The rising population is leading to a huge load of traffic, thus increasing intense workload on the employees, in every sector. Any customer service work can be a challenging and rewarding experience. Repetitive workload may decrease the productivity of employees resulting into the dissatisfaction of customers. The primary goal of any industry is not just to satisfy customer requirements but also create an amazing user experience so as to retain the clients. The 21st century is marked with the growing era of Artificial Intelligence. More than 90% of worldwide industries have been streaming on AI platforms and the rest are on the similar path to adopt it. Thus, at many organizations ‘CHATBOT’ are playing role of ‘Smart Assistants’ or they are utilized to augment the human counterpart.
We all know Alexa, Siri and Google bot. These are famous examples of Chatbots. Chatbots are tiny programs that help simulate interactions with customers automatically based on a set of predefined conditions, triggers, instances and utterances. They are specifically designed to solve customers’ day to day queries and get all the latest information about the industry preferably without having any human intervention. Thus, they provide 24X7 customer support at significantly lower cost. One of the sectors which is tremendously getting benefited with this technology is the Banking Sector. Banks have adopted Chatbots as one of their most prominent business tools.
With all this preamble, let’s go to the nitty-gritty of technology stack of chatbot.
It’s easy if we choose a framework for building chatbot. There are many frameworks available but the popular ones are dialogflow, QnA Maker and RASA. While first two are from Google and Microsoft the third one is open source. We have to create intents, entities, and utterances to get desired responses. Once the users’ flows are finalized for desires use cases, the initial data in terms of Questions and Answer needs to be provided to this agent. The above mentioned frameworks have basic Natural Language Understanding (NLU) techniques which helps to generate basic responses. But, as the conversation becomes bit complex we have to start training the data so that it can be part of framework and would learn from it consequently.
Business applications of Chatbots for entertaining simple client queries are growing rapidly. In fact, over 59% of millennial's have interacted with Chatbots. Many e-commerce websites are already headed towards the adoption of this tool for their customers’ satisfaction. In our opinion, Chatbots will have a vast and increasing market in the upcoming years. What is your opinion towards adopting Chatbots as a business tool? Do comment your views!