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Creating a Chatbot: Types, Use Cases, and How to Choose

 

In the wake of greater digital initiatives and a constant push for customer-centric organizations, chatbots have not only been widely embraced; they are somewhat of a norm when it comes to enhancing the customer experience. This is evidenced by the more than 1.4 billion people currently using chatbots.

The conversational AI market is estimated to reach $77.6 billion by the end of this year, and by 2027, global chatbot revenue will surpass $455 million. These numbers clearly paint the picture of how quickly chatbot spending is growing, validating that companies are using them more and more. 

Businesses can save money on customer service workers by using conversational AI chatbots to handle routine customer service requests. By 2023, all of these cost reductions might amount to $11 billion annually.

Nowadays, an increasing number of companies are using chatbots to automate specific aspects of client interactions, relying less and less on service agents, and bringing in much-needed cost savings and efficiency. For example, chatbots are expected to handle between 75 and 90% of healthcare and banking queries in 2022, which more than proves the point of how incredibly valuable chatbots are.

Have you ever had to wait longer than expected for a service rep to give you an answer to a service or product question? Maybe as a result of having to wait so long, you abandoned your cart or decided to go with a competitor. These are simple scenarios of poor customer experience and customer abandonment that are easily remediated by chatbots. Think of chatbots as advanced digital assistants that easily streamline interactions between people and services, and as a result, enhance the customer experience and engagement rates. Thanks to less human intervention, chatbots bridge the gap in customer support that demands greater speed, accuracy, and quality. 

Chatbot types

The first-ever chatbot was called ELIZA and it was created by Joseph Weizenbaum. The date of creation? 1966! This pioneering bot appeared to pass the Turing test but flunked some of its other tests; yet, it was enough to lay the groundwork for future advancements in chatbot technology. We now have access to highly developed chatbots, which have become increasingly popular over the past few years due to the excellent service they offer.

Nowadays, thanks to the road paved by ELIZA, we have different types of chatbots.

1. Rule-based chatbots

A rule-based bot can only understand the choices it has been programmed to understand. The bot's conversations follow rules that have already been set. Rule-based chatbots are easier to make because they use a simple "true or false" algorithm to understand what users are asking and give the right answers.

Pros of rule-based chatbots:

  • It’s easier and faster to create a rule-based chatbot than AI-powered or intelligent, autonomous chatbots.
  • Rule-based chatbots are easier to set up, faster to get up to speed, and cheaper to develop.
  • Rule-based chatbots are easier to use and don’t need to learn over time to understand your client’s needs to give them the right answers; it all comes from a simple decision tree.

Cons of rule-based chatbots:

  • Rule-based chatbots can’t learn on their own and their questions are only based on a set of rules set up by you.
  • The client experience of a rule-based chatbot is very straightforward (perhaps, a little too straightforward).
  • With rule-based chatbots, if your clients ask questions outside of what is programmed to understand, it will fail to provide an effective answer.
  • Rule-based chatbots can’t provide a personalized experience and are prone to “break down” when people use different grammatical structures than what it’s programmed to understand.

2. AI-powered chatbots

This robot has an artificial brain, which is also called artificial intelligence. It has been taught to understand open-ended questions with the help of machine-learning algorithms. Not only does it understand what is being asked of it, but it also understands what is being said. As the bot talks to people and learns from them, it keeps getting better. The AI chatbot figures out the language, context, and goal, and then responds in the right way.

Pros of AI-powered chatbots:

  • AI-powered chatbots can learn on their own and can be set up to talk in your brand’s tone of voice or even in a specific, local dialect.
  • AI-powered chatbots become experts in the field you assign to them, giving you access to uninterrupted expertise for your clients and removing boring tasks from employees.
  • AI-powered chatbots take care of client requests from start to finish and provide services without human interaction, 24/7/365.
  • AI-powered chatbots can understand different languages and dialects, tailoring the way they talk to your customers. They can understand goals and feelings.
  • Thanks to their continuous learning, AI-powered chatbots will keep getting better.

Cons of AI-powered chatbots:

  • AI-powered chatbots take a significant amount of time to program and train.
  • While smart, AI-powered chatbots can run into scenarios where they don’t always understand what the client is talking about as they don’t necessarily understand the context.
  • AI-powered chatbots must be continuously maintained and updated with changes or questions that may have been missed.

3. Intelligent and autonomous chatbots

These are Machine Learning (ML) bots, which can learn from what the user tells them and what they ask for. As they learn from the data they are given, they will be able to figure out what to do on their own or with little to no help from a person. They understand certain words and phrases based on what they have been taught or what they have learned in the past.

Pros of intelligent, autonomous chatbots:

  • With little human intervention, ML chatbots can figure out how to proceed on their own.
  • These ML chatbots can understand words and phrases based on training and historical data, improving the customer experience and personalization.
  • They can help increase sales by solving customer questions instantly.
  • When compared to human agents, ML-based chatbots possess a large repository of knowledge, ensuring quick and accurate responses.

Cons of intelligent, autonomous chatbots:

  • Complex to program and train, as well as time-consuming.
  • Even though they can partially handle emotions and context, ML chatbots can sometimes miss unpredicted requests.
  • Lengthy maintenance and ongoing optimization is needed.

Benefits of chatbots for your business and customers

With the COVID-19 pandemic, conversational AI chatbots became even more useful. With companies having to work with fewer staff and millions of people relying solely on online orders to keep their homes stocked, the demand for good customer service was much higher than the number of customer service reps who could handle the volume. This was mostly because it was cost-prohibitive to hire service reps in equal measure of demand, the requests being processed were twice as many, and the requests were harder to address.

Online chatbots can replace human customer service reps by providing instantaneous assistance to website visitors and directing them to the information they require. A more streamlined and successful approach to customer service is beneficial for both your business and the consumer since it increases the likelihood of a client making a purchase as well as the size of that purchase. Conversational AI chatbots yield a lot of benefits that go beyond the time frame of the pandemic:

  • Costs less and there are fewer support tickets.
  • Automates common inquiries
  • Makes your customer service team available 24 hours a day, 7 days a week. Lets you grow without hiring more full-time employees.
  • Improves the customer experience as a whole
  • Boosts the number of sales
  • Increases the number of people who use a website.
  • Looks like it belongs on your website. Lowers the number of people who leave.

Diving deeper, one of the biggest benefits is the increased satisfaction rate. Chatbots' primary function is to offer instantaneous assistance to online consumers before, during, and after their purchase. They can mimic the in-store experience digitally if designed to react to questions in natural language. In fact, some chatbot adopters have reported a user satisfaction rate of 80% from using chatbots.

Another important benefit is the automation of customer inquiries. Chatbots can provide real-time, ongoing support to clients any day at any time, giving companies the chance to automate most operational aspects of their workloads. One chatbot adopter reported automating 90% of their inquiries thanks to chatbots.

With so many reasons to use them and so many benefits, it's easy to see why so many companies are getting on the chatbot bandwagon.

Chatbot use cases

Chatbots are used by businesses to help their internal and external customers and leads.

Sales: Chatbots that are advanced and keep up with the latest trends in artificial intelligence can make it possible for customers to book appointments automatically from your website or Facebook page. For example, chatbots for e-commerce companies can:

  • Help customers through the buying process and make it easy for them to make quick decisions.
  • Suggest products based on what people have bought before. This makes the service more personal.
  • Give all the FAQs real-time customer service.

Of course, there are some issues or concerns present in using chatbots for sales. For instance, about 34% of clients say they would feel comfortable using chatbots when shopping online, meaning chatbots are not what most customers want. Another concern is customer feedback. By employing a chatbot, you can't listen to what your customers are saying. This means you might not get feedback on problems that are getting worse or good ideas about ways to improve the sales experience.

IT services support: Chatbots can be set up to solve the most common problems that employees have when using IT infrastructure. Chatbots can handle things like outage alerts, knowledge management, system status updates, password updates, regular scans, and more. 

In contrast, one of the greatest drawbacks of using chatbots in IT service support is that users can easily tell when they are getting answers from a computer/robot. Additionally, given the technical nature of IT service requests, it's easy for reactive chatbots to get stuck. If the question has nothing to do with what you taught it, it won't understand it. This can make the customer angry and make the user's experience bad.

Marketing: For better customer engagement, customers want quick responses from chatbots that are powered by artificial intelligence. Chatbots can answer your customers' questions right away and make them happier, which makes them more likely to stick with your brand. 

  • Schedule an appointment: People who spend a lot of time on your pricing page or click "Get more info" are probably very interested in your product. Chatbots can set up a call with your sales team right away.
  • Recommend products: You can track your customers' interests, preferences, and needs with chatbots. You can give them suggestions for products or services based on these things. Product recommendations are very important in e-commerce, the fashion industry, and the beauty industry.
  • Place an order: With a chatbot, users don't have to go to a website to place an order. Customers spend less time and effort buying from you because they can place orders and pay right in the chat. For that, you need to link your chatbot to PayPal or another service that lets you transfer/receive money.

Even though AI chatbots can mimic what people say, in marketing, understanding the customer's voice and tone is critical. This means that, sometimes, chatbots fail to relate to how a person might feel. Also, a lot of customers want to connect with brands in ways that AI chatbots can't do yet.

While powerful and effective, chatbots demand work from your marketing team. The marketing field requires a human element, and while chatbot automation is great, it can’t be a replacement for human interaction. Additionally, it’s recommended that you don’t overuse chatbots for marketing or nurturing as too many emails or chats can cause people to unsubscribe.

How to create a chatbot

Chatbots can take many forms. Customers who shop with them online will benefit from their ability to recommend products, answer questions, and handle returns. They can help with airline reservations, food delivery from restaurants, stock market updates, financial planning, and more.

The purpose for which a chatbot is designed determines how it is written. What you may not be completely familiar with, however, is how to build a chatbot, despite your familiarity with the concept and its many applications. 

In simple steps, creating a chatbot involves:
Step 1: Figure out what kind of chatbot you're making.
Step 2: Pick a channel to watch.
Step 3: Pick the stack of technologies.
Step 4: Plan out what you want to say.
Step 5: Teach the bot what to do.
Step 6: Give the chatbot a try.
Step 7: Put the bot to work and keep it running.

Easy enough, right? Let’s review chatbot creation in greater detail, shall we?

Selecting a messenger platform or web application

The easiest way to make a conversational chatbot is to use a platform. Platforms are easy to understand. They let you design chatbots by dragging and dropping pre-made parts, so you don't have to know how to code.

Some platforms come with ready-to-use templates to make the building process easier. You can use them as is or change them to fit your needs. Chatbot platforms are a good choice for brands that don't have a lot of technical knowledge but don't want to pay for outside developers.

You can also build your chatbot from scratch, but you might want to consider that it takes a lot of time, yet it gives you complete control over it. You can make your AI agent fit the needs of your customers, give it the power to solve hard problems and connect it to any platform you want.

Chatbot architecture

Pro tip:  Determining the best architecture for a chatbot requires knowing its intended domain. 

A chatbot could, for instance, respond to your questions. The chat may have been interrupted, and you may return to it at a later time. It's up to you, as the developer, to decide whether or not your chatbot will keep track of past conversations. A pattern-matching architecture is supremely suitable for some areas. 

However, a greater domain is necessary for chatbots that deal with different domains or services. Advanced, cutting-edge neural network designs like Long Short-Term Memory (LSTMs) and reinforcement learning agents are your best choice in these scenarios. Because chatbots are used in so many different ways, their structures evolve to meet the specific requirements of each bot.

Integration with existing IT products

Pro tip:  A chatbot will be truly valuable once it’s genuinely integrated with your IT products. 

Few will argue that integrated AI chatbots are much better than a smart system without integration. Imagine a client of yours needs to connect with your client service area. Thanks to an integrated API, they can easily access a chatbot without needing to open a separate page. 

With API integration, you can work with more complex bots with expanded features, you can automate processes, and provide a plethora of available solutions to your client’s satisfaction.

Chatbot cloud solutions

Pro tip:  For your chatbot, you can employ a hybrid strategy by making use of cloud computing while also integrating with on-premises infrastructure.

Should your chatbot be on-premises or on the cloud? Each option has its unique set of advantages and drawbacks, as it all depends on your business's unique needs. For potential clients, using a hybrid approach means that even if they are stuck using old legacy apps, they may quickly and easily make the transition to more conversational systems by integrating an API.

You may find chatbots that were made to work on the cloud, and there are plenty of them. In spite of cloud computing's popularity, cutting-edge products are still available for in-house deployment. Having the chatbot co-located on the same premises as the organization's operational platforms could be the most efficient setup. Having the chatbot on-premises also ensures that all communications and transactions with customers are kept private.

Here are some pointers for both on-premises and cloud-based chatbots:

Customization

Pro tip:  For a personalized experience, you need to know what customers want and be able to guess what questions or problems they want to solve. This will help determine whether a cloud or on-premises chatbot is a better fit.

  • Cloud-based chatbot - The cloud simplifies chatbot development. Multiple channels are easily integrated. Businesses may not have much flexibility with customer-specific adaptations. You pay for what you use and the charges vary by usage.
  • On-premises chatbot -  With on-premise chatbots, you can tailor processes as needed. Businesses can integrate any working client applications into the workflow. The on-premise option gives firms in-house maintenance and support, reducing or eliminating vendor dependence.

Deployment

Pro tip:  When deploying an AI chatbot, you must plan and keep a close eye on your virtual assistant's evolution to validate you are on the right deployment path. 

  • Cloud-based chatbot - Businesses use cloud computing to deploy chatbots, which can come in numerous forms. Public, private, multi-cloud, and hybrid clouds. Companies can remotely access these resources anytime, anywhere.
  • On-premises chatbot -  On-premise deployment uses in-house resources. Companies can manage resources internally and maintain the solution and all connected processes in-house.

Cost

Pro tip:  The cost of a chatbot depends on whether you build it yourself or pay a monthly fee for the software. And if you choose a chatbot provider, the plan and company you choose are also important.

Cloud-based chatbot - Cloud-based solutions have different plans based on consumption and need. A cloud-based chatbot is priced by monthly or annual subscription. Support, training, and updates cost extra. 
On-premises chatbot - On-premise costs include server hardware, power usage, space, support, training, and upgrades. Also, implementation costs are lower which helps reduce ownership costs.

Testing chatbot with messengers

There is a widespread lack of testing and understanding of user intent in chatbots. They fail because they are unable to recognize or follow human speech or direction. Chatbots can only take off if people really utilize them. When interacting with a chatbot, a customer's intent refers to what they hope to accomplish.

That's why it's crucial to make sure your chatbot is designed and tested with a variety of input values and formats. Customer intent must be programmed into chatbots. Chatbots require a large amount of data to reflect the input values that they must be able to process, due to the wide variety of ways in which individuals communicate individual intent.

All the various types of intent are represented in this set of training data. These data points are used by chatbots to develop mathematical models for intent recognition. Then, it must be tested to make sure it behaves as intended across all possible conditions.

by Svitla Team
November 29, 2022

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