Posted On: September 30, 2020 | 8 mins read
As the world moved into the digital era, companies found themselves growing at massive rates, with their influence spreading across various sectors, industries, and regions. But with great power, came great responsibility. Companies found themselves talking to hundreds of thousands of clients, fielding questions from basic product features to in-depth company policy. Frequently Asked Questions (FAQs) became the go-to tool for transmitting information to customers about the most common issues and topics – but these lists were either limited or too long and tedious. Companies need a smarter, more intuitive way to communicate with their clients and answer the most pressing questions first. But you can’t answer all questions unless you have all the answers first. Enter knowledge management, and by extension, the knowledgebase FAQs.
The simplest definition is an online repository of information, available for use to the general public as well as internal departments and employees. Curating a knowledge base begins with knowledge management – which involves the accumulation of data to be stored through smart and intuitive AI that identifies the top traffic topics relating to a brand, and providing solutions for the same.
While a basic FAQ list will look to answer basic questions, a knowledge base will allow companies to diversify and detail the information on offer – allowing customers to self-serve and solve problems without the need to reach out to sales executives or your customer service team. It is more in-depth and comprehensive, covering a wider range of questions than would be possible on a traditional FAQ list.
As technology advanced, new solutions became available to companies, allowing them to tackle problems faster, and more accurately than before. Of these solutions, KM drove artificial intelligence has been the one to take the world by storm – allowing machine learning and other programs to take over labor-intensive tasks such as the identification of problematic channels, and the search for solutions.
AI chatbots were pioneers in the field of revolutionizing FAQs, allowing users to ask the bot specific questions and receive precise answers, instead of trawling through a frustrating list of endless questions.
AI would explore thousands of the most common questions and solutions, compiling solutions into an easily accessible knowledge base for consumption. Questions that didn’t receive a lot of traffic could be marked as redundant – and removed in favor of more pressing doubts. Knowledge management systems allowed companies to follow this process, constantly updating them on the latest or projected customer requirements, issues, and solutions.
This level of AI, however, came with its own hurdles. Early AI, at the end of the day, was still human intelligence-driven. For an answer to appear to a relevant question, the AI still required a basic A-B matchup between the two – a human who writes solutions and then tells the bot what answer best fits which question. This allowed the AI to quickly draw on this information later at a customer’s behest.
Recent innovations have broken this barrier, however, with the advent of software and technology like Natural Language Processing (NLP) expanding the capabilities of your knowledge base – and reducing the need for human interaction at any point in the customer journey.
NLP is an upcoming field in AI that allows computers and machines to decode and understand unstructured human language. Picture your E-Mail inbox sorting mails into different folders based on content – from important emails to spam. This is made possible through text classification, an NLP function that allows AI bots to examine paragraphs of text for certain keywords, analyze past messages to understand the importance of the mail, and sort it into the right category automatically.
While still a young field, NLP finds itself at the center of a lot of new-age technology – from writing software like Grammarly to notifications for appointments that extract data from your calendar, and smartly remind you accordingly.
To sum up, NLP is changing the game-breaking the ceiling in terms of machine-human capabilities and allowing machines to understand our language. This enables a compilation of smart solutions gathered from vast stores of data across sources.
It is well known that when it comes to positive engagements and interactions with customers, nothing serves to boost Customer Experience (CX) like a personal, human touch. While traditional AI could gather data basis filters applied by a human, NLP also attempts to decode the sentiment behind the data. This takes our understanding of the issues our consumers face to the next level without an additional investment of human resources.
This will only grow in importance as companies continue to expand. While KM plans bring huge volumes of data to your fingertips, it’s tools like NLP that help further analyze and structure the information, pick the most relevant points for discussion, and presents them in a convenient manner.
Perhaps the biggest feature of NLP is the transformation of unstructured data into a usable knowledge base which also helps in making knowledgebase FAQs. Features like text or speech recognition attempt to understand the complexities of human language and dialects, and translates them into a language that the machine can understand. This improves the way we can communicate with machines in the future, thus allowing them to better understand and service our needs.
While a knowledge base will provide you with a vast store of data and AI programs designed to extract useful information, it is vital this information reaches the right people, through the right channels. In large companies, information channels are often crossed and overlapping – what is useful for a sales team might also be useful to R&D. With this in mind, it’s important to induce a culture of knowledge sharing at your organization.
Efficient knowledge management will allow for the creation of a knowledge base and FAQs basis the most pressing issues brought up by consumers, and a good knowledge management plan will ensure the right information reaches the right team, be it external or internal.
For example, should a new customer service representative face a common question, he no longer needs to understand the situation first before responding – a knowledge base will allow him to isolate the most common problems and solutions faced by his team, and will allow him to serve the customer instantly, leaving little layover time for downloads or extra communication between teams.
Similarly, external-facing FAQs channeled through AI tools like chatbots allow customers to seek out solutions to their problems from the company’s knowledgebase FAQs – saving countless hours in man-power and support initiatives.
The need of the hour in the service industry is self-service, and most successful companies today employ KM firms or software to build powerful knowledge bases/knowledgebase FAQs and channels for the successful disbursement of information. While the world of AI and data management changes every day, knowledge management remains an essential ‘first step’ in the long journey of customer success.