Call Center Intelligence (CCI) through AI Solutions
By: Akaash B
Tags: Naturallanguageprocessing, Business, Chatbots, Call Center, Artificial Intelligence

Call Center Intelligence (CCI) through AI Solutions

Over the years many organizations have tried to build a solution in order to anticipate the necessities of their customers and provide solutions to their queries related to products/services. Call centers tend to customer’s inquiries through telephones which can be inbound (e.g. Attending to customer’s queries) and outbound (e.g. Telemarketing).

Everyone would have faced the worst queuing up, possibly pressing your keypad a whopping number of times and listening to softly played music until you talk to an actual live agent.

At the end of all the fuss, we end up repeating all the information again.

Hmm! Frustrating, ain’t it?

Pain points of a Call Center:

These can be discussed in terms of:

Customer Experience

Huge Business Expenditure

Customer Experience:

Deep and Complex IVR (Interactive Voice Response) Tree. Customers Repeating Information more than once Agents searching for information thereby increasing the wait time.

Huge Business Expenditure:

The number of requests to call-center has increased massively in the past decade. The preference of the people has always been voice rather than other services such as chats or emails. Most of them are just routine calls, such as troubleshooting network issues (Internet Service Providers) and Debit card blocking/unblocking complaints when it comes to Banking Sectors. Live agent resources could be cut down if these routine calls are avoided to an extent.

“Focus on the Solution, Not the Problem”. With the advent of Natural Language Processing (NLP), chatbots can easily decipher our intent, emotions, and sentiments based on the way we interact.

AI Powered Assistants: A Solution:

There is ample evidence that Artificial Intelligence simplifies many routine things and daily tasks, changing our lives for the better. AI has been the buzzword around the business circle making it an unavoidable technology to account for. Creating computers that can understand natural language has always been the technology that surrounded homo sapiens’ speculation. The growth in Natural Language Understanding has quenched the thirst of longing.

Natural Language Understanding(NLU) and Natural Language Generation(NLG) are very promising areas of Artificial Intelligence. According to the GlobalNewsWire forecast, the global NLP market accounted to hit a market value of $28.6 billion in 2026. Chat-bots exploit NLU, i.e., in simple words it develops the ability to understand what the user actually says.

Call centers are the best market to implement NLU algorithms, where chatbots could perform routine works and also works as an advisor to the live agents.

Let's check out different places where an AI agent becomes handy.

Source: Virtual Agent (Attends routine topic)

Virtual Agent: Automates most common transactions and passes on complex transactions to live agents. It propagates all the context gathered during calls to the live agent. More interactive, informative, and quicker than IVR.

Agent Assist: Pulls out contextually similar contents from the Knowledge Base and presents it to the Agent, thereby reducing the waiting time.

Conversational Topic Modelling

Conversational Topic Modelling: Discover the topics for which customers reach out to you and how they articulate. This is essential to update the Knowledge Base and produce more improved results in the future. Thereby the system gets better and better.

How to make AI better Agents?

AI can be better agents and the one way it does it is through AI-powered Knowledge bases. In customer service jobs, agents have to quickly search through relevant documents to find a solution to a customer’s problem and this has to be pretty quick. An AI-powered Knowledge base can quickly traverse through the documentation by using key-phrases and deliver this straight to the agent thereby reducing the time. This surely inspires confidence in the brand/product.

So, how to build a powerful Knowledge Base?

The main factor here is to understand what the customer wants, it includes the discovery of the topic on which customer has to be serviced. Each time a customer calls, call logs are collected in order to generate training data for different topics. Any ML algorithm can be used to predict the needs of every user.

Creating a Powerful Knowledge Base

Once the topic is chosen, important keywords and top sentences used by the callers to articulate those topics are collected. ML algorithms along with human-supervised validation make the system more robust.

Whenever an AI agent fails, the stored recording serves as the Knowledge Base thus making the chatbots better over time and much more adaptive to specific business cases.

AI: ALL THE WAY

The world now is adapting more to AI-driven solutions. It is pretty clear that AI cannot take the position of human beings but sure it can assist them thereby increasing their productivity and is a boon for any business.

For all the promotions around AI Chatbots, few companies have embraced it in call center operations. But the rate of acquisition is going to rise up in the following years primarily because of the cost reduction and personalized experience it offers. And sooner chatbots being drafted to other businesses is not too far away.

Let’s democratize the AI! Let’s make AI for everyone possible!

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