Armed with smartphones and inherent inquisitiveness, today’s customers demand and value personalized experiences above all else. Because of this paradigm shift in customer behavior, engaging them with compelling experiences is becoming more important than ever before. And while many companies are already tapping into innovative technologies like predictive analytics, personalized recommendations, and even augmented/virtual reality (AR/VR), nothing compares to the customer satisfaction achieved by implementing a simple software that talks to your customers – conversational AI.
What is Conversational AI?
Imagine a smart assistant communicating and empathizing with your customers, understanding their requirements and assisting them accordingly. Unlike menus, touchscreens, or mouse clicks, a simple conversation like this takes a user closer to final action faster. And conversational AI can help you achieve this!
Conversational AI refers to a type of artificial intelligence designed to help software understand and interact with people in the most intuitive way possible – using natural language. It enables businesses to deliver automated and personalized communication experiences using voice assistants, chatbots, and messaging apps.
An amalgamation of technologies like machine learning (ML), natural language processing (NLP), speech to text (and vice versa) recognition, user authentication, and intent and domain prediction; conversational AI is changing the face of Human-Computer Interaction (HCI).
Moving away from the pre-programmed decision tree bots (rule-based), AI bots learn as they go! They are no longer based on linear and simplistic if/then logic. The most significant advantage that conversational AI has over rule-based bots is the identification of user contexts and intentions. They can thus decipher a user’s query and deliver a personalized experience.
Integrating conversational artificial intelligence across automated customer-facing touchpoints can eliminate the need for page-hopping or a heavily click-driven approach to interaction. Instead of performing multiple actions and browsing through heaps of irrelevant information, customers can simply ask an AI-enabled bot to find what they need. Examples of companies utilizing conversational AI include AirBnB’s machine learning algorithm to discover and fuel in-app messaging intent, the State Bank of India’s personal banking bot (ILA), and Starbuck’s AI-powered ordering system.
Why is Conversational AI a global trend today?
Conversational AI’s adoption is being driven by a dual mandate. While brands need new ways to carve a larger share of the highly competitive marketplace, they also want to cater to customer needs. Conversational AI holds the key to achieving both objectives. It can change every aspect of when, where, and how brands engage with people. Conversations can be a short one-off request/response or part of longer-running customer engagement. Conversational AI empowers brands to deliver intelligent, superior and personalized customer experience.
53% of existing Facebook Messenger users say they are more likely to shop with a business that they can contact via a chat app.
Businesses using automated conversations to connect with customers can easily extend that experience across multiple platforms, devices, and channels to cater to the entire user base. From websites and mobile apps to messaging platforms, social media, and voice-based assistants, conversational AI solutions can be deployed across all touchpoints to create a seamless customer experience. Apart from the fact that customers find conversational AI solutions more friendly and easy to use, there are various other reasons for companies to dive into this technology.
- Saves time – Conversational AI provides quick responses and fast customer service. This is especially attractive to customers who do not want to go through the tedious process of connecting with a customer service desk for generic queries.
- Easy real-time access – Customers can seamlessly connect with a chatbot via the channel of their choice. Additionally, the conversations are synchronous which reduces the risk of information discrepancy.
- Increases efficiency – One of the key benefits of implementing a conversational AI solution includes increased operational and customer support efficiency. The high number of calls and emails no longer takes a tool on the customer service teams. With automated operations and lowered customer acquisition costs (CAC), companies can focus on other business functions.
- Online relationship management – Companies can holistically manage social media interactions and branding engagements through synchronized and personalized conversations.
- Efficient customer query pipelining – Conversational AI can efficiently cater to customer needs and provide necessary information taking the load off the management staff. They can address general queries and keep on learning to solve the complex ones.
- Superior CX with lowered customer support complaints - By identifying individual users through their messaging profiles, it is possible to drive highly personalized interactions. Sales questions can be answered before the purchase, ultimately reducing friction during the buying process.
- Handles the entire customer cycle – A customer can complete a transaction, receive detailed purchase information, and receive after-sales information. It covers the complete loop – from seeking product information to sharing product feedback.
- Higher levels of collective intelligence – The technology is capable of tracking purchase patterns and monitoring customer data to provide the best personal support in real-time. The interactions get better and better as more users use the bot!
- Reduced cart abandonment rate – With a persistent communication channel that takes the context forward, customers are intrigued to look more and buy more. They can also pause the conversation or restart it later without having to replay the entire process. This also eliminates the hassle of dealing with support centers.
How is conversational AI disrupting markets and consumption models?
The way people communicate with each other has intrinsically changed. This shift is significantly altering their expectations from brands as well. Modern customers want products and services delivered to them effortlessly and immediately. They do not want a prolonged “buying journey” and prefer “instant gratification”.
Consequently, the volume of automated communication between brands and people has increased over the past few years. While the apps and the frequency of sending messages to businesses may vary by region, its global momentum is undeniable. Conversational AI technology is also improving with time. For example, advances in speech recognition software are helping reduce word error rates; and machine translation has improved exceptionally from the early Siri days.
The market is experiencing continuous surges of development. The goal has shifted from covering simple FAQs to broader perspectives. Bots are increasingly being designed to answer complex questions like: “When should I board a flight to City X to reach by Day Y?” or “Which is the best time to do a conference call if my business partner is in CST time zone and I am in IST?” The technology has come a long way from a hardcoded database that covered the basic website navigation and reminder notifications. NLP now even covers speech to text and vice versa capabilities. Conversational AI today not only understands spoken, written, and gestural communication but also communicates back to humans via their preferred medium!
You can now find AI-enabled conversational interfaces within hospitals as medical assistants. They can give financial advice or provide educational support making sure that the visitor’s experience is top-notch. The omnichannel presence of such bots and the data collected by existing conversational AI solutions is also helping build a better understanding with the user. The interaction is seamless and becoming more humane in every way.
We are yet to get beyond “I don’t understand” triggered as a response to unexpected queries. For this, advancing the ability to interact more naturally is very critical. Another checkpoint for the same goal is for bots to recognize and understand emotions, personality traits and even identify the tone of the conversations! Advances in conversational AI are catching up with this aim and intricately scripted training algorithms may slowly make it a possibility too.
Gartner predicts that by 2021, 25% of enterprises across the globe will have a virtual assistant to handle support issues.
As conversational AI becomes a business imperative, innovative brands are looking beyond chatbots, voice skills, and smart speakers to an omnichannel, multi-device, and multi-modal future of highly contextualized and intelligent engagement for lead generation, customer engagement, customer support, feedback, and insights.
The scope of Conversational AI – Today, tomorrow
Conversational AI originated with chatbots built for specific tasks within a limited domain to provide basic responses embedded in the interactive framework. Since then, it has expanded to provide an abundance of services and callouts tailored to produce an enriched user experience (UX) via both voice and text commands.
For AI-human relationship to reach its fullest potential, scientists are experimenting with machine learning to provide as much context to computers as possible. Two specific techniques are leveraged for this purpose-
- Supervised learning: Teaching with examples, and
- Reinforcement learning: Trial and error
Continued advances in cognitive technologies are eventually making it possible to provide more accurate and relevant dialogues to customers, giving rise to increased use of conversational AI solutions for enterprise and B2B applications. Several organizations are launching conversation AI for all kinds of niche services. Today, bots can even answer questions about products and services, schedule appointments, and even direct users to additional resources – virtually eliminating the need for a human operator.
Future iterations of conversational AI will assuredly provide personalized assistants (even in local languages) that both serve and predict user needs. Its greatest strength will reside in its ability to engage in human-like discussions across various scenarios. Consumers will increasingly communicate with businesses on their favorite chat platforms, just as they currently do with friends and family. At the same time, enterprises will need to be able to hold seamless, synchronous conversations with customers across channels!