The impact of machine learning (ML) and artificial intelligence (AI) in e-commerce is compounding. ML-driven customer service is helping boost sales and optimize various aspects of e-commerce operations, from product adoption to successful ordering of products.
Artificial intelligence in e-commerce is not the same as in other industries – it is significantly more complex and demanding. As a result, there is a high risk of impacting the customer’s buying experience, trust in the product, and loyalty towards the brand.
Conversational AI is helping overcome the risks by enabling e-commerce businesses to adapt to the new normal. But, unfortunately, most e-commerce firms are influenced by myths about using AI in customer support automation and are missing out on its benefits.
Debunking the Five Popular Myths Around Using AI In Customer Service Automation
Let’s demystify some of these popular myths, which will help you gain a better perspective and help you leverage the power of automation in driving your business towards a successful digital transformation.
Myth 1: AI in customer service automation requires tremendous operational support
The truth is that using AI in customer support automation does not require enormous support from your sales/marketing or your customer support operations team.
Over the years, AI has moved from human-supervised learning models to unsupervised learning models (independent learning). By using unsupervised learning, ML algorithms automatically and effortlessly learn unfamiliar things and achieve significance in day-to-day lives.
AI-powered conversations help you engage your customers and answer their questions without unnecessarily requiring many salespeople to interface with customers. Seventy-six percent of customers receive conflicting answers, for the same question, from different support agents – this is a real difficulty. It causes not only chaos but also a loss of confidence and reliance on the brand.
Conversational AI, backed by your data, stories, and guidance, can help you make the most out of every interaction. Automating your customer support is the way to build a clean and confident process that works for you, your hard-working sales team, and your devoted customers.
Myth 2: Conversations can go wrong with AI
Yes, there is always a chance something will go wrong during a conversation, especially with humans. We have personal intent, social bias, and our thoughts come in the way when we talk. However, AI transcends these.
Given the accelerated pace of product innovation and high turnover, agents routinely have to put the customer on hold while doing their research. While the agent ferociously searches through knowledge bases and FAQs and skims through multiple web pages, documents, and spreadsheets, the customer loses patience. Using Conversational AI in customer service automation brings down your Average Handle Time (AHT) by reducing the manual effort needed to gather and deliver insight from multiple sources.
Advanced customer care automation tools continually learn from agent interactions and content sources to instantly surface the correct passages from relevant documents, helping your business reduce the turnaround time, and deliver timely, data-driven, and context-based customer experience.
Myth 3: Conversational AI requires an immense amount of data
When an annoyed buyer calls an e-commerce vendor to complain about a significant defect in their product, they anticipate immediate answers. They expect the agent to be well prepared, explain the rationale, and offer the best resolution. However, with no context for the incoming call, agents are at a disadvantage. They need to study and understand the issue, review account history, assess potential options, and fix the problem – this is immensely time-consuming. Hence, you need a data-driven, analytical and automated approach to initiate the conversation and solve such complex cases.
Conversational AI requires smart data that is accurate, relevant, and genuinely responds to customers’ questions. With appropriate data in place, conversational AI can have a meaningful and intent-driven dialogue with users. The ability of true conversational AI to understand not only the intent but also the context and subcontext helps it maintain the dialogue and appropriately provide the user with the right response. The question, therefore, is: How much data do you need, and where do you find it?
Let’s answer this question by asking another question: How is your sales team currently answering the same questions?
The answer is they have the data and content to review and respond to customers without missing the context.
AI needs the same data you already possess to get started. Once commenced, AI solutions are programmed to constantly collect data, learn and evolve into more intelligent customer service providers – it becomes an ongoing, self-propelled cycle. There are now proven techniques that require a small amount of data from the enterprises and immensely reduce the training time to only a few minutes.
Tailored conversational AI solutions can drill into a customer’s interaction history, examine usage and activities, search for irregularities, and evaluate potential recommendations in a manner of seconds.
Myth 4: AI will ultimately replace human interaction
In reality, AI chatbots complement human support teams and help them free up their time to handle more critical customer interactions.
AI-powered chatbots are intelligent, and nowadays, they can speak and interpret natural language, thanks to progress in natural language processing (NLP). By automating customer service through AI, you can free up agent time for more complex cases. Thus, you are eliminating the linear cost of scaling to add more agents to support more users.
Advanced conversational AI technology helps chatbots understand user tone and sentiment and handle conversations appropriately. Thus, AI chatbots add immense value to human agents by providing essential support in assisting new and recurring visitors, gathering information from consumers, answering questions, helping buyers follow up on their orders, and reminding agents of their daily critical tasks.
AI can power the initial stages of your interaction, gather relevant customer data, and help answer questions. But when things get rough, you need your best human customer support representative. The overall goal of using AI in customer service is to keep customers engaged, satisfied, and barrels of revenue rolling in.
Myth 5: Most customers don’t like interacting with AI chatbots
People don’t like being treated like tickets. Your customers don’t mind sharing their issues with chatbots as long as they are comfortable with the conversational flow. Today, not only are people enjoying talking to chatbots, but they’re often more honest with them than their human counterparts. In fact, millennials and young buyers are more likely not to want to talk to another human as long as they are getting self-help in some way, including a chatbot.
Machines don’t judge and possess an unconscious bias, which is a great advantage for customers. With advanced NLP and machine learning in place, conversational AI has become more natural and customer care automation more feasible.
As digital messaging becomes the favored channel to engage with brands, 90 percent of consumers expect a prompt response round the clock. As a result, sales agents are compelled to handle multiple messaging platforms to address those questions, often dealing with multiple conversations parallelly. Instead of switching between applications, hunting for answers, and copying and pasting information, AI can be seamlessly embedded into messaging platforms to present contextual solutions and provide instant customer satisfaction.
Exceptional customer service is inevitable in e-commerce. A 2018 report by Gladly says, 92% of customers say they would cease purchasing from a company after three or fewer poor customer service experiences. Twenty-six percent of those customers stop after just one unsatisfactory experience.
To sum up, customer service automation needs to be coupled with live customer touchpoints via call, chat, or messaging. Creating a personalized experience for every customer is the goal of Conversational AI, especially in e-commerce and B2C companies. American inventor and futurist Ray Kurzweil said, “Artificial intelligence will reach human levels by around 2029. Follow that out further to, say, 2045, and we will have multiplied the intelligence – the human biological machine intelligence of our civilization – a billion-fold.“
Thus, we need to keep up with the flow of global digital transformation, be willing to adapt to change, and implement the right and relevant solutions as and when required. The time is now.
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