Chatbot in Customer Service and the Role of Data Science

Chatbot in Customer Service and the Role of Data Science

Implementation of chatbots is extensive. These tools have leveled up the arena customer service. Small and large brands online are focusing on chatbot services for smooth customer interaction. An interaction that may offers solutions, consider requests, provide answers to queries, etc. This is a relatively new service that is ameliorating customer service across multiple verticals at an affordable budget. Time for Decoding More: The year 2017 has witnessed a paradigm shift in using chatbots. In fact, a huge number of businesses started to invest in this virtual live-chat agent that runs on the complex code and data science. Needless to say, chatbot is a trustworthy service. And trust is a much-favored human trait. Talking about data science, this very field of Big Data combines statistics and computation for a thorough interpretation, apt in making a better decision. Data science breaks down the messages into human understandable natural languages using several techniques and these are: 
  • Natural Language Processing
Natural language processing a.k.a NLP is one of the major reasons behind the superlative customer service. It is a technique that acts as a sensor to understand the patterns of human conversation. However, the finest NLP engines must be trained enough with well-structured codes to decipher all the variations with ease. 
  • Clustering of Phrase
Phrase clustering is another tactic for improved comprehension of semantic similarity. The logic is simple here. The similarity in the meaning of incoming messages makes the messages akin. So, common parts of speech or even word frequency don’t really matter much. Thanks to incredible data science, things are now simpler for both the businesses and customers.
  • More with Similarity Threshold
This tactic deals with clustering similarity threshold for a meaningful conversation. For example, topics like delivery, return or refund are common for an e-commerce company. Questions of these sorts get clustered together with approximately 90 percent similarity threshold. At times, the clusters may get saturated. However, developers have the flexibility to adjust the threshold limit for a broader spectrum of clusters.
  • Dealing Nuances
Users have different ways of writing. For instance, the word ‘please’ on text-based chatbots are often written as ‘plz’, ‘pls’, ‘pleez’ and so on. A powerful algorithm of phrase clustering is applied in such situations to understand the user intent in a better way. Bottom Line: Chatbot over the past couple of years has emerged out to be a self-service technology. Starting from generating automatic communication to extensive customer service, these new-age tools are an absolute delight to business. There is much more to this. With the constant progression in data science and even AI, chatbot services promise a gamut of opportunities. For those who are planning to explore this vertical of customer service can contact us for more insights and offerings.

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