A chatbot is a program or software application that automates conversations and interacts with customers online rather than providing direct human assistance.
There are two types of chatbots: rule-based bots and AI/ML (artificial intelligence/machine learning) based chatbots.
Rule-based chatbots, also called decision-tree bots, work on pre-defined rules. These chatbots provide responses or solutions based on a series of pre-defined rules.
Example: A rule-based chatbot can be used for booking an appointment with a doctor.
In a typical scenario, one has to visit a doctor’s clinic personally or should call the concerned person to book an appointment.
If you are availing the assistance of a chatbot for appointment booking, a patient can raise a request over the chatbot, and the chatbot will collect the required information (date and time of appointment, name, age, gender, etc.,) using a set of pre-stored questions.
The chatbot will confirm scheduled appointments after checking the availability for the requested date and time.
Using a chatbot for an appointment scheduling system frees you from the time-consuming act of making appointments and enables your patients to make an appointment when it’s convenient for them.
AI/ML chatbots use natural language processing (NLP) and machine learning. These bots can process and simulate human conversations more or less entirely on their own.
Example: An AI chatbot can be used as a virtual customer service agent.
A chatbot that uses AI and ML can understand user intent and provide personalised responses for their queries.
These types of chatbots can also use your existing knowledge base and provide responses for FAQs 24/7 without the help of a customer service agent.
When compared to a customer service agent, your chatbot (virtual customer service agent) can interact/deal with multiple customers at the same time.
AI and ML based chatbots are highly useful for call centres and other industries. Instead of employing a large number of customer service agents, you can implement a chatbot which can provide faster and accurate responses to your clients.
How to make a chatbot that learns from responses?
A combination of AI and a rule-based chatbot is an excellent solution to create a chatbot with learning capabilities.
Before forming a response, a chatbot (combining AI and rules-based functionality) understands the context and intent of the question. Pre-programmed scripts and machine learning algorithms are used to respond to chatbots.
When appropriately trained, a chatbot can determine the best responses for any query or message it is presented with. Chatbot chooses the best response from a pre-defined set of responses.
The more one interacts with a chatbot, the smarter it becomes.
Following these points to help you build a chatbot that learns from responses.
1) Why do you build a chatbot?
Planning and executing the steps in building a chatbot will be easier if you understand its purpose and objective.
Identifying the actual challenge will enable you to determine the purpose of a chatbot and its objectives. The challenges faced by your business and users can be identified based on your previous experience with them.
Some business owners realise that it is a mistake not to attend to website visitors properly, which results in low conversion. So they either employ a full-time staff to chat live with website visitors or create an AI chatbot to make this process automatic.
Software companies use AI chatbots to provide automated customer support by integrating the bot into their backend systems via APIs. Ex: AVLView.com uses a chatbot on the partner portal to help reseller partners to identify and resolve their clients’ GPS connectivity, login issues and fleet diagnostics.
Customers’ historical use cases, suggestions, comments, and concerns can prove valuable when figuring out the actual challenges. After determining the purpose of the chatbot, you should ensure that the chatbot you build will accomplish the desired goal.
The chatbot you are planning to develop must be beneficial to your business. Additionally, the information and data obtained from chatbot users should improve the user experience and generate revenue over time.
2) Identifying the target audience
Identifying your target audience is the next step after determining the purpose and objective of your chatbot.
Analysing your client base in detail can help you determine the target audience and create a chatbot tailored to their needs.
A person’s preferences, needs and expectations change over time. For example, the present generation prefers online portals to collect information, book a movie ticket, or to pay telephone bills.
So identifying a user group who prefers to use chatbot can help you achieve your goal easily.
The following factors can help you identify your target audience:
- Location and
How to determine the target audience?
- Conduct market surveys and research – Conducting a market survey or research is time consuming yet the most effective way of determining your target audience. The ‘market pulse’ and the intended audience can be better understood through market research or survey results.
- Make use of the available market analytical tools – Making use of the available market analytics tool can be of great help if you utilise this in the proper manner. Examples are Google analytics, Microsoft Power BI, Zoho analytics etc,.
- Analyse existing user’s database – The most economical way to identify your target audience is to analyze your existing user database. The most important advantages of this method are immediate availability, minimal time consumption and faster results.
3) Creating an impressive and effective chatbot design
The next step after identifying your target audience is to create a fruitful chatbot design that can deliver the expected results.
The design stage is one of the most crucial steps in building a chatbot. Any mistakes made at this stage will affect the results of the chatbot you create.
Before designing a chatbot, you should keep in mind that the user experience of your visitors must be impressive so that the user feels like they are having a conversation with your chatbot a second time.
Providing timely and relevant information/responses can keep the user engaged in a chat conversation. Moreover, a chatbot conversation should be backed by anticipatory and suggestive questions or answers.
During every stage of the conversation, the chatbot must be able to create an impression to the users that he/she is receiving personalised messages or responses. This will enable you to request a user to provide any required information, which can significantly help improve the chatbot experience.
One way to improve the conversation’s look and feel is by using visual elements. Visual elements can help you convey abstract and complex information most effectively.
A movie-booking chatbot, for instance, could provide a link to a short video clip or an image depicting a scene from the movie that can grab the audience’s attention.
A well-designed chatbot can help you collect user data/information in the easiest way possible.
Any information or unanswered questions raised by the users can be helpful to you in training your chatbot to provide your users with a better experience next time they are involved in a conversation.
Any irrelevant or vague information can force a user to switch to your competitors, resulting in declining sales and revenue.
Ultimately the chatbot design should be in such a way that it should be able to deliver the following benefits:
- Provide your brand’s unique identity
- Provide timely and accurate response/information
- Make the user feel that their queries are appropriately answered.
- Train the chatbot using received data/information
- Collect user data/information, and the last and the most important thing is;
- You must be able to use the collected data to improve the chatbot user’s experience.