Consider these:
- Hi there! Your phone bill is due on the fifth. Would you like to set up auto-debit?
- Press 2 for your last bill payment summary.
Both are automated and provide self-service options. But the first one assumes charge and reduces the effort a customer needs to make. The second one is reactive and puts the onus solely on the customer. The first is a chatbot, the second is an interactive voice response system. Both address the problems of scale when it comes to customer engagement. Yet, the tide turned within a decade.
Fundamentally, the way enterprises interact with customers has changed. It is no more a set of punched-in numbers or even keywords. It is a full-blown, two-way conversation requiring chatbots to listen, understand and act. All in real-time.
Also Read:- Reimagining HR with Chatbots and Analytics
Mostly because digitally empowered customers will not hold the line. They will not even make the call. They want enterprises to be up and ready to take notes – wherever they want, through the channel of their choice – all the time. Twitter, Facebook, and Instagram are now extensions of contact centers – if enterprises are not listening, the world is.
That is customer experience (CX) one-O-one, and at the very basic level – it’s human-first.
The Human-First CX, Delivered by AI
The ambit of what CX is understood to be has broadened. It is no more the transactional activity, guiding a potential customer to the buying stage. Rather it is a lasting relationship – nurturing prospects to become customers to evangelists.
And artificial intelligence (AI) quietly orchestrates it from the front desk to the back office. As conversational AI – powering the Alexas and Siris of the world, it helps enterprises humanize automated customer interactions. It enables them to listen – not only when a customer reaches out – but throughout the customer journey.
How Conversational AI changes Transactions to Interactions
Conversational AI turns the enterprise-customer relationship on its head. Enterprises are not monolithic ‘Oracles’ to which customers come. They now become active listeners, looking for not just intents but sentiments, to strengthen the relationship. The customer, on the other hand, gains autonomy and is not limited to enterprise-owned channels to be heard.
Here is how the meaning of ‘customer experience’ changes for both the customers and the enterprises:
- Personalized and at scale: With conversational AI, there is always a personal assistant available for customers. It knows their names and demographic details. Maybe even when they are reaching out because it analyzes social media activity or historical behavior. For every customer. Take for instance digital bankers that know a customer’s premium due dates and the risk appetite for customized investment advice.
- Meaningful and contextual: Since the conversational AI knows names and reasons, customers can get to the point straightaway. With natural language processing, AI-powered chatbots or digital assistants engage customers meaningfully – answering queries or asking questions. If the AI recognizes anger, it can redirect the customer to an agent. Over time, these conversations enhance with heuristics and machine learning. Thus, creating a self-learning model that analyzes data and works insights into feedback loops for enterprises.
- Seamless and responsive: Understanding intents or responses are one part. With conversational AI, enterprises need to break away from organizational silos and allow data interoperability across departments such as billing, sales, and marketing. The visibility and insights thus made available will provide a seamless customer experience, while being more responsive and receptive. A fair bit of effort lies with internal teams and those responsible for implementing AI engines – and working in tandem will be key.
- Near real-time and self-serviced: In an age where customers are spoilt for choice and do not have an incentive to wait, real-time intervention makes all the difference. With conversational AI, enterprises can address customer queries or grievances in near real-time. Most of it is through promoting self-service options, where a human agent need not come in. However, the human-in-the-loop approach is advised for unique cases.
Also read:- 4 Ways That AI Is Improving the Customer Experience
Get the Human-First Approach to Conversational AI with Apexon
Of course, setting up a chatbot or a voice-enabled digital assistant requires expertise and in-depth knowledge of internal processes. There are unique challenges that conversational AI poses – such as understanding accents, digital ethics, and security, or even confusion triggers. Getting the AI to talk is easy, but ensuring it makes sense is the game.
This requires continual investments in training and testing the AI engine. Enterprises that have deployed bots and digital assistants need technical and domain expertise to ensure the AI continues to deliver on expectations. And they turn to strategic partners like Apexon to make this possible.
Apexon offers enterprise-specific, contextually aware conversation engines to power through conversational AI journeys. We have helped global enterprises cash in on immense possibilities and substantially enhance the customer experience across channels – all with AI that knows what it is talking about.
We can help you too. And you won’t have to punch in numbers, visit: Apexon.com