Your Complete Guide to Chatbots 2022
Today, if you’re a citizen of the digital planet, chatbots are probably somewhere in your life. You may not even realize how fully integrated they are, and how much you rely on them. Personally and professionally, chatbots offer information, guidance, and even fun. If you need help or lose your way, chances are a chatbot is already helping get you back on track.
So what are these constructs? Where did they come from? What are they made of? How do they work? Are they smart—or not? What do they know about us? Let’s explore the world of chatbots and dive beneath the technical jargon to simplify their role. And on the way, we’ll glance into the future: the trends, progress, and destinations that await chatbots.
CHAPTER 1: Chatbots: Who thought them up, anyway?
Chatbots have an impressive genealogy. We can attribute their origin to the polymath Alan Turing, who made major contributions to code-creation, mathematics and computer science. His vision of intelligent machines in 1950 set the stage for what we call artificial intelligence, the foundation for chatbots.
Initially, the phone tree offered a primitive, clumsy version of a chatbot, guiding people on an often confounding journey of one option after another to arrive at the customer service they wanted (if their patience held out). But the underlying concept of “reading” a customer’s intent eventually merged with advances in artificial intelligence to create the modern chatbot and its offspring, the virtual or digital assistant. AI had found its way out of the ivory tower and into the everyday lives of people by providing a convenient, swift, and intuitive grasp of our needs.
How chatbots grew and evolved
Earliest chatbots were called ELIZA and PARRY. These explored the applications of the Turing Test (“The Imitation Game”)—a deceptively simple three-person interaction in which a computer tries to fool a human into thinking it is another human. PARRY was evaluated against the Turing Test, but testers were no more successful than random guessing in distinguishing humans from machines. The Turing Test has become the gold standard for determining artificial intelligence; to date, no computer has passed it, though some claim to have come close. But even today’s most sophisticated supercomputers eventually fail. Some find that reassuring.
But chatbots had their own destiny, and they were now off and running. With time, developers innovated powerfully provisioned chatbots on AI technologies, including deep learning, NLP, NLU, and ML algorithms. As computer science galloped ahead, chatbots could finally access massive amounts of data. Scalability soared, and enterprises celebrated the arrival of workable solutions to guide countless interactions with their customers over just about any device. There was good cause to celebrate: rather than one human addressing another one at a time—a costly, cumbersome process—business now had a universal helpmate that could fulfill requests simultaneously while hoovering up useful new information in the process.
USE CASE: 8 X 8 deploys ”Otto” AI-powered Virtual Assistant to scale customer support and decrease case volume by 60%
Read how 8×8 supercharged existing resources to automate self-service handling of mundane tasks. With Aisera, they achieved a precipitous drop in case volume, decreased the number of chats handled by live agents, and improved agent productivity by 50 percent.
CHAPTER 2: What are today’s chatbots made of?
In a simple answer, electrons. A chatbot is a computer program which is instructed to imitate and intelligently process human conversation, both written and spoken. It enables humans to interact with an array of digital devices as if they were communicating with another individual.
A chatbot can be as simple as a basic program that responds to a query in a single line. Or it can be as sophisticated as the machine learning-fueled virtual assistants that absorb fresh information and deliver sophisticated, personalized answers gleaned by data-gathering and processing. Like humans, they expand and grow in understanding and relevance as they are exposed to information.
Where do linguistics, syntax, and abstraction fit in?
Language is much more than simple sounds and utterances. It doesn’t even have to be vocal; sign language is as sophisticated as spoken language. Humans have developed countless languages, although they all have certain rules and structure in common. That’s because our ability to understand and produce language is biological in origin, evolving as the relevant areas of our brains expand. AI-driven language has matured at phenomenal speed to replicate not just sounds but the abstract intents and context that are the hallmark of our species, and that infants rapidly acquire. Chatbots have now moved out of their own infancy to take their place in our global civilization.
Chatbots, informed and driven by Artificial Intelligence (AI), are also shaped by rules. Natural language processing (NLP), Natural Language Understanding (NLU) and of course Machine Learning (ML) along with many other capabilities combined to enable a binary machine to be intelligent and communicative. There’s practically no limit to what chatbots can be taught—or can teach themselves—to do.
We have differentiated chatbots into classifications based on their utility and complexity. As we’ve mentioned, you’ll need to decide on the type of chatbot that is ideal for fitting your own business needs. So let’s explore a little deeper into their classifications.
Task-oriented (declarative) chatbots are basically interactive FAQs; they are the simplest, commonest form of chatbot. They perform one function using rules, NLP, and a bit of ML to deliver automated, conversational responses to user queries. These rule based chatbots are ideal for service and support, as they provide specific and structured responses. Examples might be hours of business or simple transactions having no variables. Thanks to NLP, they can “seem” conversational, but their function is limited.
- Scripted or quick reply chatbots are basically a hierarchical decision tree whose branches are predefined questions that users navigate until the chatbot has produced the answer the user is looking for.
- Menu-based chatbots offer users the ability to make selections from a predefined list, or menu. Thus, the chatbot gains a deeper understanding of what the customer needs.
- Keyword recognition-based chatbots are more complex. These assess or evaluate what the user types and respond accordingly, relying on keywords from customer responses. They use customizable keywords and AI to fuel responses. A drawback: these chatbots struggle with repetitive keyword use or redundant questions.
- Hybrid chatbots merge menu-based and keyword recognition-based bots. Users can select from the options of having questions answered directly or use the chatbot’s menu to choose when keyword recognition is ineffective.
Data-driven, predictive (conversational) chatbots are also called virtual or digital assistants. Their ability to conduct predictive intelligence and analytics lets them personalize conversations based on user profiles and past user behavior.
- Contextual chatbots demand a data-centric focus. They use AI and ML to remember user conversations and interactions. These memories fuel their functionality and the chatbot is able to grow and improve over time. Rather than relying on keywords, contextual chatbots utilize questions that customers ask and how they phrase their conversation to provide answers and self-improve.
- Voice-enabled chatbots are the ideal destination for this technology. These chatbots use language and spoken dialogue with agility, as well as empathizing with user emotion to elicit insights, provide accurate responses and carry out tasks. Developers create these chatbots using text-to-speech and voice recognition APIs. Amazon’s Alexa and Apple’s Siri are examples of consumer-oriented, data-driven, predictive chatbots and are far more personalized, sophisticated, and interactive.Highly context-aware, voice-enabled chatbots educate themselves via ML. Digital assistants can learn user preferences and previous choices, offer personalized suggestions, and even anticipate a user’s unique, individual needs. They can initiate conversations and pull information from simpler chatbots in real time to offer a richer response.
USE CASE: McAfee elevates customer satisfaction 74%
by automating self-service
McAfee achieved phenomenal gains in service agent efficiency by offering self-service on the consumer portal for instant issue resolution. Read how the system leveraged knowledge articles and delivered sharp, context-based responses to boost auto-resolution and agent productivity by three-quarters.
CHAPTER 3: Getting Down to Business
Chatbots and today’s dynamic, ever-expanding global business environment are made for each other. The value chatbots bring to businesses and customers is almost incalculable.
With chatbots, businesses discovered they could reach out to customers in a personalized and informative way—without the expense and complexity of hiring actual humans to address mundane, routine queries. These common questions were a black hole of cost-inefficiency and tedium for personnel. Companies had tried to address these volumes of easily answered questions via FAQs and troubleshooting guides, but they were only effective up to a point. People would become overwhelmed searching through long lists of FAQs or online guides, seeking their own specific situation.
But chatbots could sail right through the avalanche of data to triage questions, identify a user or customer instantly, and offer informed interaction on their specific issues. They could also hand off the conversation to a live person if things got too complex or exceeded their knowledge. After some initial hesitation, people quickly realized the convenience of chatbots. One of their major advantages to both company and customer is that they enable self-service. As it turns out, people would much rather quickly solve their problems and get back to their own occupation than interface with another human, however friendly and personable.
Chatbots enrich the customer experience
Comprehensive surveys indicate that chatbots are increasingly the preferred method for transacting with businesses for certain purposes. Chatbots enable a level of seamless service and convenience that exceeds what humans can offer in many circumstances. Far from being a cold, mechanistic experience, chatbots actually create a positive conversation. People get accustomed to interacting with them and trust them as authentic sources of reliable information.
One major benefit is that chatbots save time for users. They shave down wait times to mere seconds in many instances. When it comes to routine customer activities within the banking, retail, and food and beverage sectors, chatbots are accepted gratefully. Whether in government or business, people can get their needs met, even potentially complex needs such as requesting or changing services and resolving billing issues.
Business welcomes chatbots like valued family members
There’s almost no limit to the uses that businesses find for chatbots. They have revolutionized customer contact centers, managing incoming communications and directing customers to appropriate services and resources. For internal functions, they can streamline and smoothe the onboarding of new employees. They can also help existing employees schedule vacations, sign up training, order new equipment and supplies, and conduct all manner of functions without calling for human intervention.
Chatbots are on duty 24/7; they don’t take vacations or get into personal conflicts with colleagues. When it comes to the bottom line, they save costs and propel operational efficiencies that businesses could only dream of. Chatbots scale almost infinitely, and can preempt and solve potential problems across the enterprise. A business that is limited to human-only transactions sacrifices major advantages such as the flexibility to scale up or down. And the ability to keep accurate records of each interaction. Now they can provide simultaneous human-like, personalized, proactive service to millions of people. Businesses can pass along the savings they realize through usage of chatbots to customers. This increases their competitive advantage, while superior service boosts the value of the brand.
USE CASE: Personalize the customer experience
and accelerate revenue growth
Put Conversational AI to work for you as a powerful tool that maximizes your marketing ROI. Read specifics here about how you can automate renewals and engage upsell, propel revenue, drive sales opportunities by tracking user behavior, and much more.
CHAPTER 4: Getting started with successful chatbot implementation
One of the delights of chatbots, from a business perspective, is that they usually require little-to-no coding. Chatbots don’t demand the hiring and training of data scientists. There is no shortage of chatbot service providers, who make it simpler for developers to build conversational user interfaces for third-party business applications. You can easily see the financial and operational benefits of implementing chatbots, and their value only increases with time.
But a business has a number of decisions and choices to consider when making chatbots part of the family. These commitments can determine whether the initiative is successful and delivers on the promise of the chatbot technology—or not.
For example, it’s critical to choose the proper natural language processing (NLP) engine. If you expect people to interact with your bot through voice, for example, then you’re going to need to invest in the appropriate speech recognition engine.
Also, you have to decide whether you want to go with structured or unstructured conversations. Businesses have to understand their own needs to make this determination because chatbots built for structured conversations are highly scripted. This simplifies programming but limits what people can ask the chatbot. For B2B communications, chatbots are usually scripted to respond to FAQs or perform simple, repetitive tasks such as delivering phone numbers quickly to sales reps.
Avoiding the pitfalls and finding your ideal solution
Gartner observes, “90% of all chatbot solution implementations failed in 2019.” That’s a daunting statistic. Building a chatbot platform is a project that all too frequently collapses into failure. That’s why only 10% of enterprises were attempting to build their own platforms in 2020, down from 405 in 2019.
Finding the right vendor to help you navigate the process smoothly is a key decision. A chatbot partner can guide you away from the pitfalls and take a major weight off your shoulders.
Why Conversational AI is the right path for so many businesses
More and more, Conversational AI assistants are taking over from early primitive virtual assistants. Let’s talk about the differences. Conversational AI takes the interaction to a more useful, intuitive and sophisticated level.
Conversational AI is capable of comprehending the nature and context of a customer request and thereby offering personalized answers. This capability is at the core of self-service, streamlining the process and delighting customers. It also frees up trained agents from slogging through mundane interactions with volumes of customers, all requesting basically the same thing. When users can serve themselves, service agents can take on more high-priority issues and reach higher in their own expertise. This makes them of greater value to the business and to their own career paths.
A crash course in Conversational AI
Conversational AI is the engine that drives chatbot functionality. Conversational AI uses various technologies such as Natural Language Processing (NLP), Advanced Dialog Management, Automatic Speech Recognition (ASR), and Machine Learning (ML) to understand, react and learn from every interaction.
Your virtual assistant first receives information either as written text or spoken phrases. If spoken (ASR, also known as voice recognition), the Conversational AI makes sense of the verbiage and translates it into a machine-readable format: text.
The virtual assistant uses NLU, which is an element of Natural Language Processing, to understand the abstract intent behind the text. After that, it formulates a response based on its understanding of the text’s intent. The virtual assistant uses the Advanced Dialog Management function to orchestrate its response and convert it into a format that’s comprehensible to a human. In doing this, it employs Natural Language Generation (NLG), another element of NLP.
The virtual assistant then either delivers the response in text to the user or relies on speech synthesis, an artificial reproduction of human speech, to relay the response via voice. Machine learning can take over at any point to learn and refine the responses of the virtual assistant over time. Your solution can self-correct and learn from experience to improve itself—all without human intervention.
Aisera is innovating the space of Conversational AI Assistants, which are becoming indispensable to business applications.
USE CASE: Challenger bank Dave puts customers first—scaling support dramatically to serve its rapidly expanding user base
See how Dave employs Aisera’s AI Customer Service solution to deliver on-demand, personalized support options. Now users can access Aisera’s 24×7 virtual assistant at any time. Dave was able to see results right away, achieving a 70 percent auto-resolution rate with self-service, plus 60 percent first call resolution (FCR).
CHAPTER 5: What is the future of this progress?
In time, AI will combine fully with 5G technology, opening up the route for businesses, employees, and consumers to access enhanced chatbot features even more easily. The speed of recommendations and predictions will increase, and sophisticated new applications will become commonplace, such as instantaneous access to high-definition video conferencing within a conversation. You can expect these advances and improvements to be introduced as quickly as they are developed because the cost-efficiency and profitability of AI are much sought-after and competed for. Think of all people carrying a fully functional personal assistant right in their wallet or even on their wrist! The world will only become more connected with time.
Easing access to collaboration platforms
Another business benefit of chatbot technology in live chat is the ability to turbocharge self-service and give users and customers the ability to autonomously resolve common IT issues and save millions of dollars. The popular messaging platforms like Slack, Zoom and MS Teams are part of that convenience and help streamline communications.
Your Decision: Unsupervised or Supervised Learning
Before you can get started on your chatbot journey, you need to fully understand two fundamental underpinnings of AI.
Supervised learning is an element of machine learning that uses labeled datasets that can train or “supervise” algorithms to classify data or predict outcomes. Unsupervised learning uses ML algorithms to analyze and cluster unlabeled data sets. The algorithms can find hidden patterns in data without the need for people to get involved, hence “unsupervised.”
Without getting too deep into the weeds, it’s the use of labeled datasets that differentiates the two: supervised learning uses labeled input and output data, while unsupervised does not. While supervised learning models are considered more accurate than unsupervised, they do require plenty of human intervention to label data. A supervised learning model might be able to predict the length of a commute. But first it has to be taught which conditions can elongate driving time, like rain or highway repairs.
Unsupervised learning, by comparison, works independently to identify the structure of unlabeled data, though it might sometimes require a data analyst using human intuition and educational insights to come up with accurate results.
If you’re looking for applications like spam detection, sentiment analysis, or weather forecasting, a supervised approach is all you need. For anomaly detection, recommendation engines, customer personas and medical imaging, you’re best off with an unsupervised approach. Keep this in mind: supervised learning is simpler but training can be time-consuming. Unsupervised learning can be less accurate but is economically advantageous under the right circumstances and can handle large volumes of data in real time.
The chatbot commitment: Too important to go it alone
As mentioned earlier, many companies have found out the hard way that wading into the complexities of developing their own chatbot platforms can wind up to be a costly and counter-productive ordeal. Experienced partnership and guidance from a specialist like Aisera can help you make the optimal choices for your AI application, right from the start. Begin your exploration of what Aisera delivers, and we’re always ready to answer any questions.
USE CASE: Zoom automates customer service and boosts revenue while responding quickly to the needs of unprecedented growth
Zoom’s success story is legendary. Read how the company automated billing and subscriptions, streamlined customer service, and delivered remarkable technical support, increasing CSAT dramatically in just six months.