Back

Chatbot Alternatives: Going Beyond Rule-Based Responses

Over the past decade, chatbots have expeditiously become a massive trend in digital marketing and customer service efforts. As a result, thousands of companies worldwide are spending millions of dollars on developing various types of chatbots to help them in their attempts to improve their customers’ experience and reduce service costs.

As a matter of fact, the global chatbot market is currently valued at $17.17 billion and is forecasted to reach $102.29 billion by 2026, registering a compound annual growth rate of nearly 35% over the forecasted period. Nevertheless, as traditional, rule-based chatbots are somewhat outdated to serve actual business needs in 2021 and beyond, the chatbot industry is growing thanks to the so-called chatbot alternatives like virtual assistants powered with machine learning, deep neural networks, and other groundbreaking advancements in artificial intelligence technologies.

Photo from Chatbots Magazine

These virtual assistants, which may come in the form of chatbots or intelligent speakers, among others, are utilized for numerous applications across nearly all end-user industries like retail, healthcare, banking, insurance, and so on. For example, the banking sector is one to frequently adopt newer technologies and chatbot alternatives, primarily due to their reliance on trust, speed, and everyday communication with clients. However, the old rule-based chatbots are not suited to facilitate communication and build customer relations through cognitive analytics by learning customer’s thinking for instant responses like modern-day chatbot alternatives can.

This blog post will help you better understand the main differences between old-fashioned rule-based chatbots and the newer chatbot alternatives that companies integrate within their processes and how AI-powered solutions like the Y Meadows software might be the best solution to automate your customer service process after all.

Rule-Based Chatbots

To begin with, rule-based chatbots depend on keywords in the queries they receive to understand what the human on the other side is communicating. Afterward, the chatbot will instantly research for predefined answers and provide a somewhat relevant response.

These chatbots became very popular when Facebook launched its Messenger platform in 2016, where rule-based chatbots allowed companies to perform automated customer support every time a customer reached out to them via chat. Such chatbots are used to answer simple questions, such as booking a room in a hotel, purchasing tickets for a sports match, or using delivery services. Using a decision tree, the client gets a set of predefined options that should lead to the desired answer.

Photo from Data Science Dojo

These chatbots are typically split into two tracks: a sales track for setting up a meeting or a call or capturing contact details; and a support track for providing generic answers or sending a URL link that contains the necessary information. For the most part, rule-based chatbots are built with a graphical interface that automatically reacts when the user presses a particular button and activates the next layer of the decision tree. Also, many of these chatbots are keyword-based, trained to respond to specific words but are limited to typos, and come with a considerable risk of providing the wrong answer and causing a very frustrating customer experience.

Chatbot Alternatives: AI-Powered Solutions

On the contrary, AI software-enhanced chatbot alternatives use natural language processing and machine learning to provide end-users a more conversational, more dynamic, realistic experience. They also have a database of answers through which they can answer customers' questions and queries to the best of their ability. The main difference here is that the database and the chatbot's abilities gradually expand over time using a process based on human reasoning.

Photo from Boost Words

These rule-based chatbot alternatives use natural language processing technologies to comprehend the intent behind every question and solve the customer's issue without any need for human assistance. In addition, these natural language processing-charged chatbot solutions use models that significantly increase the chatbot's functionality as they're able to identify thousands of different questions written by humans.

Because customers tend to ask bots questions by writing it the same way as communicating with a human representative. While rule-based chatbots may fail to understand the customer's intent because of their limited capabilities, which we explained above, chatbot systems enhanced by NLP solutions like Y Meadows can do a much better job.

First, the intelligent chatbot uses a text classifier to recognize the intent and understand the meaning behind the query. Afterward, it creates a sequence of additional questions and answers using the dialogue tree that helps to specify the precise issue that the customer wants to be resolved and how it will be done. Apart from that, API integration with the back-end systems permits the chatbot solution to perform the customer's task instead of only providing a URL link to self-service instructions.

NLP models like ours can help your AI-driven chatbot to identify thousands of different customer questions and provide viable solutions as no rule-based chatbot can. If you're curious to know more about our chatbot alternatives, please get in touch with our customer support team or request a demo to acknowledge what our powerful, sophisticated solution can do for your business.

Photo from Y Meadows

The Business Value Of AI-Powered Chatbots

Chatbot alternatives in the form of AI-powered chatbots provide a more significant value to businesses when compared to basic chatbot solutions because they can automate a high volume of bottlenecks, require less data for training, and solve more complex issues and requests. Nevertheless, the most significant business value of AI-powered chatbots comes from personalizing messages and automatically performing the tasks on the customer's account.

Customer verification and API integration with a sound knowledge base can allow the intelligent solution to see the customer's historical data and better understand their ongoing problem. Thus, the chatbot's response is entirely personalized, and it can even perform a task for the customer, like activating some service that your company offers.

Photo from The Business Journals

In addition, the ever-improving NLP algorithms permit the next generation chatbot alternatives to reach over 95% text classification accuracy by confirming the intent with the customer on the other side. So, suppose a client asks a question or submits a request that the AI-powered solution doesn't have a high enough confidence level to predict the customer's intent. In that case, it will ask a specific question like "Would you like to change your home address or company address?".  

Which Type Of Solution Is Best For Your Business?

Asking about the differences between outdated rule-based chatbots and AI-powered chatbot alternatives ultimately leads to a simple follow-up question: which one is better, and which one suits your business and situation?

Rule-based chatbots’ most significant trait is their simplicity. They are relatively easy to implement and come at a friendly price in most cases. But, on the other hand, they can lead to much lower satisfaction and resolution rates. Rule-based chatbots are frequently just legacy systems from the early age of the technology that haven’t been brought up to date yet.

Chatbot alternatives which are powered by artificial intelligence are way more complex. But, at the same time, they’re powered for complexity and resolving issues that rule-based solutions could never do. They can still answer simple questions but are also equipped with technologies like NLP and machine learning to participate in more complex, nuanced interactions with customers. The ML, NLP, and conversational AI capabilities make them a better fit for organizations that genuinely look to leverage natural communication in their customer service efforts.

Final Words

Nowadays, tech-savvy customers are looking for the best and most personalized customer experiences like at no other point in human history. With an AI-enhanced chatbot solution, your company can quickly provide high-quality support and conflict resolution any time of the day, simultaneously for a large number of clients. And while 90% of customers expect an online portal for customer service, the need for AI-powered software solutions will only continue to rise. Now is the perfect time to deploy an intelligent software solution to your existing system so that your organization doesn’t get left behind.

MORE ARTICLES

You Might Also Like

1/11/22
Leveraging AI To Advance Customer Service

Everywhere around us, customers' preference to be served by human agents rather than intelligent machines is commonly cited. But, what if AI in customer service can help to deliver more human experiences than what humans can do?

Read more →
1/5/22
The Changing Landscape Of Ticketing Systems

In the customer service domain, ticketing system providers have had to update their products in order to serve their customers better. Read more to learn about the top new trends that we are seeing in customer service ticketing systems.

Read more →
12/28/21
Implementing A Ticketing System

For exceptional customer service, companies need to incorporate a sound ticketing system. Learn more about systems that can help your business fast-track support and issue resolution.

Read more →
View All Articles