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Why Chatbots Are Ineffective And Why AI, Machine Learning, And NLP Are All Better Solutions

While chatbots may have had their fair bit of fame, they are certainly not as efficient as they are hyped up to be. Of course, the idea of talking to a regular chatbot seems like it would be a fast and efficient way to solve your customer care request, but is that really the case? Even though these so-called intelligent chatbots have taken over crucial tasks like customer service, marketing, and sales in thousands of organizations worldwide, are these chatbots ready to take up more? Well, we are sorry to disappoint you at the very beginning of this blog post; but most of today’s chatbots are pretty ineffective and actually fail to clearly understand human context, display emotions, and retain clients. But, first, let’s take a look and see how it all started.

The year was 1966 when the MIT AI laboratory introduced the first chatbot named Eliza with the sole purpose of simulating human conversations by utilizing pre-programmed responses. Unfortunately, Eliza had no framework for comprehending the context of the discussion whatsoever, and it answered customer queries based exclusively on user prompts.

After the emergence of Eliza, years and years of extensive research and development have led to the rise of intelligent chatbots, which were able to answer more complex questions and requests, perform different tasks, and execute small-scale business operations for customer service and data collection. An example of an intelligent chatbot is a company chatbot that automates interaction with potential leads and assigns human marketers and sales agents to such leads using social media.

Photo from Board Of Innovation

Nevertheless, the popularity of chatbots has also led to an influx of not-so-intelligent bots in many industries that aren’t smart enough to answer more complex questions and entertain a simple discussion. These ineffective chatbots, which are somehow prevalent today, lack the backing of suitable algorithms powered by machine learning, natural language processing, and emotional intelligence, which are all subfields of artificial intelligence technology.

This blog post will discuss why most chatbots prove to be ineffective for organizations in reaching their customer service goals and how AI-powered solutions are becoming indispensable to organizations and customers worldwide.

Chatbots Are Still A Liability For Organizations

Even though chatbots appear to be well-received, you should know that it's not all a bed of roses. In fact, if the chatbot experience ends up being a negative one in the customer's eyes, almost 75% of customers state that they won't use that chatbot ever again. As a result, instead of providing top-class support and helping them with their queries, companies that employ unsophisticated, rule-based, ineffective chatbots could drive their clients away just like that.

In addition, most consumers admit that they're more frustrated by chatbots that can't answer their questions than human customer service representatives in the same situation. Also, companies should never attempt to hide the fact that they're using chatbots while interacting with the people who bring in the profits for their company since three-quarters of all customers want to know whether they are talking to a chatbot or a human agent. Moreover, 50% of the customers surveyed say they can clearly distinguish between a chatbot and an agent and won't look kindly on companies' efforts to fool them.  

Photo from Medium  

As you can tell, chatbots are not fully autonomous yet, which makes them a potential liability for all companies that strive to provide top customer service and experience. And when customers feel like they are getting nowhere with the automated responses from their ineffective chatbots, customers are left inclined to believe that the organization is cold and indifferent, which can seriously damage its brand.

Below, we’ll go through the top five reasons why chatbots may fail and what you can do to avoid them in the first place.

5 Reasons Why Chatbots Are Ineffective

Their Intelligence

As you may already know, there are two main groups of chatbots: rule-based chatbots and AI-powered chatbots. There’s a list of established rules in advance for the rule-based chatbots, which practically determines how the chatbot will respond to different customer queries. Typically, each customer request gets searched for a specific keyword, and if the keyword from the request is found, the answer will be immediately displayed to the customer. Nevertheless, if there’s no pre-defined answer, these ineffective chatbots can’t answer the query, and users might get easily frustrated.

Luckily, the situation is totally different with AI-powered chatbot solutions. Although they are also set up using a set of rules at the beginning, thanks to groundbreaking technologies like machine learning and natural language processing, these chatbot solutions can learn from each interaction with a customer and continuously improve the quality of their answers. By implementing the Y Meadows’ NLP-powered software, your chatbot might be able to grasp better insights about how your clients feel about your products and brand, locate opportunities for improvement, and gain a competitive advantage over your rivals.  

Photo from Chatbots Magazine

Unluckily, rule-based chatbots are much more prevalent within organizations at the moment. However, even though AI-powered solutions are more expensive and more complex to implement, you should strive to fit one into your extensive company budget as it’s an investment that will definitely pay off.

Wrong Use Cases

Despite the fact that chatbots are a buzzing keyword these days, that doesn’t mean that they are an appropriate tool for every business out there. As a result, many companies get very enthusiastic about this new technology that they are even willing to introduce it without even researching the potential benefits or problems it may bring for their use case.

But, if you’re considering the possibility of deploying a chatbot solution in your scope of work, that’s precisely what you need to do. So, before you buy a chatbot solution of any kind, think about whether you’ll have a genuine use case for it. Usually, chatbots are a good solution for answering standardized requests and questions. Still, they are often not suitable for resolving more complex customer problems or individual complaints, especially if they aren’t adequately equipped with natural language processing and machine learning technologies.

No Clear Scope

Many companies try to use chatbots and think they can solve all problems for everyone. Regrettably, that can cause particular problems. Basic, unsophisticated, relatively ineffective chatbots need a  clear scope of the topics and use cases they can cover, or they will hardly be able to provide a satisfactory user experience.

No Transparency

As we mentioned earlier, when organizations deploy chatbots, they should always stay 100% transparent about it because many customers find it frustrating when they think they’re talking to a human agent only to realize that it’s actually a chatbot. For that reason, you should never create false expectations. And if you’re using a chatbot to interact with your clients, make sure you indicate it at the beginning. Also, if you hand over the conversation to a human representative, let the customer know. This way, they’ll know who they are talking to at all times.

Photo from The Guardian

Lack Of Analysis Functions

Like any other software, chatbot solutions need uninterrupted analysis and a structured process to improve further their functionality, which many companies are not really aware of.  

Once the solution is launched, it’s often assumed that the work is entirely done, but in reality, this is where the real work starts. The data basis for chatbot improvements can be anything from user statistics and conversation protocols to surveys after each conversation. Companies should continuously monitor and analyze the available data and implement adequate measures for improvement.

Why AI, Machine Learning, And Natural Language Processing Are Key In Making Customer Service Experiences More Satisfying

These days, companies welcome hundreds of customer support requests, queries, and questions from different customer channels. These channels include emails, tweets, customer support tickets, chat conversations with human agents or chatbots, and more.

This data that organizations deal with daily is often unstructured and scattered, making it more challenging to utilize and successfully manage. But, at the same time, if organized, this data can increase the speed while resolving customer service issues and decreasing the volume of incoming support queries. So, how can your business reduce response times while being way more efficient than rule-based chatbots in maintaining your customer happiness with your customer service?

Photo from SAP Blogs

The best way to tackle this issue is by streamlining and automating particular customer service processes using natural language processing and machine learning, both subcategories of artificial intelligence.

Natural language processing, or NLP in short, is a category of artificial intelligence that allows computer programs like chatbots to process and analyze unstructured data, mainly text one. When combined with machine learning, NLP can be a powerful tool to transform unstructured data loads into something more structured and functional. It can also help your customer service agents acknowledge patterns in your gathered data and provide natural conversation with clients when implemented within virtual assistants or chatbots.

Y Meadows software is entirely dedicated to empowering your customer service efforts, be it your agents or your chatbots, to increase their overall productivity and improve the customer experience for the better. Y Meadows solution utilizes advanced AI, NLP, and ML technology to handle customer requests and empower the entire process. It can help your organization deal with incoming support tickets, reduce response times, and analyze each client’s request to respond with highly relevant information and attachments simultaneously.

Bottom Line

In conclusion, we are not saying that you shouldn’t employ chatbots in your customer service efforts. In the end, the ability to maintain an ongoing relationship with your customers 24/7 has immense value. Nevertheless, it’s always important to integrate human contact at necessary intervals to sustain customer satisfaction so that you don’t make your company look disconnected from the people who actually keep you in business.

To achieve this by implementing sophisticated software, we advise you to get in touch with our sales agents and request a demo from our AI-powered software to make your chatbot more efficient and your clients more content with your service.

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