Mar 19, 2019

Conversational AI Chatbots

Conversational AI is the ability of a computer program to have a conversation with a human. This technology is used to create chatbots, which are computer programs that can mimic human conversation.

Conversational AI is powered by natural language processing (NLP), which is the ability of a computer program to understand human language. NLP is used to interpret the user’s question, then find and return the best answer from a pool of data. A number of guiding principles are important.

6 Core Principles of Guided Conversational AI Chatbots

  1. Available everywhere: An enterprise should offer assistance on all the major touchpoints where consumers want to interact, both modern (digital) and traditional (voice). Based on the customer’s devices and preferences, a guided chatbot should determine the best channel for the current interaction. The chatbot should detect presence across devices and give customers the option to switch to a more effective channel. For example, the chatbot can sense that a caller is also on the website, and offer to present complex information using visuals over the web rather than read it out over the phone.
  2. Intent driven: Since humans may be imprecise in their communications, the ability to understand consumer intent is critical. Intent prediction goes beyond natural language processing. The platform needs to combine behavioral, transactional and external signals (such as time, weather, product availability, flight schedules) to anticipate intent or disambiguate a vague request.
  3. Conversations with a chatbot: Conversations are more effective when the message is meaningful and relevant, based on the individual’s demographics, preferences and interests. If there is a request for product information, the chatbot should personalize the response by highlighting the features and capabilities that are likely to be most useful. Furthermore, the form of a response — such as its wording, phrasing and tone — should adapt to the customer’s interaction style.
  4. Agent blending: Chatbots need to know what they don’t know: by design, certain intents may require a human agent. Chatbots should also know when they are failing. If frustration is detected, the chatbot should escalate to a human agent before the customer abandons the chat session. In these situations, a human agent should continue the journey where the chatbot left off, without requiring the customer to start over. The chatbot can remain engaged: it can suggest responses or lookup answers to assist the agent in real time. When the conversation is back on track, the agent may re-invoke the chatbot to handle routine tasks, such as payment collection, account updates, or terms and conditions.
  5. AI-enabled conversational chatbots: Gartner states that customer self-service will increase from 50 percent of customer interactions in 2018 to 64 percent in 2022 due to advancement of AI capabilities. By learning from the end-to-end outcome of customer interactions, including actions performed by human agents, AI-enabled conversational chatbots can continuously improve to better predict intent and optimize conversations.
  6. Enterprise ready: Chatbots need to integrate to CRM and other enterprise systems to apply business rules and perform transactions. As chatbots handle sensitive consumer data, security is paramount. These solutions will have to achieve enterprise levels of security, reliability, scalability, manageability and connectivity.

Incorporating chatbots into your customer experience can make a huge difference but the key is to ensure core abilities are in place that can deliver a successful and positive experience for customers.

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