Businesses have had to adapt to a rapid increase in digital interactions during the past year, regardless of industry or consumer base. Many businesses have resorted to AI to manage swings in consumer demand and crunch enormous volumes of data and derive relevant insights from it, as customer behaviour and expectations are changing faster and more unpredictable than ever.
Contact centers are swiftly evolving into a proving ground for AI-based products as well as a means of introducing their benefits to consumers through fulfilling interactions. Examining the various AI use cases in the contact center can provide significant insights beyond customer service, information that any company can utilize to improve flexibility and efficiency.
Contact centers have long been data gold mines. According to a Gartner 2020 poll, the volume of customer service interactions has risen to levels 40 percent greater than expected during the pandemic.
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Simultaneously, demand for speedier and more personalized service has grown. When contacting a business, 83 percent of customers expect to speak with someone right away, according to the 2020 Salesforce report “State of the Connected Customer.”
Contact centers looked for tools to help lighten the burden as resources were stretched thin, call volumes were higher, and employees were dealing with more unpredictability than ever before. The enormous issues these teams were confronted with—and the necessity to act quickly—led them to identify AI-based technologies that go well beyond the popular self-service chatbots. These advantages of these technologies have shown to be both powerful and practical.
Machine Learning (ML), natural language understanding (NLU), and Natural language processing (NLP), are tangible AI capabilities that have shown to be the right fit for the demands of contact centers, allowing them to extract insights from conversations, client history and sentiment. Cloud platforms enabled large-scale data collection, allowing for greater flexibility in managing demand spikes.
Contact centers were able to handle the spikes and swings in consumer enquiries during the pandemic by combining the scalability and flexibility of cloud with the deep, quick insights of NLU, NLP and ML capabilities to deliver more effective self-service and better prepared agents. Customers will notice more tailored experiences as a result of this. For employees, that’s meant customer relationships and interactions that are easier to manage and learn from.
In many respects, the success that contact centers have had is a key proof of concept for AI in the field of customer experience. The underlying ML, NLP, and NLU technology that powers AI solutions in contact centers can be extended to create a variety of other tools for both broad and industry-specific applications. These solutions are also iterative, meaning that they may be taught and improved over time to provide efficient experiences. They free up resources not only for IT, but for the entire company, allowing business leaders to focus on other strategic initiatives.
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All of this adds up to a compelling business case for practical AI, but ML, NLP and NLU tools in the contact center have provided an additional key benefit: they have normalized the usage of AI for customers in situations that could otherwise be mundane or upsetting. Customers ca
n see first-hand the benefits of AI tools when they connect with brands that use it effectively, associating them with more natural, personalized, and fulfilling experiences.
Other industries too can learn from contact centers, by taking the technologies and strategies that work best for them. With the recent success as a roadmap for contact centers, these forward-looking businesses can magnify the positive impact of AI on the customer experience.
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