Once upon a time, chatbots could merely serve a customer based on a static script coded into their rules. This was a challenge because customers would often employ slang, shorthand or expressions that were not part of the script, thereby limiting the ability of the chatbots to respond satisfactorily. Now, the advent of intelligent search powered by machine learning (ML) and natural language processing (NLP) has given rise to conversational chatbots. According to Allyson Barr, Chief Marketing Officer at Attivio, these chatbots deliver a more satisfactory productive experience.
Today, NLP elements like parts of speech tagging, lemmatization, synonym expansion, classification, entity extraction, key phrases, and sentiment analysis are enabling chatbots to understand user questions and the intent behind them. For instance, tools such as semantic search are helping systems comprehend the meaning of a query and respond appropriately. Additionally, automatic spelling correction and advanced syntax have increased the efficacy of search findings. In addition, lemmatization is allowing chatbots counteract common typing errors and identify precisely what the customer is inquiring.
Employing the entire technology in the background means the chatbot will be able to turn a frustrated user query into an answer that absolutely solves the problem. Furthermore, the chatbots can now automatically locate the most relevant answer by mining information from across the organization, regardless of where it resides.
Significantly, ML is equipping chatbots to evolve along with the user queries. ML uses clicks as a signal of popularity. Additionally, ML analyzes user behavior and builds relevancy models that enable chatbots to learn and improve as content, data, and user activity grows and evolves.
In sum, intelligent search is empowering chatbots to gain contextual understanding and interpret language inputs better. These advances are enabling them to serve customers better and offer an enriched user experience.
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