Based on the popularity of AI algorithms, conversational robots have become more and more popular in recent years and are being promoted and used in all walks of life. From basic button-based automation bots to NLP-driven conversational chatbots, what’s the difference? The most important thing is how should we design a conversational robot that meets business needs? Let’s discuss it together here.
First of all, it is important to differentiate between the various types of chatbots available in the market. What are the levels of chatbots from simple menu/button based chatbots to conversational AI chatbots? There are different types of chatbots and they use different technologies, so let’s take a look at what are their respective characteristics.
As the name suggests, this type of chatbot allows the user to choose from multiple options, which are presented in the form of a menu or buttons. Depending on what the user clicks on, the bot prompts him with another set of options to choose from, and so on.
As you can guess, their structure consists of very basic button forms (single choice, multi-select, etc.), and because of its simplicity, they represent most chatbots. These bots can answer preset questions and help users navigate a website or online store, thereby facilitating their purchasing journey. The downside is that they are less effective when solving complex requests involving a large number of variables. In fact, once the user’s query falls outside the preset range, this type of chatbot cannot provide any help and ultimately leaves the user feeling very disappointed and frustrated.
With this type of chatbot, the user enters a word or a phrase and the bot identifies the key words in the query Character. This type of bot uses a basic analysis engine to process these keywords and match them against a preloaded vocabulary.
The advantage of this is that the robot will only reply to the content manually loaded into the system and will not deviate from the topic, so that the business can control the automatic messaging of the vocabulary very friendly.
On the other hand, this type of chatbot is limited by its inability to recognize misspelled words or slang. They are also highly contextual and can be significantly inadequate when used outside of their context. Ask the library chatbot the question "book a hotel" and it may return books about hotels.
This type is the most advanced artificial intelligence chatbot so far. They use artificial intelligence and natural language processing to provide the best experience for their users. Thanks to these technologies, the bot considers the different words that make up the sentence, analyzing them as well as any available context to gain a contextual understanding of the question. So it can apply that understanding to the solution of the query.
The main advantage of conversational chatbots using NLP is that they understand the meaning behind the words, and the algorithm-based advantage is also able to understand misspellings, thereby providing a better user experience for users.
I believe that every enterprise has already used chatbots with conversational AI technology, and the robot's capabilities can also reach different conversation "levels" . Let’s take a specific case as an example to explain what these different stages look like.
Suppose a company develops an NLP conversational chatbot for internal use to answer employee questions on various HR matters. A team member wants to know how many days of annual leave he has left, and he asks the chatbot.
The first level answer lies in telling the employee where he can find the answer to the question, usually on his payroll or HR software. This is the simplest, most basic level of conversation you can easily achieve when designing a conversational chatbot.
The second level answer is slightly more evolved, as the bot can redirect the employee to a specific internal system, such as HR software in this case, where he can find out how many days of annual leave he has left .
Finally, the more advanced third-level answers allow the chatbot to automatically and seamlessly log the employee into the HR software so that he directly accesses the information he needs. Bots at this stage can even prompt employees to request some annual leave via a calendar or form without leaving the chat platform. This stage obviously means that the conversational chatbot can be integrated with a third-party platform or software to be able to retrieve information into another system. This is one of the technical prerequisites for robots to provide such interactions and services.
Having a conversational chatbot using NLP technology is a very good start, which can bring a good competitive advantage to the company and reduce the cost. This works for the purpose, but you must also ensure that the interaction with the bot is qualitative and engaging to your users. So how do you design a bot that users will reason to talk to? Here are some tips and previous practice examples.
As the name suggests, a chatbot script is a scenario where pre-designed conversation messages (business processes) are used as responses to user queries. Of course not all queries will require a script: simple FAQ type questions will be answered with a one-time request, but transactional queries will require a script. In practice, the bot must follow a specific conversational flow to gather the details needed to provide specific information, such as the car insurance quote bot previously developed at an insurance company.
This process will obviously present different prices based on the input information of the chatbot and the vehicle volume information, but please keep the following suggestions in mind when writing the process:
No matter what the goal of your conversational chatbot is, you must ensure People understand it. This means that every response given by the bot must be clear and free of any ambiguity that could lead to misunderstanding.
This may seem obvious, but most companies or Botmasters forget this simple rule. It results in a very confusing and impractical conversational interface, which completely defeats the purpose of designing the bot in the first place.
In addition to designing a clear and unambiguous process, we must also make the robot's answers as short as possible. The reason is simple: the more they read, the more likely it is that users will feel confused, tired, and distracted. A good way to do this is to break down the conversation, i.e. break the bot’s messages into smaller chunks.
Personalization is your bot’s advantage. In fact, we have to define what kind of personality we want the conversational chatbot to have, determining its tone, what language it will use, how it communicates, etc.
Designing a character that users like is a tricky problem. Give it too little personality and the interaction feels bland. Overuse it can quickly become annoying...
To sum up, designing a quality conversational chatbot is not an easy task, but I hope these tips and practices Experience will help when designing intelligent robots.
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