Since the dawn of conversational commerce, chatbots have been a benchmark in the field of business communications. However, chatbot technology has struggled to be an effective tool as they find it difficult to provide the human touch in conversations. As the struggle for dominance between the efficacy of human agents and chatbots continues, recent developments with regard to the integration of artificial intelligence have given chatbots a significant push.
Speed, convenience and effortlessness are essential to a good conversational experience. Conversational AI is transforming the way people interact with business chatbots as they provide a human-like, organic experience that reduces the customer’s effort in explaining their needs.
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AI chatbots – the need of the hour
Chatbots were invented as a way to reduce human effort in interacting with the customer, allowing the machine to handle the queries and complaints of the customer. They have been incorporated in all walks of life, from social media sites, blogs, e-commerce websites to government sites and mobile apps. However, since the crux of conversational commerce and computing is based on personality in general, brands are giving importance to humanising their approach and offering their clients a unique experience.
With the emergence of digital commerce, customer expectations have evolved into a more informed and instantaneous requirement. So, in order to understand the nuances of conversational commerce, let us first delve into the expectations of the current consumer.
What a modern E-commerce consumer expects
The needs and expectations of consumers have been rising progressively over the years as they are well informed. As per the latest research by Forbes, 72% of consumers look for their answers online. The report further suggests that 70% of white-collar employees prefer to use conversational platforms for their day-to-day needs. Brands are progressively catering to such a need of being given the right information at every step of the purchase cycle with the help of conversational AI. Implementing an AI chatbot ensures a quick and accurate response to the client’s demands.
Despite the need for prompt and accurate response, the customer isn’t truly satisfied until the conversation has a human context and personality. This particular bottleneck has puzzled chatbot makers for years. Thus, conversational AI helps meet this particular demand by comprehending complex scenarios, identifying emotional sentiments and interacting in a personalised, human-like manner.
AI chatbot – Speed of a machine, voice of a human
The working of an AI chatbot completely depends on the parameters of its utility. While question-answer bots are simple and require a smaller skillset, virtual assistants harness the full potential of AI and ML to imitate and reproduce an organic conversation.
An AI chatbot is trained with a large number of conversational logs to identify the intent of the user, extract relevant entities, analyse and come up with the most appropriate response. This process can be achieved with three basic methodologies.
1) Pattern Matches: In this method, the bot groups the text given by the user and tries to find a similar pattern in the database with the help of AIML – Artificial Intelligence Markup Language. Once the pattern is matched, it gives the appropriate response accordingly. This process is demonstrated by a simple example:
2) Natural Language Understanding (NLU): In this method, the bot converts text into structured data to understand the given input by the user using three concepts given below:
Entities – An entity represents an idea to the AI tool. For example, the payment process in an e-commerce chatbot.
Context – Here, the AI separately stores the phases of a conversation during a chat. With context, the AI tool can easily relate expectations with the necessity of comprehending the last question.
Expectations – Here, the AI tries to fulfil the user’s needs by identifying the command in the user typing text and providing the appropriate response
3) Natural Language Processing (NLP): This process is similar to NLU as the chatbot converts input text or speech into structured data. Then the AI uses this data to choose a relevant answer using tokenization, sentiment analysis, named entity recognition and dependency parsing.
Is AI chatbot winning the customer service game?
In the world of constant communications, chatbots have turned out to be quite an effective business catalyst that connects with the user to attain valuable information to generate business. Virtual assistants like Google Assistant, Alexa and Siri use information from various platforms to build on a better and human-like experience. But the question remains if AI chatbots are superior to human agents in the field of conversational commerce.
Tal Shnall, a certified customer service trainer & keynote speaker, talks about AI changing the world of conversational commerce as he states: “AI Chatbots are change-makers in how customers connect to a brand and organisations today. More and more companies are already using the technology with their valued customers. While technology is advancing and part of every business success, we must not lose the human touch of creating emotional connections with our customers. Can chatbots generate human emotions? Can chatbots build relationships of trust? Will machines exceed customer expectations? All of these questions create opportunities for entrepreneurs to innovate and wow the customer expectation to deliver excellence.”
All in all, AI chatbots operate 24 hours a day, 365 days a year, are cheaper to implement and come with vast banks of information at exceptional speed. However, it is a long way from resembling a human-like experience. In the coming years, at the pace that AI is advancing, it will surely bring a paradigm shift in the world of conversational commerce.