Today’s business landscape has become insanely competitive. Customers crave personalized experiences. And they have higher demands than ever before. At the same time, 67% say they prefer self-service over talking to one of your agents. Add to that executives pinching pennies and slicing budgets. They’re looking for innovative ways to be more productive. But at the same time, they still need to meet (and exceed) customer expectations.
Conversational AI has flown into the picture, cape and all, to help. And, one of the best ways to add conversational AI to your org is through a NLP knowledge base.
In this guide, we’re going on a journey to understand the advantages of an NLP knowledge base. We’ll outline the steps you’ll need to take to get started and then we’ll look at a few best practices, too.
But first –
What is an NLP Knowledge Base?
An NLP knowledge base is a database that stores information that an AI chatbot or conversational AI can access to provide natural language responses to customers. This type of KB lets chatbots understand humans and respond.
To be a little more technical, an NLP knowledge base uses machine learning algorithms to analyze words, phrases and sentences. Then it takes that analysis, uses the context in the inquiry, then serves up the right answers.
For instance, if a customer asks, “What’s your return policy?” A chatbot can access your NLP knowledge base and answer. All without human involvement. What’s more, your entire team can use these KBs internally, too. You might have a team member ask the AI chatbot for product resources or HR questions like, “How do I file an expense report?” The NLP knowledge base will grab the correct info and send it back to the user.
Benefits of NLP Knowledge Bases for Businesses
An NLP knowledge base can provide a ton of benefits to your business. For instance, it’ll boost your customer experience and team efficiency. And, because it’s always on, you’ll see increased scalability and major cost savings.
Enhanced Customer Experience
Research from the Temkin Group found that companies that earn $1 billion annually can expect to earn, on average, an additional $700 million within three years of investing in customer experience. That seems to make a pretty strong case to invest in your CX.
With an NLP knowledge base, you can start giving customers personalized experiences at scale. This, of course, leads to a more positive customer experience. And since your chatbot can respond conversationally, your interactions are more natural. More engaging. Plus, you can up CSAT and loyalty by providing quick, accurate, relevant responses.
Improved Efficiency
An NLP knowledge base can help you automate your repetitive, monotonous tasks. That reduces the time and effort your team spends on simple customer inquiries. And then, with all the low-hanging fruit off their plates, agents can focus on the more complex issues. The tech can help you improve efficiency, decrease response times, and cut hold and wait times.
Automate Your Work
Capacity’s enterprise AI chatbot can help:
- Answer FAQs anytime, anywhere
- Find relevant documents within seconds
- Give surveys and collect feedback
Increased Scalability
An NLP knowledge base lets you help more customers without adding staff. Because a chatbot can handle several inquiries at the same time, more people are getting the answers they need, faster. This can be particularly useful if your company has seasonal fluctuations in demand.
Cost Savings
Turnover in your contact center is probably, well, not awesome. On average, contact centers see average turnover hovering around 35-45%. But when you add automation like an NLP knowledge base, turnover becomes less of an issue. Natural attrition can happen and automation can replace those agents, saving you big time.
What’s more, chatbots can handle more inquiries with fewer resources. That helps you reduce operational costs and focus resources in other company areas.
Steps to Implement an NLP Knowledge Base
Implementing an NLP knowledge base can be a daunting task. We get it.
With the right approach and tools, businesses can successfully implement a chatbot to understand and respond to customer inquiries naturally and engagingly. Here are some steps to consider when implementing an NLP knowledge base:
Step 1. Define Your Goals
Before starting the implementation process, defining your goals and objectives is essential. What problem are you trying to solve? What outcomes do you want to achieve?
By defining your goals upfront, you can ensure that your NLP knowledge base aligns with your business objectives and provides the value you seek.
Step 2. Choose the Right Platform
Choosing the right chatbot platform is essential for successful implementation. You need to select a platform that provides natural language understanding, can scale with your business, and integrates easily with existing systems.
It’s also important to consider the customer experience you want to provide. Does the platform offer features such as personalization or sentiment analysis? These features can ensure that customers have a positive, engaging experience with your chatbot.
Step 3. Train Your Chatbot
Once you’ve chosen a platform, you must train your chatbot to understand and respond to customer inquiries. This involves creating an intent classification system to identify the intent behind a customer’s query and generate a relevant response.
Step 4. Design the Conversation Flow
Designing the conversation flow involves mapping out the different paths a conversation with your chatbot might take. This includes defining the different intents and responses your chatbot can provide and setting up prompts and follow-up questions to keep the conversation going.
Using AI support platforms like Capacity, companies can use guided conversations to empower users to choose their own paths. Using self-service, users can get specific answers instantly.
Step 5. Test and Refine Your Chatbot
Once you’ve built your chatbot, it’s important to test and refine it to ensure that it’s providing accurate and relevant responses. You can do this by conducting user testing and gathering customer feedback. It’s also crucial to monitor your chatbot’s performance over time and adjust as needed to improve its accuracy and effectiveness.
Step 6. Integrate with Other Systems
To get the full benefit of your NLP knowledge base, try integrating it with other software. For instance, pair it up with your CRM or CCaaS platform. This can help you track your customer inquiries in a single location and provide a more personalized experience.
Step 7. Provide Ongoing Support and Maintenance
To keep your chatbot effective for your business, supporting and maintaining it continuously is important. This involves monitoring performance, updating responses, and making changes based on feedback and evolving business requirements, even after it’s been launched.
Natural Language Processing Knowledge Base Best Practices
Implementing a natural language processing knowledge base can be a powerful tool for businesses to enhance customer experience, improve efficiency, and reduce costs. Here are some best practices to consider when building and maintaining an NLP knowledge base:
1. Focus on Accuracy
One of the most critical aspects of an NLP knowledge base is its accuracy. Customers expect chatbots to provide accurate and relevant responses to their inquiries, so it’s important to invest time in training and refining your chatbot’s algorithms to ensure that it’s providing the most accurate answers possible.
2. Design for Natural Conversation
Natural conversation is a key element of an effective NLP knowledge base. Chatbots should be designed to mimic natural human conversation, with prompts and follow-up questions that keep the conversation flowing and engaging.
3. Personalize Responses
Customers expect personalized responses to inquiries; an NLP knowledge base can help. Chatbots can use customer data and insights to provide personal responses tailored to each customer’s needs and preferences.
4. Provide Clear Navigation
Straightforward navigation in your NLP knowledge base is crucial for guiding customers to the correct information. This can be achieved through well-organized categories, subcategories, prompts, and follow-up questions that help guide customers to the necessary information.
5. Provide Human Support
While NLP knowledge bases can handle many customer inquiries, it’s crucial to provide a way for customers to connect with a human support agent if needed. This can help resolve more complex issues and provide a more personalized experience for customers who may prefer speaking with a human.
By following these best practices, businesses can build and maintain a practical NLP knowledge base that enhances customer experience, improves efficiency, and reduces costs.
3 Common NLP Knowledge Base Mistakes to Avoid
NLP knowledge bases can be a powerful tool for businesses to improve customer experience and efficiency. However, there are some common mistakes that companies should avoid when implementing an NLP knowledge base:
Over-Reliance on Automation
Maintaining a balance between NLP knowledge bases and human support is important to automate and handle customer interactions effectively. Over-reliance on automation can lead to frustrating experiences for customers who may prefer speaking with a human for more complex issues.
Lack of Personalization
Customers expect personalized responses to their inquiries. Failure to personalize responses based on customer data and insights can lead to generic responses that fail to meet customer needs and preferences.
Poor Navigation
It is essential to have easy-to-use navigation in your NLP knowledge base to assist customers in finding the correct information. If the navigation is unclear, customers may become frustrated and struggle to find the necessary information.
Selecting the Best NLP Knowledge Base Platform
Choosing the right NLP knowledge base platform is critical to the success of your customer support and automation efforts. Here are some factors to consider when selecting an NLP knowledge base platform:
- Scalability: The platform you choose should be able to scale with your business needs. As your customer base grows, you will need a platform that can handle the increased demand for support while maintaining high accuracy and efficiency.
- Integration capabilities: The platform you choose should be able to integrate with your existing tech stack. This will ensure a seamless transition and avoid the need for costly and time-consuming development work.
- Pricing: The cost of the platform should be a consideration, but it should not be the only factor. Look for a platform offering a range of pricing plans that meet your needs and budget.
Recommended Platform – Capacity
One platform that meets these criteria and stands out in the market is Capacity. Capacity is an AI-powered support automation platform that connects your entire tech stack to answer questions, automate repetitive support tasks, and build solutions to any business challenge.
Capacity’s low-code platform is accessible through conversational AI. It enables graceful human handoffs and intuitive task management via a powerful workflow automation suite, robust developer platform, and flexible database that can be deployed anywhere.
Want to provide top-notch customer support? Try Capacity for free today!
