16 Apr
What is the objective of Inquiry ChatBot?
Chatbot’s main objective is to reduce the repetitive task’s workload in a smoother way. And the second object can provide 24/7 customer service to clients. ChatBot is not only for customer inquiry purposes. We can use different ways. For example
- Advice assistant for Cancer patients to give tips who is undergoing chemotherapy.
- For doctors, it acts as Drug informative.
- In the Insurance field, for clients, chatbots act as educational assistants, .etc.
Why do we need Inquiry ChatBot?
Employers feel too much boredom by doing repetitive tasks or jobs, and these tasks under-utilize human resources. Telecommunication companies face big challenges today with these types of repetitive tasks. In telecom companies, there are so many departments. If some customer or user wants to know any information from companies, customers are waiting too long to speak to an agent.
A chatbot is very useful at this task; it can directly connect to all the department processes internally and can resolve issues spreading over various departments with ease. For the customer, the experience is much smoother.
Companies can use ChatBots to solve these uncomfort tasks. Companies can also save a huge amount of money by using it to reduce appointing customer support agents and training them to solve customer queries. Not only this task, but ChatBots are also useful in different ways. By adopting ChatBots, telecommunication companies experience profits in different ways.
- Increment in income — Chatbots can help and examine client conduct. They can also convey modified offers, which will, in turn, increment the income.
- Reduce costs — Chatbots in Telecommunication are the ideal method to give minimal effort, low cost, and strong help.
- Increased client maintenance or retention — Chatbots give customized or personalized customer service and omnichannel experience, and in this way, improve consumer satisfaction.
How does an AI-based solution work in Inquiry ChatBot?
All telecommunication customers or users experienced long call duration to reach out to customer agents who resolved their issues. Due to this, customers sometimes get too frustrated, especially when they are in a hurry or if the issue is fairly low-key.
ChatBot provides a solution for this such that it can easily understand human speech and provide a direct solution to client or customer issues by using Natural Language Processing techniques. This experience is very comfortable for clients too. In addition, it is basically definitely realized that few parts of approaching inquiries to the client service focus are more appropriate for bots to deal with.
We can build chatbots with different types of chatbots, such as generative-based chatbots and another one is retrieval-based chatbots.
Complete process
Data extraction
For ChatBot model building, we can collect a huge amount of data from telecom customer services such as call conversions or messages from existing conversations that are either from phone calls or messages or SMSs. In this stage, our goal is to collect or gather as much as possible data from customer services because a huge amount of data will help Chat Bots too much while learning.
Data annotation
Whatever data we collected or gathered from the customer services user communications or interactions is completely unstructured data. We need to do annotations on it. Without annotated data, the Chatbot does not perform well.
In this stage, annotators will annotate data with different types of topics. For example, a few questions and answers are related to recharge offers; few are related to balance, etc. So annotators will annotate data with all those topics. Then ChatBot models will learn patterns like what type of answers they will provide based on topics while training or building models.
So the annotation process in Chatbot really helps to model to perform well in real-world scenarios. Without an annotated data model can’t know what topic-related questions are asked by customers. We can use this annotated data for further activities in the way of building our ChatBot model.
Data processing
After extracting data from customer services, we should apply to preprocess techniques to the dataset. For Chatbot we have to focus more on grammar-related text preprocessing techniques. Because while converting speech to text of call conversion, sometimes the conversion model cannot predict the exact words of users due to the low quality of audio, etc. So if we apply spelling correction preprocessing techniques that will help our chatbot model, we can apply different text processing techniques in this stage, such as stemming, lemmatization, spelling correction, etc.
We need to apply other types of techniques like convert emojis or emoticons to words when we collect data from customer service messages. We should perform well in this process. Because the result of this directly affected the ChatBot model.
Model development
After completing all the activities mentioned earlier, we need to build a model for Chatbot by using a preprocessed dataset. Here we have to select which type of Chatbot we need to build our Chatbot problem statement or goal.
- Generative-based model :- in this model, the Chatbot doesn’t use any predefined repository. This is an advanced form of Chatbot that uses Deep learning techniques or algorithms to respond to customers’ queries.
- Retrieval-based model :- in this model, the Chatbot uses a repository that has responses to the queries. Here we need to choose an appropriate response based on the questions.
Generative-based Chatbot performed well as compared to retrieval-based models. Because it easily captures the style of questions and their customer responses etc. Generative-based models are suitable for complex queries and retrieval-based models are well performed on simple queries.
We can build chatbot models using sequences to sequence Natural Language Processing architecture to produce or generate sequential data in the correct form.
Sentiment analysis
Sentiment analysis is a layer on the head of a chatbot’s natural language understanding (NLU) engine. It is a usefulness that permits the Chatbot to ‘comprehend’ the client’s state of mind by investigating verbal and sentence structuring clues. This not just empowers organizations to comprehend the effect of their products/services yet additionally to change their systems according to the end customer’s sentiments. The organization built up an influencer chatbot empowered by sentiment analysis, which helped them improve business execution.
Topic modeling
Topic modeling gives us techniques to sort out, comprehend, and sum up vast textual information collections. An accurate forecast of discussion points can be a significant sign of making intelligible and connecting with chatbot systems. Topic modeling helps in: Discovering shrouded useful examples available over the collection of client conversational datasets.
Inference and deployment of the model
Now, this is time to test our trained Chatbot where users’ interactions happen similar to telecommunication customer services. We have to test all possibilities. If results are satisfied, then we can deploy or lunch our model to specific customer services from where we collect data. Otherwise, we have to re-perform the above activities.