Indian luxury goods company Titan is confident that its chatbot will double the traffic to its site from 1.5 million to 3 million by 2019 financial year. When they launched it, it became an instant hit and went viral and therefore boosted sales. The potential of Artificial Intelligence technologies is such that it will rapidly be deployed in the world in the coming years.
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Chatbots are those Artificial Intelligence systems that one can interact with through text or voice interface. The interactions with these systems can be simple and straightforward like asking the bot about new collections in a merchandise or complex like asking to troubleshoot the problem with internet service. In this blog post, we’ll be giving some pointers as to how to build an efficient chatbot.
Important Aspects to Consider While Building a Chatbot
What exactly is the purpose for which you are building the Artificial Intelligence chatbot? This aspect should be clearly stated. The most common reason for using Artificial Intelligence chatbot is that processes can be automated for increased efficiency and accuracy. Solving operational challenges and deciphering things through the help of big data are other benefits of using Artificial Intelligence chatbots. Solving work and data complexities will obviously increase the expertise and efficiency of the business.
A chatbot designer can better build the system when he is well acquainted with the goals of the system. The quality of user engagement your website can deliver will be directly proportional to the way the designer has understood the goals. There are other things to keep in mind when designing an Artificial Intelligence chatbot like structured and unstructured interactions.
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Understand Customer Intent
If you own a business or a website what do you expect the customer wants from you. This aspect should be clearly understood. You should be familiar as to how your customer engages you (chat, phone, social media, etc.). You should know what actions they perform and how they enter your sales channel and customer service departments. Reach a consensus with your department heads to clearly understand customer intent.
Structured Interactions
The queries which a customer asks will be like how to pay, what is the refund procedure, etc., and such FAQ-like questions are then structured. This interaction will provide information on contacts, services, products, etc.
Unstructured Interactions
These kinds of conversations are hard to predict as it includes freestyle plain text which is beyond simple keywords. The queries being asked are not simple and can’t be answered with a ready response. It has to be processed and the results should still be put out instantly as if the chatbot and the customer are conversing. Here AI understands the communication context through complex NLP analysis.
Domain Knowledge
This gives an answer to what should the bot understand from the input given by the user. Certain AIs and bots can answer amazingly or perform specific tasks very expertly. But they are restricted to that only. This phenomenon is why technocrats call AI as idiot savants.
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Personality
Bots often have been characterized by having an annoying lack of comprehension and mindless responses. Therefore, they are classified as less human and more robotic. It’s better to portray your bot as a non-human character rather than as a female or a millennial.
There needn’t be a tight coupling between domain and personality. The titan bot needs to know about products, discounts, and exclusive offers, but the domain doesn’t imply any kind of personality. A shopping bot can have the personality of a helpful person or be devoid of it entirely.
Natural Language Processing
The bot needs to be programmed with the right NLP software. The ideal NLP software doesn’t use keywords from customer input. Rather the knowledge of sentence structure, idioms and pattern recognition is used to determine the intent behind customer input. The bot is therefore programmed to identify things that people want from it. The Natural Learning Processing engine works by detecting and extracting entities using libraries used for tasks like named entity recognition and tokenization. Tokenization filters down all the words in a sentence without punctuation marks whereas named entity recognition looks for words in pre-defined categories. A library called normalizer identifies the most commonly done spelling errors, expands contractions and abbreviations.
Additional NLP tasks would be needed to measure content and intent. This can enable the NLP engine to understand the relationship between words. Words are parts of speech, and tagging the relationships between words takes a sentence and identifies its nouns, verbs, adjectives, etc. Dependency parsing is used to identify subjects, phrases, and objects. More complex NLP tasks can be included in the chatbot like sentiment analysis. If a customer is becoming frustrated with the chatbot, then the bot can escalate the token to a human customer rep.
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There are a lot of alternatives for building an NLP engine and that depends on your bot functionality and the language. Python is most preferred for its competent Machine Learning libraries like NLTK, SpaCy, and Pattern. For those who don’t want to develop their own NLP engines, Wit.AI and API.AI can be used to create question-and-answer chat routines. This will train the chatbot to recognize normal requests and entities. When such routines are created, the services will use Machine Learning to make the bot answer to similar chat routines based on input and data from other bot platforms.
Artificial Intelligence Chatbots will naturally be connected to a database. NoSQL databases are commonly used for chatbots. MongoDB is the most preferred among the NoSQL databases for its document-oriented option especially for firms who want to do analytics on the data they gather. This would then be fed into Machine Learning systems to enhance the bot’s performance.
Using Non-coding Frameworks for Artificial Intelligence Chatbots
Platforms like Flow XO, Bottr, etc. can be used to create a chatbot system. Through this, it is not possible to build NLP-based Artificial Intelligence chatbots that deal with unstructured data, but one can build sample Artificial Intelligence chatbots that run well on cloud systems. There would be drag-and-drop templates to create bots with the help of these frameworks.
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Using Coding Frameworks for Chatbots
One needs to be well acquainted with programming languages to develop chatbots this way. Storing data, giving AI capability and producing analytics is all possible with these frameworks. These frameworks include Wit.AI, API.AI, and Microsoft Bot. Here is where NLP based functionality of chatbot can be better simulated and advanced AI can be inculcated into the Artificial Intelligence chatbot system. Highly complex unstructured interactions can be answered when chatbots are built this way.
An Artificial Intelligence chatbot is not so different from a typical app. But the chatbots have different requirements than web or mobile apps. Other apps are feature rich and complex but chatbots should be lightweight and fast.
Key Takeaway
Gartner says that 85 percent of the customer interaction with a brand will be through a chatbot by 2020. This shows that Artificial Intelligence chatbots are clearly the future of the site-customer interface. It may replace many jobs but then again for every job lost to AI there will be more created. The trend will pick up where companies will increasingly use the chatbot to drive traffic and boost sales. Therefore, it would be nice if one knows more about building an Artificial Intelligence chatbot or better yet be able to build it himself. If that’s your intention, then our AI training will be a great help to you.
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