How Cognitive Capabilities and AI are better resolving your customer queries

“I’m in!”

The above stated statement can range from an affirmation to an assertive sentence providing the location of the speaker. In plain language, its literal meaning can vary from “I agree” and “I include myself” to “I’m inside (something)” or even “I have logged into my mail”. Here, the context of the entire conversation acts as a premise for the statement and understanding it becomes necessary to deduce to its actual meaning.

Customer service is all about understanding customers and providing a valid and relevant solution to their queries. These queries can be related to raising a complaint, obtaining product information, or seeking a resolution to a complaint. In understanding the user’s requirement, one of the key players is the Natural Language Processing (NLP) capability.

Natural Language Processing and its limitations:

Natural Language Processing, also known as NLP, is the process of applying computational techniques to deduce natural language comprising written text or speech. It is a taxonomy-based approach which identifies keyword and matches them with the taxonomy tree to understand the meaning of a statement. Though it has relatively high accuracy, it cannot identify the statement’s semantic variants which can have a meaning other than the predefined sets.

Thus, NLP alone cannot help the system in understanding a user’s requirements by itself. A majority of ‘chat bots’, as they are called in mass market parleys, are NLP at different scales and do not implement Artificial Intelligence and hence follow the abovementioned approach. The user’s intent in such cases needs to be well understood and the context of the user query needs to be established. This can be achieved only by complementing NLP with machine learning and cognitive capabilities.

But why do you need to be careful?

Implementing customer assistance that leverages NLP alone can, given its limitations, cause an adverse situation for the brand. These services have limited use cases that can be easily identified by the customer during interaction. Once being aware of the fact, a majority of customers tend to lose interest in the conversation – resulting in lower customer satisfaction.

Moreover, this issue resolution methodology by and large follows the ‘brute force’ or ‘canned approach’ technique without issue resolution. In such systems, responses are driven by URLs fetched from indexed queries to answer databases where keyword mapping leads to a set of the most probable or matching answers. These answers lack accuracy and can vary dramatically for a similarly phrased statement. Such a gap in experience hampers the customer experience and creates a negative perception about the brand rather than adding a positive value to it, the goal behind all customer service operations.

So, NLP is ineffective without the assistance of AI sciences including machine learning, deep learning and cognitive sciences.

Advantages of an AI-based system:

One of the key deficiencies of human-driven customer assistance is the lack of consistency in responses. A good agent can give great responses resulting in a happy customer, while on a bad day, the same agent can cause a completely different situation. This problem is addressed with cognitive NLP systems where responses are always standard and consistent. While NLP can be called the science of literal translation of the user’s speech or text, AI acts as the brain that makes it understandable to the system – especially with contrast to the context of discussion, user intent, and its logical implementation – and helps it respond like a cognitive system with human-like answers.

70 to 75 percent closure of customer queries, be it informative or issue resolutions, is a milestone for companies to achieve. This high accuracy and completion rate can easily be attained with the help AI platforms. Also, customer care issues can move to virtual assistants and provide satisfactory results only when such systems are incorporated. With machine learning, the system also improvises consistently and adopts a strategy that is best suited for a case-specific customer. This further increases its effectiveness along with enhancing customer satisfaction, improving conversion rates, and most importantly boosting growth of the business.

Making the system cognitive is imperative to offer well-tailored solutions to customers without affecting the brand perception. Smarter implementations using NLP and AI – especially with cognitive assistance – have proved their viability even across complex industries including banking, insurance, and telecom. As they are now focussing on new market segments, businesses across sectors are expected to experience increased flexibility while also substantially reducing their operational expenses. 

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