In the digital age, access to timely and accurate information is crucial for businesses to function effectively. One of the tools that has significantly improved information accessibility is the Suggested Knowledge Articles feature in knowledge management systems (KMS). This feature plays a pivotal role in optimizing customer service, internal support, and IT help desks by recommending relevant articles based on user queries or system activity.
This article explores what suggested knowledge articles are, how they work, their benefits, implementation strategies, challenges, and their impact on user experience and organizational efficiency.
What Are Suggested Knowledge Articles?
Suggested Knowledge Articles are contextually recommended pieces of content that aim to resolve a user’s question or issue based on keywords, previous interactions, or specific actions taken within a system. These suggestions often appear in real time as users type their queries or raise support tickets.
This feature is commonly integrated into:
- Customer service platforms (like Zendesk, Salesforce, Freshdesk)
- IT Service Management tools (like ServiceNow, Jira Service Management)
- Self-service portals and intranets
- AI-powered chatbots and virtual assistants
The goal is to empower users—whether customers or employees—to solve their own problems without waiting for human assistance.
How Do Suggested Knowledge Articles Work?
Suggested articles rely on natural language processing (NLP), machine learning, and search algorithms to match user input with relevant content in a knowledge base.
Core Mechanisms:
- Keyword Matching: The system scans the user’s input and matches it with keywords or tags in knowledge articles.
- Search History & Behavior: Algorithms consider previous queries, interactions, and frequently accessed articles to improve accuracy.
- AI and Machine Learning: Over time, systems learn which articles are most effective in resolving certain types of issues, enhancing future recommendations.
- Contextual Awareness: In advanced platforms, the system understands the context of the user (e.g., logged-in status, product version, location) to suggest the most relevant articles.
- Ticket Categorization: In ITSM platforms, once a user starts typing an issue or categorizes it (e.g., “password reset”), the system proactively suggests articles tagged with the relevant resolution steps.
Benefits of Suggested Knowledge Articles
1. Reduced Ticket Volume
One of the most tangible benefits is a reduction in support ticket submissions. When users are presented with relevant knowledge articles at the right moment, they often resolve issues themselves.
2. Improved First Contact Resolution (FCR)
If an issue requires human intervention, agents can see suggested articles related to the ticket and use them to provide faster and more accurate solutions.
3. Faster Onboarding and Training
New employees can benefit from suggested articles without having to know where to look, shortening the learning curve and boosting productivity.
4. Improved User Satisfaction
Whether it’s an internal employee using an HR portal or a customer trying to fix an app issue, getting instant answers improves satisfaction and trust.
5. Consistent Information Delivery
Suggested articles ensure that users and agents access up-to-date and standardized information, reducing discrepancies in support responses.
6. Enhanced Knowledge Base Utilization
Many organizations invest heavily in developing a robust knowledge base. Suggested articles ensure this resource is used more effectively.
Implementing Suggested Knowledge Articles
Successfully implementing this feature requires a strategic and well-planned approach:
1. High-Quality Content Creation
The effectiveness of suggested articles hinges on the quality of your knowledge base. Articles should be:
- Clear and concise
- Well-categorized and tagged
- Regularly updated
- Written in user-friendly language
2. Metadata and Tagging
Proper tagging (e.g., keywords, product versions, departments) helps the system categorize articles accurately for better recommendations.
3. Integration with Support Systems
Integrate your knowledge base with CRM, ticketing, or chat platforms to enable seamless suggestions during user interactions.
4. AI and Analytics
Choose platforms that use AI to refine suggestions based on feedback loops—e.g., did the user find the article helpful? Did they still submit a ticket?
5. Feedback Mechanisms
Allow users to rate articles and leave comments. Use this feedback to refine both the content and the suggestion logic.
6. Monitoring and Optimization
Track key metrics such as:
- Suggestion click-through rate (CTR)
- Article helpfulness rating
- Ticket deflection rate
- Search success rate
These help in continuous improvement.
Common Use Cases
1. Customer Support Portals
When a customer starts typing a query like “can’t log into my account,” the portal suggests articles about account recovery, password reset, and common login issues.
2. IT Help Desks
In ITSM platforms, users opening tickets for issues like “printer not working” see recommended articles related to troubleshooting steps.
3. HR Self-Service
Employees searching for “leave policy” on an intranet get suggestions from HR documentation, reducing queries to HR teams.
4. Chatbots
Chatbots use real-time input to recommend knowledge base articles before escalating to a live agent, reducing response time.
Challenges and Considerations
1. Content Overload
Too many suggestions or irrelevant ones can overwhelm users. It’s crucial to prioritize quality over quantity.
2. Poor Article Quality
Outdated or vague content can lead to user frustration. Regular audits and updates are necessary.
3. Bias in Recommendations
AI systems can reinforce biases by continually suggesting popular articles even when more accurate ones exist. Balancing popularity with relevance is key.
4. Search Engine Limitations
Basic keyword-based systems may not understand user intent well. Investing in semantic search and NLP capabilities can improve relevance.
5. User Interface (UI) Design
Poor placement or visibility of suggestions can reduce engagement. Articles should be easy to notice and interact with.
Best Practices
- Design for Self-Service: Keep the end-user in mind. Structure articles to be easy to scan, with clear steps and visuals.
- Leverage Analytics: Regularly review metrics to understand what’s working and where improvements are needed.
- Use Smart Tagging: Implement automatic tagging using AI to improve article discoverability.
- Encourage Knowledge Sharing: Involve front-line agents in content creation—they know common issues best.
- Promote a Knowledge-Centered Service (KCS) Culture: Encourage the continuous creation and refinement of knowledge based on real issues.
The Future of Suggested Knowledge Articles
As AI technologies advance, suggested knowledge articles will become even more intelligent and personalized. Here are some trends to watch:
- Predictive Suggestions: Systems that anticipate user needs based on behavior and past queries.
- Voice-Activated Support: Integrating knowledge article suggestions into voice assistants and smart IVRs.
- Multilingual Suggestions: Automated translation and localization to support global audiences.
- Video and Interactive Content: Replacing static articles with step-by-step video guides or interactive walkthroughs.
- Augmented Reality (AR): In technical fields, AR glasses may soon suggest repair procedures or troubleshooting steps in real time.