more contextual data about a customer or issues through the automated system that remembers and analyzes every past interaction. Thus, if a client calls the contact center to follow up on her complaint, she does not need to repeat her entire file to a new agent. Based on in-depth data analysis, the system collects more insights into the customer’s engagement with the company. By leveraging this knowledge base, human agents continue to become more effective at solving problems. Ultimately, don’t treat a customer like a claim ticket number. She is a human first and she is treated as such. Humans need data, information and solutions, but they need empathy from their fellow human beings.

Based on the use case above, companies need to identify the right balance between technology and connectivity to provide customers with a unique contact center experience. Overuse of automation, for example, simply providing an automated chatbot or auto-response ID, can leave customers frustrated and unhappy in emergency situations. Consider your favorite taxi aggregator service that only offers an automated chat option when multiple drivers cancel your ride request after midnight. On the other hand, too much reliance on human agents makes the system inefficient and leaves room for errors or other undesirable violations of service level agreements. Both of these extreme situations are brand detrimental and should be avoided. A better way is to critically analyze every aspect of the contact center experience through deep data analysis and incremental investment in automating the parts where there is an engagement gap. Continuous analysis keeps the customer experience intact and also identifies areas where technology can help improve efficiency. Thereafter, it is important to continuously train agents to focus only on the critical parts of the conversation and expand the use of technology to peripheral parts of the business based on initial experience. Identifying an experienced and knowledgeable technology partner makes this journey very rewarding for companies.

Rashi Gupta is chief data scientist and co-founder of

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