Businesses are always confronting the challenge of how to successfully guide customers’ journeys. They need to create touchpoints that inspire and support while managing a system that they can track consistently. It is no wonder, then, that the contact center domain is obsessed with phrases that assess efficiency and impact: time to response, average handling time, call abandonment rate, customer satisfaction score, etc.
Thankfully, businesses can gain more control of these factors by leveraging machine learning and artificial intelligence to create intelligent contact centers. Most importantly, they can help their customers have a more enjoyable and efficient journey while simultaneously making the process of interacting with them easier.
How AI Can Transform Contact Centers
Here are some of the ways that companies can integrate AI with customer service to solve call center pain points and create opportunities to get closer to customers:
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Behavioral pairing:
- AI can detect shared personality traits or personal qualities between agents and customers. Then, it can match them up. This dimension of interaction is often overlooked in the average contact center. Although callers and agents can be paired up based on agents’ knowledge, performance, and skills, the fundamental aspect of the connection between two people in interaction is largely ignored. Behavioral pairing helps to plug this hole by predicting interpersonal behaviors and pairing callers with agents based on algorithms.
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Self-service and chatbots:
- An AI-powered customer service assistant can empower customers to solve their own problems while maintaining a conversation context.
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Search knowledge:
- AI connects your team to a full-text search knowledge base built on natural language processing. This helps agents find the necessary information to give fast and proper answers.
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Sentiment and emotion analysis:
- Unfortunately, AI can’t replace the empathetic powers of human agents, but it can do a basic scan and provide on-screen prompts with analysis of callers’ moods or other indicators. This way, businesses can resolve queries more tactfully.
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Flagging compliance risk:
- One of the most important roles of AI in customer experience is its ability to alert agents to compliance risks based on a comprehensive knowledge base, customized to particular regulations. This can save companies a lot of heartache — and a lot of money.
These are just the beginning functionalities that an AI or machine learning contact center can offer. The real benefits are the satisfaction it can bring to modern consumers — who have increasingly high expectations when it comes to company interactions.
Consider that 95% of people view customer service as a very important factor in deciding whether or not they will remain loyal to a brand. What’s more, more than 75% of consumers expect customer service agents to have access to their past interactions and other information. Unfortunately, nearly half report being disappointed by agents’ apparent lack of context.
AI can provide the transparency people are looking for.
Picture a bot collaborating seamlessly with a human agent, providing her with insights from former conversations, consumer profiles, and other knowledge bases as the present conversation unfolds. In turn, if the bot doesn’t understand a query or picks up on a negative tone, it can generate an alert so that a human agent can step in.
This power is already being used to great effect. Take major Swedish bank SEB. It has introduced a virtual assistant called Aida, which uses natural language processing to assess conversations. Then, it scours its massive stores of data to provide answers. Crucially, the virtual assistant works in tandem with human customer service representatives, and when it can’t answer a question, Aida turns the caller over to a real person.
The Challenges of Contact Center Automation
The above opportunities are certainly reason enough to consider adapting AI solutions for your customer service, but there are some inevitable challenges associated with building an intelligent contact center.
Most of these occur when businesses harbor misguided expectations for how AI will improve their lives. Many imagine that AI will replace human roles and dramatically reduce their contact center costs. But the reality — a much more exciting reality — is that AI will increase the opportunities for agents to focus their energy and skills on doing what AI can’t: forging meaningful human connections with customers.
In fact, companies that employ AI for customer engagement will actually create many more new jobs than they eliminate. The World Economic Forum estimates that AI will create approximately 58 million jobs by 2022. Because of the collaborative nature of great AI tools, it’s always necessary to have a human backup or supervisor monitoring automatic interactions and adding crucial meaning (a human touch) to solutions.
How to Implement Machine Learning and AI
There are two keys to companies being able to use machine learning and AI solutions to face their contact center challenges: integration and goal-setting.
Businesses must be ready to integrate their chosen solution in a way that assists and learns from the skilled humans on their team, rather than trying to replace their duties. The benefit of using AI in contact centers, for example, is that the technology can collaborate with humans. AI can transcribe massive quantities of speech effortlessly, but it needs the human eye and ear to detect nuance in the speech.
Businesses also need to set goals for their implementation to help them stay on track toward making their processes better, rather than just testing out technologies for the sake of it. An automation road map can be a great resource to map out sensible use cases for how call center AI could improve your business goals, not to mention guide your choices toward revenue-saving tools and solutions that will work for your particular customer base.
The challenge of establishing and maintaining contact with all of your unique consumers at once never ends. But by incorporating the intelligent processing of AI and machine learning tools, you can turn the challenge into a golden opportunity.
