The versatile chatbot is seeping into every industry, solving customer queries much more efficiently.
The Global conversational AI market is growing exponentially. Reports estimate a CAGR of 21.9% and a rise in valuation up to US$13.9 billion by 2025 from US$4.8 billion in 2020.
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The conversational AI market size is valued at US$46.29 billion by 2028 and is expected to grow at a compound annual growth rate of 30.75% in the forecast period of 2021 to 2028. Also, the increasing demand for AI-based chatbots to stay connected as well as informed during COVID-19 has been directly influencing the growth of the conversational AI market.
Online businesses are expanding to previously untapped markets across the globe, and with that, customer services are growing at an exponential rate. The rise of Natural Language Processing (NLP) and deep learning technologies in the last ten years have given new amplitude to simulate human conversation and dialogues.
In this regard, Verloop.io, a customer support automation platform, encapsulated a report by analysing nine million queries from brands across the ecommerce, real estate, BFSI, logistics industries located in India. According to the report, 83% of the total chats during the last year were completely handled by Conversational AI.
The report also reveals the following:
- The highest accuracy rate that conversational AI has reached is for English, which is close to 73%, followed by Bahasa with an accuracy rate of 65%.
- Almost 85% of queries in the banking sector were resolved by conversational bots without human intervention.
- In the ecommerce space, 60-70% of queries were resolved without escalating to a human agent.
- Brands that adopted Verloop.io’s customer support program, CSAT (Customer Satisfaction Score) jumped to 3.8
- The most preferred medium for brands to solve customer queries was WhatsApp (49%) followed by Website/Facebook (30%) and Apps (21%).
The Tech Panda spoke to Gaurav Singh, Founder and CEO of Verloop.io about how brands are realizing the efficiency of conversational AI.
Gaurav Singh
Conversational AI chatbots are helping customer-facing brands to move from a messenger service to a quicker automated and personalised multi-lingual live chat platform via different social media channels
“There are businesses of all types that are investing heavily in innovating new ideas to delight the customer, achieve excellent Net Promoter Scores, and reduce their customer service cost. Conversational AI chatbots are helping customer-facing brands to move from a messenger service to a quicker automated and personalised multi-lingual live chat platform via different social media channels,” he says.
Based on the report, certain sectors are showing high demand for conversational AI, as well as tier 2 and 3 cities, he says.
“We are focused on industries where there are large volumes of pre-sales and post-sales support required. This typically includes banking, financial, insurance, retail, ecommerce, EdTech, travel, logistics, foodtech amongst many others. As we see the penetration and focus of brands shift towards tier 2 and 3 cities, it’s no surprise that technology will follow. We have seen healthy growth in chat and voice volumes for the same,” he reveals.
Challenges conversational AI is solving in CX
For the brands of today, ensuring that a customer’s journey is nothing short of delightful is a key to unlocking growth. With the saturation of acquisition channels and overload of advertisements, the real moat or the differentiation any B2C brand can tout today is their support.
However, digital savvy customers of today expect support to be delivered through the medium and time of their choice. This is where Conversational AI is charting the path for more effective customer communication, says Singh.
Conversational AI bridges the gap between the monotony of automation with the personal attention of interacting with a human. It does this by delivering messages instantly through intuitive, natural, and personalised free-flowing language
“When you connect with your customers quickly and clearly, you keep them engaged with your brand and deliver a better customer experience. It is a paradigm shift in the way brands interact with their customers,” he says.
“Conversational AI bridges the gap between the monotony of automation with the personal attention of interacting with a human. It does this by delivering messages instantly through intuitive, natural, and personalised free-flowing language,” he adds.
The future outlook of conversational AI in markets
Deciphering complex scenarios, knowing customers’ previous context and history, understanding human sentiments, and training company-specific live chat data are equally important to improve the self-service accuracy and security standards of any Conversational AI chatbot.
Even lead generation, business tools and API integration, says Singh, can be a major relief for any company to provide accurate customer experience, increase sales, provide multi-channel support, and improve organisation efficiency. But the future, he says, is hands-free.
Millennials today prefer hands-free conversations to text-enabled bots, which can be building voice assistants and next-gen AR can take the engagement level and usage to the next heights
“Millennials today prefer hands-free conversations to text-enabled bots, which can be building voice assistants and next-gen AR can take the engagement level and usage to the next heights,” he says.
“The first phase of Conversational AI was simple rule-based chatbots that took the users through a pre-selected flow. With better technology, now Conversational AI is able to break through a strict flow and offers a flow that is indistinguishable from having a human conversation,” he explains.
Post pandemic changes in conversational AI adoption
In a post-pandemic world, Singh predicts even greater adoption for Conversational AI as brands focus on offering better customer experience at reduced cost.
“As brands and customers mature, the chatbot space will also mature with it. Simple rule-based FAQ bots with limited language capabilities will not be able to help brands a lot,” he says.
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Similarly, hard rule-based chatbots that do not have a narrow set of machine learning models for specific industry use-cases will fail to show the impact brands truly require to digitally transform their customer support.
“We foresee chatbots getting industry-specific. As per the data that we have, we have seen that the industry-wise queries differ substantially. We see current chatbot providers maturing into other channels,” he adds.
