Step-By-Step Guide to Building a Chatbot Knowledge Base
Without data, AI chatbots are just a pretty face with nothing upstairs. An important first step in using automation and natural language processing to help your customers is to connect your chatbot to your knowledge base.
AI is gaining a lot of traction among businesses as cost-effective assistance for customer service teams. But what companies don’t always realize is that AI requires engineering, structuring and high-quality data to be the super helper companies want.
That’s why having an existing knowledge base is crucial when creating an AI chatbot. It’ll help you build the information architecture of your bot that’s compatible with the chatbot provider you choose.
Not to say your business must have an existing knowledge base to get started with an AI chatbot. There are plenty of chatbot providers that can get a pre-built chatbot up and running on your website within a day by connecting it to their existing central knowledge bases. If you need a chatbot to schedule appointments or collect email addresses before immediately forwarding visitors to your agents, then these builders are likely optimal.
But if your chatbot is meant to assist agents and help customers, they need access to your source data. Many AI chatbot options need this structured data to understand unstructured intents, interpret it and give a structured response.
Think of it as onboarding. You wouldn’t just sit a new employee behind the computer without training or materials.
Preparing your chatbot to be self-sufficient will help reduce your service costs — because you’re helping more customers at one time without needing to hire more agents — and increase its ROI. You can potentially save thousands per year if your chatbot takes over all chats where a canned message was used.
A chatbot can also help grow and improve your knowledge base. You can regularly update it with questions that it frequently forwards to your agents, or for revising answers if multiple customers have trouble understanding.
Now let’s take a look at how to create a knowledge base chatbot.
- Create a knowledge management strategy
- Choose your infrastructure
- Determine and collect the data your AI needs
- Make the data simple and accessible for AI
- Adjust the language to fit your chatbot’s persona
- Get started with self-learning AI from OMQ x Userlike
Create a knowledge management strategy
Knowledge management (KM) is the process of collecting, storing, organizing and sharing internal information. A knowledge management strategy puts this information to use, aligning your whole team and helping you meet objectives.
Chatbots are highly self-preserving - they can collect, analyze and grow their own intelligence (and your knowledge base) from conversations. Chances are you created a knowledge base as part of your KM strategy. Now you want a chatbot that shares this information, alleviating agents and empowering your customers.
But they need a solid starting point, which is where your strategy comes in.
If you haven’t created a strategy, this is a good place to start so you have a clear purpose for your knowledge base and chatbot.
In order for your chatbot to have a fruitful ROI and reach its targets, it needs to be correctly integrated with your KM. This means carefully planning what types of information it will have access to so that it’s actually useful for your customers.
Explicit knowledge — such as manuals, guides and reports — is ideal for training your bot. Other types of “know-how” knowledge gained from experience ( tacit and implicit knowledge ) is better handled by agents, which is why we recommend integrating a chatbot with your customer messaging software.
For more help on this topic, read our post, “How to build customer knowledge across your company.”
Choose your infrastructure
Before connecting your chatbot, you need to know what to prepare. To know what to prepare, you need to pick the infrastructure you’ll use to power your bot. This could be something developed in-house, an existing chatbot infrastructure you’ll tailor to your business, or an advanced AI platform, like IBM Watson, so you can create a self-learning chatbot using an API.
Other services, like OMQ, let you create a chatbot that is already connected to their preexisting knowledge base. Answers, images and links can be customized to fit the company. OMQ’s AI is ready without training but learns from conversations over time. This is a significant benefit for those who don’t have an existing knowledge base or one that’s unideal for a chatbot.
Pre-built knowledge base chatbots are also a good place to start if you want to build your own knowledge base. You can take what your chatbot learns and start creating FAQ pages and articles suited to recurrent customer inquiries.
Once you decide on an infrastructure and understand what information it will need for your chatbot, you can start preparing your data.
Determine and collect the data your AI needs
Chances are you already have a knowledge base or FAQ page that you want to use with your bot. If so, feel free to skip to the next step to learn how to prepare this information for your chatbot.
If not, collecting and preparing data, even if it’s in a simple spreadsheet document, will make this process easier. First, start by gathering the questions your customers and employees ask each other most often. Consult live chat transcripts, emails and conversation logs from phone calls to create your list.
Next, organize this information into categories. For example, these could be:
- Product questions General questions about your products/store (e.g. “How do I add a credit card to my account?” )
- Problems Common problems customers have (e.g. “I forgot my password. Can I create a new one?” )
- Pricing/plans Questions about your pricing structure or plans. Could also include cancelations or upgrades. (e.g. “Can I pause my subscription for a couple of months?” )
- Frequent requests Could be feature requests or questions about discounts, sales, free try-ons, free shipping and more. (e.g. “Can I change the shipping address on my order?” )
- Feedback Positive and negative feedback given to your store/products. (e.g. “Thank you for the tutorial video, I found it really helpful for setting up my device. Do you have more videos or guides with tips and tricks?” )
Not all of these inquiries are suitable for a chatbot to help with, so we recommend connecting it to your agents via software like Userlike. Then it can still collect difficult, complex questions, ask for relevant details and forward the contact to an agent.
These prompts will also help you decide which topics to cover in knowledge base articles and what types of helpful infographics or tables to create. Though a chatbot will proactively help customers with their inquiries, published content is also nice to have on your website for those who don’t want to chat.
For more knowledge base creation help, I recommend reading Melissa Rosen’s article for GrooveHQ, “How to create a knowledge base: building self-service for customer support.”
Make the data simple and accessible for AI
Your data will need to be reworked into a conversational format that fits the infrastructure you chose. For example, when creating a chatbot using an API with a development portal like IBM Watson’s Workspace, you’ll create intents, entities and dialog to train Watson to understand different types of requests. These are often short, easily digestible questions and answers.
The information should also be clear enough for customers to understand. If you’ve already created a FAQ page, then it’s likely already tailored to your audience. Internal knowledge bases will need more work.
For example, if a common inquiry is, “I keep getting an error message when I try to pair my phone with my smartwatch” and possible answers are “try restarting your network settings and your devices” and “make sure you have Wi-Fi and Bluetooth turned on,” then you could write the information like so:
Inquiry: Received error message while trying to pair device
Solutions: Restart network settings. Restart devices. Enable Wi-Fi and Bluetooth.
It’s okay if it sounds robotic at this stage, your focus should be on dissecting your knowledge base into pieces to train your AI. This will make it easier to write flows because you have all possible intents and answers to make a script with.
Looking for better customer relationships?
Test Userlike for free and chat with your customers on your website, WhatsApp, Facebook Messenger, Telegram and SMS.Read more
Adjust the language to fit your chatbot’s persona
When building a user-friendly chatbot, consider its tone and personality. This will help you better understand your chatbot when writing its script and create a more enjoyable, meaningful experience for users.
The complexity of this step will depend on your chatbot’s role, persona, and your company’s brand image. Use your agents as inspiration for creating your chatbot’s voice. The level of poise and professionalism you expect from your agents should be reflected in your bot for consistent, dependable service.
For in-depth help with this step and do’s and don’ts, read our post, “How to find the right chatbot persona for your brand.”
Here are a few more principles to know:
Reply in short sentences. Walls of text aren’t necessary for explaining complex topics such as your terms and conditions. Link to relevant pages on your website or your FAQ page instead.
Avoid having your chatbot say “What can I do for you?” It’s an invitation that can lead to disappointment. Instead, introduce its name, state that it’s a chatbot and outline its capabilities ( “I can help with questions about shipping, returns and missing packages” ).
Don’t use “yes” and “no” answers. An intelligent chatbot still struggles to pick up on nuances in conversation and adjust its answer to fit the context. If a customer asks, “Is delivery free?” the chatbot could say, “Yes, it is.” But if they ask, “Do I have to pay for delivery?” it sounds silly if the chatbot says, “Yes, it is.” It’s better to teach the bot to answer, “Deliveries are free.” Then it avoids sounding unintelligent and frustrating the user.
Incorporate the user’s request in the response. This makes the chatbot sound more intelligent and evokes empathy. For example, if a user asks, “Do you have stores in Seattle?” a chatbot could say, “Unfortunately we don’t have stores in Seattle, but we do in Portland and Vancouver.”
Get started with self-learning AI from OMQ x Userlike
Outsourcing the development of a knowledge base chatbot is a great option for businesses that would rather use an existing trustworthy solution than build their own bot.
OMQ offers an interactive help page and knowledge base chatbot solution fit for convenient customer self-service. Their chatbot is connected to their existing knowledge base and answers can be edited to include your business information.
Their AI chatbot understands and analyzes customer’s intentions and chooses the appropriate answer from its knowledge base. When it doesn’t understand a question, it hands over the conversation to an agent.
This is a must if you want to make sure that your customers’ needs are met. According to our study on consumer chatbot perceptions, customers want it to be easy to escalate an issue to a human agent if a chatbot can’t help them reach a solution.
OMQ’s chatbot is compatible with Userlike’s customer messaging platform, which lets you assist customers on the most important channels: website chat, WhatsApp, SMS, Telegram and more.
We encourage businesses to connect their chatbot to their customer messaging software, which is why we have a chatbot API that supports your AI bot. Once connected, you can take advantage of our chatbot features, such as button options, carousels and media sharing.
If this is an option you’d like to explore for your business, we encourage you to sign up for Userlike (you get a free 14-day trial so you can see if you like us) and talk to our team about getting set up with the chatbot API by clicking on the chat button.
To learn more about the OMQ Chatbot, visit their chatbot page and click “Request Demo'' to get started.