How to build a Smart Chatbot: 2022 Guide

Technological advancements have allowed businesses to get an additional edge. As a result, industries are open to implementing the right technologies to meet customer expectations and outgrow competitors. Primarily, industries with a mass consumer base have relied on technology due to the pandemic. Implementation of chatbots into businesses such as banks, airports, and hotels is one such example.

A successful chatbot implementation can lead to a massive step-up for overall business flow. Exceptionally, chatbots can be a gamechanger where many users’ queries need to be answered within a limited period. However, implementing a “smart” chatbot is a sophisticated process on its own. The crucial first step is picking the right chatbot solution among a conventional rule-based or intelligent chatbot. This article covers how brand should develop and implement the best chatbot with essential components for their applications.

Traditional chatbots were primitive with their operation, where they would answer or respond to queries based on a set of rules and conditions. Such traditional chatbots had minimal flexibility for any addition or improvements. For growing businesses, smarter chatbots were a need. Therefore, Artificial Intelligence emerged as a provision to build innovative and intelligent chatbots.

By leveraging Artificial Intelligence, chatbots can perform much more efficiently. The ability of AI algorithms to identify and predict patterns can help chatbots. However, much more is needed in an efficient chatbot than just feeling with patterns. When dealing with a human input source, chatbots need some more sophistication when using Artificial Intelligence.

Chatbots are the latest way to communicate with customers without human involvement. However, these customers may have different backgrounds, communication manners, and sentence formation. For a chatbot to communicate with such users, they would need to process and understand a variety of queries. That is where Artificial Intelligence comes into the picture.

The Recurrent Neural Networks

Among different AI algorithms, the Recurrent Neural Networks is useful to process variable-sized text and identify the right response accordingly. The Recurrent Neural Networks (RNN) is one of the competent AI algorithms for the function of chatbots. Within different RNNs, Classical RNNs have the condition of “vanishing memory.” However, This problem can be solved through other advanced versions of RNNs such as LSTM. RNNs have been widely used, and some simpler AI models still use them.

Transformers

While RNN has been a conventional choice, Transformers are the latest AI algorithm for Natural Language Processing. Transformers use a “Self-attention” technique, allowing AI to process parts of the input sequence so as to predict the proper response. Through Transformers, many expression variants can be dealt with as the AI can automatically detect important phrases and predict an output accordingly. Slowly, Transformers are slowly taking over different branches of NLP applications with models like BERT, RoBERTa, etc. However, these models are large and training or maintaining them can be challenging.

Dealing with natural language

When interacting with humans, conversations can be complicated. There are numerous variables to account for in any query. It might include fragmentations, formations, multiple intents, as well as past references. Additionally, users can make errors, such as typing errors or incorrect spelling in the queries. These items are easy to understand for any manual representative. However, they can be challenging for an AI algorithm that relies on keywords.

Therefore, ML models have to rely on filters to identify and correct any errors. Such corrected sentences can then be further analyzed in segments to identify the intentions behind them. Additionally, if relevant background information is available, a chatbot can present a peculiar response. Such background data can include any user’s data like previous orders, queries, and interests. Through this information, a chatbot can not only provide accurate responses but can also be contextually correct and personalized.

Intents & Sentiments

Another complex item that an AI algorithm has to account for is users switching intents. Often, any user might change their intent during the communication. For instance, if a user is making a purchase, he might also request a balance check in the middle of a conversation with the bot. In such cases, the chatbot would have to answer their recent intent and follow up on the previous one.

While the intent is crucial, sentiment also plays a significant role in customer experience. Based on the choice of words and how they are represented, AI can identify the user’s mood. Through sentiment understanding, AI can categorize the conversation or even onboard a human agent to help with any such query. Sentiment analysis can also allow organizations to understand vital areas to improve.

Visualization & performance

AI is a great add-on for chatbots and can make chatbots efficient in most applications. However, there are a few more advantages where smart chatbots can make the user experience even better.

Visualization is what sets chatbots apart. In comparison to human support agents, chatbots can use graphs, pictures, and user guides instantaneously. Through visualization, chatbots make the job of query resolution efficient and easy.

Additionally, chatbots do not lag. Unlike manual assistance, chatbots can work 24×7 and maintain their speed regardless of the traffic. They are answerable all the time and can answer any query almost instantaneously. Quick resolution plays a game-changing role in converting any customer’s negative experience into a positive one.

Also read our article on the key to developing effective chatbots? Training – the Smart and Fast way.

Pre-training of AI models

Pre-training is a method to train a Machine Learning model on a huge quality data set that is already available and identified. This method is often used for the use cases of Natural Language Processing such as chatbots. A large corpus of data allows chatbots to find correlations between words, phrases, and intent. Here the quality of data is as important as the AI model itself. Such an AI model is adaptable for various applications and tasks. Through pre-training AI models rigorously, users can make the most out of chatbots.

Progressive implementation

Implementation of these chatbots is as important as the development process. For a successful and efficient chatbot implementation, one can start using chatbots for a particular area or domain. For instance, a company can begin using chatbots to book travel tickets only where the chatbot only has to answer from predefined data. Once initiated and implemented, AI can be trained further for numerous different roles. Progressive implementations allow users to make the most out of chatbots with minimum turnaround time as the chatbot can grow and train later.

For customers, operators, and organizations:

Through this approach, AI chatbots can answer some straightforward queries for support staff. This work allocation allows support staff to be available for essential queries where a chatbot can not help. This way, customers don’t have a long waiting time, organizations can identify areas of improvement, and manual agents can focus on crucial items rather than common questions.

The secret formula behind exercising a perfect intelligent chatbot is using the right AI algorithms, a quality dataset, a progressive implementation method, and fallback support with manual agents.

To learn more about how your brand can build smarter Chatbot and provide a better customer experience, contact us today to book your demo.