Chatbots and virtual assistants with advanced natural language processing (NLP) are transforming customer care and how businesses engage with their customers.
NLP enables marketers and advertisers to process and understand text strings, applying sentiment scores. This data is derived from various sources, including chat and voice logs, as well as audio and speech-based conversations.
Powered by artificial intelligence (AI) and large language models (LLMs), these advanced technologies facilitate more sophisticated and contextually aware customer interactions that closely mimic human conversation. They assist marketers and advertisers in hyper-personalizing messages and offers, building brand loyalty, and enhancing campaign effectiveness.
Verint, a customer engagement solutions firm, pioneered chatbot infrastructure, introducing some of the first chatbots to organizations like the U.S. Army. The company has launched over 50 specialized bots to help businesses enhance their customer experience.
According to Verint’s State of Digital Customer Experience report, a positive digital experience is crucial to customer loyalty. The report found that 78% of consumers are more likely to become repeat customers if they have a positive experience on a digital channel, while 64% have switched to a competitor following a poor experience.
NLP in the context of chatbot and virtual assistant development is a common topic. What is not as commonly discussed is what it takes to do it right and the downsides of getting it wrong, according to Jason Valdina, senior director of digital-first engagement channel strategy at Verint.
“NLP enables these essential customer experience [CX] automation tools to understand, interpret, and generate human language, bridging the gap between humans and bots to provide next-level customer service,” he told CRM Buyer.
New Trends in AI for Digital CX
The key to effective chatbots and virtual assistants lies in the accurate implementation of NLP, which allows bots to understand customers’ intentions and provide relevant responses, Valdina offered.
It needs to be fine-tuned and continually updated to capture the nuances of an industry, a company, and its products/services. These elements enable sophisticated, contextually aware interactions that closely resemble human conversation.
“It is crucial to recognize changes in sentiment to know when to connect the customer with a live agent. Properly implemented NLP equips chatbots with this level of contextual awareness critical for successful customer interactions,” he explained.
Verint found that consumers want to use chatbots for self-service. The problem they face, however, is that most of what is deployed is built badly.
To fix that flaw, companies must go beyond using simple word trees. A new breed of conversational AI must understand a wide range of customer intents and deliver efficient and effective service.
All-in-One Approach a Better Strategy
According to Valdina, Verint uses a digital-first strategy to provide a “single pane of glass” for customer engagement, giving agents a holistic view across all engagement channels. That could be a more productive approach for some of its clients, who cling to phone, email, chat, social media, and messaging interactions siloed on different data platforms.
This omnichannel desktop experience provides them with a comprehensive view of data for a single way to engage regardless of the channel. Consolidating telephony, videoconferencing options, and other channels into one platform significantly streamlines business operations and enhances the customer experience.
Combining digital (social messaging) and traditional (voice) communication methods ensures brands provide a seamless experience across all touchpoints. There is also an emphasis on CX automation, whether automated email responses or proactive chat, to increase efficiency and allow faster and more personalized support.
“If an organization has a diverse range of 15 or more digital and traditional customer engagement channels, Verint offers a unified platform that helps brands meet those customers wherever they are,” he said.
AI-Centric Call Centers Ring Better Human Deployment
Leveraging AI in the call center makes customer interactions more efficient and successful. Targeting small daily opportunities with AI optimizes and improves customer interactions. These micro-moments are critical to scaling improvements and making impactful changes.
“A 30% reduction in average handling time, for example, means your company has 30% more capacity to work on things that need human attention,” explained Valdina.
AI insights and predefined criteria benefit call center agents. When human agents have to delay offering an unhappy customer a discount until manager approval is garnered, the risk of churn heightens.
With the help of AI, unhappy customers at risk of churn can be identified and provided with real-time solutions, such as a discount or voucher, to show goodwill. At the same time, the agent determines the best way to address their concerns, he added.
No More ‘Sorry, I Didn’t Understand’ Botsense
By leveraging its language models with third-party tools and open-source resources, Verint tweaked its bot capabilities to make the fixed-flow chatbot unnecessary. It developed proprietary language models with its Verint Da Vinci AI to build a large volume of anonymous customer conversations flowing through its platform.
Da Vinci powers all Verint applications and is embedded into business process workflows to maximize CX automation. All of Verint’s AI models are continuously trained on customer engagement data to ensure that they are fine-tuned and can perform successfully.
“Access to this type of data allows us to create customized language models that are finely tuned to the nuances and vocabulary used within each industry, providing more accurate and relevant responses to customer inquiries,” Valdina noted.
For example, the company’s hundreds of airline industry customers are the basis for NLP models Verint built that are typical for its specific customer interactions. It’s not just one giant NLP model; it is dynamic.
One model handles foreign languages, another performs escalation scenarios, and a third has industry/domain expertise. This setup enables a chatbot to switch between the language models in the same interaction as the conversation shifts.
“Brands need to dynamically utilize multiple language models to deliver dynamic conversational experiences at the same time as the conversation shifts. This capability is what can create a memorable customer experience and set a brand apart from the pack,” he said.
Advanced Inventory of Next-Gen Bots
Good bots don’t just deliver information; they deliver resolutions, according to Valdina. Verint created a group of high-end, very good bots. Some of them include:
- Intelligent Virtual Assistant (IVA) can book a flight, change a medical appointment, or sell a product to the customer – right in the chat.
- Real-Time Agent Assist (RTAA) provides real-time guidance based on acoustic (nonverbal), linguistic (verbal), and desktop activity to employees working from anywhere.
- Automated Quality Management (AQM) evaluates calls, identifies non-compliance, and assigns coaching for 100 percent of voice and text interactions.
- Interaction Wrap-Up Bot uses deep learning to provide market-leading real-time transcription accuracy, which is fed into generative AI algorithms, such as ChatGPT, for automatic interaction summarization.
- Interaction Transfer Bot provides seamless transitions from self-service to assisted service.
- TimeFlex Bot lets agents earn and spend “Flex Coins” to create shift changes as needed. Flex Coins are earned when agents make changes that improve the forecast performance.
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