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Let’s take a trip down memory lane and go back to literature class. When analyzing books, many teachers argue that it’s the thoughts of the author that matter. While us as students believed the reader’s interpretation since the reader is on the receiving end. It’s actually both that play a significant role, since art is entirely subjective.
But, what about in the case of human-technology interactions? Then, the customer’s interpretation is always going to be the winner. There’s nothing subjective there. “The customer is always right!” as the saying goes. Therefore, while developing and designing a chatbot, it’s always a matter of customer intent and entity recognition.
First, how do chatbots work?
A chatbot is a software that simulates human conversations. Users communicate using a chat interface, as if they’re texting another person. These bots interpret the words given to them and provide pre-set answers.
When training a commercial chatbot, you have to feed it a lengthy set of FAQs that cover the topic you want it to answer. You can then deploy it either on a website or through other channels where customers are more likely to reach out with their queries.
Now, here’s where the problem lies, if you were to ask the bot a question slightly out of its curriculum, it would fail tragically, regardless of how good the NLP system behind the bot is.
Here’s a quick example
Imagine you own a flower shop. On your website, you have a chatbot that runs suggestions to your customers based on various questions that they might have, so you have fed the bot with the following FAQs:
Now suppose a customer comes to your website and just asks, “What flowers do I buy?”
Based on the NLP system running in the background, it might provide an error message, or it displays those pre-determined answers stated above based on a probability score.
Had this conversation happened in person, the florist may follow up with more questions like “For what occasion?”
Adding the functionality to segregate Intent and Entity in your chatbot FAQ's solves this issue. It gives your chatbot the ability to pose intelligent questions to draw more information from a user instead of resorting to fallback responses.
Understanding intents and entities
Let’s go back to the example of the flowers.
“What flowers do I buy for my birthday?”
On looking at this statement, we can understand two basic things:
The former is the Intent, the latter, Entity. They more or less are the building blocks for most queries. Now, if you run a gift shop, you might not just be selling flowers; you might also sell gift cards, chocolates, cakes, and much more. In that case, you would want to add two or more Entities with the Intent of buying.
To break it down further, let’s define a few terms.
In the graphic above, we can understand that the user’s intention is to read the news.
So after integrating these scenarios into a chatbot, we now open up the possibility of having a more human-like interaction between a Customer and a Bot.
Take this interaction as an example:
Customer: “I want to buy something.”
Bot: “What would you like to buy?”
Customer: “I’d like to buy flowers.”
Bot: “Great! For what occasion?”
Customer: “It’s my anniversary.”
Bot: “Would you like to buy Roses?”
Customer: “Yeah! I’ll pick them up in the evening at 8”
Bot: “Sounds good!”
Customer: “Thanks! Bye!”
In this example, the customer tells the bot their Intent. The bot keeps asking questions to determine the Entities associated with this Intent, effectively helping them place an order tailored to their needs.
The 2 types of entities
Why use an intent-based chatbot?
An intent-based chatbot offers several benefits in terms of the end user experience.
Designing your chatbot
One of the 1st steps a UI designer must focus on while working on a chatbot is picking up common customer questions.
An easy way of going about it is by collecting data from customer queries through calls, live chat, or other channels. However, at times the language is not concrete enough for humans to understand, let alone chatbots.
Here are 2 versions (or inputs) of the same situation-
Here, the first is clear and direct, while the second expects the chatbot to understand the underlying customer intent, which is to look for a nearby highly-rated Italian restaurant.
The entity that the chatbot picks could be ‘restaurant,’ ‘dinner,’ ‘take <noun pronoun=""> out,’ and respond accordingly.</noun>
Another example could be something like this-
Now, the second input suggests that the customer is looking for an iPhone repair shop. The entity in this example would be ‘broken,’ and what do you do with something broken? You have to fix it.
Therefore, you've got to design the structure to support the same. So, NLP is training and designing UI for chatbots to understand the intent and context of the conversation, which will further help conversation designers create the dialogue flow and relevant content.
Let’s look at intent, layout, and entity identification from the customer’s perspective. What is it that customers, or people generally do when they want to express their feelings, emotions, intentions, or requirements?
As we’ve mentioned above, customers either state their requirements directly or they beat around the bush. They can either ask questions, use exclamations, or keep browsing with the available options that the chatbot provides. They do what they feel is expressive enough and don’t call for extra effort unless incredibly frustrated. So, how do you go about the design for the chatbot? Of course, it’s easy to understand what the customer is looking for when they directly state it, but even human inputs can be vague and outrageously confusing.
So, when you’re running a business and want to build a bot. You have 2 options.
You can either:
The first one is a red flag for your business. However, the second one is a powerful tool to bet your money on, and why not? Machines aren’t unruly anymore. There’s a common misconception that devices work in a systematic order yet unintelligent manner. Perhaps, this was the case back in the 50s but not today. The advancements in technology and NLP are training chatbots to compete with humans in the most technically challenging and intellectual fields.
This is the kind of intelligence and design that’s inbuilt in chatbots. It trains them to understand customer intent in a conversation.
We’re building a conversational interface that’s helping machines train themselves to interact with humans. Thanks to neural networks, bots are now intelligent enough to collect customer queries, provide efficient customer solutions, figure out what answers they don’t have, and train themselves to learn and bridge the gap. This training is making chatbots contextually more aware and emotionally robust, and intelligent. And the overall design works well for business.
Register with Engati to get started on your chatbot journey!