This column is authored by Sindhu Joseph, CEO & Co-Founder, CogniCor
The competitive business landscape that we live in has seen multi-billion dollar companies spiralling down the market. The reason can simply be attributed to a business’s inability to understand its customers. One recent example has been Nokia, whose CEO’s heart-breaking admission of defeat took the internet by storm. The company, which had the pole position across leading economies before the advent of smartphones, was ill-prepared for the upcoming wave of smartphones… and along with it the increasing aspirations of its customers. However, it gave a strong message to businesses across the globe. A brand must leave no stone unturned to understand its customers and evolve accordingly. One such evolution lately has been the deployment of virtual assistants.
Virtual assistants, also known as chatbots, are automated programs that are used to engage with customers online. Such assistants are the new cognitive interface for the businesses to drive customer engagement and revenue while achieving substantial OpEx reduction. However, as a business caters to diverse customers, it has to address different sets of requirements and needs differently. Becoming aware of the customer intent, in this context, has become essential.
Understanding Customer Intent
In day-to-day life, there is no better way to understand someone than by getting into an engaging conversation with the concerned person. This is precisely the same tactic leveraged by virtual assistants. Prospective customers participate in detailed conversations with virtual assistants. These conversations are analysed to understand the true customer intent. But the degree of precision in this process depends on a chatbot’s inherent approach. The overarching objective of any virtual assistant is to drive the user through conversations so that all requirements of the user are well understood.
All chatbots primarily follow two approaches, i.e. Natural Language Processing-driven (NLP-driven) approach, and NLP-driven approach with AI capabilities. A majority of chatbots use Natural Language Processing without AI capabilities. They follow brute force or canned methodologies – which are rendered ineffective in complex market paradigms. In such an approach, customer queries are classified into different request categories. These categories, in turn, are mapped to pre-defined answers. Such models are largely inflexible and force a user to follow a pre-determined flow of user conversations. As a normal user, an easy way to identify such chatbots is to observe if requests are being answered such that it gives an approximate – but not ‘precise’ – query resolution. As they fail to deliver quality experiences to the user, user churn rates of such chatbots are very high. In these models, intent mapping fails to achieve this predominant objective.
Enters Artificial Intelligence
AI-driven models follow an entirely different approach in understanding the underlying customer intent. Contrary to the above mentioned method, they use the cognitive representation as the basis for understanding the customer intent. Intent – be it single or multiple – is extracted by framing the relevance of concepts, features, properties, as well as emotions. Such models have the capability of reading the intent more accurately and also equipping a platform to recognise multiple intents at once. Understanding the actual intent and conversational context is not possible if machine learning and cognitive capabilities do not augment Natural Language Processing capabilities of such platforms. Managing the context of discussions during these engagements is equally important and they are interlinked with decoding the true intent. While knowledge graphs and maps are built as part of AI modelling, cognitive mappings are well defined which in turn makes the intent analysis very accurate.
Every virtual assistant must have an ‘inferring intent’ function executed with precision. Without understanding the intent, we cannot expect it to be engaging in a meaningful conversation where customer queries will be resolved and most optimum actions performed to achieve the desired end-result. For instance, how customers should be serviced with an issue resolution or be presented a product for sale depends on this fundamental building block driven by natural language processing algorithms.
As systems become smarter with innovations and evolutions in natural language processing, machine learning, as well as cognitive algorithms, chat bots also get smarter with improved understanding of user intents, discussion contexts, and required call to action. This helps to push the evolution of chatbots and is making them more consistent and effective in targeting the customer. Thus, enhancing the end-user experience. With it, the NLP-driven chatbots with AI capabilities are paving the way for a redefined customer engagement future.