What is Conversational AI and How Does it Work?

Written by Emily Peck on Mar 10, 2023

Conversational AI is growing more prevalent every day. Not just in business, but for entertainment purposes as well. Whenever computers have conversations with humans, there’s a lot of work engineers need to do to make the interactions as human-like as possible. Whether you’re messaging with a chatbot, responding to an automated email or speaking with a virtual assistant, computers are working hard behind the scenes to interpret what is being said (sometimes called “intent classification”), determine the appropriate response, and respond in a way that’s natural and easily understandable to humans. This article will highlight the key elements of conversational AI, including its history, popular use cases, how it works, and more.

What is Conversational AI?
Conversational AI vs Chatbots
Why Conversational AI is Becoming So Critical Today?
How does Conversational AI Work?
How To Build Conversational AI
Conclusion

The concept of Conversational AI has been around for decades, but it wasn’t always something that was wildly talked about. According to data from Google Trends, interest in “conversational AI” was practically non-existent from 2005 through 2017. However, over the last 3 years, interest in Conversational AI has grown exponentially.

In the US, search volume for conversational AI has never been higher.

What is Conversational AI?

At a high level, conversational AI is a form of artificial intelligence that facilitates the real-time human-like conversation between a human and a computer.

It’s important to note that conversational AI isn’t a single thing; it’s a combination of different technologies, including natural language processing (NLP), machine learning, deep learning, and contextual awareness.

Conversational AI vs Chatbots

The main difference between Conversational AI and chatbots is that chatbots have much less artificial intelligence compared to Conversational AI. We’ll touch on the differences even more in the next section. With that said, there is a lot of ambiguity surrounding the differences. The discrepancies are so few that Wikipedia has declared – at least for the moment – that a separate Conversational AI Wikipedia page is not necessary because it is so similar to the Chatbot Wikipedia page.

Conversational AI vs chatbot - wikipedia thnks they are the same — Wikipedia says that Conversational AI and Chatbots are too similar to have separate pages.

Why the confusion?

One reason why the two terms are used so interchangeably is because the word “chatbot” is simply easier to say. A chatbot also feels tangible to our imagination – I visualize a tiny robot that has conversations behind a computer screen with people. Whereas a conversational artificial intelligence is more conceptual than physical in nature.

One common application for conversational AI is to be incorporated into chatbots. Chatbots provide convenient, immediate and effortless experiences for customers by getting customers the answers they need quickly. Instead of scrolling through pages of FAQs or sitting through long wait times on hold to speak to an agent, customers can receive a reply in seconds. However, not all chatbots use AI, and not all AI is used for the purpose of powering chatbots.

It might be more accurate to think of conversational artificial intelligence as the brainpower within an application, or in this case, the brainpower within a chatbot.

Perhaps you’ve been frustrated before when a website’s chatbot continually asks you for the same information or failed to understand what you were saying. In this scenario, you likely engaged with a scripted, rules-based chatbot, with little to no AI.

There are several notable differences between conversational AI chatbots and scripted chatbots. Traditional scripting chatbots require companies to write out all the responses to anticipated customer questions beforehand. Next, these responses are matched to keywords. Whenever a customer’s reply or question contains one of these keywords, the chatbot automatically responds with the scripted response.

Conversational AI chatbot comparison table

Scripted chatbots have multiple disadvantages compared to conversational AI. First and foremost, these bots cannot provide the correct response if a customer uses a phrase or synonym that differs even slightly from what has been pre-programmed, whereas conversational AI chatbots can understand the intent and meaning of the words given by the customers. Companies that implement scripted chatbots or virtual assistants need to do the tedious work of thinking up every possible variation of a customer’s question and match the scripted response to it. Think about the Comcast example above. When you consider the idea of having to anticipate the 1,700 ways a person might ask one straightforward question, it’s clear why rules-based bots often provide frustrating and limited user experiences. Compare this to conversational AI chatbots that can detect synonyms and look at the entire context of what a person is saying in order to decipher a customer’s true intent.

Scripted chatbots are also unable to remember information across long conversations. Because it’s impossible to write out every possible variation of a back-and-forth conversation, scripted chatbots need to repeatedly ask for information to match a response to a pre-set conversational flow. This rigid experience does not provide any leeway for a customer to go off script, or ask a question in the middle of a flow, without confusing the bot. Meanwhile, conversational AI chatbots can use contextual awareness and episodic memory to recall what has been said previously, provide a relevant reply and pick up a flow where it left off. All in all, conversational AI chatbots provide a much more natural, human-like interaction than their scripted counterparts.

Why Conversational AI is becoming so critical today

Businesses use conversational AI for marketing, sales and support to engage along the entire customer journey. One of the most popular and successful implementations is for customer service and customer experience, a $600B industry with a lot of repetitive knowledge work.

Because AI doesn’t rely on manually written scripts, it enables companies to automate highly personalized customer service resolutions at scale. This makes every interaction feel unique and relevant, while also reducing effort and resolution time. As a result, customers tend to report higher levels of satisfaction.

The more advanced conversational AI chatbots can enable companies to analyze and identify when customers have questions and issues to identify common pain points to preemptively intervene before a customer ever reaches out.

Conversational AI chatbots for CX are incredibly versatile and can be implemented into a variety of customer service channels, including email, voice, chat, social and messaging. This helps businesses scale support to new and emerging channels to meet customers where they are.

Learn 5 strategies for successful customer service automation in this exclusive Freshworks webinar.

Top Conversational AI Applications and Use Cases

Automate resolutions to common FAQs – Many businesses have 5-7 different kinds of questions that make up over 50% of the total customer service questions by volume. A powerful AI can interpret the various different ways people might ask the same question. For example, an airline might deploy a travel chatbot to resolve highly repetitive questions, like “can I change my flight?”, without human agent intervention.
Increase customer engagement – Conversational AI can proactively reach out to customers at key points along the customer journey or based on behavior signals to provide information at the exact moment of relevance. This can help to drive revenue, decrease churn and eliminate frustration.
Improve product accessibility – Businesses are relying on artificial intelligence to provide more inclusive services to all of their customers. A powerful AI can leverage NLP and NLU to automatically translate text, or even text to speech. By doing so, businesses can help those with disabilities use their products better.

Fintechs need to provide a stellar customer experience across the board.
Learn more in our eBook today.

How Does it Work?

Earlier we mentioned the different technologies that power conversational AI, one of which is natural language processing (NLP). NLP isn’t different from conversational AI; rather it’s one of the components that enables it.

NLP is frequently interchanged with terms like natural language understanding (NLU) and natural language generation (NLG), but at a high level, NLP is the umbrella term that includes these two other technologies.

Because human speech is highly unstandardized, natural language understanding is what helps a computer decipher what a customer’s intent is. It looks at the context of what a person has said – not simply performing keyword matching and looking up the dictionary meaning of a word – to accurately understand what a person needs. This is important because people can ask for the same thing in hundreds of different ways. In fact, Comcast found that there are 1,700 different ways to say “I’d like to pay my bill.” Leveraging NLU can help AI understand all of these different ways without being explicitly trained on each variance. Sophisticated NLU can also understand grammatical mistakes, slang, misspellings, short-form and industry-specific terms – just like a human would.

Once a customer’s intent (what the customer wants) is identified, machine learning is used to determine the appropriate response. Over time, as it processes more responses, the conversational AI learns which response performs the best and improves its accuracy.

Finally, natural language generation creates the response to the customer. This technology leverages its understanding of human speech to create an easy-to-understand reply that’s as human-like as possible.

More advanced conversational AI can also use contextual awareness to remember bits of information over a longer conversation to facilitate a more natural back and forth dialogue between a computer and a customer.

An example of this technology that most of us carry around with us every day is a voice assistant like Amazon’s Alexa or Apple’s Siri. These assistants use AI to assist with voice-to-text and text-to-speech applications. Their ubiquity in everything from a phone to a watch increases consumer expectations for what these chatbots can do and where conversational AI tools might be used.

How To Build Conversational AI

We’ve gone over the advantages of conversational AI and why it’s important for businesses. Now, we’ll discuss how your organization can build and implement for your business.

While some companies try to build their own conversational AI technology in-house, the fastest and most efficient way to bring it to your business is by partnering with a company like Netomi. These technology companies have been perfecting their AI engines and algorithms, investing heavily in R+D and learning from real-world implementations. With customer expectations rising for the interactions that they have with chatbots, companies can no longer afford to have anything interacting with customers that’s not highly accurate.

There are a few simple steps that go into creating a strategy for conversational AI:

Step 1: Define your goals

Are you trying to increase customer satisfaction or decrease resolution time? Do you want to alleviate mundane work from your human agents? Can you introduce proactive customer service to solve issues before you even know they exist?

Step 2: Train your AI

Train your AI based on your historical tickets. That way, you can leverage your existing data to understand how your customers have asked a specific question in the past, increasing the accuracy of your AI.

Step 3: Design journeys and workflows

Design conversations and user journeys, create a personality for your conversational AI and ensure your covering all of your top use cases.

Step 4: Integrate

Depending on your use cases, you might want to also integrate with your other back-end systems like your CRM or accounting software. This way, the conversational AI can actually pull in data from these sources to resolve customer service issues on an individual basis without human intervention.

Step 5: Measure

You’ll want to measure the impact your AI is having on your customer service KPIs, including first response rate, average handle time, CSAT, AI and human agent collaboration, and more.

Step 6: Optimize

Over time, as the AI has more customer service interactions, you can uncover further opportunities to train the AI and empower it to solve even more tickets. You can also help retrain the AI if it did not provide the correct response in a specific scenario, enhancing the experience over time.

Conclusion

Conversational AI is growing in popularity and for good reason. More and more businesses are beginning to leverage this artificial intelligence to improve their customer support, marketing, and overall customer experience.

Customers care more today about every interaction they have with a company. There is an inherent demand for immediate, effortless resolutions across an increasing number of channels. Even one bad experience can turn someone off from ever doing business with a company again. Conversational AI can help companies scale the experiences that people expect by providing resolutions to everyday questions and issues in seconds. That way, human agents are only brought in when there is a complex, unique or sensitive request.

For more information on conversational AI and chatbots, discover how to provide brilliant AI-powered salesforce chatbot solutions to every customer, every time. Depending on the industry you serve, you may also be interested in checking out our eBooks on telecom and media and entertainment.

The 12 Most Important Customer Service Expectations

Written by Dylan Max on Jul 14, 2022

It’s no secret that customers today have high expectations when it comes to customer service. In order to keep up with the competition, businesses need to meet (or exceed) these expectations. Every business has customers, no matter what field or industry, and it’s vitally important to be aware of what they expect.

What are Customer Expectations?

Simply put, customer expectations are the standards that customers have for a product or service. These standards can be based on many factors, such as previous experiences, what they’ve heard from others, or even societal norms. And when customers don’t feel that their expectations have been met, they’re likely to take their business elsewhere.

There are a few key things to keep in mind when it comes to customer expectations:

– They’re constantly changing: What customers expect today may be different tomorrow, so it’s important to stay on top of trends and changes.

– They vary by customer: Not all customers have the same expectations, so it’s important to tailor the experience to each individual.

– They should be met or exceeded: Meeting customer expectations is the bare minimum — businesses should aim to exceed them whenever possible.

The 12 Most Important Customer Service Expectations

Name: Netomi
Brand: Netomi
Rating: 4.8 (388 reviews)

While customer expectations can differ depending on the company or industry, there are some common themes that businesses should be aware of. Here are 12 of the most important customer service expectations:

1. Honesty and Transparency

Customers today expect honesty and transparency from businesses. They want to know that they can trust the company, and they want to be able to easily find information about the product or service. For example, if a customer is buying a product online, they should be able to easily find shipping costs, return policies, and other important information. Companies should not try to hide this information or be vague about it.

2. Friendliness and Courteousness

It seems like it should go without saying, but sometimes it’s easy to forget about basic decency. Friendliness and courteousness are two of the most important customer service expectations. Customers want to feel like they’re valued, and they want to be treated with respect.

3. Being Understood

Customers want to feel like they’re being understood, and they want businesses to take the time to listen to their concerns. This includes everything from active listening to providing support in their language of choice. Customers want to feel like they’re being understood, and they want businesses to take the time to listen to their concerns. This includes everything from active listening to providing support in their language of choice. Active listening is a customer service technique that involves giving your full attention to the customer, paraphrasing what they’ve said, and checking for understanding. This can help to make sure that the customer feels heard and that their issues are being addressed.

4. Innovation

Innovation is one of the most important customer service expectations in today’s climate. With new technologies and platforms constantly emerging, customers expect businesses to be on the cutting-edge. They want companies to be constantly improving and adapting to change. Innovation also means being able to meet customer needs in new and unique ways, like using chatbots or artificial intelligence. Innovation should never be made at the expense of smooth function–after all, the latest and greatest technology is hardly great if it hampers your customer service agents from giving customers the best possible experience.

5. Proactivity

Customers today expect businesses to be proactive, and they want companies to anticipate their needs. This includes everything from proactively addressing problems to offering personalized recommendations. A proactive approach means identifying potential issues and taking steps to prevent them before they happen. It also involves awareness of customer needs and offering solutions or recommendations before the customer even knows they need them.

6. Speed and Efficiency

Customers today want speed and efficiency when it comes to customer service. They don’t want to wait on hold for hours, and they expect problems to be resolved quickly and efficiently. Quick reaction time from customer service is essential in today’s climate.

7. Multi-Channel Service

In order to meet customer expectations, businesses need to provide multi-channel service. This means that customers should be able to reach out through a variety of channels, such as phone, email, chat, or social media. And they should be able to receive a consistent experience no matter which channel they use. In addition to this consistent access, they also want continuity–a conversation that begins on one channel should continue on another.

8. Privacy and Security

With all of the recent data breaches in the news, it’s no surprise that privacy and security are top customer service expectations. Customers want to know that their personal information is safe, and they want businesses to take steps to protect their data. This includes everything from ensuring that data is encrypted to offering secure payment options.

9. Personalization

Customers today expect a personalized experience, and they want businesses to take the time to get to know them. This includes everything from using their name to providing tailored recommendations. Not only this, but customers also want the feeling that they’re more than just a number. They want to be treated as individuals, and they want their customer service experiences to reflect that.

10. Empathy

In addition to being understood, customers also want businesses to show empathy. This means that businesses need to be able to put themselves in their customers’ shoes and understand their needs. This is more than just a sympathetic ear or a kind word–it’s about being able to truly understand what the customer is going through. The Human connection is an important one and should be a cornerstone of any customer service approach.

11. Constant Availability

With the rise of online shopping, customers now expect businesses to be available 24 hours a day, seven days a week. This means that businesses need to have customer service representatives available around-the-clock to answer questions and resolve problems.

12. Customer Self-Service

Finally, customers today expect to be able to serve themselves. This means that businesses need to provide easy-to-use self-service options, such as FAQ pages, online chatbots, and step-by-step guides.In addition, businesses need to make sure that these self-service options are easily accessible and easy to use by their customers. Efficient and convenient self service is not only great for customers, but it’s also cost-effective for businesses.

How to leverage AI in the quest for customer service excellence

There is power to be realized in partnerships and the strategic combination of forces, and companies using chatbots for customer service can further enhance the CX, taking it to new heights. While platforms can streamline the agent experience and can decrease resolution times, omnichannel conversational AI platforms like Netomi are now acting as the first line of defense when it comes to customer support. Netomi’s virtual agents sit alongside human agents to supplement and enhance the capacity of support teams, ensuring the seamless resolution of customer queries.

No matter which platform you choose to implement in your customer service operations, the Netomi AI helps you by taking the best course of action with every incoming support ticket:

The AI can automatically resolve common, highly repeatable tickets without having to loop in a human agent (‘auto-pilot’ mode)
For more complex tickets, the AI can gather information from the customer or back-end systems, and even draft a response for review, before handing off to a human agent (‘co-pilot’ mode)
For the most complex tickets that require a human hand, the AI can summarize and route tickets to the most appropriate agent for the task

By leveraging the out-of-the-box Netomi virtual agent integration, companies enhance both the agent and customer experience, while also reducing costs. Other chatbots don’t sit natively within the agent desk, but with Netomi, virtual and human agents work alongside each other, creating an efficient and ultra-powerful customer service team.

The 13 Best Live Chat Software Tools for 2023 [Review and Key Features]

Written by Emily Peck on Jul 11, 2022

Finding the best live chat software for your business is a crucial step in providing top-tier customer service. Options range from simple live chat apps to robust customer support platforms for complex sales processes.

According to many reports, conversational customer service is on the rise. Customer inquiries over live chat channels have jumped 36% in 2021. This jump represents the highest increase of any other communication channel.

What is live chat software?

Live chat software is a service that allows customers to communicate with companies in real-time. Live chat provides customers an easy way to access customer support and information. Live chat software enables support agents to interact with customers on a company’s website, mobile app, over social media channels, or via text message.

Why is live chat important?

Think of live chat as an instant messaging service. Live chat is convenient, immediate, and has grown into a popular offering that customers have come to desire and expect. In fact, 79% of customers say they prefer to chat with agents solely because of the immediacy it offers in comparison with other channels. On top of that, 63% of millennials prefer to have their basic customer support queries answered through a live chat widget versus traditional channels.

What are some of the benefits of live chat software?

Here are 3 main benefits of using live chat software:

A cinch to install
Adding live chat functionality to a website or within an app is quick and easy to implement.
Enables agents to serve multiple customers concurrently
This translates to faster service times and lower operating costs.
Opens a personal and casual feel
Unlike the formality of phone calls (and without the hold times), live chat has a back and forth exchange more similar to what one might experience with friends or family.

How to evaluate live chat software tools?

Here are some of the key questions to keep in mind when evaluating live chat software tools:

Does it work well with the people, processes, and tools that your agents already use?
Does it include AI to automatically respond to common queries?

To help your team realize the full benefits of live chat software and make the most of these powerful tools, we break down the core highlights and features of the best.

The Best Live Chat Software in 2023

These robust tools are unlocked by an equally robust customer service chatbot, such as Netomi’s, which can streamline the entire customer journey and offer immediate responses to customer queries, with its native integration with leading agent desk platforms such as Zendesk.

1. Best live chat software for mid-sized to enterprise brands: Netomi

What makes Netomi a top live chat software in 2023?

Netomi’s AI platform helps companies automatically resolve customer service tickets via email, chat, messaging, and voice. Netomi holds the record for the highest automation rate, automatically resolving over 80% of routine queries.

Other key features

Full customization, allowing agents to customize the chat experience such as embed customer forms to collect data, accept uploaded files, and send a copy of the chat transcript to a customers’ email
Natural Language Understanding (NLU) for 100+ languages, to fully understand the customer’s intent, for human-like conversation with high precision
Native integrations with agent desks such as Freshworks, Gladly and Salesforce, making it easy for agents to use the systems they already use
An intuitive analytics dashboard that displays real-time performance data on key customer service metrics such as AI resolution rate and engagement metrics, also enabling teams to track how well the AI is performing

2. Best live chat software for teams already using HubSpot or another CRM: Hubspot Live Chat

What makes Hubspot one of the best live chat software solutions in 2023?

Businesses can harness HubSpot’s live chat tool to automatically connect chatters to the right people, route customer inquiries to services teams, and pass leads to the salesperson who owns that relationship. Affording teams complete context and a clear view of every interaction, all conversations are automatically saved and stored in both conversation inboxes and on the contact’s timeline.

Other key features

Integration with HubSpot CRM offers full visibility into customer and prospect information, helping your teams provide better support and close more deals
Real-time support enables agents to interact with customers or prospects in real-time to understand their direct pains and needs
Targeted welcome messages that can be created for different web pages or audience segments, so you can connect with the site visitors who matter, and right when they’re most engaged
Contact activity pages allow team members to view historical conversations and their outcomes all in one place, including page views, form submissions, and sales activity

3. Best live chat software for businesses of all sizes who want to deliver rich experiences over chat and across different channels: Zendesk’s Sunshine Conversations

What makes Sunshine Conversations one of the best live chat software solutions in 2023?

As Sunshine Conversations is natively integrated with Zendesk, every single conversation is automatically captured. The Sunshine Conversations Cloud is a software platform that enables businesses to communicate with their customers across popular messaging apps. With Sunshine, businesses can craft rich interactions from scratch, to drive revenue and captivate customers across their entire journey.

Other key features

A conversation-focused and centralized agent workspace, allowing agents to see all interactions in one place, and maintain relevant and personal conversations on any channel
Conversation extensions, which allow teams to create custom interactive experiences inside the chat window of any messaging channel. Customers can pick concert seats, or view their shopping cart from right inside the chat (see below use case of a hotel booking!)
Integrations with Instagram, WhatsApp and Facebook Messenger, enabling agents to stay on top of conversations and customize conversations
Integrations to link data to and from all systems, such as CRM, order management system, and inventory database, with platforms such as Netomi
Live chat analytics to track agent metrics over time to see the impact of wait times and missed live chats, keep track of all past chat conversations, view actionable data on chat volume, visitor experience, and measure the positive impact of chat on your overall bottom line

Note: if you already use Zendesk for your help desk and chat, check out how Netomi can transform your CX with a best-in-class Zendesk chatbot.

4. Best live chat software for small businesses: ClickDesk

What makes ClickDesk one of the best live chat software solutions in 2023?

As the leading live chat app for more than 180,000 small businesses in 100+ countries, ClickDesk has been designed to be simple to use from any device, anywhere, anytime. An integrated helpdesk, live chat, and social media platform, ClickDesk touts itself as a “cross-platform software with a global outlook.”

Other key features

Advanced reports, analytics and metrics, such as daily web chat stats, weekly chat stats, and ticket status offer actionable data, enabling teams to make better business decisions
Proactive chat greetings, enabling agents to welcome visitors with custom messages based on user behavior, location, URL or the time spent on a page (such as : ‘I see the weather is splendid in London today, are you looking for a leather coat’)?
Keystroke preview that offers insight into what visitors type before they send it, allowing agents to understand their visitors more completely, and anticipate their helpdesk questions

Learn more about how Netomi’s fully customizable chat widget can help your team offer 24/7 support over chat!

5. Best live chat software for companies looking for a full-ervice helpdesk tool, with 24/7 problem-solving: LiveAgent

What makes LiveAgent one of the best live chat software solutions in 2023?

A tremendous time saver, LiveAgent’s simple (copy & paste) integration connects support teams with their customers within seconds. When customers reach out through the chat application, agents receive a new ticket in the platform’s universal inbox, allowing for seamless communication and prompt replies. The platform routes new incoming chats to the right team members, and dynamically adapts online chat availability as agents log in and out during their shift.

Other key features

Video live chat software, so issues can be effectively solved via screen sharing
Proactive chat invitations that assist website visitors during different customer journey stages by inviting them to live chat with agents
Real-time typing view, allowing agents to preview the text that a customer is typing at the moment, giving them the necessary time to analyze the situation and provide accurate answers, faster
Chat embedded tracking allows companies to track events of live chat sessions in Google Analytics to evaluate the impact of live chat on conversions on their websites

6. Best live chat software for converting website visitors: LiveChat

What makes LiveChat one of the best live chat software solutions in 2023?

From email and website to WhatsApp and SMS – LiveChat enables customer support teams to be everywhere their customers are and connect across multiple channels. One of the software’s highlights is its integrations with popular tools such as MailChimp, WhatsApp Business and Google Analytics. With the Mailchimp integration, for instance, website visitors can opt-in to email marketing during their chat experience, with no additional forms needed.

Other key features

Inactivity messages to stay in touch with customers when agents are too busy to reply instantly (e.g. “Please leave your email and I will respond to you later”)
Chat tags that can be added to chats to give them context, also allowing teams to filter reports using tags to compare numbers between different types of cases
Chat boosters to provide customers with instant answers, allowing teams to sync their chat widget with their Knowledge Base to offer self-service prior to chatting
Chat ratings to gather feedback to gauge whether customers are happy with their live chat experience and improve customer service, such as post-chat ratings and customer satisfaction reports

7. Best live chat software for smaller businesses and websites that are new to using live chat: Olark

What makes Olark one of the best live chat software solutions in 2023?

Olark’s live chat software and customer data tools help businesses learn from every online interaction. With Olark, businesses can listen to their customers online, learn from live chat data, and, in turn, improve their sales support. Additionally, the platform offers ‘PowerUps’ — specialized live chat features for sales, growth, and customer service, such as live chat translation and visitor insights, that are available to add to any plan with a flexible monthly subscription.

8. Best live chat software for advanced sales and marketing teams: Pure Chat

What makes Pure Chat one of the best live chat software solutions in 2023?

Born with the simple premise to “help entrepreneurs and small teams have better conversations with leads and customers,” Pure Chat is ideal for teams looking to connect with their website visitors throughout the customer lifecycle by leveraging data and insights from chat conversations and user behaviors, to provide a personalized experience at each and every touchpoint.

Other key features

A mobile-optimized chat widget for websites and a mobile app for both iOS and Android devices, so teams can efficiently manage live chat while on-the-go
Direct integrations with popular software products such as Google Analytics, HubSpot, and Infusionsoft; teams can also use Zapier to integrate with over 1000+ other applications
Historical chat transcripts, enabling agents to easily reference previous conversations

9. Best live chat software for teams already using Slack, Microsoft Teams, and Webex: Social Intents

What makes Social Intents one of the best live chat software solutions in 2023?

With Social Intents, agents can chat with their website visitors directly from Microsoft Teams, Slack, and Webex. The platform is ideal for companies that want to use their existing workflow without switching between different tools.

Other key features

Canned responses, allowing for agents to reply to customers instantly, send default responses for common questions, and make the live chat process more efficient
Proactive Invites to reach out to potential customers with automatic chat invites, with chat triggers based on URL, time, a visitor’s past interactions, and knowable visitor info (such as country and device)
Customizable chat widgets to fit a company’s brand, complete with custom colors, logos, text, default responses, and tab styles

10. Best free live chat software: Respond.io

What makes Respond.io one of the best live chat software solutions in 2023?

Respond.io has a small footprint with a potentially big impact, with a “forever free” tier for small teams or individuals who want the benefits of a live chat but without the overhead. Respond.io can provide a better understanding of each customer profile with the inclusion of omnichannel chat history, contact import, and real-time analytics. While Respond.io is a solid choice for desktop communications, it doesn’t have a mobile component, limiting how customers can engage.

Other key features

Free, but also offers a paid tier
Includes a “supervisor dashboard” for managers
A workflow system provides easy delegation

11. Best live chat for mobile users: Podium Webchat

What makes Podium Webchat one of the best live chat software solutions in 2023?

Where some other live chats might neglect mobile, Podium directly engages mobile customers. Users who interact with the Podium Webchat provides an SMS number, and continued assistance comes through text messages. This allows businesses to meet customers where they are–on their mobile phones–rather than in front of a computer.

Other key features

Integration with Google Analytics
Easy implementation with few steps
Fully customizable with user’s brand

12. Best live chat for sales: Smartsupp

What makes Smartsupp one of the best live chat software solutions in 2023?

A live chat is great for customer service, but it can also be a great way to enable sales and conversions. By engaging with your prospects and customers in real-time, you create an opportunity to build relationships, answer questions, and close deals. Smartsupp purports to get the most out of live chat for a business by specifically focusing on conversions. Their platform also includes video recording of customer activity on the site, so employees can more effectively address customer intentions.

Other key features

Free version with additional paid tiers
Optional chatbot implementation
Integrations with all major online shopping platforms

13. Best Live Chat For the Medical Field: SnapEngage

What makes SnapEngage one of the best live chat software solutions in 2023?

SnapEngage is a great choice for medical providers because their live chat is HIPAA compliant. If you don’t know what that means, it’s basically a set of regulations that protect patient privacy. Any businesses in the medical industry have to abide by these rules, and SnapEngage makes it easy to do so with their software. Not only is their chat software compliant, but they also offer a number of features specifically for the medical industry.

Other key features

enables interactions with patients from point of access to diagnosis and beyond
medically enabled but robust offerings that cover all industries
detailed and helpful support documentation

Conclusion

Instantaneous and frictionless, live chat enables customer support teams to connect with their customers on their preferred channels, offering assistance in the moment of need, in a conversational and user-friendly manner. As not all live chat software platforms are the same, it is important to select the one that can deliver the most impact to your support team and your customers.

Email Support: The Pros and Cons of AI Customer Service Tools (Updated July 2021)

Written by Can Ozdoruk on Jul 25, 2021

Customers today demand more. To meet expectations for convenience, companies need to offer omnichannel programs and email support system solutions. While channels like Twitter, Instagram, Facebook Messenger and WeChat gain prominence, email support will dominate as the digital channel of choice for customer service in the foreseeable future.

According to Hubspot⁴, 62% of customers want to communicate with companies via email for customer service. This compares to 48% who want to use the phone, 42% who like live chat, and 36% who want “Contact Us” forms. An aside: the best chatbots can work on email (as opposed to just chat and social channels).

Consumers prefer to communicate with customer service 47% more often over email compared to live chat.

An email support system is superior in many ways. It’s not asynchronous and therefore is inherently more convenient. A person can send an email to a company and walk away, checking for a response when it’s convenient for them. A person is not committed to engaging in a real-time conversation which can get interrupted or lost due to connectivity issues or by simply clicking to a new Web page. Email also keeps a record. Emails can be stored and accessed later. On a phone call or Webchat interaction, no record exists.

Learn more about companies using chatbots for customer service.

Why Aren’t Traditional Email Support Systems Meeting Expectations?

Email is the most used digital customer service channel according to Forrester ¹, but 62% of companies do not respond to customer support emails². This presents a real risk to customer loyalty and satisfaction.

For the companies that do respond by email, they are not doing so quickly enough. Customers expect businesses to respond to their emails within an hour³. The average response time to a customer service request is 12 hours and 10 minutes². Furthermore, only 20% of companies are able to completely answer questions on the first reply².

From these numbers, it’s clear that email customer support needs to be improved. Customers want resolutions in their inbox. Making this worse, customer expectations for service in every channel are increasing; they expect faster response times and better responses. Companies like Amazon and Zappos have set the benchmarks for customer service, and people now demand quick, convenient support from every company they do business with.

“The handful of companies that respond promptly and accurately to customer emails increase trust in their brand, bolster customer satisfaction, and boost sales both online and offline.”

How Can Conversational AI Impact Your CSAT?

When launching an AI-powered Agent, email is often forgotten. Most companies start with social channels or live chat. Email, however, offers incredible benefits in terms of training an AI and delivering high customer satisfaction (CSAT).

AI Agents managing email conversations need to be more advanced than those deployed on a website or social channels. This is because email messages are typically longer and contain multiple intents. It’s important that you leverage a conversational AI platform that has the ability to decipher the intent from longer messages within the right context to be able to accurately resolve a ticket and reduce frustration.

Conversational chatbot AI can eventually manage over 50% of emails without human intervention, according to what we see from our customers. This offers the convenience and immediacy customers want on their preferred channel which keeps CSAT high.

Discover the key questions to ask when scheduling a chatbot demo.

Should You Launch a Virtual Agent on Your Customer Support Email?

Training an AI is initially done using historic data.

When you’re training an AI, it’s important to not focus on how you think people ask a question, i.e., the FAQs listed on your online help center or knowledge base, but how people are actually asking questions. In one example, Comcast found that their customers ask the simple question “I want to see my bill” in 7,500 unique word and phrase combinations.

By training with historic data, you’re able to set the AI Agent up for success by giving it knowledge and confidence to correctly classify intents across a larger number of utterances. The more data that’s available, the more accurate an AI will be. Because email has been used by companies for much longer than other channels, companies tend to have a much larger dataset.

Email is the most desired and used channel by customer service organizations and their customers.

When providing and omnichannel experience, it’s important to note that your customers ask things differently based on the channel in which they are seeking support. Chat is usually succinct. An email might contain multiple questions and additional detail. In a nutshell, people will ask things differently on email than other channels. So using your troves of historic email data will be most useful if you’re using it to train an email-based Conversational AI.

How Can Email Impact Reinforcement Learning?

People don’t expect an answer immediately on email like they do on chat or voice. This allows companies to start leveraging an AI Agent “behind the scenes” and conduct reinforcement learning. In this type of training, an AI Agent has no direct interaction with the customer. Instead, an AI reviews every incoming email and suggests a response to a human agent. The AI learns how the human agent responds in order to build confidence over time. Because the expectation for instantaneous support does not exist with an email support system, you have the leeway to train with real interactions without disrupting the customer experience.

Excited to deploy AI for your email? We can kick start improving your email channel by free analysis of AI fit in your organization. Just ask us how.

References

Forrester: 2018 Customer Service Trends: How Operations Become Faster, Cheaper — And Yet, More Human https://www.forrester.com/report/2018+Customer+Service+Trends+How+Operations+Become+Faster+Cheaper+And+Yet+More+Human/-/E-RES142291
Super Office: Customer Service Benchmark Report https://www.superoffice.com/resources/guides/customer-service-benchmark-report/
Toister: Get Ready to Respond to Customer Email Within One Hour https://www.toistersolutions.com/blog/2015/4/13/get-ready-to-respond-to-customer-email-within-one-hour
Hubspot: https://blog.hubspot.com/news-trends/live-chat-go-to-market-flaw

What’s The Difference Between Conversational Chatbot Solutions, Rules Based Chatbots, and Traditional AI?

Written by Can Ozdoruk on Apr 23, 2020

The History of Chatbots

As you may already know, chatbots are software that use natural language processing (NLP) to engage in conversations with users. But that doesn’t mean that all types of chatbots are created equal. Below, we are going to demystify three common terms for chatbot that you may be hearing across the industry: conversational chatbots, rules-based chatbots, and AI.

You can include these bots in mobile applications, messaging apps, websites, email, and even voice platforms like Alexa. Online retailers are integrating their chatbots with Shopify to increase revenue.

Along with countless benefits, many companies use chatbots for customer service as a way to provide immediate resolutions to common issues.

Conversational chatbot solutions and artificial intelligence have never been more popular than they are today. In fact, data from Google Trends shows that interest in chatbot solutions has increased ten-fold over the last 5 years.

Interest in chatbots over time, from January 1, 2004 through October 2020, according to Google Trends

During this explosion of interest, “chatbot” has evolved into an umbrella term that may inaccurately describe what a chatbot can and cannot do. Chatbots and conversational AI technology are often used interchangeably. In reality, the capabilities between chatbot technology and artificial intelligence are very different. We’ll explore more about what separates some chatbots from others below.

Chatbots vs. AI. What exactly is the difference?

It’s important to understand why modern artificial intelligence chatbots (also known as Conversational AI or AI agents) differ greatly from first-generation (rule-based) chatbots. The first chatbots adopted by companies were based on stringent rules and rigid decision trees that often led to frustrating user experiences. On the other hand, modern chatbots are more forgiving when it comes to following strict rules, enabling users to engage naturally in conversation.

More companies are looking to virtual assistants and conversational interfaces to provide anytime, anywhere customer support. So, it’s important to have a clear understanding of different technologies. That’s because the scope of a conversational agent initiative and the end-user experience is vastly different. Rules-based chatbots are limited to very basic scenarios. On the other hand, AI-powered virtual assistants are capable of engaging in natural language understanding, participating in 1:1 conversations due to machine learning, deep learning, and conversational experience.

Companies must ensure that they are adopting the right technology for their business and their customers. This is because the customer experience plays a critical role in consumer buying decisions and loyalty. Here is an example between modern conversational AI and basic bot technology:

If a person asks a question that a chatbot has not explicitly been trained to handle, it is easily confused. Conversational virtual assistants enable users to engage in natural, human-like conversation.

What Are Rules-Based Chatbots?

Rules-based chatbots can automate customer service in very specific scenarios. For example, looking up an order status or browsing through a product catalog. Basic chatbot technology moves the conversation forward via bot-prompted keywords or UX features like Facebook Messenger’s suggested responses. (As compared to typing in a question in free-form, using slang and engaging naturally in a conversation).

Basic chatbot platforms have limited, if any, natural language processing. Typically, the bot will ask a user a question and display a few responses in which a person can select from or it will identify a specific keyword in a user’s question. Based on a person’s input, the conversation moves forward on a specific path. With pattern-based bots, what a user says must explicitly match with how a bot was pre-trained in order for it to understand and move the conversation forward.

In regards to this, variations of a question must be pre-trained for a chatbot to accurately understand what a person is trying to say. For instance, a virtual assistant is trained to understand “Where’s my order?” If a customer asks the same question slightly differently, “Is my package arriving today?”, the bot will not accurately understand the intent of the question is “order status.”

That is, unless it has been explicitly trained to do so within the labeling and learning provided in its training data.

Rules-Based Bots And The User Experience

Chatbots lack semantics and advanced Natural Language Processing to understand the context of a message.

The user experience with rules-based bots is often alinear. If a person says something that is not preempted, a chatbot will get confused. The virtual assistant will most likely repeat the same question until it understands a response. For example, a chatbot designed to help people order a pizza will not know how to respond to a customer asking for nutritional facts as they are selecting toppings.

How to Train Rules-Based Bots

Chatbot training is a manual process and requires programming every flow and utterance of a question. A human workforce also identifies and implements ongoing improvements.

If you’re deploying a rules-based bot, make sure that you select a very specific use case. Fandango, for instance, has a bot that asks people for their zip code and pulls up movies playing locally. In another example, The Wall Street Journal lets users type in a stock symbol to get live stock quotes. These use cases are very specific and well defined and work well for bots.

Be upfront with your customers on a chatbot’s capabilities. You’ll need to provide an alternative method of getting support for other matters (i.e. I’m the Order Tracking bot. To find your order, type in your confirmation number below. If you need something else, please call ….).

Conversational Chatbots

Conversational chatbot solutions are AI-powered virtual agents that provide a more human-like experience. In opposition to rules-based chatbots, they are capable of:

carrying on a natural conversation
understanding the meanings of words
understanding misspellings
continuously improving over time

Because of these important differentiating features, conversational chatbots provide a greater user experience through the use of natural language processing and leverage semantics to understand the context of what a person is saying.

Discover the key questions to ask when scheduling a chatbot demo.

Conversational AI Examples

Here’s a quick example scenario of how conversational AI works: “I got the side table delivered yesterday but it looks like it might have been broken while in route. There’s a crack in the front. Can you help me? I would like my money back.” An AI-powered virtual agent would be able to decipher that a person is looking to return an item and receive a refund. An AI thinks like a human, not a robot, and is able to maintain a conversational flow.

AI-based chatbots that are conversational use machine learning technologies to understand, contextualize, and predict to accurately respond to user inputs. They enable companies to provide hyper-relevant personalized engagement, not generalized support. This can be done by training algorithms used in these chatbots with historical data from real user responses and can be optimized with ongoing user feedback (reinforcement learning). Like humans, AI virtual agents are able to decide the next best action based on a variety of things including contextual-factors, customer profiles, sentiment, or business policies. Furthermore, it can alter how it responds based on real-time sentiment analysis. For instance, an AI Agent treats a person who checks the status of their (on-time) flight differently based on how they react. A virtual agent would presume that a person who responds with “Oh no!!” that they are likely to miss their flight.

Two Different Types of Conversational Chatbots: Generative vs Retrieval

Continuing, there are two subclasses of learning-based chatbots: generative chatbots and retrieval chatbots. Generative chatbots can dynamically create responses in real-time, and retrieval chatbots select from a pool of responses based on the person’s message to the bot.

AI-based chatbots leverage semantics to understand the context of what a person is saying. Therefore, these bots can engage more naturally in conversation, and respond to more inputs without being explicitly trained on every single way a person might phrase their question, like the flight example above. Traditionally, these bots may not have been as accurate as pattern-based methods and used to take a long time to train. However, there are a few companies, like Netomi, that have built robust NLP engines that accurately understand user inputs up to 95% of the time, which means scaling and training are now exponentially easier and the end-user experience is much better than pattern-based bots.

Conversational chatbot solutions powered by AI also support multi-turn dialogue. This is the ability to switch between various user questions within a single conversation. This is what sets apart a human-like AI versus building chatbots. An AI-powered virtual agent responds without getting confused if a person pivots the conversation. For instance, a person can ask about the price of checking a bag in the midst of checking flight status. In conclusion, AI can also understand more short-form and slang than chatbots.

How to Train Conversational Chatbot Solutions

AI training is a combination of supervised and unsupervised learning. AI can learn from historic data. With customer service, this includes customer support email, chat and messaging logs, to identify and group together similar questions and scenarios. Training is on auto-pilot. An AI learns how a situation has been handled and teaches itself to act in the same way.

AI also uses deep reinforcement learning to improve over-time based on real-life interactions. AI-powered virtual agents are able to determine patterns based on how end users are responding in various circumstances. This is based on things like customer segmentation and contextual factors. For instance, if meal-delivery customers have issues with changing their subscription day, an AI would learn to proactively offer this information.

The richness of the technology has Gartner predicting that by 2021, 15% of all customer service interactions will be completely handled by AI¹. The best AI chatbots tend to be the most self-sufficient when it comes to adapting. When you hear about terrible chatbot fails, those are likely stemming from less-sophisticated bots and/or an improper way to set them up – basically launching a bot without enough training.

Key Trends in Chatbot Technology

There are a few emerging trends that are propelling the sharp rise in the adoption of conversational chatbots. Take a look at these key chatbot trends:

Personalization – personalization involves chatbots tailoring the interaction based on customer profile and behavior. For example, an AI bot could provide a hyper-relevant cross-sell recommendation by learning that a customer prefers certain brands or types of products. By incorporating customer experience personalization, chatbots respond on an individual level, providing more meaningful interactions.
Voice recognition – voice recognition enables faster, hands-free interactions for users, making AI bots even more convenient. Examples of voice recognition can be found in the array of personal assistants, including Google Assistant, Siri and Alexa. Companies, including WestJet, are also launching skills on voice platforms to provide yet even more choice with how customers receive support.
Machine learning operations (MLOps) – MLOps is a strategy used to automate and operationalize machine learning workflows. This strategy plays a role in chatbots by improving the speed and ease with which bots can be trained and improved. With such automation, bots are ready for market faster and can be more frequent, and easily updated.
Memory and context – many brands store customer information in customer relationship management systems (CRMs). When integrated into chatbots, CRMs can provide valuable information that enables chatbots to continue previous conversations with customers or look up specific details about the user. While this is often limited to profile details for privacy, chatbot engineers are working on ways to make queries more secure to enable broader interactions.

Exploring a new CRM solution? Learn more about two of the industry leaders in our Intercom vs. Zendesk review.

What To Keep In Mind As The Differences Between Chatbots vs. Conversational Chatbots With AI

Conversational chatbot and AI adoption is skyrocketing. In fact, according to Accenture, 60% of surveyed executives plan to implement conversational bots for after-sales, customer service, and social media. Accenture isn’t the only organization projecting big movement within the chatbot space – just take a look at these very telling chatbot statistics. At first glance, chatbot technology and AI-powered conversational interfaces appear very similar. When you go below the surface, though, the technology could not be more different. The initial training, the ongoing improvement, and the end-customer experience are not even close to being in the same league.

Interested in learning more about artificial intelligence and chatbot technology? We’d love to discuss how our powerful AI chatbot platform provides the frustration-free experience your customers expect. Don’t use a robotic, limited chatbot solution that plummets your CSAT. Let’s chat.

References

Gartner: 4 Trends in Gartner Hype Cycle for Customer Service and Customer Engagement

Netomi’s highlighted as a Growth Vendor in Forrester’s New Tech: Conversational AI For Customer Service Report!

Written by Can Ozdoruk on Jul 26, 2019

We’ve been featured in Forrester’s New Tech: Conversational AI For Customer Service, Q2 2019 report. This latest Forrester AI report is available to check out here.

The report focuses on the incredible growth of conversational AI within the enterprise, noting that “conversational AI tools could rewrite the rulebook for customer service, demanding new metrics and service process design.”

The report found that “thirty-one percent of global telecommunications technology decision-makers who are significantly involved in contact centers cited customer-facing chatbots as a top priority for technology investments.” That’s just the tip of the iceberg when it comes to telecommunications industry trends.

Contact centers are increasingly turning to AI customer support to deflect tickets from expensive human agents, enable human agents to focus on more complex issues and scale service to new channels, according to Forrester.

Netomi’s msg.ai was featured as a Pure Play, Growth-stage technology provider. Pure Play providers “focus almost exclusively on building conversational AI for enterprises. They rely on integrations with existing communication channels that their customers have deployed and, as such, tend to be very vendor-agnostic.”

“We’re so excited that our technology is being recognized by the trusted and sought-after thought leaders at Forrester. We’ve worked tirelessly to create a sophisticated AI solution that addresses the real customer service issues facing enterprises, including skyrocketing costs as the number of channels, increase while customer demands also quickly rise,” said Puneet Mehta, Founder / CEO, Netomi. “Never before has customer service had such a direct impact on a company’s bottom line. It now plays a fundamental role in people’s buying decisions. Our AI can multiply a company’s workforce to enable companies of all sizes to scale the immediate, convenient and personal support to every customer, any time, anywhere.”

Netomi’s AI customer service platform automatically resolves over 50% of a company’s incoming customer service queries on customer support email, chat and messaging, freeing up human agents to focus on more complicated customer issues. Netomi’s AI also boosts agent productivity by acting as the first line of defense, gathering information from customers or business systems, before routing to the right agent. Netomi can be deployed on any channel, in any vertical – from AI in retail, to telecom customer service, to operating a travel chatbot, and so much more. The technology can also recommend replies and actions to human agents to approve, edit or reject.

Discover the key questions to ask when scheduling a chatbot demo. To learn more about the Netomi AI platform, get in touch today.