Conversational AI Explodes To Close CX Gap

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COVID-19 has dramatically accelerated the adoption and implementation of digital transformation initiatives. Nowhere was this more evident than in customer experience (CX). Organizations have quickly adopted new technologies such as chatbots powered by artificial intelligence (AI) to meet customer expectations of timely response to questions and problem solving.

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Chatbots are an example of how AI can be used to augment human capabilities, providing customers with a convenient way to interact with organizations 24/7. In the context of customer service, they can provide an efficient and cost-effective way to handle large volumes of customer inquiries. This frees up human agents to focus on more complicated questions. With the increase in customer demand for digital channels during COVID-19, organizations that have invested in chatbots have been able to scale customer support and answer questions from prospects.

Conversational AI

In the world of customer service, “automation” has been a dirty word. The customer wants to talk to a person, not a machine. Employees are itching to be replaced by an automated system. However, as voice search, smart speakers and voice assistants become more and more accepted and accepted, automation becomes indispensable for delivering an excellent CX.

Conversational AI empowers customers and employees to quickly and easily get the answers they need, without having to queue or jump through hoops. At the back end, automation of the total experience brings all your customer, product, and employee data together in one place. This makes it easy to track customer journeys, identify areas for improvement, and provide consistently accurate answers to questions. In today’s customer-centric world, automation of the total experience is essential to providing an excellent CX and employee experience (EX).

Conversational AI enables human conversations between users with websites, applications and devices through texts, speech and commands. The five key elements that come together to realize conversational AI are as follows:

Conversational customer-facing user interface that receives and delivers input and outputNatural Language Processing (NLP) engineDialogue managerSearch engine that traverses data repositories through business integrationsMachine learning (ML) capabilities

When all the components are in place, the conversational AI experience can leverage many of the aspects that make human language such a versatile and rich medium of communication.

Automation of the total experience

Total experience automation is an approach to CX and EX that automates conversations across platforms at the front and integrates with business systems at the back. The goal of total experience automation is to provide a more seamless, efficient, and personalized CX and EX by leveraging conversational AI to automate interactions. The challenge is to improve the quality of the experience while controlling costs in an age of exploding data, channels, customer expectations and employee turnover.

To do this, businesses must have a robust back-end infrastructure that can connect customer data from different sources and enable real-time communication between front and back-end systems. By doing this, they can provide a more cohesive CX and allow employees to focus on higher value tasks. In addition, automation of the total experience can help businesses reduce costs by automating low-value, repetitive tasks, such as forecasting demand and reducing supply chain disruptions. is at the forefront of conversational AI by providing a highly efficient interface with stakeholders. They currently serve more than 1,200 customers in financial services, retail, energy, education and gaming, including Unilever, P&G, Schlumberger, Roche and Amazon.

The core of the front-end is multilingual. It supports more than 100 languages ​​through more than 35 text and voice channels, including WhatsApp, Google Business Messages, Apple Chat, Instagram, Messenger, Viber, WeChat, Alexa, Telephony and others

The platform is built on its own NLP engine. The Natural Language Understanding (NU) and NLP engine amplify the self-learning capabilities of the Dynamic AI Agents through multifactorial intent recognition, effective engagement, and instant resolution – all in real time and with 98% accuracy.

The predictive/AI layer predicts the future conversation or allows third-party tools to predict conversations and manage the workflow. This allows end users to place orders, allowing partners to use data feeds to build models for future predictions with little data or feature engineering.

Use cases

Diaggio has a bartender assistant who assists bartenders in making cocktails while also assisting with demand management using predictive analytics.

Asian Paints customer interactions create real-time forecasts of inventory needs.

American Shipping customers place orders and receive status updates during the shipping, transportation and delivery process used to forecast shipping demand.

AI + Human Emotion

AI and automation have the ability to ingest and analyze massive amounts of information and data in milliseconds.

Yellow-ai provides an interface between the customer and backend systems. It skips the front office and also provides an NLP interface. Dynamic AI agents uniquely learn from all human-answered questions to rapidly reduce future AI-to-human transfers, achieving 60% automation in the first 30 days of going live.

Humans have emotional and empathic abilities. While isn’t trying to synthesize that, it does have the ability to bring people in at the right time of the interaction. This creates training points to improve the models.

For example, when it comes to complex conversations that require empathy, the end user looks for human intervention. As for a customer looking for details on COVID-19 protocols, they want to connect with a human being. This includes calls that may involve customers at risk of churning or high-value transactions. The key to overcoming these challenges and creating an effective conversational AI strategy is to enable seamless transfer to humans, leveraging the collaborative intelligence of humans and AI.

The more seamless and successful these transitions become, the faster we will see the adoption and adoption of conversational AI chatbots across all industries.

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