In this fast-paced world, where decisions are made in a matter of seconds, the way and the medium a brand chooses to communicate with its customers could either cement their relationship or leave permanent cracks. Personalized, prompt messages are the way to win customers and keep them happy. HubSpot research finds 48% of consumers want to connect with a company via live chat than any other means of contact. The research adds that consumers like using chatbots for their instantaneity.
Thus, if you are still asking if your business should adopt a chatbot, you’re asking the wrong question. Rather, the answer you need to seek is what chatbot architecture should you opt for to reap maximum benefits.
Role of a chatbot architecture
A chatbot is a software that drives communication with humans via a conversational platform, either in written or spoken form, to help the latter with a task. A chatbot architecture is very similar to any other web application architecture working on a client-server model. The only difference is that the data the architecture works with is unstructured.
What it looks to the naked eye is that the user asks a question and the chatbot responses. The client-server receives raw data from the messaging interface. The architecture has a middle layer that parses the text and derives insights. Then it summons the backend API to perform the intended action. The process of understanding the input, crafting a response, or using a suitable predefined response is the work of architecture. In short, the architecture is the semantics of operation guiding the chatbot’s functions. Different configurations are added to the architecture to speed up data processing.
Depending on the business need, the context of communication also needs to be interpreted.
Different chatbot architectures
With a rule-based or scripted architecture, pattern-matching bots parse user ‘input text’ word-by-word against <pattern> stored in the repository. When it finds a suitable <template>, it sends that as a response.
Opposed to matching the input against a <pattern> and choosing a response, pattern matching algorithms compare the input against <responses> in the repository to find a suitable response. Algorithms reduce the classifiers to a manageable structure to give a simple solution.
Artificial neural network
This generative-based chatbot is built on a deep learning model that produces a response at the go and does not resort to canned responses. Each sentence is broken down into different words, which are analyzed in the neural network. Past messages and chat analysis render context and make the input layer along with the input text.
Natural Language Processing (NLP)
The most evolved of all chatbot architectures, the NLP-based model uses speech recognition to convert language (input text) into a structure. It identifies the user’s input. It uses a dialogue management plugin to provide a happy route to the conversation. The bot takes feedback at the end of each conversation, and with the aid of reinforcement learning, it learns from its mistakes and improves its performance.
A sub-part of NLP, Natural Language Understanding (NLU) has 3 components — entity, context, and expectation. An entityis an idea and expectation is the task that the user expects to fulfill with the assistance of the bot. For instance, pizza is an entity for a chatbot and placing the order is the expectation for a bot designed to order food.
NLU does not need to store previous conversations, it just needs to store the present or the latest state. For instance, if the chatbot’s first question is what do you want to order? And the answer is pizza, the current state is ‘ordering a pizza’. The chatbot need not remember the question but just the state to render a context to communication.
An architecture as per your needs
AI-based chatbots are in trend, but that is not why you should adopt one. A chatbot architecture should start with keeping your user base in mind. What tasks are your customers trying to accomplish, how can chatbots help them with those? These are a couple of questions that need to be pondered upon.
It also depends on your business requirements. For instance, if you want your chatbot to pick up a conversation that was left by the user mid-way, you should opt for neural network architectures like Long Short-term Memory (LSTMs) and reinforcement learning. These kinds of architectures are also needed when the chatbot deals with a broader domain or multiple domains. If you operate on a narrower domain, a pattern-matching domain should work fine for you.
Best practices to design a chatbot architecture
Define the scope
Creating a chatbot becomes much easier if its purview is well-defined. For example, a chatbot can book a table, pull out information on loyalty benefits and change passwords, but the bot needs to be trained to carry out such tasks. If not, it is as good as a random topic for the bot.
User behavior and expectations
There is no one way to categorize human behavior, while some may use the text input box as a search engine and enter just one word or a phrase, others might respond well to humor and wit. In fact, these are not two exclusive categories of human behavior at all; one individual might exhibit both behaviors. A conversational bot might find it difficult to decipher the intent without the surrounding context. Thus, this needs to be kept in mind while creating the training data and designing the architecture. Also, building in humorous responses for some nodes can work wonders.
Acknowledge limitations, handover to humans
Design the architecture such that it can detect multiple visits to one node, pointing to conversations going in circles. This is indicative of a conversation going haywire. The chatbot needs to accept that the present query is beyond its purview and handover to a human. This would prevent frustration among users.
For a chatbot architecture to perform optimally, it is essential that the bot is trained well. The bot can either be trained manually by a domain expert, can map FAQs to answers, company documents, and policy documents and ask the bot to train itself. However, if the plan is to build a sample training document from scratch, it can be done using an interactive learning model. This is a user interface application that provides action choices to the user once the bot responds. The model improves its predictions next time onwards.