How to Get the Most Out of Intelligent Virtual Assistants

Learn what sets intelligent virtual assistants apart from chatbots, and how to maximize their potential.

The intelligent virtual assistant market grows at a rapid rate. Market size will reach around $5.1 billion by 2028, rising at a compound Annual Growth Rate of 24.3% during the forecast period.

The technology uses speech recognition and voice response to build fully virtual characters that communicate with users.

The intelligent virtual assistants’ artificial intelligence (AI) is becoming invaluable in customer service, as an example. It engages customers by:

Giving product-related information
Accessing your bills
Making payments
Advising you on who to speak to next in the event of a greater problem

Here’s how to get the most out of IVAs and the opportunity they present.

What are intelligent virtual assistants?

An Intelligent Virtual Assistant (or IVA for short) is a machine learning based system that can understand human language and respond to queries in a fluid manner.

These intelligent AI assistants are not only capable of presenting a multiple-choice selection of answers to the user. They can also understand user intent.

IVAs use artificial intelligence to create a system that gets smarter over time. They gather data from previous queries, then use machine learning to refine its processes.

Nuance is a great example. Their IVA “delivers intelligent, conversational engagements that increase self‑service usage and improve customer satisfaction.”

So, let’s say you’re in the market for a new phone. The IVA will automatically pull up your records, recognize your current device, and offer trade-in and purchase options based on the perceived intent of your interaction.

Furthermore, it personalizes engagement based on the context of the conversation and your personal buying history.

Difference Between an Intelligent Virtual Assistant and a Chatbot

Did you ever notice that a chatbot can feel slightly, well, robotic? That’s because a chatbot is kind of like a gumball machine. It only offers responses that the developer fills it with.

So, just like you slip in a quarter (remember carrying change?) and receive only one of the items inside the gumball machine, a chatbot will only give you what it has to offer. In other words, tell a chatbot “X” and you’ll always get “Y” in response.

But whereas chatbots provide only set answers to specific set questions, IVAs take in user queries and provide more accurate answers in real time and in context.

It’s more of a vending machine situation here. IVAs recognize and anticipate your wants and needs and feed you correspondingly.

Basically, here’s the tale of the tape between the two.

Chatbot	Intelligent Virtual Assistant
Rule-based structure handles straight-forward FAQ	AI better interprets queries and offers more specific answers
Robotic answers	Interaction more resembles human speech
Understands only specific inputs with no margin for error	Processes queries with spelling errors, slang, grammatical mistakes, or overly confusing language
Uses auto-assigning routing logic to connect with agents	Connects users based on agent availability and capabilities
Static buttons prompt users’ next steps	Provides users with autosuggest responses based on the start of their text inquiry

How are intelligent virtual assistants built?

IVAs basically function as conversational AI. That’s the range of technologies that bring together machine learning, natural language processing (NLP), contextual awareness, and other advanced tools to facilitate a real-time dialog between human and computer.

Foundational to conversational AI, then, is the machine learning aspect: algorithms that automatically improve as they acquire data.

Thanks to conversational AI, interacting with IVAs is more akin to chatting with a friend than seeking assistance from a machine.

These devices are therefore more advanced than regular chatbots. They’re more human-like and even personable, generating responses that are more natural and aligned to real human conversations.

Indeed, this requires loads and loads of conversational data.

Conversational Data Collection

That refers to naturally occurring dialogue for the purpose of machine learning. This can come in the form spoken or typed exchange of sentiments, observations, opinions, or ideas between two or more parties in short or long form.

Conversational data further trains AI to replicate the ebbs and flows of human conversations. It’s not just programming different languages and dialects, but also phraseology, pronunciations, filler words, slang, and other variables.

There are four main sources for this kind of data:

If you’re looking solely for the data, then ask for it as is. Having said that, you can label the data to whatever degree you require by whatever means you gather the data.

Lastly, to get the most out of IVAs, err on the side of human speech transcription to ensure accuracy and inclusivity, and to handle complex environments and use cases.

Let’s Get Your IVA Talking

Understanding natural conversational speech is therefore crucial for a seamless user experience. Presently, we offer phone conversations, text chat transcripts, and any other unique scenario you may require.

Additionally, we provide full transcription plus classification and labeling services.

We also offer both in-field and crowd-sourced speech and conversational data collection services to capture the exact conversations you need.

Or maybe you’re in need of a high quantity of data with a fast turnaround? Then we have proprietary data collection app that connects us to thousands of participants worldwide.