[The following is an excerpt from an article our Chief Technology Officer Patrick Nguyen wrote for CMSWire explaining why conversational chatbots are key to successful enterprise deployments.]
Gartner predicts that by 2020, 30 percent of our interactions with technology will be through "conversations" with smart machines. A chatbot therefore needs to possess cognitive capabilities to understand human dialogue and navigate business systems to resolve customer issues. A number of guiding principles are important:
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 bot 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.
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.
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 bot 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.
Bots need to know what they don’t know: by design, certain intents may require a human agent. Bots should also know when they are failing. If frustration is detected, the bot 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 bot left off, without requiring the customer to start over. The bot 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 bot to handle routine tasks, such as payment collection, account updates, or terms and conditions.
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.
Bots need to integrate to CRM and other enterprise systems to apply business rules and perform transactions. As bots handle sensitive consumer data, security is paramount. These solutions will have to achieve enterprise levels of security, reliability, scalability, manageability and connectivity.
Incorporating bots 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.
Read Patrick’s full article, Deploying a Bot? Remember the Conversational Advantage here.