“Just think about this for a second, if someone speaks the way you speak and knows what you like, wouldn’t the chances of conversion be higher in that case?”
The integration of AI technology to improve the experience of customers visiting the platform stemmed from it. As the industries started to gravitate towards a more customer-centric approach, the rise of AI chatbots took center stage.
This article deals with how that happened and how it helped businesses to improve their customer experience.
The Inception of Chatbots and Limitations that Led to AI Integration
The story of Chatbots starts in the year 1966 with Joseph Weizenbaum’s program “ELIZA”. The program worked on the concept of pattern matching and substitution for stimulating conversation with the user. This was a breakthrough that even passed the Turing Test.
Later on, there were many different chatbot programs such as PARRY, Jabberwacky, Dr. Sbaitso, and A.L.I.C.E, up till ChatGPT and Google Bard. It is also important to know that the first chatbot for customer service was rolled out in the year 2011 by the company Intercom. The tool has grown since its release till date and is right now being used by more than 30,000 businesses around the world.
However, there were limitations with the early version of these Chatbots that started the AI renaissance in the Chatbot industry.
Limitations of a Traditional Chatbot System
Out of the many limitations that these chatbots had primarily there were four that led to their upgrade. These were:
- These were rule-based systems that only responded in a predetermined way and didn’t get the context majority of the time.
- They didn’t provide enough personalization that made the customer experience devoid of human touch.
- They were incapable of handling complex requests which led the request to be passed on to a human operator.
- They didn’t evolve with time and in order to have new & more accurate responses, the dictionary had to be updated.
Traditional chatbots didn’t use technologies like Natural Language Processing. It made those chatbots much inferior to what we have now in terms of both personalization and contextual understanding.
Need for Personalization and Contextual Understanding for Chatbots
The use of chatbots is not limited to a single industry. In fact, almost every industry is utilizing chatbots for multiple use cases that they have. Therefore, the need for personalization and contextual understanding of chatbots stems from the various use cases organizations have across the industry.
Some of these use cases are:
- Automation of website support
- Use of Chatbots for Sales & Lead Generation
- Chatbots used for customer segmentation
- Engagement of users on social media
- Chatbots for supporting human resources operations
And, there are many more where they came from. Initially, these tasks were conducted via human operators. This required tonnes of cost and other resources. However, the companies were still not able to provide a 24*7 operational infrastructure.
As the number of internet users catapulted, it created a huge gap in terms of what was required and what was provided. The buffer between when the actual request was made and the time at which the operator was assigned started to widen more.
The requirements became stringent when companies had to focus on providing an enhanced customer experience as well as support. This gap was filled by chatbots that can entertain customers 24*7. These chatbots were capable of attending to queries at any time of the day or night and passing them on to the customer support team in the form of tickets or queries for resolution.
Adding to it, the competition to provide better support didn’t stop there. As the companies started to hyper-customize their product offerings and customer experience as per their preferences. This required the integration of personalization and contextualization at the chatbot level too. Therefore, AI chatbots came into being.
Examples of AI Chatbots for Businesses
Right now, the market of AI chatbots is already flourishing. As per a report by Markets & Markets, the chatbot industry is estimated to reach $5.4 billion by 2023. Also, the chatbot market is estimated to reach $15.5 billion by 2028 with a CAGR of 23.3%.
In this situation, there are plenty of the best AI chatbots that are available and will amplify the growth of the market. These AI chatbots are:
- ChatGPT
- Netomi
- WP-Chatbot
- Alexa for Business
- Zendesk Answer Bot
These AI chatbots are being used industry-wide and with the expected growth, there are plenty more players to come in the market.
AI Chatbot’s Personalization and Contextual Understanding- How it Improves the User Experience?
The usage of AI chatbots by any chatbot development company can improve its user experience and customer service quality in multiple ways. To gain perspective, this is how these pieces of incredible technology are capable of pulling it off.
Response to Every Query
Unlike traditional chatbots, AI-based chatbots are capable of responding to a wide range of queries. It is because their dictionaries are not limited to a few queries. Instead, these AI chatbots are trained on a diverse range of datasets that make them capable of responding to relevant answers that makes sense.
Human-like Response
The responses generated by traditional chatbots are often monotonous, robotic, and repetitive in nature. However, with an AI chatbot, the major USP (unique selling proposition) of the chatbot is that it holds the conversation mimicking a human.
Capability to Handle Complex Queries
The majority of chatbots available previously in the market were not capable of processing complex queries. In order to generate a relevant response, the input provided by the users was supposed to be articulated well. However, that is not the case with everyone. On the other hand, AI chatbots constantly evolve and learn with every conversation. This makes it highly likely to respond to any query, however, complicated it may be.
More Flexibility
Traditional chatbots were trained on industry domain data. This meant that they couldn’t have been used effectively with other industries or anything beyond the use case they have been trained for.
On the other hand, AI chatbots based on models like GPT (generative pre-trained transformer) have loads of data and can further be trained more in order to fit a particular industry, niche, or operational use case.
This provides enhanced flexibility to organizations deploying a single chatbot system for multiple multiple use cases of a company. These chatbots are also capable of processing more customer queries as they are capable of comprehending a wide variety of data.
Wrapping Up!
AI chatbots are already a mainstream technology. However, with the release of ChatGPT, this market has become even more advanced. Right now, there are plenty of Generative AI systems available such as Google Bard, Bing AI, Jasper, etc. Each of these chatbots is based on AI models that can be implemented easily in any organization’s process for customer support. These have the capability to provide extensive support to their users and will be doing it in the future with upgraded sensibilities.
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