How Knowledge Management Makes Chatbots Better

According to a recent study, 40% of millennials claim to use chatbots on a daily basis. This astounding number highlights the importance of AI technology in the commercial world. However, chatbots are inherently limited. These programs are only as smart as the rules and algorithms used in their creation.

During the development phase, software developers attempt to understand what questions the end user, from employees to customers, routinely ask. Using these questions as a guide, they then create algorithms that sift through the typical query process to arrive at a final answer. Regardless of how thorough these developers are, it’s not possible to program chatbots for every possible scenario. Connecting the AI to a knowledge database, on the other hand, provides a vital link to information that allows chatbots to answer a greater variety of queries with improved accuracy and depth of response.

How Chatbots Source Information

Chatbots function in a unique way. Their process begins with a set of rules that defines their reactions to specific prompts. A system may be set up to play a greeting when a voice is detected, open a menu when the keyword is given, or present other information based on the user’s request.
When a query is entered into the system, the AI proceeds to search through its knowledge database for applicable solutions. Based on the words that were used to ask the question, the chatbot searches through its files to find related information. Using algorithms as the decision-making engine, chatbots can narrow down the data to a handful of possible solutions which are presented to the user. At that point, the user can either tell the chatbot to proceed with a chosen solution or go back and try again.

Source material is an essential part of chatbot functions. Some AI’s are preloaded by engineers with the data used to discover solutions. Others are connected to a knowledge management database that allows the program to dynamically generate solutions from the source material. With AI learning capabilities, chatbots can quickly discover and catalog the best and most frequent solutions to repeat issues. Over time, these programs naturally become more efficient.

How Poor Source Material Negatively Impacts Chatbot Performance

A rich and comprehensive database is vital to a well-functioning chatbot because rely on their source information to find answers to user questions. If the information they are given is incomplete, it’s unlikely they’ll be able to find the right answers. So instead of getting an answer, users are presented with an additional step in their quest for a resolution; they need to connect to a live agent. This wasted time and inconvenience only adds to end user’s frustration, which can have a negative impact on CSR’s and ultimately threatens the call center’s ability to retain employees.

On the other hand, when chatbots are fully integrated into knowledge management systems, they are able to pull from the organization’s full catalog of data. This means they can solve more problems without the need for agent interaction. Proper integration allows call center managers to use their AIs for more than just basic transactions like taking payments and providing balance inquiries.

Stop Trying to Predict the Future

It’s asking a lot of a software engineer to expect them to understand all of the complexities and nuances that exist within the contact center function of a business. Because they aren’t subject matter experts in the questions, answers, policies, and procedures that are central to a contact center, it can be difficult for them to decide what information needs to be included in an AI’s internal database. This can severely limit an AI’s capabilities and actually make it harder for callers to find what they need.

Knowledge base integration helps removes the guesswork from building a successful chatbot. Rather than selecting information based on what a team of developers believes is useful, integrating the AI with a knowledge base gives it access to more comprehensive information. Instead of wasting time trying to predict every possible question and variation that may be encountered, the AI can concentrate on learning for itself what your customers really need. With full access to a complete database, your AI can handle a greater variety of queries, identify more relevant information related to caller issues, and provide more satisfying resolutions without the need for agent involvement.

Learn how AI-powered chatbots can improve your call center processes by requesting a free demonstration of Shelf. This knowledge base management software is designed to relieve the burden on your agents, increase customer satisfaction, and positively impact your metrics.

Author: Colin Kennedy

Colin is the COO and Co-founder of Shelf.io and a 2x software entrepreneur.