Integrating a chatbot with your knowledge base

With the emergence of conversational AI, the growth of the chatbot landscape has accelerated. In fact, according to a recent report by Grandview Research, the global chatbot market is expected to "expand at a compound annual growth rate of 23.3% from 2023 to 2030."¹

The report suggests that this boost can be explained by the need to offer 24/7 assistance to customers, reduce operational costs by automating repetitive tasks, and address increased demand for self-service resources.

Cutting-edge customer service teams are already experiencing these benefits with their chatbots equipped with knowledge bases.

However, for an AI chatbot to offer reliable and relevant answers, it needs to be fed with quality content – and, for that, there is nothing better than a structured knowledge base. By doing so, support teams ensure that customers have access to accurate information quickly and easily.

But it's not just companies that can benefit from a chatbot with a custom knowledge base. Consumers are also increasingly adopting these tools, with a KPMG study² finding that 69% of people use chatbots and virtual assistants.

In this article, we’ll cover the definition of a chatbot and a knowledge base, showing how they can work together to simplify support workflows. We’ll also guide you through building a knowledge base for a chatbot, with actionable tips to help you make the most of these valuable, cutting-edge tools.

What is a chatbot?

A chatbot is a conversational technology that interacts with humans through text or voice-based interfaces. It uses an input-output model, where the user sends a command to the system, which processes it and returns an appropriate response.

There are two types of chatbots: rule-based and AI.

Rule-based chatbots follow a predefined script of rules created by a human. Their objective is to anticipate users' needs and provide the answer that best corresponds to them. However, their ability to understand human language and handle complex queries is quite limited.

On the other hand, AI chatbots use Natural Language Processing (NLP) to interpret what the user says. These chatbots seek to extract the context of the conversation to generate an appropriate, relevant response, both in terms of content and tone.

As a result, AI chatbots can provide a more natural and personalized user experience compared to rule-based chatbots.

In short, AI chatbots offer an invaluable advantage with their ability to rapidly absorb information from extensive databases. This quality makes them an excellent choice for integration with knowledge bases, facilitating effortless learning and instant adaptation.

What is a knowledge base?

A knowledge base is an online library that provides self-service support to customers and employees. It contains data about products, services, or company procedures, and is designed to help individuals find the information they need without the assistance of a help desk or customer support representative.

There are two types of knowledge bases: internal and external.

Internal knowledge bases are used by employees to access relevant intel about HR, IT, and corporate policies, among other things. External knowledge bases are employed to support customers and usually contain information about how to use a product, industry trends, and other helpful news or tips for a brand's target audience.

What is an AI chatbot powered by a knowledge base?

An AI chatbot powered by a knowledge base is a conversational technology that integrates two solutions: the knowledge base and the AI chatbot. The former serves as the chatbot's database, containing information about the company's products, services, and procedures. The latter interprets the queries received, correlates them with the content available in the knowledge base, and builds an answer.

What makes this chatbot stand out is its ability to offer natural-sounding responses, ask for clarification if needed, and escalate the query to a human support representative when the knowledge base doesn't have enough information.

Why is it important to connect chatbots with knowledge bases?

Now that you’re aware of what a chatbot with a knowledge base is, let's delve into the two main reasons why you should integrate these technologies.

A chatbot with a knowledge base provides quick and accurate responses to customers' questions

A chatbot with a knowledge base can tap into a vast repository of data, find the relevant piece of information based on the received prompt, and answer customer queries within seconds.

But that's not all. This powerful duo is able to combine speed with consistency, assuring that customers get access to accurate product details, FAQs, and  troubleshooting guides.

By freeing up support reps from answering a high volume of repetitive and low-complexity questions, companies allow them to focus on more intellectually challenging activities.

It makes customer support more efficient

Another great advantage of an AI chatbot equipped with a knowledge base is its ability to make customer support more efficient.

A year-long research study by Stanford University and MIT³ analyzed the practical effects of generative AI on more than 5000 customer service representatives at a Fortune 500 software company. The findings revealed a significant improvement in productivity, averaging a 14% increase overall. Notably, the newest or lowest-performing employees experienced a remarkable 35% increase in their performance.

In fact, our report, "State of AI in Customer Service 2023,"⁴ highlights that a substantial percentage of surveyed support teams believe that the adoption of AI tools can make room for new roles. These include: 

• Chatbot developers (58%)

• Chatbot analytics (51%)

• Chatbot data collection (49%)

• Conversation designers (39%)

• Chatbot strategists (38%)

Scott Donnelly, Head of Customer & Digital Operations at Total Synergy, believes that blending this emerging technology with team upskilling is the key to unlocking value for everyone involved, from businesses and support reps to customers.

How to build a knowledge base for a chatbot

1. Choose a knowledge base platform

The first thing to do when you’re considering implementing a knowledge base is to research and compare the providers available on the market.

Make sure to look for a platform that’s easy to use and offers customization options, so it’s able to streamline your workflows and adapt to the unique needs of your business.

It's also important to consider integration with your existing tech stack. If there are tools that you are absolutely not willing to give up, it’s critical that you know they’ll synchronize with the prospective knowledge base.

For example, to integrate Intercom’s AI-powered chatbot Fin with a knowledge base effectively, you should begin by selecting the sources of information you want it to use.

Fin can utilize both your Intercom Articles and external support content. You have the flexibility to decide which specific sources Fin should draw from when responding to customer queries.

2. Identify the information to include in the knowledge base

One idea for how to identify the company information that should be included in the knowledge base is through conducting an audit. This will help you identify frequently asked questions from customers and look for gaps in the information provided on the website or by your reps.

This way, you ensure that the articles in your knowledge base contain the correct information and are optimized to work with the chatbot, so that customers' main needs are answered.

3. Set up the knowledge base

The last step is to set up the knowledge base. You can do this by:

• Creating categories and subcategories for the information

• Writing clear and concise articles that provide answers to common questions

• Ensuring that the articles are easy to search and navigate

Do you need help to get your support articles ready for a chatbot with a knowledge base? Watch this video:

Prepping support content for AI - Avoid ambiguity

How do you train an AI chatbot with a custom knowledge base?

The short answer is, you don't! Keep reading to understand why. 

1. Integrate the chatbot with the knowledge base platform

An AI chatbot with a knowledge base can learn instantly, which means it doesn’t need training to get good at answering your customers’ burning questions. All you have to do is integrate both tools and watch the magic happen.

Let’s take Fin, our AI chatbot, as an example again. If you don't have an Intercom Help Center or wish to use other support content, you can sync external content with Fin.

Simply provide public URLs (like website pages or blog posts), and Fin will import and keep the content synced weekly. Make sure to use basic URL stems for optimal results. However, note that Fin currently cannot interpret images or multimedia content from external sources.

If it’s your first time implementing a chatbot with a knowledge base, you must read this article: Get ready for AI bots by optimizing your knowledge base.

2. Regularly review and update the articles in the knowledge base

As the chatbot uses the knowledge base as a source of information to answer customer questions, it’s essential that the articles in that base are accurate and optimized to work with AI.

A good knowledge base article does the following: 

• Uses simple language and avoids ambiguity.

• Restates questions, so the answers aren’t quoted out of context.

• Is logically formatted with headers, which makes it easy for both customers and bots to scan content for the right information.

• Includes text explanations alongside screenshots or other rich support content, as bots can't watch videos or understand images.

The support team must also review the articles regularly to ensure that the information is up to date and in accordance with your company's current policies.

3. Evaluate the performance of the chatbot with a knowledge base

The key to improving any process is to have objective parameters to evaluate its success or failure. In the case of customer interactions with chatbots powered by knowledge bases, keep track of how it impacts the key customer service statistics, including: 

• First contact resolution, which indicates how often your customers’ queries are resolved after their first interaction with your company’s support team.

• Customer satisfaction, which reveals how happy your customers are with your products, services, and experiences.

• Deflection rate, which shows how many customers were able to solve their problems with self-service resources.

4. Make adjustments as needed to improve the customer experience

Once you’ve accumulated enough of these metrics, you can make informed decisions about any necessary changes that could provide your customers with a better, more satisfying experience. 

Imagine, for example, that after implementing a chatbot with a knowledge base, the volume of customer inquiries directed to human reps decreases by half. You might then consider increasing your investment in this technology while also providing training to upskill your reps, so they’re capable of solving more complex problems.

Meet Fin, our AI bot for customer service

When companies connect their chatbots to well-designed knowledge bases, it's not just customers who benefit from a more agile and contextual service. Support reps are also rewarded with more time to dedicate to the kind of complex tasks that challenge them intellectually or require uniquely human capabilities like empathy.

If you believe that both your customers and your employees deserve a better experience, the time has come to meet Fin, our AI-powered chatbot. Using advanced AI language models, Fin solves complex problems and provides safer, more accurate answers than any other AI bot on the market.

Built for scale, Fin is conversational by design, which means it can keep up with the interactions and ask clarifying questions through web, mobile, or app. And because it’s able to learn from users, it gets smarter over time.

Start a free trial today and find out how technology can be an ally in your business.

Sources

¹ "Chatbot Market Size, Share & Trends, Analysis Report By Application (Customer Services, Branding & Advertising), By Type, By Vertical, By Region (North America, Europe, Asia Pacific, South America), And Segment Forecasts, 2023 - 2030". Grandview Research.

² "Trust in Artificial Intelligence". KPMG and The University of Queensland. This study was conducted with over 17,000 respondents in 17 countries.

³ “Generative AI at work”. Erik Brynjolfsson, Danielle Li, and Lindsey R. Raymond. This study analyzed the impact of generative AI-based conversational assistants using data from 5,179 customer support agents.

⁴ "State of AI in Customer Service 2023". Intercom.