Conversational AI: What It Is & What You Should Know

Every organization wants to deliver exceptional customer experiences. Customers can be fickle and aren’t afraid to abandon a brand because of a perceived negative interaction.

Prioritizing the customer experience is imperative in today’s competitive landscape. Customers have high expectations. Just one poor experience can wipe away any loyalty. According to a study, companies lose more than $75 billion in revenue as a result of bad customer service.

To meet these demands and ensure consistency and quick responses, you need technology platforms that empower customers to self-serve and enable agents to answer questions faster.

The answer? Conversational AI.

The use of AI in business operations isn’t new. For example, customer service technology often uses chatbots—which, to be fair, are rarely powered by true AI. There’s a big difference between chatbots and conversational AI. Chatbots are useful, but the issues with delivery of service occur when companies focus solely on these for simple queries, such as where their order history is. When companies fail to consider the rest of the customer journey and the human interactions with service, either automated or with an agent, it becomes a strategy that doesn’t address the big picture.

Technology should drive faster resolutions, convenience for customers, and greater agent productivity. Chatbots alone won’t help you achieve this. Human-centered, conversational AI will.

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chapter 1
Chapter 1

What is Conversational AI?

It’s no secret that customers have elevated expectations regarding the companies with which they do business. With high competition and the ability for buyers to find what they need in a few clicks, building relationships with customers matters and impacts whether they remain your customer. The customer experience is quickly becoming the make-or-break determinant of customer loyalty.

With this in mind, leading organizations are turning to new forms of technology to meet the needs of their growing customer bases. One of the most effective solutions? Conversational AI.

But what exactly is conversational AI? Simply put, it’s technology that allows machines to engage in human-like conversational experiences with people.

chapter 2
Chapter 2

How Conversational AI Works

Conversational AI is a term used to describe chatbots that simulate a human-like conversation with a machine. While standard AI chatbots function via if-then statements and simple keyword-based decision trees, conversational AI understands human language.

In order to accomplish this, conversational AI utilizes the following components to deliver these human-centered conversations:

  • Natural language understanding (NLU), allowing AI to comprehend text in the same way humans do.
  • Natural language processing (NLP), working with NLU to process the data and produce that human-like back and forth conversation.
  • Machine learning (ML), allowing the chatbot to continue learning as it ingests more data.
  • Contextual awareness, enabling the chatbot to understand the situation of users or devices.

The combination of these components allows for the conversational AI process to unfold. That process works like this:

  1. A user inputs information.
  2. That information is analyzed using NLP and NLU, gathering meaning out of the words.
  3. A reply is generated using natural language generation.
  4. Machine learning is applied, analyzing user inputs to refine replies overtime.
chapter 3
Chapter 3

What is Conversational Technology?

Conversational technology is an AI-based set of tools that allow users to find information or perform certain functions through speech and natural human language. These tools enable customers to do business with you via phone or an online chat functionality.

Benefits of Conversational AI

There are multiple benefits of conversational AI, including the following:

  • Integrate it into existing workflows, then optimize them
  • Provide a smart search tool that uses natural language understanding (NLU)
  • Act swiftly with past solutions and answers at the top, allowing agents to apply them in a click
  • Vote on answers to solutions, which enables continuous learning for the algorithms
  • Filter knowledge sources.
Chapter 4

Conversational AI vs. Chatbots

The main difference in the battle of conversational AI vs chatbots is that chatbots are built with basic keyword-based decision trees while conversational AI utilizes machine learning and natural language processing to understand the intent of the messages being received.

This means that a customer support tool that incorporates conversational AI is able to understand what a user is asking based on the sentiment of the message rather than simply the keywords that are included. Typical chatbots are incredibly limited in this regard, only providing an accurate response if the user’s message includes a keyword from a predetermined list of topics.

In terms of adoption, the current landscape for AI in customer service shows increasing utilization. In 2019, Gartner predicted 25% of customer interactions would include AI in 2020. In another report, 54 percent of customers surveyed said they use AI daily—an increase from 21 percent who said the same in 2018. That number could be far greater considering the disruptions and transformations from 2020 and beyond.

But what kind of AI are companies using, and is it really helping them solve challenges?

Simple chatbots only follow rules

What most companies are deploying is simple chatbots or manual tagging based on keywords in the tickets. Chatbots are cruder and more robotic. They do use AI in some ways but have limitations because they must follow the “rules” of decision trees to branch to possible answers to questions. Most of the time, these could be very simple, such as, “How do I do XYZ in the software?”

Chatbots focus on keywords but not much beyond that. They return answers; however, in too many cases, the answers aren’t relevant. Chatbots also have issues with “learning” because they only derive information from decision trees. If those don’t get updated, like when you add a new product or service, they’ll be of no help.

For chatbots to provide a great customer experience, the customer’s questions have to be super simple. Anything with the slightest complexity, such as changing a subscription or asking about an invoice, and the chatbot can’t respond.

Customers expect chatbots, and they are willing to use them. But they have low expectations of what the tool can provide.

Simply adding chatbots to your customer service flow could frustrate customers more and increase the time it takes to manage their problems.

But what kind of AI are companies using, and is it really helping them solve challenges?

Chatbots can act as “deflectors”

Another issue with standard chatbots is their deployment in isolation. Their only purpose is to prevent customer requests from reaching a support team. This act of “deflection” is self-serving to the company’s metrics but doesn’t help customers. Yes, it will reduce ticket backlog, but customers don’t get answers.

Alternatively, human-centered conversational AI incorporates a chatbot as one part of a larger customer service strategy. Its purpose is to answer customer inquiries quickly and accurately. Only true AI can understand past ticket history to serve answers. It also knows when it’s best to route the customer to a human for more complex matters.

Not adopting this technology could jeopardize how customers see your brand. A company with minimal self-service and customer support options may appear to be behind the times or just too inconvenient for them to engage.

Here’s an example. Suppose a customer wants to inquire about specifics to a product not included on its page before they buy. With human-centered AI, a chatbot could provide the answer but also acknowledge the intent, which may color the message to drive that conversion. A decision-tree chatbot would likely only offer what’s already listed on the product page, leaving the customer frustrated.

chapter 4
Chapter 5

What is Conversational AI Used For?

How is conversational AI being implemented in various organizations? Here are a few of the most common use cases.

Manage higher volumes of inquiries

There are often fluctuations in inquiry volume. That could be after a new product release or other internal factors. Much of the time, there is an expectation that tickets will grow at certain times, such as during the holidays. For retailers, shipping companies, and others moving products throughout the ecosystem, their customer contact numbers can double or triple.

Conversational AI can help you tame that uptick in traffic and supply customers answers they need via self-service. You’ll likely see patterns in the same questions, so self-service can handle many of these. That will dramatically decrease the number of people that need human support, and you’ll be able to route more accurately.

Collective knowledge reduces time spent on tickets

Your agents hold critical insights and information, and conversational AI can help you tap into that by sourcing pasting tickets, knowledge bases, and time-saving shortcuts. It solves the challenge of manual work involved in locating all these resources. It makes an agent’s job easier.

AI can pull knowledge from anywhere and centralize it, so it’s easy for agents to access. Having this in their corner translates to reductions in time spent on tickets.

Maintain a healthy CSAT while also increasing cases closed

Organizations often have apprehension about how technology could impact the human element. Companies want to keep CSAT up while also improving efficiency. With conversational AI, you achieve both without compromises.

Assist technology enables agents to be ready to respond to questions as they arrive. There’s no need for special indexing or updating of data. When agents open a new case, AI automatically provides relevant past cases, knowledge articles, and macros. Customers receive accurate and friendly service, so the interaction is quick, accurate, and friendly.

Conversational AI By Industry

Over the past 10 years, innovative companies have implemented conversational AI across all kinds of industries with high levels of success. And it’s predicted to continue. In fact, the global conversational AI market is forecasted to reach $15.7 billion by 2024.

How can various industries use conversational AI in customer support? Here are some examples:

Conversational AI for Customer Service

Whether customers have a simple question or need detailed assistance from an agent, conversational AI improves the entire customer support process. Some of the most common benefits? Lowering customer wait times, enhancing self-service, and reducing ticket backlog for agents.

Conversational AI platforms also provide improved in-depth assistance, thanks to their ability to detect sentiment and surface past relevant tickets. And at the end of the day, a better customer experience leads to increased customer retention, benefiting all parties involved.

Learn more about conversational AI for customer service here.


Conversational AI for SaaS

Another industry that’s utilizing conversational AI in a big way is SaaS (software as a service). These organizations are constantly looking for ways to provide excellent service in a scalable way, and conversational AI is an incredible tool for accomplishing this goal.

Helping customers reset their passwords without a human agent, responding to questions and concerns at a faster rate, and decreasing customer churn are some of the most common use cases we see in SaaS.

Learn more about conversational AI for SaaS here.


Conversational AI for Ecommerce

Ecommerce companies are utilizing conversational AI in multiple functional areas, including support, sales, and customer loyalty—driving sales and increasing retention. A common use case is contactless customer support, such as when a customer has a question about shipping speed or is need of a refund.

Learn more about conversational AI for ecommerce here.

Conversational AI for Insurance

The insurance industry is finding value in conversational AI in many ways. One of the most popular is automating the most repetitive aspects of insurance—providing quotes, answering frequently asked questions, and providing instant support to customers.

Conversational AI also provides insurance agencies with the ability to analyze internal data and gather valuable insight, allowing them to proactively advise customers to meet their needs.

Learn more about conversational AI for insurance here.

Conversational AI for Travel

Complicated logistics are an important aspect of every area of the travel industry. Because of this, it’s imperative that organizations can adequately respond to customer concerns and requests in a timely manner.

Whether dealing with itinerary issues like needing to change a flight or cancel a hotel reservation, or helping customers book new travel, conversational AI can play a pivotal role in growing sales and improving customer experiences.

Learn more about conversational AI for travel here.

Conversational AI for Financial Services

Often a little resistant to new technology, many financial services companies are now embracing conversational AI as a way to improve experiences for both customers and employees.

Financial organizations like banks and mortgage lenders often receive high volumes of common requests. The ability to automate responses to questions like, “Can I open a new account?” or “Can you help me transfer funds?” is a game changer. Financial institutions are also seeing large cost savings from implementing conversational AI in their support and sales organizations.

Learn more about conversational AI for financial services here.

Conversational AI for Education

Whether a student is enrolled in a traditional school or a virtual classroom, conversational AI is enhancing the experiences of both teachers and students across the board. When students have common questions or need to access information after hours, a conversational chatbot is the perfect way to provide that support.

Other schools are even utilizing conversational AI in their admission processes. This can relieve burden from administrative staff while providing prospective students with instant access to assistance.

Learn more about conversational AI for education here.

Conversational AI for Retail

The world of retail is rife with customer interactions: customers ordering items, returning others, packages being tracked, and questions being asked. Even large support teams can find this volume of requests overwhelming.

Say hello to conversational AI. Retail organizations utilizing conversational AI are finding that it helps them eliminate redundant tasks, streamline processes, and provide better experiences for both customers and support agents alike.

Learn more about conversational AI for retail here.

chapter 6
Chapter 6

How to Get Started With Conversational AI

The customer experience is now more important than anything else. Modernizing your processes with human-centered AI ensures you continue to delight customers while also increasing agent productivity. AI as a tool can deliver so many benefits—you’ll just need the right platform.

Forethought provides end-to-end customer support optimization with conversational AI. Our customers report faster resolutions, happier customers, less agent strain, quicker onboarding of new hires, and decreases in tickets.

Modernizing your processes with human-centered AI ensures you continue to delight customers while also increasing agent productivity.


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