Customer service bot is a bot that uses artificial intelligence (AI) and machine learning to answer basic customer questions via a business messenger. It can recognize and answer multiple forms of the same question and can be trained to give instant responses using your preferred voice or text.
Here's why:
- AI chatbots use your existing information and resources, like FAQs or knowledge base articles, to help answer and resolve your customers’ questions.
- They can recognize and answer multiple forms of the same question and can be trained to give instant responses using your preferred voice and tone.
- Chatbots offers instant resolutions *Your business can offer 24/7 conversational support *Chatbots continuously learn
- You can tailor answers to different types of customers
- Your team has more context on each customer
- A consistent user experience is created
Chatbots and AI received a major facelift in recent years. The once futuristic-yet-a-pain-in-the-butt digital assistants are being fueled with more data to better serve customers and drive sales.
Is it worth the time and money investment? It depends on your industry and company needs. But today’s sophisticated bot infrastructures have a lot to offer that most businesses can benefit from.
This chatbot was build using following frameworks, libraries and softwares.
To run this project you need to follow the following steps.
These are the prerequisites you need to build this bot as well as run it.
$ pip3 install rasa==1.10.8
$ pip3 install rasa[spacy]
$ python -m spacy download en_core_web_md
$ python -m spacy link en_core_web_md en
$ pip3 install pandas==1.1.0
$ pip3 install nltk==3.5
$ pip3 install fuzzywuzzy==0.18.0- Create conda environment and create project in this environment
- After installing requirements in above Modules LIST
- To add custom component to rasa
- Add current working directory of this project in your python environment variable - eg: PATH = D:\Projects...\Restaurant-Bot-Automation
- To set the console channel Timeout in seconds
- Go to Anaconda3\envs{your_rasa_env}\Lib\site-packages\rasa\core\channels\console.py
- And set DEFAULT_STREAM_READING_TIMEOUT_IN_SECONDS=200
$ rasa train$ rasa train -c spacy_config.yml$ rasa run actions --actions actionserver.actions$ rasa shell --debug$ rasa shellIBM estimates that 265 billion customer support tickets and calls are made globally every year, resulting in $1.3 trillion in customer service costs. IBM also referenced a Chatbots Magazine figure purporting that implementing customer service AI solutions, such as chatbots, into service workflows can reduce a business’ spend on customer service by 30 percent. Here are the key areas for how customer service chatbots help businesses to deliver better support.
- Deliver instant customer support
- 24×7 availability
- Deliver seamless hybrid support along with live chat
- Easy scalability
- Reduce your cost of customer support with bots
- Reduce support tickets
- Collect customer feedback & information
- Automate your social media support
- Minimize IVR frustrations
- Better chatbot support = higher customer happiness!!
For more examples, please refer to the Article
See the open issues for a list of proposed features (and known issues).
Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
- MIT license
- Copyright 2020 © Aditya Mangla.
Aditya Mangla - @aadimangla - aadimangla@gmail.com - adityamangla.com
Project Link: https://github.com/aadimangla/Rasa-Chatbot