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2020 | OriginalPaper | Chapter

Language Model-Driven Chatbot for Business to Address Marketing and Selection of Products

Authors : Amit Kumar Kushwaha, Arpan Kumar Kar

Published in: Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation

Publisher: Springer International Publishing

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Abstract

Artificial Intelligence has been increasingly gaining acceptance across advanced functions in numerous fields and industries. This includes marketing, customer support, and leads generation in healthcare, transportation, education, and off late in e-commerce. Machine learning as a subset of artificial intelligence techniques provides various algorithms that enable machines to learn from historical data and make realtime predictions on numbers and texts. Most of the businesses nowadays are trying to increase their reach and making sure that they are available to cater to the customers when they need help. This also enables the companies to market and respond to the queries of potential customers on a realtime basis. Chatter robots or chatbot is one such application of machine learning which allows the business to provide round the clock support to customers and potential leads for marketing questions. Most of the business fail to venture in the domain of hosting chatbot on the website as they do not have enough conversational data with them to train the machine learning algorithm and wait for years to collect enough sample. With the proposed language model-driven chatbots, businesses starting fresh in the domain of the hosting this application can use the user-generated content on social media to fuel the backend framework for the chatbots and start hosting the application.

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Title: Language Model-Driven Chatbot for Business to Address Marketing and Selection of Products
Authors: Amit Kumar Kushwaha
Arpan Kumar Kar
Publisher: Springer International Publishing
Book: Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation
Print ISBN: 978-3-030-64848-0

Electronic ISBN: 978-3-030-64849-7

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-64849-7_3

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