Many knowledge base managers start out thinking comprehensive documentation is enough. Just write everything down, organize it well, and users will find what they need, right? Well, not quite. Managing a knowledge base is actually more like tending a garden – it needs constant pruning, adjusting, and replanting based on what's working and what isn't.

Let's dive into how you can use a data-driven approach to make your knowledge base work harder for your users.

Start with the Search Data

Your search analytics are gold. They tell you exactly what your users are looking for, often in their own words. Here's what to look for:

Failed searches are particularly revealing. If users consistently search for "password reset mobile" but find no results, that's a clear signal you need content addressing mobile password resets. Similarly, if they're using different terms than your documentation (like "login issues" instead of "authentication errors"), it's time to update your content to match their language.

Track User Behavior

It's not just about what users search for – it's about what they do afterward. Are they bouncing quickly from certain articles? That might indicate the content isn't addressing their actual needs. Do they need to visit multiple articles to solve one problem? Maybe it's time to consolidate that information.

Pay attention to time-on-page metrics, but interpret them thoughtfully. A long time spent on a troubleshooting guide might mean it's thorough and helpful – or it might mean it's confusingly written. Cross-reference this with success metrics like "Was this helpful?" ratings and support ticket data.

Listen to Your Support Team

Your support agents are on the front lines every day. Set up regular check-ins to understand:

• Which articles they frequently share with customers
• What content they wish existed
• Where existing documentation falls short
• Common customer misunderstandings they encounter

Use Seasonal Data to Plan Updates

Many businesses see predictable spikes in certain types of questions. An e-commerce company might see more shipping-related queries during holiday seasons, while a tax software company might see spikes in certain topics during tax season. Use this historical data to plan your content updates and ensure your most-needed content is fresh when users need it most.

Make Data-Driven Decisions About Content Retirement

Not every article needs to live forever. Use your analytics to identify content that's:

• Rarely accessed
• Frequently bounced from
• Related to deprecated features
• Consistently rated as unhelpful

But before you delete anything, consider whether the content needs updating rather than removing. Sometimes low traffic indicates poor findability rather than lack of relevance.

Create a Regular Review Cycle

Don't wait for problems to emerge. Set up a regular schedule to review:

• Most visited articles (monthly)
• Search analytics (monthly)
• User feedback and ratings (quarterly)
• Overall content audit (annually)

This helps you stay proactive rather than reactive with your content management.

Measure Impact

After making changes, track the impact. Look for:

• Improved search success rates
• Better article ratings
• Reduced support tickets on related topics
• Increased self-service resolution rates

Document what works and what doesn't – this becomes your playbook for future improvements.

Leverage AI-Powered Analytics Tools

If you're looking to streamline this entire process, modern knowledge base analytics platforms can be game-changers for your content strategy. Ariglad uses artificial intelligence to analyze support tickets in real-time, eliminating the need for manual data collection and analysis. Instead of waiting for monthly reports, Ariglad can continuously monitor user interactions to highlight content gaps and opportunities for improvement. This means your team can focus on creating and updating valuable content rather than spending hours sifting through analytics trying to determine what needs attention.

The Bottom Line

Building a great knowledge base isn't a one-and-done project. It's an ongoing process of listening to your users through data, making informed improvements, and measuring the results. By taking a data-driven approach, you can continuously evolve your content to better serve your users' needs.

Remember, the goal isn't just to have documentation – it's to have documentation that actually helps users solve their problems quickly and effectively. Let the data guide you to that goal.

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