The increasing popularity of chat-based communication is also giving rise to chatbots. Thanks to chatbots, your company can engage with an unlimited number of customers at any given time without involving customer service, which elevates operational efficiency and cuts costs.
Today, 23% of organizations use chatbots in the administrative department, while 20% use them for customer service.
Chatbots enable companies to resolve customer issues or answer questions without putting a strain on human resources. At the same time, chances are you've already encountered a chatbot and know that an unhelpful chatbot might be a detractor. By using chatbots, businesses can provide personalized, human-like service while customers get timely help, provided the chatbot is set up right. But let’s start from the beginning.
A chatbot is a computer program that imitates and processes human conversation and allows customers to interact with digital devices as if they were talking to a real person. From customer service to online shopping - companies employ chatbots in different ways: sales teams can provide pricing or shipping information, while customer service teams are relieved from answering repetitive requests to resolve mundane issues.
Chatbots vary greatly in their complexity. It could be a simple program to answer straightforward queries with a single-line response or a sophisticated virtual assistant that evolves as it processes new information. Generally, chatbots can be classified into two types:
Rule or task-based chatbots use predefined conversational paths. They typically follow a hierarchical pattern: every reply leads to new options for the user to choose from until the desired answer is reached.
Such chatbots can only respond to the most common questions about the business; they represent a kind of upgraded FAQ. If users ask something outside of predefined queries, rule-based chatbots cannot answer them. So although these chatbots do use natural language processing (NLP) to imitate real-life conversations, their capabilities are limited.
Data-driven or conversational chatbots are much more advanced and interactive. They are contextually aware, can learn from users’ past experiences, and anticipate future needs.
Data-driven chatbots are developed using NLP and conversational AI and can facilitate much more intelligent conversations with users, as long as they are trained using suitable datasets. These are the kind of chatbots we refer to in this post.
Once you decide to build a chatbot to use in your business operations, the next step is to choose a chatbot provider. There are many different chatbot technology vendors, and as they operate using diverse methodologies, not all of them might be a good fit for you.
Choose a provider whose framework matches your communication channels and features best to ensure your needs are covered. Some chatbot providers you can look into: Microsoft Bot Framework, IBM Watson assistant, Kore.ai, and Boost.ai, among others.
Regardless of the technology you choose, the most important part is training the chatbot. And a chatbot can only be as good as its training data.
You will need lots of conversational training data to develop a helpful chatbot successfully. The quality of this data will affect your chatbot’s ability to recognize the right intent and respond accordingly. Intent indicates a customer's goal when interacting with the chatbot; for example, an intent could be that a customer wants to upgrade their subscription plan.
Intent recognition is crucial to chatbot functionality because your chatbot’s accuracy in understanding intent will ultimately determine its success. Identifying intent can be a challenge because humans converse and explain themselves differently. Some use complete, grammatically correct sentences, while others make a lot of typos or use keywords.
Because intent plays a significant role in chatbot development, the data to train chatbots is also known as intent data. Varying definitions of intent data exist, including behavioral datasets used in marketing and sales campaigns, but in this case, we mean conversational training data specifically.
The predefined intents in conversational data can be limited, causing chatbots to misinterpret users’ intent, provide faulty responses, damage your company’s image, or even cost you a client. Therefore quality datasets that fit your use cases are essential in chatbot development.
Finally, your training data has to be classified or annotated into the correct intents so that it can be used to automate and reduce manual processing. For example, someone loses their bank card, explains the situation to the chatbot, and wants to freeze their bank account. In this case, the label or the annotation could be “freeze account.”
You may already have intent data in your system - using existing customer conversations to build your chatbot is possible. However, note that to use it, you need to have collected this data in accordance with privacy regulations.
If you don't have such data, you can use a data vendor like StageZero. We can collect intent data for any use cases you might need and annotate it. With over 10 million of global users, we can generate as much training data as necessary to build a great chatbot in any language.
Learn more about how StageZero can help collect intent data and provide annotation.
