In this chapter we’ll cover chatbot fundamentals, including what a chatbot is, how it works and why it’s important.
So, if you’re just getting started with chatbots, or want to strengthen your knowledge, this chapter is for you.
Listen to this chapter:
What is a Chatbot?
A chatbot is a computer program that allows humans to interact with technology using a variety of input methods such as voice, text, gesture and touch, 24/7 365.
For several years chatbots were typically used in customer service environments but are now being used in a variety of other roles within enterprises to improve customer experience and business efficiencies.
Known by a variety of different names such as a conversational AI bot, AI chatbot, AI assistant, intelligent virtual assistant, virtual customer assistant, digital assistant, conversational agent, virtual agent, conversational interface and more, chatbots are growing in popularity.
But just as chatbots have a variety of different names, they also have varying degrees of intelligence.
A basic chatbot might be little more than a front-end solution for answering standard FAQs.
Chatbots built using some of the bot frameworks currently available may offer slightly more advanced features like slot filling or other simple transactional capability, such as taking pizza orders.
But, it’s only advanced conversational AI chatbots that have the intelligence and capability to deliver the sophisticated chatbot experience most enterprises are looking to deploy.
For the purpose of this guide, all types of automated conversational interfaces are referred to as chatbots or AI bots.
Why are Chatbots so Popular?
Smartphones, wearables and the Internet of things (IoT) have changed the technology landscape in recent years. As digital artefacts got smaller, the computing power inside has become greater.
But mobile apps and data-heavy activities don’t go hand in hand. Wading through complicated menus isn’t the fast and seamless user experience businesses need to deliver today.
In addition, consumers are no longer content to be restricted by the communication methods chosen by an organization. They want to interface with technology across a wide number of channels.
A conversational AI bot offers a way to solve these issues by allowing customers to simply ask for whatever they need, across multiple channels, wherever they are, night or day.
How do Chatbots Work?
On a simple level, a human interacts with a chatbot.
If voice is used, the chatbot first turns the voice data input into text (using Automatic Speech Recognition (ASR) technology). Text only chatbots such as text-based messaging services skip this step.
The chatbot then analyses the text input, considers the best response and delivers that back to the user. The chatbot’s reply output may be delivered in any number of ways such as written text, voice via Text to Speech (TTS) tools, or perhaps by completing a task.
It’s worth noting that, understanding humans isn’t easy for a machine. The subtle and nuanced way humans communicate is a very complex task to recreate artificially, which is why AI bots use several natural language principles:
Natural Language Processing (NLP)
Natural Language Processing is used to split the user input into sentences and words. It also standardizes the text through a series of techniques, for example, converting it all to lowercase or correcting spelling mistakes before determining if the word is an adjective or verb – it’s at this stage where other factors such as sentiment are also considered.
Natural Language Understanding (NLU)
Natural Language Understanding helps the chatbot understand what the user said using both general and domain specific language objects such as lexicons, synonyms and themes. These are then used in conjunction with algorithms or rules to construct dialogue flows that tell the chatbot how to respond.
Natural Language Generation (NLG)
Delivering a meaningful, personalized experience beyond pre-scripted responses requires natural language generation. This enables the chatbot to interrogate data repositories, including integrated back-end systems and third-party databases, and to use that information in creating a response.
Conversational AI technology takes NLP and NLU to the next level. It allows enterprises to create advanced dialogue systems that utilize memory, personal preferences and contextual understanding to deliver a realistic and engaging natural language interface.
Chatbots can trace their history back decades, but it wasn’t until internet usage became more mainstream that the chatbots as we recognize them today, started to be used to support customer service functions.
Here’s a breakdown of some of the more prominent moments defined in chatbot history:
Turing Test, 1950
The Turing Test asks the question of whether machines can think, and was asked in 1950 by Alan Turing in his 1950 landmark paper, “Computing Machinery and Intelligence”. In the paper, Turing proposed a test where an interrogator had to determine which player was a human and which a machine through a series of written questions.
Despite criticisms and flaws, the test is still performed regularly today.
In 1964, MIT computer scientist Joseph Weizenbaum started development on ELIZA, what would turn out to be the first machine capable of speech using natural language processing.
Symbolically named after Eliza Doolittle in George Bernard Shaw’s Pygmalion, ELIZA was able to fool many people into believing they were talking to a human simply by substituting their own words into scripts and feeding them back to users to maintain the conversation.
By the early 1970s, psychiatrist Kenneth Colby had taken the principles behind ELIZA a step further. With the introduction of PARRY, Colby adopted more of a conversational chatbot strategy than ELIZA using a model of someone with paranoid schizophrenia to help increase believability in the responses. In 1973 a conversation was set up between ELIZA and Parry.
RACTER, the “artificially insane” raconteur, was written by William Chamberlain and Thomas Etter. It was reportedly said that the book ‘The Policeman’s Beard’ was written by the Chatbot Racter. However, Racter was never released publicly.
Jabberwacky is a chatterbot created by British programmer Rollo Carpenter. It was one of the earliest attempts at creating AI through human interaction. The chatbot was designed to “simulate natural human chat in an interesting, entertaining and humorous manner”.
Loebner Prize, 1990
The Loebner Prize was launched in 1990 by Hugh Loebner. It takes the format of a standard Turing Test with judges awarding the most human-like computer program.
Dr. Sbaitso, 1991
Dr. Sbaitso was a computerized psychologist chatbot with a digital voice designed to speak to you. It was an artificial intelligence speech synthesis development, created by Creative Labs meant to show off the sound card’s then-impressive range of digitized voices.
A.L.I.C.E. (Artificial Linguistic Internet Computer Entity) also referred to as Alicebot, or simply Alice, is a natural language processing chatterbot first developed in 1995, who has won the Loebner three times. Alice was inspired by the ELIZA program.
Elbot is the cheeky chatbot who uses sarcasm and wit, along with a healthy dose of irony and his own artificial intelligence to entertain humans. Elbot was created by Fred Roberts and Artificial Solutions. In 2008 Elbot was close to achieving the 30% traditionally required to consider that a program has passed the Turing Test.
The Smarterchild chatbot was developed by ActiveBuddy Inc. by Robert Hoffer, Timothy Kay and Peter Levitan. It was available on AOL Instant Messenger MSN Messaging networks. The chatbot offered fun personalized conversation and was considered a precursor to Apple’s Siri and Samsung’s S Voice.
Mitsuku is a chatbot created from AIML technology by Steve Worswick. It’s a five-time Loebner Prize winner (in 2013, 2016, 2017, 2018, 2019). Mitsuku claims to be a teenage female chatbot from Leeds, England. Her intelligence includes the ability to reason with specific objects, she can play games and do magic.
IBM Watson, 2006
Named after IBM’s first CEO, Thomas, J. Watson, Watson was originally developed to compete on the American TV program, ‘Jeopardy!’, where it defeated two of the former champions in 2011. Watson has since transitioned to using natural language processing and machine learning to reveal insights from large amounts of data.
Siri first came to the public’s attention in February 2010 when it was launched as a new iPhone app. Apple subsequently bought the company and integrated the voice assistant into the iPhone 4S at its release in October 2011, bringing voice applications into the mainstream consumer market for good.
Google Now, 2012
Google Now was developed by Google, created specifically for the Google Search Mobile App. It uses a natural language user interface to answer questions, make recommendations, and perform actions by passing on requests to a set of web services.
Siri remained perhaps the most famous of mobile voice assistants until Amazon launched Alexa. Already familiar with giving commands to their phone, Alexa caught consumers imagination and launched the now-immense market for smart home speakers.
Tay was a chatbot created by Microsoft to mimic the speech and habits of a teenage American girl. The chatbot caused controversy and was shut down only 16 hours after launch, when it began to post offensive tweets and became increasingly paranoid.
Woebot developed by Woebot Labs is an AI-enabled therapy chatbot designed to help users learn about their emotions with “intelligent mood tracking.”
2020 and Beyond
Expect to see enterprises planning for an intranet of conversational AI applications that can work together seamlessly, sharing information.
In this chapter we’ll cover the different types of chatbot technology.
We’ll talk about linguistics, machine learning and a hybrid model approach.
We’ll also look at AI chatbot development and integrations.
Listen to this chapter:
Types of Chatbot Technology
The majority of chatbot development tools today are based on two main types of chatbots, either linguistic (rule-based chatbots) or machine learning (AI bot) models.
Linguistic Based (Rule-Based Chatbots)
Linguistic based – sometimes referred to as ‘rules-based’, delivers the fine-tuned control and flexibility that is missing in machine learning chatbots. It’s possible to work out in advance what the correct answer to a question is, and design automated tests to check the quality and consistency of the system.
Rule-based chatbots use if/then logic to create conversational flows.
Language conditions can be created to look at the words, their order, synonyms, common ways to phrase a question and more, to ensure that questions with the same meaning receive the same answer. If something is not right in the understanding it’s possible for a human to fine-tune the conditions.
However, chatbots based on a purely linguistic model can be rigid and slow to develop, due to this highly labor-intensive approach.
Though these types of chatbots use Natural Language Processing, interactions with them are quite specific and structured. These type of bots tend to resemble interactive FAQs, and their capabilities are basic.
These are the most common type of bots, of which many of us have likely interacted with – either on a live chat, through an e-commerce website, or on Facebook messenger.
Machine learning (AI Bots)
AI-powered chatbots are more complex than rule-based chatbots and tend to be more conversational, data-driven and predictive.
These types of Artificial Intelligence chatbots are generally more sophisticated, interactive and personalized than task-oriented chatbots. Over time with data they are more contextually aware and leverage natural language understanding and apply predictive intelligence to personalize a user’s experience.
Conversational systems based on machine learning can be impressive if the problem at hand is well-matched to their capabilities. By its nature, it learns from patterns and previous experiences.
But, to perform even at the most rudimentary level, such systems often require staggering amounts of training data and highly trained skilled human specialists. In addition, a machine learning chatbot functions as a black box. If something goes wrong with the model it can be hard to intervene, let alone to optimize and improve.
The resources required, combined with the very narrow range of scenarios in which statistical algorithms are truly excellent, makes purely machine learning-based chatbots an impractical choice for many enterprises.
Hybrid Model – The Ultimate AI Bot Experience
While linguistic and machine learning models have a place in developing some types of conversational systems, taking a hybrid approach offers the best of both worlds, and offers the ability to deliver more complex conversational AI chatbot solutions.
A hybrid approach has several key advantages over both the alternatives. When considered against machine learning methods, it allows for conversational systems to be built even without data, provides transparency in how the system operates, enables business users to understand the chatbot application, and ensures that a consistent personality is maintained and that its behavior is in alignment with business expectations.
At the same time, it allows for machine learning integrations to go beyond the realm of linguistic rules, to make smart and complex inferences in areas where a linguistic only approach is difficult, or even impossible to create. When a hybrid approach is delivered at a native level this allows for statistical algorithms to be embedded alongside the linguistic conditioning, maintaining them in the same visual interface.
Building conversational applications using only linguistic or machine learning methods is hard, resource intensive and frequently prohibitively expensive. By taking a hybrid approach, enterprises have the muscle, flexibility and speed required to develop business-relevant AI applications that can make a difference to the customer experience and the bottom line.
Here’s a video explaining the benefits of a hybrid approach using both linguistic and machine learning models:
It’s essential to define business value and goals at the beginning of a project. By knowing the features needed to achieve the desired result it’s possible to shape the implementation, bearing in mind any business restrictions such as time or budget.
Whether it’s a proof of concept, pilot or full production project it’s important to stay true to these goals before moving on to other phases within the project. Otherwise it’s tempting to be distracted by cool chatbot features that aren’t necessary to achieve the end goal.
Think Big, Start Small
Enterprises are moving beyond short-term chatbot strategies that solve specific pain points, to using conversational interfaces as an enabler to achieve goals at a strategic level within the organization.
Consider the wider strategy but start with a smaller project in order to see the results and measure the success before deciding on the next phase. Ensure the technology used for Artificial Intelligence chatbot development can scale to meet future needs.
Take Control of the Chatbot Landscape
In large enterprises it’s not uncommon for several proof of concept (PoCs) and pilot chatbot projects to be currently underway, unseen and often un-coordinated by the CIO. For businesses this poses two main concerns — a duplication of resources and potential security risks.
In recognition of the need to bring together teams tasked with delivering the innovative solutions that will drive the business forward globally, enterprises are forming Centers of Excellence.
Skillsets are no longer spread across the organization but focused on collaborating and developing Artificial Intelligence chatbot solutions to solve problems, improve productivity and make the business stronger.
Collaborate With All Stakeholders
The combination of CIOs taking control of the chatbot landscape, the continued business-driven initiatives from departments looking to build their own applications, and the push from developers to build conversational systems at a ‘skunk work’ level is creating an interesting and dynamic set of stakeholders.
Choose a chatbot technology that is advanced enough for developers to rapidly build a complex proof of concept that can still be easily understood by business users, even from day one.
Going Live Isn’t the End
Launching a chatbot is only the beginning. It can always do better and increase customer satisfaction even further.
Make provisions to provide continual and continuous improvement to the system. It doesn’t have to be time intensive, much of the process can be automated. At the same time, it’s also essential to have KPI reporting in place and to use the traditional measuring methods already used by the organization, such as first call resolutions rates.
By enabling the AI bot to continue to learn and improve, the value of enterprise chatbot solutions will increase.
Connectors harness the power of back-office technology to deliver even greater intelligence and capabilities by integrating a chatbot into business systems, communication platforms and more. Reach users on any channel, deliver more personalized answers based on behind the scenes processes, and execute tasks on customers’ behalf.
People use a variety of channels and devices in communicating with others. Not only is it important for organizations to be available on all channels relevant to its audience, but the experience needs to be seamless across those channels too.
For example, a person might use a Facebook Messenger chatbot on their smartphone to start a conversation on the commute home and want to continue it later that evening using a smart home hub, before moving to their smart speaker or watch to conclude it.
Channels often deployed for chatbot use include: Amazon Alexa, Android chat, Cortana, Discord, Facebook Messenger, Google Assistant, iOS Chat, IVR by Twilio, IVR by Nexmo, IVR by Cisco, LINE, Microsoft Teams, MS Bot Framework, Skype, Slack, SMS by Nexmo, Telegram, Twitter, Wechat, WhatsApp, or a custom app for mobile, in car or home.
Connectors can also include enterprise backend software, Live Chat, ASR/TTS and Knowledgebase such as: Blue Prism, UiPath, Salesforce.com, SAP, Amadeus, Bold360, Cention, Live Chat Inc., LivePerson, Google ASR, Amazon, Apple, Microsoft, Nuance, and RightNow.
In this chapter we’ll cover the reasons chatbots fail and what to avoid when building your conversational AI chatbot strategy.
Listen to this chapter:
Chatbots Failing to Deliver
It’s claimed that chatbots increase customer engagement, improve the brand experience and deliver actionable insight to the business. So why are so many chatbots failing to deliver on their potential?
The answer lies in the restrictive nature of most chatbot technology. Few chatbots offer the rich, humanlike conversation needed to engage users, nor can they guide off-topic users back to the subject at hand. They can’t ask qualifying questions if clarification is required. And, they are not able to deliver over the different channels and languages by which customers want to communicate.
The main issues can be categorized into four main areas:
A Lack of Training Data
It’s a common misconception that machine learning systems somehow work completely on their own, without any human supervision. This is not true.
Just as a linguistic based conversational system requires humans to laboriously craft each rule and response, a machine learning system requires humans to collect, select, and clean every single piece of training data, because using machine learning to understand humans takes a staggering amount of information. What comes naturally to us as humans – the relationships between words, phrases, sentences, synonyms, lexical entities, concepts etc. – must all be ‘learned’ by a machine.
For enterprises that don’t have a significant amount of relevant and categorized data readily available, this can be a prohibitively costly and time-consuming part of building conversational AI chatbot applications.
Poor Conversational Understanding
An even greater problem is the risk that the machine learning systems do not understand the customer’s questions or behavior.
In a linguistic based conversational system, humans can ensure that questions with the same meaning receive the same answer. A machine learning system might well fail to correctly recognize similar questions phrased in different ways, even within the same conversation.
There’s also the issue that pure machine learning systems have no consistent personality, because the dialogue answers are all amalgamated text fragments from different sources. From a business point of view, this misses the opportunity to position the company and its values through a consistent brand personality.
Ease of Creating Global Appeal
Organizations need to support their customers in different languages – a problem that will only increase over time. Hence, AI-based chatbots need to be fluent in many languages, with the ability to learn more when needed. But this is only part of the problem, because they frequently need to support a variety of platforms, devices or services too.
Most chatbot development technology requires a great deal of effort and often complete rebuilds for each new language and channel that needs to be supported, leading to multiple disparate, solutions all clumsily co-existing.
These types of chatbot solution cannot reuse assets from the original build, nor can they surface the same chatbot solution through multiple devices and services.
Regulations Protecting Data
Data is at the heart of conversational AI, and is used to personalize the conversation, improve the system and deliver actionable insight to the business, so it’s essential that enterprises can reap the benefits while complying with regulation and legislation.
While GDPR is an EU regulation, the ramifications impact enterprises around the globe. It’s likely that regulation will increase throughout many countries in the future. For organizations, the challenge is not just in storing the data, but also in retrieving the information for export or deleting in a secure and auditable way.
Furthermore, many chatbot technologies restrict access to the conversational data generated, meaning businesses lose one of the key benefits to implementing a conversational bot. Without this data, businesses are effectively blind to their customers.
In this chapter we’ll discuss how chatbots stack up against live chat, and why AI chatbots are the future of delivering an enhanced experience through customer support.
Listen to this chapter:
Chatbots are the Future of Customer Support
Chatbots offer several advantages over live chat or contact center agents. Although reduced costs are clearly a key incentive, it shouldn’t be the only consideration. There are several other advantages in offering your customers an intelligent automated self-service option.
While there will always be customers that prefer to speak to a live agent, what happens when it’s out of hours; or at peak times when your phone lines are jammed? A chatbot is available at your customers’ convenience over any number of different channels, not just your staffed hours and channels.
An Artificial Intelligence chatbot is built to recognize, understand and respond to specific queries and problems in seconds. They can even offer up ‘best match’ queries mid-interaction, saving even more time for the customer. By contrast most agents typically must refer to standardized macros for common queries – all taking extra time.
Accuracy is key to reduce first time call resolution rates and to ensure customers return to the chatbot the next time they have a query. Most advanced conversational systems can solve 80% of queries automatically because of their high level of understanding, often achieving 98% accuracy.
Chatbots ensure that legal notices are never forgotten or that industry regulation isn’t accidentally breached. In addition, customers and companies alike can track conversations to ensure transparency and accountability. Furthermore, important updates and changes can be centrally rolled-out and a proper audit trail maintained for compliance proposes where needed.
There are only so many queries a live agent can handle at once. Live chat allows agents to help more than one customer at a time, but call center agents must finish one call, before starting another. A conversational bot can handle millions of conversations simultaneously, all to the same high standard.
But there is still a need for the human touch…
Sometimes there is no substitute for the empathy live agents can deliver or the kind of intelligence that needs creativity or judgement to resolve a query. In these situations, it’s often the human ability to draw parallels with similar experiences that allows for problems in complex or unusual circumstances to be resolved.
Therefore, it’s essential for a chatbot to be able to seamlessly handover to a live agent when the need arises. Ensuring that all the information already gleaned during the conversation is transferred too, so the customer doesn’t have to start from the beginning again.
They allow enterprises to build advanced conversational applications using either linguistic or machine learning, or (ideally) a hybrid combination of both. Some can integrate into back end systems and third-party data sources to deliver answers that might need more than one information source to truly personalize the response.
A graphical user interface (GUI) is essential to enable both developers and business users to have visibility into the system. A visual, drag-and-drop style user environment also makes it easier for business users and subject matter experts to correct a dialogue flow or update an answer.
Data analytics from chatbot applications need to feed back into the system in real-time to increase personalization within a conversation and to automatically deliver suggestions for system improvements. While the GUI provides business critical data about customers preferences and delivers an accurate picture of the “voice of the customer”.
A conversational AI chatbot application shouldn’t just be something that is built and then forgotten – a tick in the box next to the word chatbot. To optimize RoI, capitalize on emerging channels or expand into new geographies, conversational AI applications need to be adaptable to tomorrow’s needs.
It’s essential that a platform has flexible connectors, SDKs and APIs to allow enterprises to seamlessly scale their application according to their needs.
The best chatbot platforms make it possible to create an application once and deploy it in multiple languages and, across multiple devices and channels, using most of the original build. It also enables for AI assets to be shared between applications, allowing for even faster creation and greater RoI.
While there are many different enterprise chatbot platforms available in the market, they are not all built equally. Enterprises would be advised to list the criteria and functionality they need from their chatbot applications before deciding on which technology to use.
In this chapter we’ll cover several capabilities an enterprise AI chatbot needs in order to distinguish itself from a basic chatbot. These capabilities are the keys to successful engagements that deliver true understanding to customers requests that deliver personalized responses.
Listen to this chapter:
AI Chatbots – the Key to Successful Engagements
AI-based chatbots deliver the intelligent, humanlike experience most people expect when they hear the words AI.
The majority of chatbots available today are not AI based. They may use algorithms to determine the meaning of a question and the likelihood of the correct answer, but if you go off the chatbot script then they are left floundering.
AI Chatbots or conversational AI systems by comparison are not only capable of understanding a customer’s intent, no matter how the question is phrased, but are far more capable too. They can for example fill out forms, make recommendations, upsell, book appointments, even integrate with third party or backend software like Robotic Process Automation (RPA), Enterprise Resource Planning (ERP) or Customer Relationship Management (CRM) systems to carry out further tasks.
Interestingly, despite wanting a humanlike interaction most people are quite content knowing they are speaking to a machine. For some it means they can go over a technical problem again and again without feeling foolish. For others a machine offers a faster, more efficient experience.
The key to successful engagement is understanding the customer’s request and delivering a response that’s personalized and relevant to the individual.
In order to do that, AI Chatbot solutions need several capabilities:
Intelligent Understanding is more than just correctly interpreting the user’s request. It’s about being able to instantly amalgamate other pieces of information such as geolocation or previous preferences into the conversation to deliver a more complete answer.
Memory allows a chatbot to remember pertinent details to reuse during a conversation or implicitly learn about a person to be reused later. For example, a mobile assistant might learn through previous requests and responses that the user clearly prefers Italian cuisine and so will use this information when asked for restaurant recommendations in future.
Sentiment analysis enables a chatbot to understand the mood of the customer and the strength of that feeling. This is particularly important in customer service type applications where it can be linked to complaint escalation flows, but also can be used in other more trivial ways such as choosing which songs to play upon request.
Personality can make a huge difference to engagement and the trust users place in the chatbot. While some companies chose to reinforce it using avatars, personality can easily be conveyed in the conversation alone. Want to meet a sarcastic chatbot? Try talking to Elbot.
Persistence allows people to pick up a conversation where they last left off, even if they switch devices, making for a more natural and seamless user experience.
Topic switching enables the user to veer off onto another subject, such as asking about payment methods while enquiring if a product is in stock. The conversational bot should also then be capable of bringing the user back on track if the primary intent is not reached.
What Makes the Best AI Chatbot? Must-Have Chatbot Features and Benefits
In this chapter we’ll cover what to look for when building the ultimate conversational AI chatbot platform strategy – including the must-have features.
Listen to this chapter:
The Top 10 Best AI Chatbot Features
As if starting your chatbot journey isn’t daunting enough, choosing the right conversational AI chatbot platform to build the best chatbot for your business can leave you reeling. To help point you in the right direction we’ve put together the top ten chatbot features you need to consider regardless of application.
It may seem obvious but there’s a world of difference between a chatbot answering a question and holding an intelligent conversation. An engaging exchange will not only improve the customer experience but will deliver the data to help you increase your bottom line. To achieve this, the user interface needs to be as humanlike and conversational as possible.
A conversational chatbot must understand the user’s intent, no matter how complex the sentence; and be able to ask questions in return to remove ambiguity or simply to discover more about the user. It needs a memory in order to reuse key pieces of information throughout the conversation for context or personalization purposes and be able to bring the conversation back on track, when the user asks off topic questions.
If you’re a multi-national company, you’ll need the AI chatbot development platform you choose to do all this, and in your customer’s native language too.
It’s very difficult to anticipate how people might use, or abuse, an AI application.
Certainly, Microsoft didn’t envisage that “helpful” members of the public would teach Tay to start Tweeting inappropriate messages. Tay was designed as a showcase of machine learning, but unfortunately very neatly illustrated the problem with some conversational AI development tools they lack the control required to supervise the behavior.
By ensuring a level of control within the chatbot application, enterprises can not only avoid awkward mistakes, but provide a ‘safety net’ for managing unexpected exceptions during a conversation, always ensuring a smooth customer experience.
Enterprise-Grade AI Chatbot Solutions
Few chatbot development platforms were built with the enterprise in mind. Consequently, chatbot features you might expect as standard such as version control, roll back capabilities or user roles to manage collaboration over disparate teams are missing.
In addition, look for features that will aid speed of development including automated coding, web-hooks to allow flexible integration with external systems, and ease of portability to new services, devices and languages.
Most chatbot development tools today are either purely linguistic or machine learning models. Both have their drawbacks. Machine learning systems function, as far as the developer is concerned, as a black-box that cannot work without massive amounts of perfectly curated training data; something few enterprises have. While linguistic-based conversational systems, which require humans to craft the rules and responses, cannot respond to what it doesn’t know, using statistical data in the same way as a machine learning system can.
A hybrid approach that combines linguistic and machine learning models is best, and allows enterprises to quickly build AI applications whatever their starting point – with or without data – and then use real-life inputs to optimize the application from day one. In addition, it ensures that the system maintains a consistent and correct personality and behavior aligned with business aims.
Personalizing an automated conversation, whether it’s simply accessing account information to answer a billing query or taking into consideration that customer’s love of Italian food when recommending a restaurant, not only delivers a more accurate response, it increases engagement too.
While some information can be learned ‘explicitly’ (such as the customer choosing a preference from a list of features), it’s the automated learning through ‘implicit’ methods (like information gleaned from, previous interactions) that really harnesses the power of conversational AI. This can then be combined with other information and data sources such as geo-location, purchase history, even time of day, to personalize the conversation even further.
Data Ownership and Analytics
One of the key considerations in choosing a chatbot platform is data. People reveal vast amounts of information in everyday conversations. Their individual preferences, views, opinions, feelings, inclinations and more are all part of the conversation. This information can then be used to feed- back into the conversation to increase engagement, train and maintain your conversational AI chatbot interface; and analyzed to deliver actionable business data.
Alongside data ownership, carefully consider the data analytics package provided as part of the platform, including the flexibility in drilling down through the information and understanding the context of conversations, as well as the level of detail provided.
Conversational applications are gradually infiltrating all aspects of everyday life, so it makes sense to ensure that conversational applications can be easily ported to existing and future devices. While it’s easy to state that applications can be built to run on a variety of platforms or services, all too frequently each one requires a completely new build. Investigating how much of the original build can be reused at the start, may save significant resources in the long term.
It’s also worth looking at how the chatbot application will support your users as they swap from device to device during the day. Seamless persistence of conversations increases engagement and customer satisfaction.
Data security is a key consideration for any enterprise, particularly when dealing with regulatory frameworks and customers’ personal information. Flexibility is essential in an AI chatbot platform to meet today’s exacting security conditions, across multiple geographies and legal requirements.
While most enterprises have no issue with a standard cloud deployment, when complying with industry regulations, or ensuring security policies are met that the cloud isn’t always an option. Where this applies, ensure that an on-premises option is available.
By adding an intelligent conversational UI into mobile apps, smartwatches, speakers and more, organizations can truly differentiate themselves from their competitors while increasing efficiency. Customization offers a way to extend a brand identity and personality from the purely visual into real actions.
And finally, before any final decision is taken, ensure you look beyond the marketing blurb. Check out real-life applications and talk to existing customers. Find out from them how easy it was to develop and build solutions; have they tried porting to new languages or services; how did they expand into new channels or devices; what benefits they’ve seen; and how they believe their Conversational AI chatbot platform will enable their digital strategy in the future.
In this chapter we’ll talk about how AI chatbots transform business by reducing costs, increasing revenue and enhancing the customer experience.
Listen to this chapter:
The Best AI Chatbot for the Enterprise
Users value chatbots because they are fast, intuitive and convenient. For enterprises, AI chatbots offer a way to build a more personalized and engaging customer experience, which in return delivers a wealth of customer information that is highly valuable in better understanding their customers and growing their business.
Here are the 10 key areas where businesses can derive value from AI bots:
Intelligent chatbots guide customers on a buying journey, driving sales conversion and revenue. Advanced chatbots can remember customer preferences and provide advice, tips and help, while gently upselling.
Chatbots help to reduce costs by enabling enterprises to service more customers without increasing their overheads. Virtual customer assistants can help curtail inbound queries by anything up to 40%, and often deliver first call resolution (FCR) rates far in excess of live agents.
Chatbots offer new channels for automated sales conversations to engage customers and provide personalized advice and support, without the overhead of having to deploy new back office teams to build and then run each new channel or network.
Customers want service now, 24/7, 365. They want to message you a question while waiting in line for coffee or use voice to make an online purchase while driving to work – and they want to do so using all of the devices and services they already use every day.
As customers start to favor online methods of communication, chatbots provide an opportunity to reignite the customer experience with increased engagement, personalized customer service and improved customer satisfaction.
Understand the Customer Better
First-person, conversational data can be used to understand trends and better interpret customer sentiment, providing invaluable insight that informs product and service development. This data can be accessed at granular levels for individualization marketing purposes; right up to macro level to identify overarching trends.
Artificial Intelligent Chatbots for Customer Experience
In this chapter we’ll cover how intelligent chatbots transform customer experience by delivering a more personalized service, and how a deeper understanding of your customer can increase customer engagement.
Listen to this chapter:
Greater Understanding of Your Customer with an AI Chatbot
One of the key drivers for using chatbots is to improve the customer experience through increased engagement and a more personalized service.
Customers want an experience that is fast and convenient. Chatbots remove the need to dig down through endless menu systems. Customers can simply ask for what they want, just as if they were talking to a live assistant—and get the right response, every time.
Chatbots are perfect for resolving customer service issues, troubleshooting common problems, helping with account administration and providing general advice. And with over 40% of inbound queries typically deflected to automated channels, there are significant cost savings too.
Chatbots Need to be Smart
But to substantially improve the customer experience, chatbots need intelligence.
While customers are used to the experience that Siri or Alexa gives them, it’s widely known that there is no personalization or intelligent understanding about their demands.
To achieve an intelligent and engaging experience, enterprises need a conversational AI chatbot platform that can deliver humanlike conversations over any channel, in any language. One that enables a chatbot capable of following the user as they switch devices and services during the day. While delivering a personalized response by remembering pertinent facts, user preferences and using back-office databases or third-party information to provide a comprehensive response.
Beyond Customer Service
Chatbots shouldn’t be thought of in isolation as, a point solution to solve a single problem. They need to be incorporated in the overall corporate strategy. For example, a customer service chatbot typically knows about an enterprise’s products and has already been integrated into a back-end CRM system.
Why not expand their knowledge and allow them to sell more too?
Stock availability, the day’s special offers, recommendations for complementary products, an Artificial Intelligence chatbot can easily have this knowledge at their fingertips. Using CRM information and other data such as past purchases, web navigation pattern and real-time analysis of the customer conversation, a chatbot can maximize the potential of every sales transaction.
Data Ownership is Essential
One of the key benefits of enterprise AI chatbot platforms is that the business owns the data the system generates. This can provide vital information – for example, exactly what stage of the purchase process and why someone didn’t complete – helping lower customer abandonment rates.
Conversational data also enables businesses to develop a greater understanding of what customers are looking for, how to improve information provided and deliver other business insights such as product purchasing trends. Even when the data has been anonymized or aggregated because of data privacy regulation, a wealth of valuable information can still be generated.
Increased Engagement Drives Revenue
Chatbots are transforming customer engagement by bringing together a variety of automated touchpoints to create a closer, more personalized conversation that has customers returning again and again. Increased engagement means more actionable data to personalize the experience even further, while delivering that enriched information back to the business.
For enterprises looking for innovative, cost effective ways to build a closer relationship with their customers, intelligent chatbots are now a critical component of a digital strategy.
In this chapter we’ll cover the primary ways chatbots are used, as well as look at some chatbot use case and chatbot examples covering the most important industries.
If you’re interested to know how chatbots are transforming business across industries, this chapter is for you.
Listen to this chapter:
Primary Artificial Intelligence Chatbot Use Cases
Chatbots can be broadly categorized by their use cases: customer facing, employee facing and on-board devices. It’s possible there’s some overlap such as a customer service app that’s used by both customers and call center staff in resolving queries.
Chatbots are primarily used in three ways:
Between Enterprises and Customers:
Highly conversational chatbot apps allow enterprises to create frictionless journeys for their customers, as they interact over a wide variety of digital channels and devices. Some development chatbot platforms enable enterprises to capture and analyze entire conversations to understand the voice of the customer.
Between Enterprises and Employees:
The best AI chatbot systems enable enterprises to streamline business process and increase productivity allowing organizations to do more without increasing headcount. For example, robotic process automation (RPA) and other AI assets are increasingly integrated into chatbots to deliver “zero intervention” solutions for high-volume processes.
Between Users and Devices:
Conversational AI is gaining strong traction in the home automation and automotive markets where reliance on clunky menu systems to operate various devices are a barrier to engagement. Conversational AI, with its ability to understand complex sentences, flexible integration capabilities and an agnostic architecture is ideally suited to these markets.
Banking, Financial Services & Insurance Chatbot Use Cases
Guide customers into performing a variety of financial operations in a conversational way and with complete safety. From checking an account, reporting lost cards or making payments, to renewing a policy or managing a refund, the customer can manage simple tasks autonomously.
Guaranteed Customer Support
Provide immediate support to existing customers and prospects through a chatbot capable of addressing all queries in real time. With each conversation the chatbot learns more about customers, delivering a proactive and personalized service.
Internal Training and Support
Help provide adequate support to employees by facilitating the most complex and time-consuming back-office operations, such as managing internal documentation or reviewing agreements, as well as providing the necessary training to new staff members.
Automotive Chatbot Use Cases
Increase Customer Engagement
Engage prospects with fast, humanlike interactions to significantly increase conversion rates and provide a solid pipeline of highly qualified leads to dealerships.
Guide customers into choosing the vehicle that best fits both needs and budget, in a conversational style. Using the information gleaned from talking to the customer, the chatbot can help configure a car, and even schedule a test drive at the nearest dealer.
Create a conversation that goes beyond the boundaries of the vehicle to interact with other services, such as charging stations or road-side assisting. Customers can talk to their in-car systems over any channel available.
Retail & Ecommerce Chatbot Use Cases
Improve Customer Experience
Address all clients’ queries and requests, whether it’s pre-purchase information or updates on shipping, over any channel they choose, in a conversational and humanlike way.
Collect and analyze information generated by the conversations the chatbot has every day to better understand the customers’ needs and preferences. This conversational data can be used to anticipate users’ behavior and place customized offers or marketing messages at the right time.
Telecom Chatbot Use Cases
Resolve Technical Issues
For customers searching through self-help FAQs and knowledge forums to find an answer to a question, the frustration is palpable. With a conversational chatbot, customers can resolve technical issues, find out the latest upgrade deal and even change their address at a simple request.
Increase Sales and Acquisition
Use a chatbot to boost cross-selling among existing customers, offering personalized plans and services based on purchase history or user profile. At the same time, chatbots can assist potential customers in choosing the right product for their needs.
Give customers the effortless experience they want by removing the frustration caused by call center queues, endless online menus or outdated FAQs. A chatbot can fill out forms, deliver technical advice, process billing queries, and even recommend better tariffs.
Manage appointments between customers and technical staff in order to simplify field operations and optimize installation and maintenance processes.
Media & Entertainment Chatbot Use Cases
Transform the Gaming Experience
Combine a conversational chatbot with other forms of AR or VR technology to offer an immersive experience that will transform any gaming experience, whether it’s an online gambling site that delivers the whole casino experience, or a role-playing game that allows the player to converse with non-playing characters in a totally natural way.
Unique Targeted Content
By analyzing a user’s past behavior, chatbots can learn about preferences and suggest new and targeted pieces of content users would love to consume – and in a conversational way, taking the entertainment experience to a new level.
Increase the amount of monetization opportunities, like subscriptions, plan upgrades and other content promotions, with the support of an intelligent chatbot that can handle the whole sales process, from discovery to final purchase.
Smart Homes & IOT Chatbot Use Cases
Connected Home Experience
Enable customers to interact and control any smart-home connected device and appliance (like thermostats, switches or smart fridges), using the power of everyday speech and language.
Interact with Smart Vehicles
Improve the driving experience, from the moment a customer accesses the vehicle until he reaches the final destination. From unlocking the car, setting the desired temperature, to planning routes that avoid busy roads and ensuring the safety of the drivers and passengers alike.
Travel & Hospitality Chatbot Use Cases
By asking simple questions, the chatbot can figure out what the user is looking for and make recommendations based on preferences, like budget restrictions and destination types. The chatbot can also include suggestions on other related services, like car rentals or travel insurance.
Take advantage of the customer data gathered during endless interactions to deliver personalized offers, upgrades or add-on extras, that will help increase engagement and drive brand loyalty.
Provide Customer Care
Provide immediate support to customers during crucial situations, for example if they need to re-book a missed flight or change a hotel reservation, wherever they are and on whatever device or service they choose to communicate on.
In this chapter we’ll cover chatbot case studies over a range of industries spanning from banking through to media & entertainment.
If you’re interested in learning how companies have leveraged AI-powered chatbots to transform their industry, this chapter is for you.
Listen to this chapter:
Chatbots for Banking: Widiba
Widiba takes intelligent chatbots to a new dimension with its virtual reality banking app which has customers giving the company a 4.8/5 on its “happiness index”.
For Italian bank, Widiba, the desire to interact with its customers started long before its launch when it garnered ideas and suggestions from over 150,000 users to help create the products and services it offers. Widiba uses Teneo to deliver the founding values of its customer service: listening, understanding, care and high-quality customer service.
Using its extensive experience of the banking industry, Artificial Solutions built Widdy, a conversational digital employee capable of sophisticated understanding of complex issues who not only helps customers, but is able to continually learn from these interactions.
Laura allows Škoda to deliver a superior customer service experience that is already having a significant impact on enhancing the customer journey and improving website conversion rates.
Founded in 1895, Skoda is one of the world’s oldest car manufacturers. A wholly owned subsidiary of Volkswagen Group, Skoda delivered more than 1 million vehicles to customers worldwide during 2017. Recognizing that the customer experience needed a different approach, Skoda embarked on a program to change how it interacted with customers online.
Developed in just a few months using Teneo, Skoda’s conversational AI bot Laura is transforming online experience. Customers can chat with Laura to discuss their needs, such as what they will be using the car for or what their budget is. Laura takes all the information the customer provides and recommends the most appropriate car from Skoda’s eight models. She can even include a comparison based on personal preferences.
Shiseido, one of the world’s largest cosmetic companies reached an influential teen audience by providing make-up and advice and tips with a unique and engaging chatbot.
Founded in 1872, Shiseido is the fifth largest cosmetics company in the world and operates in 120 countries and regions. Despite being steeped in history, innovation has always been at the heart of the business and Shiseido is using Teneo to develop a closer relationship with its younger customer base.
Available on both iOS and Android, the chatbot application Beau-co (beautiful girl), enables Shiseido to be a reliable source of beauty information for Japanese teenage girls. With Teneo’s highly-evolved, natural language capabilities, customers can converse with Beau-co about all manner of beauty related topics such as how to apply eye make-up, as well as specific Shiseido products.
Julia’s ability to answer queries fast means her Net Promoter Score is frequently higher than that of the call center agents.
Vodafone is one of the world’s largest telecommunications companies and provides a range of services including voice, messaging, data and fixed communications. Using Teneo, it has developed a variety of applications to deliver an enhanced online self-service experience to its customers driving customer engagement.
Equipped with the intelligence to learn, reason and understand, and then apply this knowledge to real customer interactions, its conversational AI bot Julia, not only assists customers with a range of tasks from technical support to invoicing queries, but provides vital, insightful data back to Vodafone.
Shell achieved a 40% reduction in call volume to live agents by answering 97% of questions correctly and resolving 74% of digital conversations with its Teneo based intelligent virtual assistants – Emma and Ethan.
Shell is a household name in energy and petrochemicals, employing over 93,000 people. It’s the global market leader in branded lubricants, which are marketed in approximately 100 countries.
Shell’s requirements included the capability of their conversational bot to provide answers and information on over 3,000 Shell products using information based on 100,000 information data sheets, 1,000 different pack options and 1,100 different physical characteristics. They would also need to recognize and be able to recommend current alternatives on 2,000 obsolete Shell products and over 31,000 competitive products.
The data required to deliver the correct answer to each possible question was spread out over a variety of different sources including an external vehicle database with over a million different vehicle and engine combinations – it was therefore essential that Emma and Ethan were capable of pulling all the relevant information together and delivering the answer in multiple languages to support Shell’s global business.
94% of respondents to Kindred’s survey rated its conversational AI betting solution as ‘innovative’ – the key brand measure for the project.
Kindred (and its online betting brand Unibet) is one of Europe’s largest and fastest growing online gaming operators, with over 13 million customers globally. Known as an innovator in the sector, Kindred is using Teneo to differentiate itself by speech enabling the betting process, making it faster and easier to place a bet.
Kindred’s customers can now place a bet by saying something as simple as “Put a tenner on a 3-0 City win”. The chatbot application intelligently interprets the user’s meaning and places the bet, asking the customer for clarification if required.
By enabling the customer to interact naturally, the app removes some of the hurdles of traditional web and app interfaces, so giving the customer the best possible experience. Conversational AI is particularly useful when coupled with Kindred’s live streaming portfolio (Kindred streams over 30,000 major events per year), meaning bets can be placed without having to exit the stream and risk missing that crucial goal or point. This further enhances the user experience allowing sports fans to effortlessly watch and live bet.
North America is expected to hold the largest market size in the global conversational AI software market, while Asia Pacific (APAC) is expected to grow at the highest CAGR during the forecast period. North America is expected to be the leading region in terms of adopting and developing conversational AI. Growing investments in AI and ML technologies, the presence of a maximum number of chatbot companies and increasing government spending on AI-based technologies are expected to contribute to the market growth during the forecast period. (Markets and Markets)
Intelligent Virtual Assistants (IVA) and chatbots are the 2 segment types in the conversational AI market report.
Chatbot Statistics: Market Segment
The Chatbots segment will hold a larger market size during the forecast period. Research and Markets states that the Chatbots Market was worth USD 17.17 billion in 2019 and is projected to reach USD 102.29 billion by 2025, registering a CAGR of 34.75% over the period.
The Chatbots segment is estimated to hold a larger market size, owing to the increasing demand for AI-powered chatbots to analyze customer insights in real-time. The AI-based chatbots can be used by the enterprises to understand user behavior, purchasing habits, and preference over time and accordingly can answer queries.
The major factors fueling the market growth include the increasing demand for AI-powered customer support services and omnichannel deployment, and reduced AI chatbot development costs.
Chatbot Adoption Growth Expected Across All Industries
However, choosing the best chatbot platform to create a conversational AI bot is key.
According to an April 2019 survey from Forrester Consulting, 89 percent of customer service decision makers in North America believe chatbots and virtual agents are useful technologies for personalizing customer interactions. But problems arise when the capabilities that chatbot companies promise to deliver just aren’t there, or require too much involvement from internal IT teams.
Ian Jacobs of Forrester says that one of the things he learnt while researching 14 vendors is that a typical request for proposal (RFP) doesn’t work for conversational AI. In his opinion, it’s almost impossible to differentiate between the products on paper. Ian recommends carrying out proof of concepts to evaluate conversational AI chatbot development tools.
Data shows that chatbot usage and engagement is on the rise.
Chatbot Usage and Engagement Market Statistics
Here we’ve organized some chatbot market statistics into yearly segments:
A Statista study demonstrates that over 64% of business respondents believe that chatbots allow them to provide a more personalized service experience for customers (Statista).
56% of businesses claim chatbots are driving disruption in their industry and 43% report their competitors are already implementing the technology (Accenture Digital).
57% of businesses agree chatbots deliver large ROI with minimal effort (Accenture Digital).
53% of service organizations expect to use AI chatbots – a 136% growth rate that foreshadows a big role for the technology in the near future (Salesforce).
In the 2019 Gartner CIO Survey, CIOs identified chatbots as the main AI-based application used in their enterprises (Gartner).
69% of consumers prefer to use chatbots for the speed at which they can communicate with a brand (Salesforce).
90% of businesses report faster complaint resolution with chatbots (MIT Technology Review).
Twice as many consumers surveyed in 2019 would knowingly engage with chatbots because they are “very helpful,” compared to 2018 respondents; 83% of consumers said they’d make messaging their primary means of contacting customer support if they could be guaranteed an immediate response (Helpshift).
74% of consumers say they use conversational assistants for researching or buying products and services (Capgemini).
By 2020, 80% of businesses plan to utilize chatbots (Oracle).
77% of executives have already implemented and 60% plan to implement conversational bots for after-sales and customer service (Accenture).
25% of customer service and support operations will integrate virtual customer assistant (VCA) or chatbot technology across engagement channels by 2020 (Gartner).
77% of customers say chatbots will transform their expectations of companies in the next five years (Salesforce).
Given the choice between filling out a website form or getting answers from a chatbot, only 14% of customers would choose the form (Salesforce).
43% of users between the ages of 16 and 64 are using voice search and voice commands on various devices (We are social).
When it comes to chatbots, 60% of millennials have used them, 70% of those report positive experiences, and of the millennials who have not used them, more than half say they are interested in using them (Forbes).
Chatbot Statistics Infographic
Gartner Hype Cycle for Artificial Intelligence, 2020
In 2019, the Gartner Hype Cycle placed chatbots on the peak of inflated expectations, a high standing they have maintained in 2020. During this period, early publicity produces several success stories – often accompanied by scores of failures.
Some companies act, however, many do not. As time passes, many chatbots providers will leave the market and projects will be abandoned. Gartner predicts that 40% of chatbot/virtual assistant applications that were launched in 2018 will have abandoned by the end of 2020.
With Facebook’s launch of its messaging platform, it became the leading platform for chatbots. In 2018 there were more than 300,000 active chatbots on Facebook’s Messenger platform, however, many of these solutions were nothing more than glorified FAQ solutions. By 2020, Facebook shifted its focus on other projects.
In the report, Gartner notes that “Chatbots and virtual assistants have reached peak interest in the enterprise as the most common uses for AI. But to improve customer experience and reduce costs, application leaders need to choose the right conversational platform as the enabling technology for developing chatbots and VAs.”
Discussing the market Gartner notes, “Chatbots and virtual assistants are, respectively, at the peak or just post-peak on the “Hype Cycle for Artificial Intelligence, 2019,” having gathered tremendous interest from Gartner clients over the last couple of years. According to Gartner’s 2019 CIO Agenda, 31% of enterprise CIOs have already deployed conversational platforms (see “The 2019 CIO Agenda: Securing a New Foundation for Digital Business”). This represents a 48% year-over-year growth in interest, and points to conversational platforms taking center stage in enterprises’ adoption of AI.”
The Gartner report recognizes 16 Enterprise Chatbot Platforms including:
Amazon Web Services
The larger market for conversational platforms, chatbots and VA offerings may include as many as 1,000 to 1,500 vendors worldwide. This Market Guide contains vendors that:
Offer an extensible platform for a variety of use cases.
Have above-average capabilities.
Have received client interest via Gartner either through mentions or inquiry.
Show differentiating functionality that is defining for a trend in the market.
By 2021, customer service interactions globally will be handled completely by AI, will increase 400% from 2017 (Gartner).
By 2022, 70% of white-collar workers will interact with conversational platforms daily (Gartner).
$3.9 trillion projected AI-derived business value growth by 2022 (Gartner).
$8 billion projected business cost savings from chatbots by 2022 (Juniper Research).
75% to 90% projected percentage of queries to be handled by bots by 2022 (CNBC).
$0.70 projected chatbot cost savings per customer interaction (CNBC).
According to Lauren Foye, by 2022, banks can automate up to 90% of their customer interaction using chatbots (Juniper Research).
By 2022, we’ll be talking to bots more than our own spouses (Deloitte).
Bots will save businesses $8 billion per year by 2022 (Deloitte).
$13.9B was invested in CX-focused AI and $42.7B in CX-focused Big Data and analytics in 2019, with both expected to grow to $90B in 2022 (IDC).
Bank systems will automate up to 90% of customer interactions using chatbots by 2022 (Chatbots Magazine).
5 billion hours projected time savings for businesses and consumers from chatbots by 2023 (Juniper Research).
$112 billion projected value of chatbot eCommerce transactions by 2023 (Juniper Research).
The global Chatbots market is valued at 840 million USD in 2017 and is expected to reach 5310 million USD by the end of 2023, growing at a CAGR of 36.1% between 2017 and 2023 (Reuters). [Source: Orbis Research]
The operational cost savings from using chatbots in banking will reach $7.3 billion globally by 2023, up from an estimated $209 million in 2019 (Juniper Research).
AI, including chatbots, will have a highly disruptive impact on insurance claims management, leading to cost savings of almost $1.3 billion by 2023, across motor, life, property and health insurance, up from $300 million in 2019 (Juniper Research).
By 2023, 30% of customer service organizations will deliver proactive customer services by using AI-enabled process orchestration and continuous intelligence (Gartner).
By 2023, chatbots are going to save the banking, healthcare and retail sectors up to $11 billion annually (Business Insider).
Companies will save 2.5 billion customer service hours using chatbots by the end of 2023 (Juniper Research).
The global conversational AI market size is expected to grow from USD 4.2 billion in 2019 to USD 15.7 billion by 2024, at a Compound Annual Growth Rate (CAGR) of 30.2% is forecast during the same during the forecast period (Markets and Markets).
The Chatbots Market was worth USD 1274.428 million in 2018 and is projected to reach USD 7591.82 million by 2024 registering a CAGR of 34.75% over the period (2019 – 2024) (Research And Markets). [Source: Research and Markets]
By 2024, AI will become the new user interface by redefining user experiences where over 50% of user touches will be augmented by computer vision, speech, natural language and AR/VR (IDC).
Annual global AI software revenue is forecast to grow from $9.5 billion in 2018 to $118.6 billion by 2025 (Tractica).
By 2025, customer service organizations that embed AI in their multichannel customer engagement platform will elevate operational efficiency by 25% (Gartner).
By 2025, AI will power 95% of all customer interactions, including live telephone and online conversations that will leave customers unable to ‘spot the bot’ (Finance Digest).
According to the current analysis of Reports and Data, the global Chatbot market was valued at USD 1.17 Billion in 2018 and is expected to reach USD 10.08 Billion by year 2026, at a CAGR of 30.9% (Globe News Wire). [Source: Reports and Data]
It’s clear that chatbots are here to stay. As the market matures, only the intelligent and capable conversational AI chatbot platforms will remain. In the next chapter we’ll look at the future of the chatbot market more closely.
Chatbots and Covid-19: Automation in Times of Crisis
In this chapter we will cover how businesses are turning to automation and self-service to ensure business continuity in times of crises such as Covid-19.
Listen to this chapter:
Unpredictable as it may have been, Covid-19 has shone a spotlight in areas of weakness within enterprises. While many enterprises had established contingency plans, these didn’t contemplate a worldwide shutdown affecting workforces, supply chains and customers.
Businesses have had to ensure continuity during the crisis overcoming quarantines, travel restrictions and an unexpected lack of access to people.
People-intensive industries have had to adapt quickly to remote working environments to guarantee the continuity of operations while customers have turned to digital channels to request information, stay in contact with friends, family and peers, make queries or carry out transactions.
Simultaneously, contact centers have consequently been overwhelmed with calls from concerned customers who have had to endure long waiting lines. The urgency of having to provide swift, omnichannel and 24/7 solutions to a huge number of customers means that companies have not had time to speculate on experimental approaches and have had to place their trust on reliable experts.
Chatbots Have Stepped into the Fray During Covid-19
When proficiently designed and deployed, chatbots have helped companies during the pandemic to share up-to-date information, combat misinformation and help mitigate damage across all sectors, from health to governments, travel, insurance, telecoms, banks and more.
Natural language processing (NLP) has also become an important feature. NLP has been used to parse social media for posts that mention specific symptoms. The need to deploy conversational AI has also been accelerated, especially as a go-to-point for people suffering mass-shortages, who were distressed during the initial stages of confinement, or learning to use new online services as a result of the closure of physical branches and stores.
At the same time, with consumers confined at home, their normal activities may have been restricted but new user habits have surfaced to satisfy their demands.
Consumers, for example, still need to stay connected and are turning to novel ways to do so online. Growing customer expectations have led to increases in queries and demands. Outsourcing customer services and sales interactions to reduce costs isn’t viable anymore because of Covid-19 induced lockdowns, and companies have been unable to address increasing demands for domestic call centers while handling sharp staff reductions.
The lack of access to workers goes in contrast to increasing customer demands for 24/7 services via the multiple digital channels at their disposal. This is where businesses have focused on the importance of digital self-service, automation and artificial intelligence to enhance contact center case resolutions and provide greater customer insights and real-time decisions.
New Customer Requests Need Quality Interactions
Modern consumers are digitally native and have high expectations of the brands they interact with. Companies that are at the vanguard of digital transformation also tend to consumers with the most challenging expectations.
With such a fiercely competitive landscape with increasing customer churn, companies are under pressure to provide the best digital technologies and customer experience.
Customer support have little leeway to get things right. One-third of consumers would question their loyalty to a brand if the customer service did not meet their expectations. Companies that provide excellent customer journeys in their contact centers have higher recommendation rates, customer retention, revenue and a greater likelihood to cross-sell and provide extra services to their clients.
Covid-19 has redefined how businesses and their employees go to work and interact. Here we can look at how some sectors have leveraged chatbots during Covid-19.
Healthcare and Wellbeing
Medical services have begun to use AI to make quicker decisions. Asynchronous telemedicine, without face-to-face meetings, is on the rise too, as are health management platforms that can allow, for example, diabetics to monitor their glucose levels through a mobile app and digital assistants that can provide personalized recommendations and address potential issues before they occur.
Health doesn’t necessarily have to mean medicine. Gyms and fitness brands have also turned to social media and apps to stay active, providing virtual classes, personalized workouts, nutritional information and tools to combat stress and provide motivation.
Covid-19 has accelerated the need for banks to provide new digital solutions to customers.
Banks have acknowledged that sooner than later, human assistance may be reduced to a minimum in their sector. Physical branches are closing, and robots can carry out the job faster and 24/7. In some cases with advanced conversational AI, they can offer a superior user experience.
Chatbots are being used effectively to enhance customer support, not only providing information and personalized advice but carrying out tasks like renewing policies, handling refunds and changing credit card limits.
By assisting in fraud prevention and managing internal operations, conversational AI has also helped banks leverage both robots and humans to provide high-quality user experiences. An Accenture report states that almost 80% of bankers think advancements in AI will enable banks to offer a “human-like customer experience”, with 76% believing that robots will be leading the way in customer interaction within the next three years.
The telecoms sector has always been quick to deploy innovative digital technology. It is also used to applying new business models and enhancing its global network with upgraded use of real-time data, new technologies and advanced customer support.
In a mobile-first world, telecoms have turned to machine learning and AI, shifting their practices to become more customer-centric. Covid-19 has accelerated the need to strengthen their customer experience to resolve issues for users with new demands and who are confined at home.
The demand for better broadband deals and smart home assistants has swamped contact centers and telecoms are using conversational AI to resolve technical issues, prevent fraudulent activities and increase workforce productivity by allowing human agents to focus on back-office operations and training while chatbots tend to the customer.
Additionally, telecoms have confided in chatbots that can learn about customers and tailor their future interactions, providing personalized advice via multiple channels and boosting sales with promotions and cross-selling that is in sync to each customer’s preferences.
By maximizing RPA integration with these platforms, chatbots help telecoms resolve queries but also carrying out seamless operations, opening accounts, suggesting better deals and making personalized upgrades
Like financial services, insurance firms have benefited from automated self-service and the ability that advanced chatbots possess to provide personalized, 24/7 information over numerous channels and in multiple languages.
Spanning diverse sectors such as motor, travel, health, life and home, insurance processes can handle large quantities of information and chatbots can be of great use either as a back-up for human agents, as a guide for customers going through self-service or as an assistant resolving repetitive, albeit data-heavy processes.
Retail and E-commerce
Covid-19 has imposed further changes to this highly competitive sector that was already witnessing the need to adapt to new digital trends. Customers expect shopping experiences to be as smooth, instant, personalized and convenient as possible. With people being confined at homes and spending a long time on their mobile devices they interact many more times with their brands through remarketing campaigns and advertising.
E-commerce and online shopping have risen dramatically as people look to ways to buy goods without heading to stores albeit through comfort or because of restrictions.
With customers using so many devices and accessing their brands through varied touchpoints there is a growing need within the sector to tend to seamless omnichannel user experiences and chatbots can provide the perfect assistance.
Covid-19 set off the alarms for many enterprises who were yet to embrace new technologies and chatbots, but the positive use cases from deploying these technologies and the competitive advantage it has given those companies who were quick to embrace conversational AI has spearheaded into the front line of most company’s plans for digital transformation.
In this chapter we’ll cover the future of chatbots, market maturity and the future of customer experience through digital transformation.
If you’re interested in the future of chatbots, this chapter is for you.
Listen to this chapter:
Maturing Chatbot Market
The chatbot market is rapidly maturing. It’s frequently no longer a series of individual projects, haphazardly put together, but a measured and controlled strategic approach that enable scalability across languages, channels and the enterprise itself.
In the coming years, it’s expected that customers will manage the majority of their relationship with an enterprise without interacting with a human and that millions of consumers will use voice-enabled conversational AI to purchase on digital commerce platforms.
While many enterprises are starting to widen the scope of their conversational AI strategy with chatbot applications, most of these bots are siloed and unable to share information.
In the coming months expect to see enterprises planning for an intranet of conversational AI applications that can work together seamlessly, sharing information. Intelligent routing will allow for the handover process between apps to occur in several different ways including the ability for a master application or super-bot to deliver it themselves and the ability to prioritize the order in which knowledge resources are delivered.
Digital initiatives topped the list of priorities for CIOs in 2019, with 33% of businesses now in the scaling or refining stages of digital maturity — up from 17% in 2018.
As businesses look to scale, they focus on three areas to support customer engagement:
Volume: ability to handle demand volatility and peak demand cost-effectively
Scope: ability to reliably support a wide range of products and services
Agility: ability to quickly respond to changes across channels when consumer tastes change
As enterprises continue to digitally mature, the conversational AI landscape continues to mature as well. In this video, we take a look at 5 major trends that are currently being seen in the market.
Enhanced Chatbot Customer Experience
Chatbots have yet to reach their full potential, and will ultimately lead to higher customer engagement levels, where the importance in how businesses and consumers interact online becomes more important.
As chatbots develop and become more sophisticated, they will not only generate significant value in both consumer and enterprise settings but will help to transform various aspects of communication.
Future conversational bots will become better equipped to handle proactive engagements, where they’re able to predict an incident and report a ticket – therefore resolving future issues before they arise, both reducing costs and optimizing support channels. They will be able to not only respond to answer your questions, but will be able to talk, think and develop emotional relationships with customers.
Chatbot NLP and ML will Become More Powerful
Chatbots will be able to understand and answer a higher average percentage of questions without human intervention, both more precisely and at speed, leading to higher average Happiness Index and Net Promoter Scores.
As the market matures, 40% of chatbot/virtual assistant applications launched in 2018 will have been abandoned by 2020. The enterprise chatbot platforms that remain will gain momentum and further develop second generation use cases, which will bring further awareness to the advanced ability some companies provide.
Providers will gravitate towards niche markets that provide the greatest cost savings, having the ability to more rapidly provide working solutions with pre-built industry knowledge packages, reducing time of deployment and enhancing personalization.
Chatbots will continue to be enhanced through machine learning data, where every industry will become more efficient in the collaboration between its chatbots and human employees.
A true conversational experience happens when a chatbot listens to inputs from a customer and understands them. Chatbots will become more intelligent and goal-oriented, where they will be able to learn about customers in real time as they communicate, which will provide a competitive advantage in delivering enhanced experiences.
The developments in natural language processing and machine learning will supply chatbots with sophisticated algorithms that will enable them to provide customers with more unique and personalized experiences, creating more authentic relationships with a given target audience.
Digital transformation refers to the process of integrating technology into business processes thereby changing customer experiences by providing more value and changing how companies operate – it’s the recreation of business in the digital age.
Digital transformation has been a topic of discussion for years for many enterprises, however 2020 is a crucial time for leaders to plan for and implement digital transformation strategies company-wide.
As AI technologies continue to grow in strength, so too does the attention that surrounds it. Today many companies are experimenting with AI and early results are promising.
In 2020, AI will continue to be the most popular trend in delivering personalized experiences in real-time. Building AI at scale that can handle personalized experiences is one of the top priorities for companies across the world.
In order to keep up in a technologically evolving environment, businesses must adapt.
The Definitive Guide (2021)