When it comes to designing modern customer self-service strategies, how do enterprises create a great human-like experience for customers when no human is involved? How do they mimic the personalization and meaningfulness of a human-to-human engagement through self-service interactions? The answer to these questions is through semantic Artificial Intelligence (AI).
Understanding user intent is a decisive factor when it comes to designing modern customer self-service strategies for enterprises. It starts with a solid knowledge base, containing the content identified as being relevant to customers' most common questions. Naturally, the more content is captured, the easier it will be for a self-service tool such as a search engine, chatbot, or conversational User Interface (UI) to extract a relevant response when faced with a customer query.
To get closer to understanding user intent, enterprises can use a feedback loop and Machine Learning (ML). Nevertheless, ML alone is not enough to get to a human-like 'understanding' of user intent because it will fail whenever there is not enough past behavior to make good predictions, or guess at the equivalence of synonyms or avoid other pitfalls of language.
That is why it is important to add a knowledge management model, specifically a knowledge graph, to the self-service mix. A knowledge graph allows enterprises to tag their content in ways that expose meaning (capturing synonyms and other nuances of language) and all sorts of relationships between different types of information. The right kind of knowledge graph system improves the quality of the knowledge base through taxonomy governance and automated classification suggestions.
Consequently, the knowledge base is transformed into a sophisticated asset that can be used to step up self-service from being enhanced only by ML, to being powered by semantic AI. In practice, when a self-service AI-powered search engine, chatbot, or conversational UI turns to a knowledge graph to find an answer to a user question, it finds enough context to resolve ambiguous language, and enough direction to move beyond the most obvious starting point to find a more relevant, or complete, answer. In other words, it comes as close as an AI can get to 'understanding' what the user's question meant, or what their search intent was.
In short, semantic AI can use the understanding and insight enabled by the knowledge graph, together with its learning abilities, to deliver personalized recommendations and answers during self-service interactions. It is key to understanding user intent and fundamental to delivering an excellent customer self-service experience.
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