Attributed to Raghu Ravinutala, CEO & Co-founder, Yellow.ai
Modern-day consumers are often characterized by the “Right Now” mindset, where they expect immediate resolutions to their queries without any delay or inconvenience. They desire personalized and seamless omnichannel experiences that allow them to interact with brands across multiple touchpoints with ease.
The question is: Are brands able to deliver on these expectations?
Traditional manual customer care methods often fall short of meeting these expectations, leading to negative customer experiences that can harm a brand’s reputation. As a matter of fact, 90 percent of Americans consider customer service a significant factor when deciding to do business with a company, making the customer experience one of the most critical aspects that businesses need to excel in. This underscores the importance of delivering exceptional customer service that goes beyond customer expectations, particularly in today’s hyper-connected and fiercely competitive business environment.
That’s where automation steps in to save the day. To stay competitive, it has become essential for brands to invest in technology and tools that enable them to provide personalized, fast, and efficient customer service across multiple channels. Fortunately, advances in cognitive technologies such as generative AI, machine learning, Natural Language Processing (NLP), and speech recognition have made it possible for brands to deliver support just the way customers want it. In fact, a survey by McKinsey found that 56 percent of businesses are adopting AI in at least one of their daily functions, while Forrester Research found that 65 percent of B2B marketers currently use AI-powered virtual assistants to engage with customers.
But why ‘Conversational AI’?
Businesses that have adopted Conversational AI solutions are able to deliver quick, effective customer care at a scale that is beyond the reach of humans alone. Conversational AI-powered customer care can streamline the complete process of managing requests with capabilities like automatic call classification and routing, reducing the complexity of the customer service process, and relieving human agents of mundane tasks. Leveraging these solutions in customer service is helping businesses not only improve overall CX but also increase operational efficiency. In fact, at Yellow.ai, we have seen up to a 60 percent reduction in operational costs through our Conversational AI solutions.
Our Conversational AI-powered Dynamic AI agents offer brands an unparalleled self-service experience by providing 24/7 availability and a faster response time. Leveraging our DynamicNLP™, these agents have the capability to comprehend inquiries and provide accurate answers, reducing unidentified utterances by up to 60 percent. They can also discern the intention and emotions behind customer queries and deliver responses accordingly.
Moreover, customers today expect a seamless experience across channels, whether it be a website, social media DMs, WhatsApp, text messaging, or contact center assistance. This omnichannel approach to customer care is efficiently enabled by Conversational AI solutions, providing uniform support across multiple channels. By leveraging Dynamic AI agents, brands can offer their current and potential customers hyper-personalized experiences. These agents are capable of delivering personalized recommendations, such as suggesting accessories to complement an outfit, conversing in multiple languages, and providing accurate responses based on deeper actionable insights, all in real-time.
Onto the ‘HOW’ behind the execution
First, businesses must invest in the right tools and solutions by choosing the right automation partner. Brands that consider adopting traditional automation solutions will encounter complex and time-consuming integration processes that pose a challenge in bringing together diverse systems. Conversely, low-code automation platforms are highly effective at providing robust automation integration.
Second, it is crucial to address the question of where to get data from and how to train the Natural Language Understanding (NLU) model, as well as NLP and speech recognition systems, in language semantics, jargon, and nuances. The training of the NLP model has been made so much easier and quicker with the launch of our DynamicNLP™ that ensures enterprises no longer need to go through the lengthy process of manually labeling the datasets for training.
Third, the key is to leverage the best of both worlds—the speed and scalability of Dynamic AI agents with the emotional intelligence of human agents. For the overstretched teams of call center workers, the use of Dynamic AI agents lessens their workloads and, in cases of complex customer issues, helps them root out the administrative issues and focus more on finding a resolution.
Finally, companies should adopt an all-inclusive approach to provide a consistent and continuous experience to their customers. Essentially, their strategy should be equipped with the ability to drive automation across different modalities, be it voice, chat, web, social, etc. It is also critical to select the appropriate channel for the appropriate use cases.
As North American consumers look for stellar and personalized customer support, brands will face more pressure to deliver services and experiences that meet their digital and smartphone-centric needs. With Conversational AI-powered Dynamic agents, brands can deal with large volumes of customer queries with high accuracy and human-like behavior. Not only will these agents meet the “Right Now” generation’s needs, but they will put brands one step ahead in the race for customer care and retention.
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Raghu Ravinutala is CEO and Co-founder of Yellow.ai, a leading enterprise-grade conversational AI platform working with 1000+ enterprises across 85+ countries.
Featured Image by DeepMind
