If you own a business with heavy customer service response demand, and your target is to provide quick and efficient response to your customers, then you have landed on right spot. We have a solution for you to take one step ahead of your business competitors. In next few minutes you will be learning about simple and precise techniques to develop a chatbot using state of the art Bots in Deep Learning
Any business which involves customer service, needs very high maintenance in terms of customer support using CSR(Customer Service Representative) Agents. Which eventually results in high cost in terms of salaries of agents as well requirement of there personal PCs to use while handling calls. Before emergence of Computer and internet it was assumed that one phone number with CSR agent is more than enough to satisfy customer in case of guidance, but now needs of customers and business have changed a lot. Each customer demands for quick and reliable response in 1 call. Fix-it-now attitude have changed requirements for CSR jobs. Specially when there is a requirement of technical support, highly skilled personals are mandatory for the job. Which results in high salaries.
Due to these changing dimensions of businesses, companies started to launch chat services for their customers. By those chat options it became possible for 1 agent to handle multiple customers at a time. Response of this kind of customer service was so overwhelming because customers feel more valuable by getting customer support 24/7 from there service provider companies.
Automation and Use of Chatbots:
Most of telecom companies got benefit from this technology of 24/7 online chat facility. Their level of dealing customer get too much smooth by implementing it. But as the law of nature, everything needs to be advanced with time. There is always some loop holes left to cover and in this technology, there are lots of loop holes left to cover up.
But thanks to newly emerged Artificial intelligence field called Deep learning, which helped a lot to cove up those holes by implementing Chat bots. These bots are capable of replying customers autonomously thorough mails, chats and most importantly through calls. Let’s start discussing Chat bots from its use and replacement of humans. While developing a good chat bot main motivation of any company is to completely automate the process of customer service representative which will eventually reduce human management involvement in whole process as much as possible.
Options to Develop Chatbot
There are two options to develop a chatbot equipped system.
First option is to use
chat bots as helper
to already working CSR agents. In that case
company will empower their
agents to use AI based chatbots to reply
queries as best as possible
while replying to queries. But in that situation agents have to put in customer
quires into system, Chat
will respond to it and then
agent will guide customer
according to that response.
But second option
is more automated
and cost cutting. Replace all agents
with chat bots and eliminate human interference in whole process of customer query calls. In this blog we will be focusing on 2nd option.
So, we are set for: building a chat bot for telecom company.
As we are focusing on Deep learning which is a branch of Machine Learning, the very first step would be data preparation. Because it should be correctly interpreted by the machine and understandable form of thousands of already available customer and Customer service representatives’ interactions to teach machine which phase/words are appropriate for the targeted industry. Considering all this process is known as Ontology.
In next phase data preprocessing will be performed by incorporating proper grammar into machine understanding and mis spelled text. Also preprocessing includes tokenizing, stemming and lemmatizing to make it learnable for bot. Usually it is done by using freely available python language toolkit called NLTK Tool which is available online also. In the last step it will create accessible parse tree provided by company chat department, which will serve bot as reference.
Once ontology process is completed and preprocessing is done, now question is which kind of bot you wanted to create. There are two available options:
- Retrieval-Based model
- Generative model
Retrieval-based model uses predefined responses repository. But in Generative model they don’t depends upon predefined responses repositories, it uses modern technique called Deep Learning to learn from scratch and generalize for chatbots. This technique is very similar to human responses and language models. These models are very hard to train but once you have trained it efficiently its results will be near to humans.
However, in the telecom industry situation, they have enough data to train chatbots for their regular queries. Generative models don’t guarantee to be a human, however they can respond better in case of surprising questions and demands by customers. For training any of given models there is a very popular technique called LSTM (Long Short-Term Memory) based on RNN (Recurrent Neural Networks). Basic concept of this network is to remember previous information or sentence in our case of chat bot and respond accordingly. Basic structure of LSTM is shown in figure below.
For further details related to RNN and LSTM please visit to this blog.
Now lets have a look on simple Code of Chatbot which will search for given query and if answer not found it will return “Sorry, I cannot think of a reply for that.” This code is taken from Github developed by lmzach09.
Chatbots are essential part of 4th industrial revolution because it is transforming user experience and job market. It is a very impactful for companies to reduce their cost of CSR department also non sop services. It requires one-time Hugh investment in the form of GPUs enabled systems but after that it will cut-off large amount of budget for customer services. 24/7 customer support is no doubt very attractive element for each customer before buying product of that particular company.