Why Now is the Time to Start Building the Next Generation of Customer Experience Enablers with AI

I recently spoke at the AI Summit, Singapore, at their Start-up Showcase. My talk was focused on the impact that artificial intelligence (AI) and natural language processing (NLP) technology can have, specifically in the customer experience vertical.

But, before I get into it, it’s important to ask “Why is good customer service critical to any business?”. The answer — because it’s typically any customer’s first touch-point with a brand. So, it had better be good!

As we move away from screens, towards chat and voice-based interfaces, a major shift is taking place in the technology that’s enabling customer experience. Given that customers are more likely to use your product through voice-enabled interfaces like Google Voice and Amazon Alexa, or even mixed reality environments, there is a deep layer of complexity to delivering great customer experience. That’s all the more so when this experience has to be provided at all times during a user’s product journey.

It is no longer possible to bank on existing methods of providing help and support, where content is on webpages and searchable through simple indexing. If your product user is in an audio-only or experiential environment, then the experience on a webpage-based system can end up being sub-par, and can result in high product drop off.

Let’s dig a little deeper:

Let’s begin by understanding what exactly customer service is. Let’s not look at it from the point-of-view of a business or a customer, but as an onlooker:

Great customer service should translate to love and loyalty — between customers and businesses. But how often does that happen? Almost never. So, for now, it’s a pretty utopian vision.

When customers call customer service, their experience is most likely to be fragmented and frustrating when they are transferred from one department to another or just made to wait for ages until someone who can actually help comes along. We’ve all been there and we’ve all hated it, haven’t we?

Let’s look at the current problem scenarios mapped to their gaps —

1. Customers post their queries across channels: Information is fragmented and scattered, so businesses are unable to understand who their customers really are.
2. Peak time is ‘always on’ when it comes to customer service: Customer service reps are bound to make errors while trying to get the entire picture.
3. The 4 big messaging platforms together have over 3 billion monthly active users: Consumers are already gaining exposure to chatbots on smartphones and other devices
4. Inability to solve issues can quickly lead to a bad reputation(since the human tendency is to criticize rather than appreciate): This results in loss of customers to competition.

But how will these issues get solved?

For customers — when they have to visit just one point for prompt response on any query or issue.

For customer service reps — when they can cut down the noise and focus on the right things.

This is where AI, powered with content and conversations, comes in to bridge the gap:

As an AI application, an NLP-based chat or voice bot can:

• Understand customer correspondence
• Extract key and relevant info
• Share it with the right people
• Record the data and answers to enquiries on its own after learning from conversations

Wherever the bot gets stuck, a human should intervene. This lets the bot:

• Hand over requests to the customer service rep and learn from the way its human counterpart handles it.
• Free up the rep to build customer relationships and convert more brand advocates.

You can go through my presentation in detail below:

AI Summit Singapore Presentation

At Kontiki Labs, we are helping businesses move to new ways of interaction by building customized (Artificial Intelligence as a Service) AIaaS applications.

We started with building Kontiki.ai — a Content Management System for chatbots — a unique platform that allows you to plug any kind of content from the web and build an NLP capable chatbot in very short time and with less development effort.

As part of the expansion from just voice and chatbots to AIaaS, we will look at using the technologies and algorithms developed during platform creation, as well as the platform itself if needed. Our deep domain understanding of chatbots, voice bots and the tech needed in training them, makes us uniquely capable of rolling out a larger AIaaS suite.

I hope you enjoyed reading this. If you did, I’d love it if you hit the 👏 button and share to help others find it. I’d be happy to connect too 🙋🏽. Reach out to me on payal [at] kontikilabs [dot] com.