Customer service is a buzzword for most organizations these days across the globe to acquire and retain customers while keeping them happy. In the digital age, with great social media and messenger reach comes great omnichannel responsibility to your customers!
This is where chatbots enter the picture, using AI to converse with customers in real time, boosting engagement and brand credibility. Chatbots will happily sit in for their human counterparts 24 hours a day to look after your customers and their needs.
In this guide, we will touch upon the importance of Chatbots, why they matter, how they help alleviate customer pain points and how you can alleviate them.
We will, in particular, go through the chatbot aspect of customer service automation. You can expect everything from the definition to the types of chatbots and what the future holds for chatbot technology. We will also show you how you can build a chatbot yourself!
But first, let us see how chatbots are a part of customer service automation.
What is customer service?
Customer service is the support you extend to your consumers throughout their customer journey, from awareness of your product till they transform into your loyal brand advocates. A lot is going on between awareness and advocacy, and customer service is the key to guiding the customer's journey as best as possible. Customer service in current market conditions goes beyond reactive issue resolution and includes proactive support instantly across a channel of the customer’s choice.
You should consider customer service a top priority because of customer retention. The cost of acquiring new customers is exponentially higher than retaining existing ones, immediately affecting your business's bottom line.
What are the challenges to customer service?
Customer service is a grueling process that requires consistency, patience, accuracy, and a mindset to help people in need. Here are some challenges that customer service teams need to address:
Serving a high volume of customers simultaneously.
Achieving low response times, especially when issue resolution would require more time.
Dealing with frustrated or angry customers using empathy
Scaling the team up or down due to unforeseen circumstances
How does customer service automation solve such challenges?
Customer service automation is the key to solving most challenges customer service poses. Customer service automation includes a collection of systems, processes, and tools that can resolve customer issues and queries without direct human intervention or minimal help from a human agent. Enterprises can use such automation across varied service channels to scale service, minimize costs, and significantly boost customer satisfaction.
Self-service is the way to go regarding customer support for routine issues, with customers wanting to troubleshoot service issues independently. This is where chatbots shine, providing immediate steps to solve any product or service-related problem.
Chatbots go way beyond issue resolution and act as a living link between the customer and the organization. You can streamline customer support and get your customers to engage in meaningful conversations that ease customer pain points while generating revenue by creating opportunities for upselling and cross-selling.
Lets now get into how chatbot technology has taken off in the last decade.
The Rise of Chatbots
The last decade has seen chatbots through a prolific rise from the failure of Facebook’s chatbot M to the phenomenal breakthrough with Facebook’s Alice and Bob Chatbots.
Since then chatbots have added greatly to the growth of businesses worldwide. Some shining examples
Emirates Vacation integrated a conversation bot within its display ads to boost engagement by 87% since its launch in 2018
Madison Reed’s AR bot Madi that helped visitors desired hair color based on a selfie they upload. This boosted engagement rates by 400% and CTR by 21%
When Covid hit, remote work and associated technologies helped minimize in-person interactions. Chatbots played a leading role in addressing queries at scale at a critical time which saved lives by the millions.
The innovation born out of necessity during the pandemic accelerated the chatbot industry to progress quickly. The requirement for chatbots and their place in a world where automation is a necessity became a sure thing.
History of Chatbots
Chatbots are all the buzz these days, but many do not know that humans have been trying to interact with computers via bots way before the time computers became a household phenomena.
Lets start right at the beginning, when computers were not a household name and more of a research device. Somewhere around the halfway point of the previous century would be a good starting point.
Alan Turing and the Turing test
Alan Turing was an English mathematician, but, more importantly, he was a pioneer of modern computer science. His most famous contribution to the field was probably the Turing test, which is the test of a machine’s ability to imitate the behavior of a human being.
While there have been many versions of the Turing test since Alan Turing first introduced it in the 1950s, what hasn’t changed is the fundamental way the test is conducted - can a chatbot convince a human being that it is not, in fact, a chatbot.
It was the early 60s, when a German American computer scientist by the name of Joseph Weizenbaum was exploring the relationship between computers and human beings. Weizenbaum is considered as the father of modern Artificial intelligence, and between the years 1964 and 1966, he built a chatbot called ELIZA.
It was not called a chatbot then, but rather a “natural language processing computer program.” The ELIZA program was written in MAD-SLIP, a programming language developed by Weizenbaum himself. The responses were given through pattern matching, which were provided in “scripts.”
ELIZA, running the DOCTOR script, mimicked the conversation between a psychotherapist and a patient in the initial patient interview. The program was so immersive, that sometimes, Weizenbaum himself forgot that he was talking to a computer program. ELIZA was the precursor to a lot of chatbots that will follow.
Fast forward to the year 1972, and the world is going through some tumultuous times. The Vietnam war is slowly winding down, and US President Nixon has ordered NASA to begin the workings of a space shuttle program. Meanwhile, at Stanford University, a psychiatrist by the name of Kenneth Colby has designed an artificial intelligence program that mimics the thinking pattern of a person suffering from paranoid schizophrenia.
A group of 33 human psychologists put PARRY to the test, and the results blew their mind. PARRY was able to fool the human examiners a whopping 52% of the time, in variations of the Turing test. Some scientists even pitched PARRY and ELIZA against each other, and PARRY beat ELIZA hand over foot, thanks to its superior programming.
Simulating human conversation has always been a topic of interest for the computer scientists, and British programmer Rollo Carpenter was no different. In the year 1988, Carpenter created Jabberwacky, a chatbot that was designed to “simulate natural human chat in an entertaining, interesting and humorous manner.”
Rollo wanted Jabberwacky to become more of an entertaining pet than talk. He built using an AI technology called “contextual pattern matching,” which was revolutionary at the time. Beating the Turing test was the aim of Jabberwacky, and Carpenter released a version of the chatbot to the internet in 1997.
And so it stayed, at the top of the chatbot hall of fame, until A.L.I.C.E came along.
ALICE was developed in the year 1995 by Richard Wallace. ALICE was one of the first chatbots to use Natural Language processing. ALICE passed the Loebner test thrice, but failed to pass the Turing test. Short form for Artificial Linguistic Internet Computer Entity, the A.L.I.C.E bot has since then become a part of pop culture, including inspiring films such as Her.
And then a few years later, SmarterChild came along.
The rise of SmarterChild
2001- Things get interesting now, as chatbot SmarterChild was unleashed onto the world.
SmarterChild was available on AOL Instant Messenger and MSN Messaging Networks. If you are from the 90s and remember using these services, then SmarterChild sat right there inside every user’s buddy list. You could message him for data on a wide array of topics, ranging from weather forecasts, sports, news and even movie timings.
Created by Robert Hoffer, Timothy Kay and Peter Levitan, the bot was quite a rage in its heyday. It is said to have interacted with more than 30 million people and accounted for more than 5% of all AIM traffic. The company that built SmarterBot, called ActiveBuddy, was eventually acquired by Microsoft in 2007.
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What is a chatbot?
At the most basic level, a chatbot is a computer program.
Like any other computer program, a chatbot has one primary objective. To carry out the activities that it is assigned to do, which, in this case, is mimicking human conversation.
This can be in the form of voice commands, texts, or both. A chatbot lets a person at the other end of the screen know that they are having a conversation with another person or imitate it as closely as possible.
According to Wikipedia, A chatbot is a software used to facilitate automated online conversations in place of a human agent through text or text-to-speech media.
Chatbots are a form of artificial intelligence which uses Natural Language Processing to simulate conversations with human beings.
Google Assistant, Facebook Messenger, and Amazon Alexa are all examples of chatbots that are a bit advanced.
A few of the other definitions of chatbots include:
“A chatbot is a computer program that simulates and processes human conversation (either written or spoken), allowing humans to interact with digital devices as if they were communicating with a real person.” Oracle
“A chatbot is a domain-specific conversational interface that uses an app, messaging platform, social network or chat solution for its conversations.” Gartner
“A computer program designed to have a conversation with a human being, especially over the internet” Cambridge English Dictionary
A magical rectangle pops up very eagerly, asking if you need any help.
This rectangle, dear friends, is a chatbot.
You type in a question, and pop comes the answer, almost like magic.
If we say it is easy to build a chatbot, we would be lying.
Having spent close to half a decade building chatbots, we know a thing or two about how they work, what customers expect out of them, and, most importantly, how you can use one for your website.
In this little guide, we will be teaching you all of these things and a lot more.
You don’t need a chatbot… Until you do!!
Customer support queries getting too hot to handle?
Leads falling through the cracks?
People asking you the same question over and over again?
You need chatbots!! But before that, a peek into the future.
By 2025... You won’t even need a website!! Yup, you heard that right, the modern website is going to be dead as more and more people move toward what Drift calls “Conversational marketing”
What is Conversational Marketing?
Conversational marketing connects website visitors and marketers in real-time. This helps curate the most appropriate response to a visitor’s query and convert the most promising prospects.
The average attention span of the customer is shrinking by the day, and people are going to be even more impatient as we move further.
You need to provide your customers with the relevant information, and you need to do it now!! What happens otherwise? Well, your customers - they just go to the competitor.
We expect that this conversational commerce revolution is already upon us, with more and more businesses using messaging platforms like WhatsApp to interact with their customers.
By the way, we at Kommunicate built a chatbot for WhatsApp too.
Why exactly do you need a chatbot?
Give your company a face: - A chatbot is usually the first point of contact your customer has with your company and hence is a reflection of your brand. Chatbots let your customers know that they are going to be taken care of in the least possible time, and a well-designed chatbot can seriously enhance the way a customer perceives your brand and your company.
Can help your sales team: - When you present the right information to the clients at the right time, there is a significant increase in your chances of closing that sale. Chatbots can be used to significantly boost the chances that your sales team has by proactively answering customer queries and guiding them through your website.
Available immediately and 24 * 7: - Chatbots don’t take bathroom breaks, don’t clock in or clock out at set times, and are available even on Sundays!! Chatbots can be of immense value in a globalized world where customers expect near-instantaneous responses to their queries, even if the companies are located in a different continent halfway across the globe.
Help you understand what your customers are looking for: - The modern customer is spoiled for choice and is always short on time. If your website does provide what a customer is looking for in the least possible time, then they are bound to navigate elsewhere. This is where a chatbot comes in handy, and if your bot can find what your customers are looking for in the least amount of time, there is no reason for them to open that next tab.
Can be used to automate your customer support: - Without stellar customer support, all your marketing, customer acquisition, and other efforts will go in vain, and chatbots offer a novel way of automating customer support. Greater automation equals reduced overheads, reduced waiting times, and increased efficiency.
Reduces customer wait time: - No one likes to wait, especially when ordering things online or browsing for items on a product website. Chatbots can greatly reduce this wait time by automating responses to the most frequently asked questions.
Can help qualify leads: - Qualifying leads involves finding if a customer is interested in buying your product or service and if they are able to afford it. While this may involve a lot of back and forth questions between the customer and your sales reps, a chatbot can solve this problem by asking your website visitors a set of very directed questions.
Chatbots help you avoid repetitive work: - Chatbots excel at doing work that involves repetition, and easily beat humans when it comes to efficiency and speed. Chatbots can take up work that is low-level and does not involve a lot of empathy or intuition, which can be taken up by humans.
Now that we know why we need a chatbot let us look at the chatbot types.
They can only be as smart as they are programmed to be. These bots work on a specific set of rules, and anything that comes beyond its purview fails to act upon. Think of these bots like a tree, with a set of predefined questions and answers. The chatbot will ask you questions and then follow up with the next logical question until you eventually arrive at an answer. In the case of a rule-based chatbot, the answers are all pre-defined.
Where are rule-based chatbots useful?
For starters, you can use rule-based chatbots in situations where the questions are straightforward and not complicated in nature. For instance, booking a show in a movie theater or a seat at a restaurant. Here are some of the advantages of using a rule-based chatbot:
Rule-based chatbots are generally faster to train and are less expensive. This makes implementing the software on your website less complicated.
Since you are defining the answers, there is greater control over the behavior of the chatbot.
Rule-based chatbots score high on security and are generally more accountable for the responses they provide.
With rule-based chatbots, you can include not just text but images, video, and also interactive media.
The second kind of bots, which have a mind of their own are -
2. Chatbots that run on Machine Learning
They are backed by Artificial Intelligence and grow smarter with time. These bots have the capability of processing natural human language and acting accordingly. AI chatbots that use machine learning understand the context and intent of a question before they give out an answer. These chatbots generate their own answers to more complicated questions using natural-language responses. The more you train these bots, the better they get at what they are doing.
Some great examples of AI powered bots include WP-Chatbot catering to WordPress customers and the US transport organization Amtrak’s very own Julie Chatbot. We will explore these in detail later on.
AI chatbots are ideal for companies that deal with a lot of data. They do take a lot of time to train initially, but that is just a necessary evil that needs to be looked over.
Where do you use AI chatbots?
AI chatbots will come in handy in these situations:
When they need to learn from the information that is gathered from a variety of sources.
When they need to continuously iterate and improve as more and more data keeps coming in.
When they follow a certain pattern of behavior.
There is a broader range of decision-making skills that needs to be imparted.
Let's now discuss the various chatbot terminologies.
To fully grasp the potential of a chatbot and what it can do for your business, you might need to get familiar with a few concepts that might come up frequently. Here are a few aspects:
Natural Language Understanding(NLU) - Human language is complex, full of nuances, idioms, mispronunciations and other factors that make it difficult for a bot to understand the intended meaning. With Natural Language Understanding (NLU), the algorithms sift through the complexities of the language and extract the required information.
Platform - A platform is a channel that hosts a conversation. It allows marketers to build and maintain chatbots for various messengers, like Telegram or Facebook Messenger.
Conversational Channel - A medium that lets you hold a chatbot conversation. SMS, WhatsApp, Facebook Messenger, etc are examples of channels.
Machine Learning - Machine learning enables chatbots to learn human language by identifying patterns and learning from their previous interactions with website visitors. It is a part of Artificial Intelligence and enables chatbots to improve over time.
API - API stands for Application Programming Interface, which allows two apps to interact with each other, and enables developers to integrate an app into existing software. Think of APIs as waiters in a restaurant, who are constantly conveying information from the patrons (front end) to the kitchen (backend), and then returning with the order (request).
Webhooks - Webhooks are API responses that extract information from chatbot conversations and pass them on to web services. This information can be in the form of email addresses, names or telephone numbers of potential customers who have visited your website.
Entities - Entities can be defined as knowledge banks used by a chatbot to provide a more personalized user experience. Entities can be text or fields describing an item, a place, numbers, or anything else that the chatbot can use to provide a more personalized user experience.
User: I want to order a Pizza
Bot: What type of pizza would you like to order?
Vegetarian or Non-vegetarian
In the conversation above, vegetarian and non-vegetarian would qualify as Entities.
Quick Reply - Quick Replies help your chatbot to give users possible options for their queries, thereby making the user experience a lot better. For instance, if the user types in “contact support,” the quick replies can be “Email ID,” “Phone number,” or “WhatsApp Number,” among others.
Hybrid Chat - A Hybrid chat is a combination of live chat and chatbot. The chatbot acts as the primary source of communication for the website visitor, and if the conversation is not resolved, it will hand over the conversation to a live human agent seamlessly. It is thus the best of both worlds.
Intent - Intent is the motive behind a chat. It is what the user wants to convey to the website via the chatbot and is usually in the form of a text message, but can also be in the form of voice or other input methods.
Intent classification: - Organizing every user input into a preset intent group helps match varied inputs with their particular intent and provide a precise response. This process is known as intent classification and helps the chatbot have more meaningful conversations and resolve queries better and faster.
Intent recognition - Simply put, intent recognition lets chatbots identify user need or requirement. The chatbot tries to make sense of the input information based on user intent to provide efficient query resolution.
Sentiment analysis - This is an offshoot of computer science using Machine Learning and NLP, that assesses the tone of a conversation or sentiment through various media such as text or the spoken word.
Sentiment analysis can aid a chatbot in evaluating user messages to recognize how the user perceives the product as positive, negative, or neutral. Google’s advanced chatbot LaMDA is trained to use sentiment analysis and is more responsive to context and user tone.
Attributes - These are packets of information that include user data such as name, mobile number, and email that chatbots collect during the process of chatting with the user. Attributes are used by chatbots to personalize their response to the user with which it is chatting.
Compulsory input - The information that a user must offer for the chatbot to function properly is compulsory input. If the user does not provide the required input, then the conversation with the bot will stall, halting the customer support process.
For instance, an app that schedules appointments with medical professionals will require an appointment ID to proceed with the user query. Without the required input, the bot will not be able to fetch the availability of the doctor and other details.
Optional input - When users can have a meaningful conversation with the chatbot and get their query resolved without providing a specific input, that information is called optional input.
For example, if a user is searching for a particular piece of apparel from an online store, then the user need not mention the brand name or the price range. Just the mention of the type of apparel is sufficient.
Decision trees - A decision tree is a diagram that displays in detail how decisions are made through set pathways based on previous decisions or inputs.
In chatbots, decision trees are useful in the form of a flowchart, showing how a conversation will flow according to the inputs of the user and the responses programmed into the Chatbot. This way, you can ensure that the conversation with the user will effectively resolve their query.
What is an Artificial Intelligence (AI) Chatbot?
Any digital experience you have these days seems seamless, thanks to the advances in Artificial Intelligence. Chatbots help you carry out complex tasks such as ordering a pizza, answering frequently asked questions on your websites, or guiding you through a complex website.
But how do they do it? And are these chatbots different from the conventional, rule-based chatbots we have been talking about so far? How can your business benefit from such an Artificial Intelligence chatbot?
An Artificial Intelligence Chatbot recognizes and responds to conversations like a human being, using a concept called Natural Language Processing (NLP). They can act as useful assistants to live agents by intelligently routing conversations to the right support agent when required.
The NLP chatbot can interpret human language, which means it can act independently in gauging what response needs to be given to a particular customer query. What this also means is that AI chatbots can understand language that is not pre-programmed or taught to it, making it that much more powerful in providing responses based on existing data.
Another important aspect of Artificial Intelligence bots is that they are constantly learning from their interactions with visitors to the website. As they are trained over a period of time, these bots will get smarter and smarter, able to analyze different situations and make complex connections, just like a human would.
Difference between Conversational AI and Chatbot
Conversational AI and chatbot are terms that are used interchangeably, but these are not one and the same. A chatbot is a subset of what Conversational AI is. Let us now look at these in more detail.
A chatbot is a rule-based computer program. You teach it to respond to a customer query using a few pre-written rules, and the bot will perform these actions without you having to intervene. If the conversation gets a bit more complex, the bot hands it over to a human being, and the customer is taken care of.
An AI-powered chatbot, as we discussed, is an advanced version of a rule-based chatbot, where it ‘learns’ user behavior based on older responses and responds to chats accordingly. AI-based chatbots use natural language processing and machine learning to understand what the user is trying to convey.
Conversational AI is the term given to technologies that can respond to text and speech inputs, interacting with customers in a human-like manner. Think of Amazon Alexa or Apple’s Siri, or even Google Assistant, for that matter. Conversational AI is perfect if you want to add a human touch to your customer support function, where it can act as the first point of contact a customer has with your business.
Having a conversational AI means businesses can be available to their customers 24*7, and customers don’t have to deal with long waiting times. Chatbots are a part of Conversational AI, and simple rule-based chatbots, based on pre-written rules and cannot provide a response outside these set rules, are thus not a part of Conversational AI.
Once you have learned all the chatbot terminologies, you will better understand how it will help your business.
Are Chatbots Universally helpful to all organizations?
Yes and no. If you are referring to chatbot technology, then yes. If you are referring to the same type of chatbot being used in all organizations, then no. You wouldn’t recommend a family buying a sportscar the same way you wouldn’t recommend an AI-driven chatbot to a company that only needs a rule-based chatbot.
The best recommendation of a car for a family of four would be a sedan or voluminous hatchback with good mileage. A family wouldn’t need the high specifications of a sportscar but can benefit from the space and reliability of a sedan or hatchback.
Similarly, if a company requires an efficient rule-based FAQ chatbot with an efficient chatbot to human handoff, an AI-driven chatbot would be harder to manage and adapt to their needs.
There is no cookie-cutter approach to chatbots that can be applied to every organization looking for service automation through chatbots. You, as a stakeholder, will need to carefully consider the type of chatbot that will best serve your team and pick accordingly.
A good way to narrow down your choice of chatbot is to look into what features you need currently or might need in the future.
Features to look for in a chatbot
Chatbot analytics - To see how well your bot is reading the intent of the user and also measure various parameters such as
Bounce Rate - People who come to your website but leave without interacting with the bot
Interaction Rate - Helps measure user engagement.
Fallback Rate - This will help measure scenarios where the bot is unable to understand the user request and reply with a relevant solution.
Chatbot Marketing - Use chatbots for a wide variety of marketing activities, including making and tracking orders, scheduling meetings, promoting products, and smoothly guiding your prospects through the sales funnel.
Omnichannel messaging support - Your customers are present on a plethora of channels, including Facebook Messenger, Website, and WhatsApp, and integrating a chatbot with these channels shouldn’t be much of a hassle. With multi-channel integrations, you can better understand customer behavior and empower your sales agents.
Easy to train - Chatbots are relatively less complex to set up, especially if you are using a no-code chatbot builder. One of the key features to look for in a chatbot is how easy it is to train the bot to carry out intended tasks. You don’t want to waste your time repeating the same thing over and over to the bot. For instance, if you tell a chatbot that your website primarily sells shoes, it will remember this and other relevant information using a technique called progressive profiling.
Easy bot-to-human hand-off - Chatbots are trained to handle a majority of the conversations, but there are situations where the bot has to hand over the reins to a live human agent. The easier this feature is to enable in your chatbot, the better. A smooth bot-to-human hand-off means the customers get a seamless experience and keep coming back to the website because they feel cared for.
Bot security and privacy - Chatbots are programmed to handle vast amounts of data, and in this era of frequent data breaches, it is important to make your bot as secure as possible. Keeping data protection at the forefront, you must specify to your customers how the data that is collected will be stored and used. If the users are unwilling to share their data, they should have the option of opting out of it.
Multilingual capabilities - Thanks to greater internet penetration and hyper localization by the bigger players, more and more customers today want to speak to your brand in their language. If a chatbot can handle multiple languages, then it can do wonders for customer engagement. Kommunicate’s own Kompose chatbot builder supports over 40 languages.
We all by now know that chatbots are an important part of your overall marketing strategy. Websites need chatbots to provide omni-channel customer support, be available for your customers 24*7, and answer questions as and when they arise.
But what happens when a customer cannot speak English? Well, to put things in perspective, only about 1.35 billion people on Earth speak English, out of a total population of 7.8 billion. That’s roughly 15% of everyone.
So what if a non-English speaking person lands on your website?
This is where Multilingual Chatbots come in.
There are a few global enterprises that have nailed the balance of features required for chatbots to excel at customer service automation. Let's check out what they are.
Now that we have covered the most desired features in a chatbot let us take a peek into the best chatbots available in the market.
What are Multilingual Chatbots?
A Multilingual Chatbot is a chatbot that can speak to a customer in multiple languages, other than just English. Previously, website owners had to build separate chatbots for each different language that the users wanted to converse in. With the help of a multilingual chatbot, you can now build a single chatbot and train it in multiple languages.
Improved Customer engagement: - If you plan to sell on a global scale, then you need to support languages from other countries too. This is because your chatbot is now dealing with customers from across the world, and many of them may not know English.
When you design a multilingual chatbot, you are ensuring that there is better engagement on your chatbot. Customer’s leave with a more positive experience, which can only help improve your CSAT scores.
Competitive advantage - If you offer a chatbot that can speak in different languages versus a competitor whose chatbot can only speak English, guess who the customer is going to go with? Multilingual bots offer a huge competitive advantage, and make it easy for you to differentiate from the competition.
Consistency - With Multilingual chatbots, customers can expect consistent responses to all their queries across different platforms, irrespective of the language that they speak.
Reduced costs - You don’t need to hire and train customer support agents who can speak different languages, if you implement a multilingual bot on your website. This directly translates to reduced costs for your business.
To get the best User Experience out of your chatbots, the conversations your customers have with the chatbot needs to be seamless. Customers should feel as though they are talking to an actual human being, and only when they ask for a live agent’s help, should they realize that they were talking to a bot. So how do we go about achieving such seamless conversations?.
With a flow designer, users can build a chatbot using a conversational messaging technique, where the user triggers a conversation, and a chatbot then guides them, step by step, through the conversational flow. You can imagine a flow designer as a map with a path drawn in it, with customer conversations acting as different paths on the map.
With a flow designer, what you are doing is trying to predict all the different directions that a customer’s conversation can go in, and provide a response through the chatbot, using a visual flow builder. Before building a chatbot using the Flow designer, you must do a deep analysis of what questions your customers might ask the bot.
Different paths on the map will have different elements that may display information, or sometimes process it. For instance, you may have a welcome message saying “Banking Services” to a banking bot, and the path may then branch out to different topics such as “Balance,” “ Last 3 transactions,” or “Check Credit card Limit.”
A Flow designer is thus a simple way of building a chatbot, and can be considered as an important step in the evolution of Low Code No Code chatbot technology.
Types of Flow Designer
Linear Flow Designer:A linear flow designer follows a sequential path to get to its objective. The chatbot presents the information or asks questions one after the other. This type of flow is useful for simple or structured interactions, where the conversation follows a pre-defined order
Menu-based Flow Designer:A menu-based flow designer gives its users menus or options to choose from. This type of chatbot is useful for providing a range of choices or navigating through different sections or features of the chatbot.
Rule-based Flow Designer: A rule-based flow designer uses predefined rules or conditions to determine the next steps in the conversation. It allows users to respond differently based on user inputs. Very useful for creating dynamic and personalized interactions.
NLP Flow Designer:As the name implies, this type of Flow Designer uses Natural Language understanding to analyze and interpret user inputs. Techniques such as intent recognition and entity extraction are used to understand and respond to user messages. The Chatbots built using an NLP based flow designer can handle more complex conversations.
Hybrid Flow Designer:A hybrid flow designer is a combination of all the types of flow designers mentioned above, using them in a blended manner to create a flexible and dynamic conversational flow. This type of flow designer is useful when the chatbot needs to handle a wide variety of user inputs and scenarios.
Advantages of using a Flow Designer when building a chatbot
User guidance is improved: Developers can create clear pathways for the users to follow within the chat conversation, using a flow designer. This step-by-step guidance reduces confusion and users get to their desired objective more efficiently, improving user engagement and satisfaction.
Better user experience: When a chatbot is able to better anticipate what the user is trying to ask, it improves the user experience. Chatbots built using the flow designer can provide the most appropriate information at the right time, which will contribute to a positive user experience.
Conversations become structured: When designers and developers use a flow designer to build a chatbot, the conversation flow of the chatbot can be organized in a structured manner.
Easier development: Implementing flow designers means that the chatbot development process is simplified. Developers can easily map out different paths, define decision points and set up rules or conditions. This improves collaboration between team members and accelerates the overall chatbot development cycle.
Makes chatbot development flexible:Flow designer enables chatbot developers to easily modify and update the chatbot’s conversation flow as needed. They can add new branches, adjust decision points, or incorporate user feedback without significant coding changes.
Things to take into account when building chatbots using a Flow Designer
User needs and goals:Make sure the type of conversations that the users are likely to have with the chatbot are aligned with their expectations.
Conversation complexity:Choose a flow designer that allows flexibility in designing the conversation flow according to the desired level of complexity
Branching and decision points: It is important to identify key decision points in the conversation where the chatbot needs to respond differently based on user inputs.
Errors in handling and validation:Anticipate potential user errors or unexpected inputs and design the flow to guide the users towards valid responses.
Flexibility:Choose a flow designer that allows you to easily modify or design the conversational flow.
Documentation support: If there are more than one team members using the flow designer to build the chatbot, choose one that supports collaboration and version control.
Testing and analytics:Make sure that the flow designer you select provides options for testing and analytics.
By considering these factors, you can build a chatbot that uses a flow designer that delivers a seamless user experience while achieving user needs.
Large Language Models and ChatGPT
Large Language Models:
Have you ever talked to a computer and have the computer respond back to you with the correct answer? Then you have used a Large Language Model in one form or another. You can define a Large Language model as a really smart computer that can understand and respond to people in a way that sounds completely natural. Almost as if you were talking to an actual human being.
How Large Language Models are trained:
These language models (for example, ChatGPT) have been trained by a trove of computer scientists and engineers and researchers. These teams collect a vast trove of data from books, websites and other sources to teach the computer about language, and the world in general. The computer then uses sophisticated Machine Learning Algorithms to learn from all that data.
While undergoing training, the computer analyzes patterns, meanings and words in the text to understand how the language works. It then tries to predict what the users may say in a conversation. The more conversations that the computers have, the better they get at responding in a human-like manner.
Training large language models requires a lot of time and powerful computers, because there are so many words and patterns to learn. It can take weeks, sometimes months to train large language models.
Types of Large Language Models:
Broadly speaking, there are 5 different types of Large Language models. These include:
Transformer-based models: They are based on a type of architecture called transformers. Perfect if you want to understand the context and relationships between words in a sentence. Ex: ChatGPT
Encoder-Decoder models: In simple terms, they use something called an encoder to understand the input text and a decoder to generate the output. Best if you want to do tasks such as text classification or translation. Ex: BERT
Pre-trained models: They are trained on massive amounts of text data before being used for specific tasks. Can be fine-tuned for a specific application such as customer support, chatbots or content generation.
Domain-specific models: There are large language models that are designed to specialize in a particular domain or industry. For example, there can be large language models specifically trained for the legal industry, which will understand all the subtle nuances and terminology of the legal industry.
Multilingual models: As the name suggests, these models generate and understand text in multiple languages. They are really useful when used for translation, cross- language communication or handling multilingual customer support.
Applications of large language models:
Large Language Models, like ChatGPT, can revolutionize the way we interact with computers and redefine our relationship with technology. One of the applications of LLMs is chatbots. Chatbots that are powered by LLMs can have conversations with us like a normal human being would, answering all our questions in a matter of seconds.
Think of these chatbots as very intelligent friends, who won’t judge you based on your questions and are trained to answer questions on almost any subject on the planet. There are chatbots like Inflection AI that even talk to you as if you were talking to a friend.
LLMs of the future can act as virtual tutors. By explaining concepts and providing personalized learning experiences, they will radically change the way children of the future approach education.
LLMs can help in language translation, understanding and translating text from one language to another, helping to break down language barriers. One other field that LLMs can excel in is content creation. Large language models can generate stories, articles and even write better copy.
Overall, large language models will continue to improve over time, and all of the above fields will be disrupted. This is in addition to other technical fields, such as programming, that Artificial intelligence is bringing a paradigm shift in.
ChatGPT - A stellar example of LLM at work
ChatGPT is an AI powered Chatbot that is built on top of OpenAI’s GPT-3.5 and GPT 4 large language model (LLM). In simple terms, it is a very, very powerful chatbot. It has been trained on terabytes of data, and can do a wide variety of tasks, most of which are so far performed by language workers.
ChatGPT can for instance, write and debug computer programs,compose music, write blogs, business case studies, and play tic-tac-toe, aimong other things.ChatGPT was trained on a large repository of online data, so that it could learn the rules and structures of language.
Architecture: ChatGPT is based on the GPT-3.5 architecture, which stands for "Generative Pre-trained Transformer 3.5." It utilizes a deep learning model called a Transformer, which is trained on a vast amount of text data to understand and generate human-like text.
Natural Language Processing: ChatGPT uses natural language processing (NLP) techniques to understand and generate responses. It analyzes the input text, processes it through its layers of neural networks, and generates a coherent and contextually relevant response.
Training:ChatGPT is trained on a diverse range of internet text data, including books, articles, websites, and more. It has learned grammar, facts, reasoning abilities, and some degree of commonsense knowledge from this training data.
Prompt-Response Format:ChatGPT follows a prompt-response format. You provide a prompt or a message, and ChatGPT generates a response based on the context provided. It can handle a wide range of conversational topics and answer questions based on its training.
Limitations:While ChatGPT is a powerful language model, it has certain limitations. It may sometimes produce incorrect or nonsensical answers, be overly verbose, or generate responses that sound plausible but are factually incorrect. It can also be sensitive to slight changes in the input phrasing, which can lead to inconsistent answers.
Bias:Like any language model trained on internet data, ChatGPT may reflect certain biases present in the training data. OpenAI has made efforts to mitigate biases during training, but it may still exhibit some biases in its responses. Care should be taken to verify information from reliable sources and be aware of potential biases.
API Access:OpenAI provides an API for developers to integrate ChatGPT into their applications. This allows developers to create their own chatbots or use ChatGPT's capabilities in various domains.
Continual Improvement: OpenAI is actively working to improve ChatGPT and address its limitations. User feedback is crucial for identifying and fixing issues, so ongoing iterations and updates are expected to enhance its performance and reliability
Integrating ChatGPT into your chatbot makes it extremely powerful, since now, your chatbot can give the same responses that ChatGPT would. This means there is less need to train your chatbot on queries that it may not understand, and you can also advertise the ChatGPT integration as an important feature of your chatbot.
To integrate ChatGPT into Kommunicate, just toggle on the”Connect with ChatGPT” option in the settings tab, and you are good to go.
Instagram had one of the fastest growths as a platform and is one of the most popular among the millennials, who form a sizable portion of the population who consume content and make purchasing decisions. We have seen the rise of “Instagram influencers” whose primary job is to gain more followers on the platform and promote products, kind of like celebrities.
If your business is not on instagram, then you are missing a big piece of the action. If you do have a presence on the platform, then a chatbot can supercharge your brand, by providing a plethora of benefits. We will now look at how chatbots can be a key to your Instagram marketing strategy. But before we dive in, let us look at:
What is an Instagram chatbot?
An instagram chatbot is an automated messaging platform that is integrated with the Instagram platform. It uses predefined rules and AI to interact with users. Instagram chatbots responds to user queries, provides them with information about your product or service, and thus enhances customer engagement.
Advantages of using an Instagram chatbot
24/7 Availability:Instagram chatbots are available around the clock. By integrating chatbots into their Instagram accounts, businesses can give instant responses to customer queries, no matter which time zone they are based out of. This enhances the brand’s value, as the customers feel that the business actually cares about them, and improves customer engagement.
Improved lead generation:Businesses can use Instagram chatbots to improve their marketing game and drill down on their lead generation efforts. Companies can use chatbots to deliver targeted and personalized messages, and nurture leads. These processes can be easily automated using chatbots, and businesses can now scale their marketing efforts, capture leads and drive conversions.
Data collectionData is the new oil, and integrating Instagram chatbots into the marketing efforts can help businesses gain valuable insights into consumer behavior. Chatbots can help customers know about customer preferences, purchasing patterns, and FAQs. Using this information, businesses can refine their marketing efforts, and make data driven decisions.
Personalization in customer interactionsBy introducing chatbots into their Instagram profiles, businesses can make their brand more “human.” Personalization is one of the key marketing techniques that all businesses are trying to achieve, and, with Instagram chatbots, personalization at scale is now possible. Chatbots can provide the profile visitors with important information, answer FAQs, and also guide the customers through the buying process. This will improve customer satisfaction and instill brand loyalty.
How to build an Instagram chatbot
Here is a Step-by-Step framework to building your instagram chatbot
Step 1: Decide the purpose of your chatbot: This can range from content delivery to lead generation, or even customer support.
Step 2: Choose the platform that supports instagram integration and also offers necessary features and customization options.
Step 3: Design bot logic and conversation flow: In this step, you must design the bot’s conversation flow, including user prompts. You should then use the platform that you choose to create the chatbot’s logic, including message triggers and keyword recognition.
Step 4: Add the chatbot to your Instagram account: In this step, you must connect the chatbot to your Instagram account, and make sure that the bot responds to messages on the platform.
Step 5: Test the bot: Based on the user’s feedback and analytics, you must thoroughly test your bot and fine tune its responses.
Step 6: Deploy the bot: It is now time to launch the chatbot on your Instagram account and monitor its performance, making sure that the desired objectives are met. For complex queries, make sure there is a proper bot-to-human handoff in place.
Step 7: Update and promote regularly: Incorporate new features into your chatbot by analyzing chatbot interactions. Also, don’t forget to promote your chatbot whenever possible on Instagram, letting your customers know that you are there for them 24 * 7.
Chatbot SDKs on mobile
Many times, you must have seen a feature where you are able to use a chatbot right within the mobile app of a business, rather than a separate interface on a website. So how are these chatbots built? The answer: Chatbot SDKs (Software Development Kits).
Chatbot SDKs are tools that enable developers to build chat functionality right into mobile applications. There are pre-built components, APIs and libraries that simplify the entire process of implementing chat functionality inside a mobile application.
Developers use Chatbot SDKs to leverage features such as Natural Language Processing (NLP), conversational interfaces and integrations with various messaging platforms. The SDKs help mobile apps understand user queries better, and give interactive responses.
Benefits of Chatbot SDKs on mobile:
Natural Language Processing (NLP): Chatbot SDKs use advanced NLP, which makes mobile apps easier to understand and interpret user queries, and reply in a human-like manner. With NLP, these chatbots can address complicated issues and engage in meaningful conversations.
Efficient responses:Mobile chatbots can automate a lot of mundane, repetitive tasks thereby reducing the need for human interference and improving efficiency. These repetitive tasks may involve things like processing orders, scheduling appointments, etc. Chatbots powered by SDKs can free up resources.
Seamless integration:Chatbot SDKs can easily integrate with mobile applications, and this means that businesses can embed chatbot functionality seamlessly. Users can access a chatbot directly within the mobile app, reducing friction and enhancing user experience.
Personalized User Experience:Chatbot SDKs make personalized interactions possible, by making use of user data and preferences. Chatbots can offer tailored recommendations and deliver targeted content to return users, enhancing the user experience.
Multi-channel support:Chatbot SDKs on mobile devices means businesses can extend their chat support across multiple channels. This means businesses can integrate with messaging apps, voice assistants and social media platforms, ensuring a consistent omni-channel customer experience.
Components of a good Chatbot SDK on Mobile
Messaging APIs:Messaging APIs make communication between the chatbot and the mobile application possible, and these are provided by the SDK. These APIs often handle how messages are sent and received, and allow developers to implement chat interfaces within the app.
NLP Engine:Chatbot SDKs typically come with NLP Engine that process user inputs and extracts entities, intents and context. The NLP Engine interprets queries that come in the form of natural language, which enables chatbots to understand user inputs and respond to them.
Dialog management: Chatbot SDKs can also include a Dialog management component that handles conversation flows. This component manages the context and logic of the conversation, allowing the chatbot to maintain context, handle multi-turn interactions, and guiding users through conversations.
Messaging platforms integration:Most of the chatbot SDKs provide additional integration with messaging platforms such as WhatsApp, Facebook Messenger or Slack. Chatbots can now communicate with users using these channels, allowing for seamless deployment across multiple platforms.
Analytics and Reporting: Some SDKs provide analytics capabilities that help you get insights into chatbot performance, user behavior and user interactions. These analytics help developers assess the effectiveness of the chatbot.
Documentation and Support:Good SDKs come bundled with comprehensive documentation, including code samples, guides and API references. They may also offer developer support channels such as forums which help implement the chatbot successfully.
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Some of the world’s best chatbot solutions
Here are some popular use cases of chatbots, where you can see various chatbots in action.
Part of one of the biggest tech conglomerates in the world, Dialogflow is a chatbot development framework that is owned by Google. Scaling will be the least of your problems here as Dialogflow is a part of the massive Google Cloud infrastructure.
Dialogflow comes in two editions - Essentials (ES) and Customer Experience (CX)
The ES edition provides a standard agent variant best suited to simple to somewhat complex agents. Conversation design centers around Intents that serve as the building blocks of design. Here contexts are used to direct conversation paths.
The CX edition is an advanced agent variant that includes highly complex agents or agents of large complexity. Here conversation design centers around flows and pages that serve as building blocks, with state handlers used to direct conversation paths.
Apart from the advanced AI that powers Dialogflow here are a few reasons why this is one of the best chatbot frameworks available:
Multichannel support with single click integration for over 20 platforms ranging from Facebook Messenger and Slack to Twitter and Kik Messenger.
Visual flow builder (in CS edition) provides a high level of abstraction. This enables users to easily edit and share their work for cross-team collaboration.
Omnichannel implementation provides seamless integration across platforms ranging from web, mobile, messenger, and more.
Cutting edge Natural Language Processing that is backed by progressive R&D
IBM Watson Assistant
IBM defines Watson as “AI for business.” Watson’s AI solutions are aimed at helping enterprises forecast business outcomes, automate complex processes, and help employees make efficient use of their time.
Watson comes from a storied background of development and uses in unique situations. It was once pitted against human contestants on a US game show named “Jeopardy!” to win the game show prize of a million dollars (USD). Its predecessor was the Deep Blue computer which played chess against world chess champion Garry Kasparov in 1997 and won.
Now, IBM Watson Assistant is an easy-to-use conversational AI platform. This AI-powered virtual agent was built for people who need to be more technical to help answer queries. Watson Assistant offers customers quick, accurate, and consistent answers across various messaging applications, devices, and channels. The virtual agent can learn from customer conversations to progressively enhance its issue resolution skills.
This is a niche chatbot for businesses that already use Salesforce and are looking to get the best out of their Salesforce CRM. As this bot is designed to work exclusively with the Salesforce CRM, the Chabot can locate the customer and lead data while using the aforementioned CRM.
Even though Salesforce Einstein is mostly made for customer support, it can also be used to execute marketing and sales tasks. It also has a bot builder to create the bot according to customer requirements.
While it works across all your current Salesforce channels, it is offered only as a part of the Salesforce Service Cloud. The automatic triage (sorting) and routing of customer support are advantageous to businesses across domains.
Just like Salesforce Einstein, this is a platform-specific chatbot that works only with WordPress sites. This chatbot helps with on-site visitor engagement through Facebook Messenger. Its no-code chatbot builder helps you build manual or automatic chatbots for your business.
WP-Chatbot is unique in that conversation history is retained in the Facebook inbox of the user, minimizing the requirement for an independent CRM. The business page on Facebook can help team members go through conversations and initiate interactions with visitors through Facebook.
While it is great that WP-Chatbot is free, there are limited branding options, and it does not work without a Facebook business page. However, it has an impressive one-click installation and enables you to customize greetings.
As the name suggests, LivePerson is popular for its live chat features, although it goes above and beyond in terms of other features. Your business can use LivePerson as an omnichannel messaging platform to promote customer engagement across various channels.
This product offering is uniquely known for its Natural Language Understanding (NLU) and self-learning capabilities. If you want to understand the best way to initiate customer engagement, its engagement insights feature can help you do so.
LivePerson’s proprietary conversational AI platform Conversational Cloud enables enterprises to build bots, and message process flows without coding.
While LivePerson’s Self-learning AI, NLU, and annotation tools help you engage more efficiently with customers, its pricing isn’t straightforward.
When it comes to Natural Language Processing (NLP) capabilities, Genesys DX is well-known throughout the chatbot ecosystem. After installing Genesys DX, your business can take advantage of bots that comprehend and respond to human language.
This chatbot platform is developed to promote customer engagement with an engagement history feature that aids enterprises in comprehending how customers interact with chatbots.
Genesys DX excels in NLP and comes with numerous prebuilt conversations, and also helps with customization when it comes to branding.
Amtrak’s Julie Chatbot
Joe Biden’s favorite form of transport, Amtrak, is a Federally owned travel organization that employs 20,000 people and sees a footfall of over 30 million passengers annually. With such as massive rail-based transport system, there is bound to be a constant influx of customer queries requiring quick and accurate resolution on a consistent basis.
Julie enables self-service for people who visit Amtrak.com, which ranges from upselling features that let customers reserve seats on the train of their choice to accommodations at nearby locations. There are also other options for users to access maps to their destinations and purchase tickets online accordingly.
The process simplifies things for travelers that need to use Amtrak by using entered data to pre-fill forms and sail through the booking process with the least amount of keystrokes and having booked their tickets of choice. The Julie chatbot also offers useful information about what they would need to take with them based on the location to which they are traveling.
Post implementation of the Julie chatbot, the booking rate has increased by 25%, with a spike of 50% in user engagement among Amtrak users. It also answers around 5 million questions of travelers who access the Amtrak website or application.
Google Chatbot Language Model for Dialogue Applications (LaMDA)
Released on May 2021, Google’s LaMDA chatbot is a successor to its Meena chatbot, released in 2018. Its latest AI chatbot is designed with dialogue in mind and built on a neural network architecture named Transformer that is open-source.
LaMDA is better at understanding the intent behind user search queries and is relying on new parameters to engage in open-ended conversations. One of these parameters includes “interestingness,” which gauges whether responses to queries are out of the blue, perceptive, or amusing and funny. However, such responses are designed to be factual to side-step issues regarding incorrect data.
Another parameter that Google is considering is “sensibleness” LaMDA grasps the context within a query and displays the same in its responses. Google also knows that with the great power of AI comes great responsibility, their newest Chatbot is aligned with its AI objectives such as being socially responsible, avoiding unfair basis, incorporating privacy design and related principles.
What do these examples tell us about where we are heading with Chatbots?
According to Gartner, Inc., 2027 will see approximately a fourth of the organizations around the world adopting Chatbots as their primary service channel. Based on a survey of 50 individuals that Gartner conducted over the months of January and February 2022, they found that 54% of surveyed entities used a chatbot in some form, including a Virtual Chat Assistant (VCA).
The key to the evolution of Chatbot technology in its various forms depends on how well organizations are at implementing it. Great implementation will require companies to balance automation and human assistance in the right proportions. When customers are met with automated self-service when they need it and assisted by agents in complex situations, chatbots will be able to ease customer service bottlenecks throughout the company.
We can also classify chatbots based on the domain in which they are use -
Chatbot based on Domain
In case you have made up your mind about investing in a chatbot and already know your user needs, here are a few chatbot examples grouped by domain.
Chatbots in Banking
Accenture, in a recent report, said that 76% of all banks surveyed believe that in the next three years, a majority of them will deploy AI interfaces as the primary mode of interaction with customers. Testimony to this belief is that leading banks have already started rolling out their chatbots in banking.
HSBC has made customer support a 24X7 affair with their virtual assistant ‘Amy’
The updated version of American Express’ Amex Bot will allow customers to query any information regarding their card and account
Masterpass enabled bots by Mastercard creates a seamless shopping experience within FB Messenger. It allows customers to transact with leading brands like Subway, FreshDirect, and The Cheesecake Factory, all without leaving Messenger.
As commerce moved from retail to online, it opened up a lot of opportunities for e-tailers. Chris Messina touted 2016 to be the year of conversational commerce, and e-commerce companies have jumped onto the bandwagon.
E-commerce Chatbots have allowed them to cater to the customer’s need for instant gratification in a number of ways:
Shopping Bots like mySimon queries across various merchants and reports back with product prices and descriptions.
Concierge Bots like Operator enable users to browse curated products and make a purchase.
Bots like CelebStyle, allow users to find products based on the celebrities they admire.
H&M’s bot learns from each customer’s preference by combining data and then makes personalized recommendations in a multiple-choice fashion.
Chatbots in Healthcare
While AI bots in healthcare are similar to AI bots in other domains, they handle critical tasks that might save human lives. Most healthcare professionals ranging from nursing staff to doctors have to deal with strict timelines and work under immense pressure. Chatbots for healthcare help in relieving their workload as well as enabling patients to access answers to critical queries.
Here are some of the ways chatbots help in the healthcare sector:
Customer service and administration: chatbots can answer FAQs so that the medical professionals are freed up to tend to pressing issues.
Locating Healthcare Services: Chatbots can guide patients to the nearest hospital or healthcare center when ill or requiring medical attention.
Triage: When healthcare organizations are overwhelmed with patients, chatbots can help prioritize cases depending on criticality. This is called triage.
Gathering patient data and feedback: Chatbots can also collect vast amounts of patient data while maintaining confidentiality. They can help incorporate feedback in real-time as well.
In the opening example of the article, I demonstrated how chatbots can help you prepare your itinerary when planning your next trip. Major players in the Travel and Tourism marketplace have embraced the disruptive chatbot ecosystem and rolled out bots for different use cases:
Pana is a virtual travel agent where users get access to human travel agents and a sophisticated chatbot.
Copa Airlines’ Web-based chatbot Ana answers simple questions like what destinations to which Copa Airlines flies and what Copa’s baggage allowances are.
HelloGbye is the closest to what bots can one day be. You can just tell the bot your travel requests (like how you do with Siri), and it will handpick and tailor make the resulting options for you.
Chatbots for Learning
Leslie is a chatbot that can help you learn English. She can define words, provide synonyms and antonyms, translate words, explain grammar and do many other things.
Chatbots for Sales & Marketing
Your sales team might be an all-human staff, and that’s a good thing, but sadly humans can’t work 24X7. That means lots of missed opportunities with regard to lead capture. Chatbots like Driftbot can plug the gap by engaging users with welcome messages, answering simple questions, qualifying them, and finally passing them to a qualified human agent.
Chatbots as your friends
Chatbots like Xiaoice and Mitsuku are more like personal companions that you can simply talk to.
As you can see, the possibilities are endless with chatbots. It is possible to build anything imaginable
As you can see, chatbots come in various shapes and sizes and can be used in a wide variety of industries.
Chatbots are great at automating repetitive tasks but they do come with a few limitations and challenges which we will now look at.
Chatbot limitations and challenges
Chatbots help you become more productive, will increase the chances of customers making a purchase from your website, will help qualify leads, and a plethora of other benefits we have mentioned earlier. But chatbots also come with their own set of challenges.
Challenges of chatbots
Here are a few challenges that chatbots currently face and might affect the implementation of your chatbot solution.
Understanding user intents: However smart your chatbots are, they are a computer program at the end of the day, and they can only go so far in understanding what the user is trying to convey. User intents are different in every case, and two people who say “Contact customer support”,” or “speak to an agent” are both saying the same thing, although they are re-phrasing it. A chatbot may not be able to identify these differences, leaving users frustrated.
Style of conversation: No one likes talking to a machine, which essentially, a chatbot is. The challenge that most developers face is to make chatbots to converse as closely to a human as possible. However, there is only so much that Artificial Intelligence can advance, and users are likely to identify that they are not talking to an actual person.
Solving a problem that doesn’t exist: Many times, companies want to add a chatbot to their website just because it looks fancy and adds a layer of sophistication. But tread cautiously when approaching implementation of chatbot in this manner. A chatbot is designed for a specific purpose and may not be the most effective solution in case you want to solve another problem entirely.
Chatbot security: Customers entrust a lot of their data to your firm, and the chatbot is the medium through which most of this data is collected. So it is no surprise that customers want to be assured that their data is safe, especially in this age of data theft and privacy concerns. It is highly important that the chatbots you build will ensure data security and are compliant with the prevalent data protection laws.
Multiple Language support: Building a chatbot in one language, say English, is half the effort if you are planning to build a bot that caters to a multilingual audience. This is because the effort required to build a bot in one language needs to be repeated to build a bot in another language. Also, multilingual bots come with maintenance challenges, and if you can’t provide support in different languages, your user experience will suffer.
Chatbot memory: An ideal chatbot will only ask you a question once, and then remember your answer for the next interaction. While this is an ideal situation, we are not there yet when it comes to technology. Chatbots don’t have the memory that humans do, and when it comes to remembering names or delivering personalization at scale, chatbots are not the best solution.
Installing a chatbot onto your website will not automatically solve all of your website’s problems. There are also situations where chatbots fail, as they come with their own set of limitations. Some of them include:
Chatbots don’t get human context:
Chatbots are computer programs that only can learn new things. In other words, they are conditioned to process only a predetermined set of inputs, which leads to frustratingly limited and predictable responses.
Chatbots are only able to answer what they have been taught. This is because they have no means of understanding language beyond the threshold defined by builders. They can’t do anything outside of what they have been programmed to do and usually don’t even know how long their batteries are going to last in the first place.
Chatbots can't make decisions:
Chatbots don't know the difference between a genuine user request or a spammy one. They don't know who they are talking to. They can't ask questions to clarify what the person is looking for. They can’t tell if the person is misinformed and needs to be educated or if the questioner is malicious and needs to be blocked.
Chatbots may save you a lot of money and time, but they come with a cost. For instance, Facebook users who have tried to use the company's assistant M reported that it took them, on average, 45 minutes to complete. You have to fill out all sorts of fields before you can even begin chatting with the bot.
Chatbot don't have emotions
Chatbots are a creation of software programmers, and although AI has made enough advancements to make computers sound as close to humans as possible, chatbots still lack empathy and emotions.
A great example of this is Microsoft’s creation, Tay. The social media platform was an AI-powered chatbot that users would talk to on Twitter and Kik. She was built to emulate a young adult, and her purpose was to improve through interactions with humans in the real world.
But Tay quickly picked up racist, sexist and anti-semitic speech from other social media users, and then began using these words herself.
NLP still has a long way to go:
NLP is at the cutting edge of technological advancements of this century, but there is still a long way to go when it comes to understanding what human beings are trying to convey to a bot. For instance, there are local dialects, slangs, mispronunciations, and a plethora of other factors which the chatbots just can’t comprehend. We still can’t confidently say that chatbots have reached a level of sophistication where they can completely replace human agents, but we are slowly getting there.
People communicate in different ways
No two users of a website are alike, but a chatbot is designed to identify only one type of user. Different users may convey the same message differently, and a chatbot may not be able to identify all that a user is trying to convey. For instance, some may use short sentences, some may use long sentences, some may use a combination of short and long sentences, etc. So the question remains: When should a bot give a response? How many chats should a bot club together before responding? The answer to these questions is yet to be found, and they will remain as limitations as far as chatbots are concerned.
The silver lining: bots as sidekicks to customer support agents
There had been many apprehensions against the popular belief that bots can one day replace customer support agents. About if they can or can’t, I believe they can’t because they lack the empathy and sensitivity of a human agent. But what they can definitely do is act as assistants to these support agents and do simple tasks like answering FAQs, suggesting help articles, and routing queries.
It is now time to see how you can go about actually building a chatbot.
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How to build a chatbot
Before you set out to build a bot, it is important to note that the tricky thing about building a bot is more of a user experience issue and not writing codes.
Typically to build a chatbot, one needs to:
Identify the problem it is going to solve and design a conversational user flow to solve it
Choosing the platform where the bot will reside (Facebook Messenger, Slack, In-app Messenger, etc.)
Server setup from which you can run the bot
Now in order to configure the bot to drive a consistent conversational flow and assist the user, one should find the answers to these 3 questions:
What’s the user’s goal?
What’s the bot’s goal?
How to handle unexpected input?
Finding answers to these questions and incorporating the learnings into designing the conversational flow will give your bot a human outlook and safeguard it against unforeseen situations.
Once the overall conversational flow is designed, the next step is to choose a platform for the bot to reside. Typically, it would be one of the major messenger platforms or the in-app messenger of your application.
In order to integrate your chatbot within social messengers, most companies have detailed documentation guides listed below:
The penultimate step is choosing a solution to build the bot. Mainly these are of two types:
Development (no-code) platforms, for people like me who can’t really code
Code-Based Frameworks, gives you a lot more flexibility, deeper level analytics, and the advantage of AI incorporation.
Below is a list of some of the most popular solutions in these categories, accompanied by their salient features.
Chatfuel: The platform is for building Facebook Messenger bots and allows you to build and launch one within 7 minutes. The platform supports content cards with the option for easy checkouts. You can also set up an FAQ or broadcast notifications to followers.
Botsify: Botsify has a drag-and-drop interface that helps you design templates easily. It supports analytics integration to give insights into what people are talking about. It also has a machine learning interface so that you can continue training the bot.
Motion AI: Hubspot recently acquired Motion AI with the aim to enable businesses to better engage, convert, close, and delight their customers across every channel at scale.
Flow Xo: They support integrations with over 100 modules and services to allow you to be where your customers are.
Microsoft QnA maker: As the name suggests, QnA maker allows you to convert your FAQs to a bot in minutes . This can be done simply by feeding in the URL of your FAQ page, and the next time your users look for any help, the bot will be there to help them with the right answers.
Other such platforms are Recast AI, Bottr, Chattypeople, Botkit, etc. For more details on non-coding platforms, read this blog on the 14 most powerful platforms to build a chatbot.
Building a bot using frameworks requires working knowledge of programming languages.
However, the bots built hence have a brain of their own. They have the ability to store data and learn from it, so much so that with time they can imitate human interactions by processing natural human language.
Now, when you set out to build a bot using one of these frameworks (details below), you will have access to a set of predefined functions and classes that would help in faster development.
Below is a list of Bot Development frameworks and their salient features:
Microsoft Bot Framework: They provide all the ingredients to build, connect, deploy and manage intelligent bots. Bots built on Microsoft Bot Framework can be deployed on any website or app, and also on messengers like Slack, Facebook Messenger, etc.
Running on Microsoft’s flagship Azure, it provides an integrated environment that’s purpose-built for bot development with Bot Framework connectors and BotBuilder SDKs. It also supports cognitive intelligence that enables the bot to see, hear and interpret in more human ways.
Facebook Bot Engine (Wit.ai): Wit.ai provides APIs that can process Natural Human Language, which can be in the form of both text and voice input. Upon processing this input, the Bot engine gauges the user’s intent and executes the intended action.
Wit’s dashboard allows you to manage the entire conversation flow by means of what they call a Story. Every Story on Wit starts with understanding human intent, based on which the Bot sends a subsequent message to the user and moves them to a suitable point in the story. The penultimate step in the story is the execution of a suitable Action, which can be anything from ordering a pizza to booking an Uber.
Dialogflow (Api.ai): Api.ai allows you to create engaging voice and text-based conversational interfaces via which your users can interact with your product. The platform is a favorite among mobile app developers and developers in the IoT space.
In order to make its conversational UI a lot more context-aware, API. ai has pre-defined domains, including those of various IoT categories. This means the platform knows ahead of time what domain any defined entities or intent applies to. This allows the system to tap into its existing library related to any particular domain.
Developers can also describe their own interactions and scenarios, and the platform will seed the same into its library to give you a more developed and robust conversational UI.
Well, saying all that was easy, but I won’t just leave you there. In the next section, I have a detailed step-by-step guide on how you can build your first bot. For the sake of this guide, we have built the bot using Kompose, Kommunicate’s own no-code chatbot builder.
A simple travel-booking chatbot built using Kompose
TravelBot is a chatbot that helps you book flight tickets from anywhere in the world.
We will build this bot using Kompose, powered by Kommunicate.
Since there are a plethora of things you can accomplish using Kompose, we will focus on creating a simple bot that books flight tickets between two Indian cities, Bangalore and Mumbai.
Let’s get cracking.
Step 1 - Creating the Bot
Before creating the bot using Kompose, you will need a Kommunicate ID. If you don’t have one already, you can create it by signing up here.
Once you have a Kommunicate ID, Login to your dashboard and click on Bot Integrations.
Next, select Kompose and click on “Create Chatbot.”
In the next screen, click on the “Blank Template” since we want to create a Bot from scratch.
In the next page, you get the option of selecting a name for your bot, along with the Avatar. Select the default language as English, select your Avatar and click on “Save and Proceed.”
The next page is the Automatic bot to Human handoff, which you can enable or disable, based on your choice.
For the purpose of this tutorial, we will enable this feature and click on “Finish Bot Setup.”
Your bot is now created. For confirmation, click on the box that says, “ Let this bot handle all the conversations,” and you are good to go.
This completes the first part of our tutorial - Creating the bot.
Step 2 - Training the Bot
Now, you have landed on the Kompose’s bot builder section.
Here, you get to create custom intents and response messages for your bot. Intents basically help the bot perceive the user’s input and decide the subsequent action.
The first part of the tutorial is to create a Welcome Message. This is the message that your bot will display when it greets a new visitor to the website.
In the section titled “Enter Welcome Message,” enter the text that you want to display that the TravelBot will greet new users with.
You can also enter additional text, or add a button with a link.
In the next step, we are going to create intents. Before that, click on “Save” for now.
In the next step, we are going to create our first Intent. We have named it as “Initial Flight Query.”
Since our TravelBot is a flight booking bot, let us assume that the travel company wants to build a bot that addresses flight routes, departure timings and their respective fares.
This intent will consist of all the possible questions that a user may ask to the bot, including:
Bangalore - Mumbai flights?
Which flights can I book from Bangalore to Mumbai?
Bangalore to Mumbai flights?
What are the available flights from Bangalore to Mumbai?
The User intent will look something like this:
In the “Bot Says” section, we will populate all the answers that the bot can give to the user for any one of the following 4 questions.
For the sake of training, we have taken 5 different flights and listed out their departure timings.
Once you fill out the details, click on the “Train Bot” abutton.
The populated screen will look something like this:
Now, we have given the users 5 different options for flights along with departure timings.
The next step is to assign the fares to each of these flights, so that the users can select the fare that is most suitable for them.
For this, you need to create 5 different intents. In this tutorial, we will show you how to create one of these intents, and you can follow the same procedure to create all 5.
Navigate to “Answer section” and click on the “Add” Button. Next, click on User says and enter all the possible questions that a user may ask the bot. In this case, the questions are:
IC 410 Rates?
IC 410 Flight charges?
What are the charges for IC 410?
Similarly, configure the answer that the bot will give in the “Bot Says” section.
Voila, you have configured the bot to answer all the questions related to IC 410. Follow the same procedure to create intents for the other 4 flight options.
Step 3: Handle small talk and unknown user inputs
The Kompose bot builder makes it super easy to build a bot, and to prove the point, there is a separate “Small talk” section right after you have trained the bot to answer all the user queries.
You can add various small talk options, questions that the users may pose to a bot that may not be related to flight-booking, and train the bot to give responses as we have explained in the earlier sections.
The procedure is the same as naming a small talk, configuring what the bot says and what the user says.
If you feel there will be a lot of small talk coming in the way of your bot, you can go to the “Settings” page and import a .CSV or .JSON file that will address all the basic small talk.
Here, we have described 3 different cases of small talk.
Once the bot is ready to handle small talk, we now move on to unknown user inputs.
Unknown user inputs:
This part of the bot is for situations where the bot does not understand the intent behind a user query. This is an unknown input, and it is good practice to transfer the user to a live human agent in this case.
Go to the Default Fallback option on your Kompose dashboard, and configure the message that TravelBot will display, in case the bot is unable to answer a user query.
In this case, we have written, “TravelBot is unable to process this request. Transferring you to one of our live agents. Please be patient.”
We can then transfer this user to a team, to a particular teammate, or even assign it to another bot.
Since we have said that we will be transferring to a teammate, we will select “Assign to a Teammate” and select the Teammate from the drop down list.
We have successfully created a TravelBot for flight bookings.
Did you notice something interesting about this whole exercise? Not a single line of code was written, and the whole thing can be completed in a matter of 10 minutes.
Now let’s see how you can test this bot and add it to your website
Step 5: Test your chatbot
Below the chatbot preview section, click the Test chatbot button.
Type in “Bangalore- Mumbai flights” and you will see a list of flights which we had input into the “Initial Flight Query” intent. Select the Flight you want from the list and type in as shown below:
Your chatbot is now ready to get to work. Next step is to add the chatbot to your website.
Step 6 - Add the chatbot to your website
Paste it just above the closing body tag on every page you want the chat widget to appear.
That’s it. Kommunicate chatbot is now integrated with your website. It’s time to activate it.
Once the Kommunicate chatbot is added to your website, you need to activate and assign all the incoming conversations to your chatbot.
You can do that from the Conversation rules section under Settings.
Enable Assign new conversations to bot and select your newly configured bot from the Select a bot dropdown. Learn more about conversation rules here.
That’s it! Now the chatbot is enabled on your website.
Test your chatbot periodically
Once you have created your chatbot, you would be able to test it from the Bot Integration section of the dashboard.
Navigate to Dashboard → Click ‘Bot Integration’ then Select ‘Manage Bots‘ for the particular bot ‘Test this Bot’
There are a few cases that need to be considered while creating a bot, testing, and deploying the bot.
Proper Welcome Message: The bot should be able to introduce itself properly & what it can do, how it will assist the user.
Basic Salutation: Bot should be able to respond to Hi, Hello, Good Morning, Thank-you, kind of message, and small-talks.
No cyclic loop: The bot should not get stuck in a cyclic look if a condition fails (repeatedly).
Fallback: If the bot is not able to handle/understand a user query then try to guide the conversation towards possible intents or handover to a human agent.
Typos: The bot should be able to handle basic typos so that chatbot interaction flow doesn’t break abruptly.
In Kompose chatbot builder, data for Bot intent analytics and Bot message analytics is readily available to analyze the bot intent count and the bot message flow. This gives more data to improve the performance of the chatbots constantly.
Maintenance and Optimization of Chatbots
As with other technologies, chatbots are an evolving space with progressive innovation and changing requirements. The chatbot you build must be adaptable to growing customer needs that change with time. You can ensure that your chatbot can stay agile and responsive to the aforementioned changes by conducting periodic maintenance and optimization.
Chatbots are most effective when they are continuously optimized to keep pace with the competitors in the space. Here are a few ways your chatbot can outshine your competitors through great maintenance and optimization techniques:
1. Periodic enhancement of protocols and responses
Chatbot development is not a one-off event. It is a continuous process involving tracking, upgrading, and testing before deployment. The progressive part of this process includes adding new data to boost intelligence and grow with customer expectations and market scenarios. You can enhance chatbot responses and protocols by:
Adding personality enhancements
Increasing its repository of interactions and responses
Adding to its knowledge base
AI-driven chatbots can be optimized using Natural Language Processing (NLP), Machine Learning (ML), and sentiment analysis. This will require little to no effort from the developer’s side. The bot will learn from its interactions with customers, the more it interacts, the better it will get.
2. Monitor user interactions
A critical part of chatbot lifecycle management is tracking user interactions and evaluating customer feedback. Using such data, you can approximate the performance of your bot and areas that need improvement. For example:
Chatbots failing to answer customer questions
Inability to understand words not in the chatbots knowledge base
Inaccurately judging customer sentiment
3. Accurately gauge when human support is needed
While chatbots can handle customer queries at scale and are highly programmable, they are also bound by limitations. When chatbots encounter a query too complex to answer, they should seamlessly transfer the customer to a human agent. The objective here is to reduce the number of human-to-bot handoffs and enable the bot to learn from live chat interactions.
Hiring experts to handle live chat queries
Enabling chatbots to assimilate new customer behavior and concepts
Using data scientists to enable enhanced chatbot learning
4. Use data and analytics to implement actionable insights
You can think of your chatbot as an evolving and active database. It stores and analyzes customer sentiment, preferences, and sales patterns to build on the existing chatbot framework. Machine learning helps chatbots extract and analyze data to enhance chatbot efficiency.
5. Implement Integrations for enhanced chatbot capabilities
Integrating your chatbot with other platforms and media immediately enhances the chatbot's capabilities. For example, customer relationship management (CRM), ticket systems, and social media integrations all add to what chatbots can do.
A great example of integrations is Facebook ads which direct the customer to Messenger on clicking. Here, the chatbot will take over and immediately greet the customer and take the conversation forward.
12 Effective Metrics to Measure Chatbot performance
Here are 12 effective metrics to track and measure the effectiveness of your chatbot:
1. Goal Completion Rate
GCR is on the top of our list because it successfully measures how effective your chatbot is by capturing the percentage of user interactions that have been successful over the chatbot.
2. Conversation Starter Messages
Companies need to initiate conversations with customers so that they stay on the website longer, so in a way, conversation starter messages help measure the organic reach of your platform.
3. CSAT Scores
Customer satisfaction scores help measure how happy the customer is with your chatbot. CSAT is measured through yes or no questions or numerically graded customer surveys.
4. Bot intent analytics
Bot Intent Analytics helps your developers assess how their messages are mapped to specific intent categories. It measures how “smart” your bot is and how it can be improved.
5. Bot Messages
The total number of messages sent by the bot during a conversation forms the basis of this next metric. This metric measures the conversation length between the customer and the bot, and we generally want this number to be high.
6. New Users
The number of new users that your chatbot has helps you gauge how popular your chatbot really is.
7. Total Users
The total users also give an indication of the amount of data that the chatbot is exposed to, and you can use this information to calculate the market size.
8. Active Users
These are users who have read the messages from your chatbot in a given time frame. With this metric, you can easily get an idea of how many potential customers you have for your product or service.
9. Engaged Users
This measures how many people actually send back messages to the chatbot, once your bot has initiated the conversation.
10. Bounce Rate
The bounce rate represents how many people are visiting your website, and leaving without interacting with your chatbot.
11. Fallback Rate
A FallBack response is one in which the bot does not understand the query from a user and gives a canned response that has been set by the bot designer. Higher fallback rates are bad since it means chatbot responses are not well-programmed.
12. Conversation Duration
The conversation duration between your chatbot and the user needs to be just right, neither too long nor too short.
In the next section, let’s see how some of the organizations in the world are successfully using a chatbot solution to automate their custom support.
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We have covered some of the best chatbots available in the global market and how they add value to organizations that adopt them. Here are some of them -
1. Kommunicate’s Chatbot for AMREF (Medical Research)
African Medical and Research Foundation (AMREF) is one of the most prominent healthcare Non-Governmental Organizations (NGOs) in the African continent, based out of Nairobi, Kenya. The organization’s mission is to help healthcare workers and train communities to be disease-free.
When the pandemic hit, AMREF was faced with the challenge of expanding its digital learning initiatives as most users were based out of different time zones. Handling user queries manually became impractical, which is when they identified chatbots as a solution to this problem. Another category of automation included enrolment queries that the students may have about any courses they would like to take.
With Chatbots, their organization could address their users' repetitive tasks, such as resetting passwords on their PCs. Any other tasks were also easily programmable, making chatbot implementation a scalable solution.
Read the complete case study here
2. Epic Bot by Kommunicate for Epic Sports (Online Sports Retail)
Epic Sports Inc is a leading US-based e-commerce website that offers sports apparel and accessories. Always looking to build on their customer service standards, Epic Sports has racked up around 400,000 reviews on Shopper Approved that averaging 4.7 out of 5. Shopper Approved is an enterprise that handles User Generated Content (UGC) to boost the reputation of online businesses.
The principal software engineer and e-commerce marketing manager, Tom Bulis states that Kommunicate’s chatbot solution is handling around 60% of incoming customer service requests. He goes on to say that Kommunicate made it possible for their business to scale its e-commerce customer base while retaining the size of its customer service team at a manageable level. Tom was also able to automate repetitive tasks performed by the customer service team by seamlessly integrating Google’s Dialogflow with Kommunicate.
Tom also stated that Kommunicate’s web platform could be integrated within a day. All that was required was a bit of development knowledge for the integration.
Read it here
3. Operator bot by Intercom for Anymail Finder’s (SaaS)
Anymail finder started out as a two-person marketing startup. It is a SaaS enterprise that lets its users locate verified employee email addresses of any company. Both founders of Anymail had to answer the same queries repeatedly over time using email. They had to answer questions like how to upload a file and how do you stand out from the crowd. This made the founders Joe d'Elia, and Pardeep Kullar write detailed articles answering questions. These articles were then arranged into preset responses and automated messages. Around 10% of visitors were met with an automated chat message based on their visited page.
Using Intercom's Operator bot, Joe and Pardeep could function as a larger team. Their experience with the chatbot and how it helped them is detailed in their self-written case study.
After implementing the Operator chatbot, 33% of buyers used the chat system before buying anything. Also, 90% of their big-ticket sales used chat automation before purchasing something. Finally, the founders managed to bring the response time down to 3 minutes within the first month of using the Intercom chatbot.
4. MongoDB’s LeadBot by Drift (Database Industry)
MongoDB is a cross-platform document database that creates scalable and highly available internet platforms. It has a flexible schema approach and is preferred by developers using agile methodologies.
The company was limited by the human limitations of their sales teams despite their success with live chat. MongoDB needed to scale its operations without scaling its team size. Its demand generation director said the company needed a messaging tool to scale the business. The tool needed to increase conversation volumes and thereby increase the sales pipeline.
Drift’s chatbot named Leadbot helped MongoDB connect their sales reps only to the most likely buyers. Also, Drift’s meeting scheduler helped make appointments with sales accepted leads. The takeaway is that MongoDB automated its lead-qualifying conversations and related scheduling. Hence MongoDB transformed more conversations into leads.
After implementing Leadbot, MongoDB saw an increase in net leads by 70%. Also there was an increase in the overall messaging response by 100%
There’s a lot going on in the chatbot space currently, and a lot to look forward to which we will cover in the next section.
The Future of Chatbots
Are chatbots the future? Will chatbots change the way people interact with websites as time progresses? Or are they just a technological fad that will give way to newer technologies as and when they arise?
In this next section, we will try to answer these and many other questions: The Future of chatbots.
Going by the numbers:
The Global chatbot industry is expected to grow from $2.9 billion in 2020 to $10.9 billion by 2026- Businesswire.
Chatbots are expected to handle 70%- 90% of healthcare and banking queries by 2022- CNBC
There are over 300,000 active chatbots on Facebook
ECommerce Transactions via Chatbots is projected to reach $112 billion by 2023.
Chatbots will save businesses 2.5 billion hours by 2023.
It is safe to say it makes sense to invest in a chatbot today if you haven’t done so already. Some ways chatbots will change the world as we know it are:
Chatbots will change the way you buy things online:
E-Commerce companies saw a huge burst in customers speaking to chatbots as the Coronavirus pandemic raged on, and the trend is far from over. According to an Insider Intelligence report, by 2024, people will spend a whopping $140 billion making purchases online via chatbots. Chatbots can make existing customers' lives easy by giving them personalized recommendations and guiding new users through the website. Chatbots have also been shown to reduce cart abandonment rates, a problem many E-commerce companies struggle with.
Chatbots may replace the conventional search engine:
Chatbots are slowly, but surely, getting smarter and may one day replace the Search as we know it. Don’t believe us? Here’s an interesting snippet: At Google’s developer’s conference held in 2021, CEO Sunder Pichai showcased the latest development that Google had made in Natural Language Understanding, a chatbot called LaMDA, designed to converse in a wide array of topics. LaMDA can talk to you about sports, politics, space, religion or anything else you would generally type into the Google Search bar. The future of search, as we know it, will be vastly influenced by chatbots, and voice search such as Amazon’s Alexa or Google’s Google Assistant are only the tip of the iceberg
Chatbots may replace entire websites:
Yup, you read that right. If recent trends are to be extrapolated, it would be no surprise that chatbots, or rather conversational apps, will replace entire websites in the future. It is already happening in countries like China, where WeChat, the country’s most popular messaging app, is being used to buy clothes, book flight tickets, and even file for a divorce. Conversational apps are an extension of chatbots, and, in the future, we will have mini-websites operating from within these apps that customers will use to carry out their transactions. And big companies like Google, Meta and Apple are all in the race to become the next WeChat. This is because they know that messaging apps are the future, and chatbots and conversational apps hold the key to getting closer to their customers.
Chatbots will change the way Customer support is done:
Chatbots make your brand accessible to your customer 24/7, but you already knew that. What you will be surprised to know is that in the future, these chatbots will get more sophisticated, able to provide your customers a better experience as they gather more and more information about them. Personalization is the key to providing superior customer experiences, and chatbots will be at the forefront of providing this personalization. Additionally, if a chatbot is unable to provide a response to a customer query, it can atleast let the customer know the waiting period before a human agent can attend to them. This will make the customers feel that the businesses value them and see them as something more than just a revenue source.
Chatbots will change the way you handle Social media and Lead Generation
Social media is a channel that businesses cannot ignore in the future, and, at the rate at which chatbots are evolving, we wouldn’t be surprised to see more and more businesses using them to up their social media game. Data gathered from chatbots can help brands better understand their customers and their preferences. Messengers bots have also been shown to increase customer confidence in a brand.
The next major disruption that chatbots will herald is lead generation. Fun Quizzes, Opinion polls, surveys, etc. that you see on platforms like Linkedin today are just the beginning of a new form of lead generation, and chatbots can definitely help in this sphere. Chatbots can be used as powerful lead generation tools, able to identify a prospect solely on the basis of their interaction with the bot. With the advancements in AI, chatbots can one day seamlessly interact with a customer and qualify them as a lead to a salesperson.
The future of chatbots will not be about replacing humans with machines but rather it will be about making them more human-like and personal. Chatbots will become more intelligent as they learn from the interactions with humans and become more like a friend than just an automated system.
For those of you who need a quick gist of all we covered, here you go:
Customer service is now a critical part of every organization’s operations to acquire, retain and convert customers into brand ambassadors
You need to care about customer service in your organization, as customer retention is far easier than acquisition and great service and support can help you here
The challenges to customer service include high customer volumes, fast response times to queries, dealing with angry customers, and scaling service teams
You face customer service challenges by adopting an efficient Customer Relationship Management (CRM) tool that includes customer service automation
A chatbot is a vital part of customer service automation and is defined as a software used to facilitate automated online conversations in place of a human agent through text or text-to-speech media
There are two types of chatbots - rule based and those that run on Machine Learning
To understand chatbots better you will need to know related terminologies such as Natural Language Understanding (NLU), Conversational Channel, Entities, User Intent and more.
You can build a simple bot to serve your business needs to see how well chatbots can handle customer queries
Ensure you subject your chatbot to periodic maintenance and optimizations through integrations, data and analytics to get the most out of your chatbot solution.
You can use standardized metrics ranging from goal completion rate and bounce rate to conversation duration to measure the effectiveness of your bot solution.
Chatbots are not a one-size fits all solution you need to pick the one that’s right for your organization. You do that by prioritizing the right features.
Chatbot features can range from chatbot analytics and marketing to omnichannel messaging and multilingual capabilities
Some of the most used or well-known chatbots in the world include: Intercom, Drift, WP-Chatbot, Salesforce Einstein, LivePerson, Genesys DX, Amtrak’s Julie Chatbot, and Google’s LaMDA.
The future of chatbots looks very promising indeed, with billions of hours being saved through smart automation and better service