AI Chatbots

A large amount of data is captured from conversational technologies like chatbots, which are a crucial channel for gathering data. Data analytics employs new approaches like DataOps to leverage data that is captured through chatbots. This data can be analyzed and integrated with the other sources of internal and external data for better marketing and customer service.

Integrating chatbots (front-office bots) with Robotic Process Automation (RPA) can enable them to navigate through back-end enterprise systems that don’t have modern APIs. RPA enables chatbots to retrieve information from these systems and handle more complex and real-time customer/employee requests and queries at scale. RPA bots can perform more mundane tasks without routing them to a human agent.By combining the power of automation from RPA and cognitive intelligence of chatbots, organizations can take their customer and employee experience to the next level, improve productivity, reduce costs and increase competitive advantage.

Companies deploying chatbots for their business processes need to standardize chatbots and make them consistent across all channels – for instance, chatbots deployed on a company’s social media pages cannot be different from the one deployed on their official page.

The next generation of AI assistants in the enterprise is the voice-based virtual assistants. Enabling chatbots to interact through voice relieves the agents and customers from the need to use devices (like mouse and keyboard) to interact with business applications. According to Gartner, By 2023, 25% of employee interactions with applications will happen via voice, up from almost 3% in 2019. Voice bots can deliver more personalized responses with contextual understanding, speech synthesis, voice recognition, and natural language processing.

According to reports, 67% of US millennials said they are likely to purchase products and services from brands using a chatbot Incorporating automated payments in the process paves the way to easier purchasing during online shopping as it eliminates the need to visit a website, as chatbots will be able to handle the entire purchasing process.

Sentiment analysis empowers chatbots with the ability to understand the emotions and mood of the user by analyzing their text or voice input. This helps chatbots to drive the conversation wisely and deliver appropriate responses. The purpose of sentiment analysis is to provide a personalized experience and make chatbots effectively respond according to the customer’s mood.Using this technology you can understand customers’ perception of the brand, enable seamless agent handoff, improve upselling/cross-selling and deliver memorable customer experiences.

A key driver of user adoption is the chatbot’s ability to understand complex responses that could have multiple intents. Chatbots should predict not only what customers want but also any key information they might have forgotten.This could be achieved by upgrading your dialog systems with new features like knowledge graph, contextual understanding, topic switching, sentiment analysis, personality, etc.

Intelligent digital assistants can be trained to easily escalate a customer issue to a human agent using rules or failure conditions. Other options include a built-in live chat support app for your agents or you can integrate your existing live chat software as well.