Text Analysis for Non-Writers: How Our Text Analyzer Improved My Documentation

I've spent the last 12 years as a software developer, and if there's one thing that's consistently made me break out in a cold sweat, it's documentation. Not reading it—writing it. The irony isn't lost on me. I can architect complex systems that process millions of transactions daily, but ask me to explain how they work in plain English, and suddenly I'm staring at a blank page, wondering if "therefore" and "thus" sound too pretentious.

That changed about 18 months ago when I discovered text analysis tools. What started as a curiosity became an essential part of my workflow, and the improvement in my technical writing has been nothing short of transformative. Here's my journey.

The Documentation Crisis That Nearly Cost Us a Client

In March 2023, our team delivered what we believed was a masterpiece—a custom inventory management system for a mid-sized manufacturing client. The code was clean, the UI was intuitive, and the system performed beautifully in testing. We were patting ourselves on the back until we received an urgent call from the client's operations manager.

"No one knows how to use this," she said, exasperation clear in her voice. "The documentation might as well be written in hieroglyphics."

I pulled up our user guide, scanning through pages I'd personally written. Long paragraphs with 35+ word sentences. Technical jargon sprinkled like confetti. Passive voice everywhere. Reading it objectively, I finally saw what should have been obvious: our documentation was terrible.

The client gave us two weeks to fix it, or they'd look elsewhere. That deadline became my personal nightmare.

The Metrics That Actually Matter for Technical Writing

Desperate for solutions, I started researching readability metrics—something I'd vaguely remembered from college but had never applied professionally. The journey led me to text analysis tools, including one we eventually integrated into our own product suite.

What surprised me most was discovering which metrics genuinely impact comprehension:

Flesch-Kincaid Grade Level: My first drafts consistently scored at 14th-16th grade level—essentially requiring a college degree to understand basic user instructions. Our target audience needed 7th-9th grade content.

Average Sentence Length: This was my biggest weakness. I traced it back to my academic background, where complexity equated to intelligence. My sentences regularly exceeded 25 words, when the sweet spot for technical documentation is 14-18 words.

Paragraph Length: This seems trivial until you see a wall of text through the eyes of a busy user. Breaking my 12-sentence paragraphs into 2-3 sentence chunks immediately improved readability scores by 18%.

Passive Voice Ratio: My documentation was hovering around 32% passive voice constructions. Getting this below 15% transformed clarity almost overnight.

Before and After: Real Examples from Our Project

Let me share an actual before/after example from that nearly-disastrous documentation:

BEFORE:

"The configuration parameters must be established prior to system initialization, and it should be noted that improper setup of these values could potentially result in data inconsistencies that might not be immediately apparent to the end-user but could manifest during subsequent inventory reconciliation processes."

AFTER:

"Set up your configuration parameters before starting the system. Warning: Incorrect settings can cause data errors. These errors might not appear right away but can create problems during your monthly inventory checks."

The first version scored a 19.2 grade level with 42 words in a single sentence. The revised version: 8.6 grade level with an average sentence length of 11.3 words.

The difference wasn't just cosmetic. When we sent the revised documentation back to the client, their team implementation time decreased from an estimated 120 hours to just 37 hours. That's 83 hours of productivity reclaimed through clearer writing.

The Unexpected Benefits Beyond Basic Readability

While saving the client relationship was motivation enough, I discovered additional benefits that I hadn't anticipated:

Lower Support Ticket Volume

In the six months after implementing clearer documentation practices, our support ticket volume decreased by 32%. When I analyzed the specific reduction, 76% came from "how-to" questions that were now effectively answered in our documentation.

More Efficient Onboarding

New developers joining our team could understand our internal documentation faster. Average time-to-productivity for new team members dropped from 4.5 weeks to just under 3 weeks—a 33% improvement simply from having clearer documentation.

Higher Client Satisfaction Scores

Our client satisfaction surveys include a specific question about documentation quality. This score jumped from an average of 6.1/10 to 8.7/10 after our documentation overhaul. That's a 43% improvement from changing words on a page.

Sometimes I wonder how many projects I've delivered over the years with subpar documentation, and what impact that had on client experiences that went unreported. It's a somewhat uncomfortable thought.

My Documentation Workflow Today

These days, my writing process has fundamentally changed. Here's what it looks like:

1. Initial Brain Dump: I write exactly as I always did—technical, detailed, and admittedly convoluted.
2. Analysis Phase: I run the text through our analyzer, which highlights problematic sentences and paragraphs.
3. Targeted Rewriting: I focus on the highest-impact issues first—sentences over 25 words, paragraphs over 6 sentences, and any passive voice constructions that obscure who should do what.
4. Jargon Check: I use our tool's "technical term density" measure to ensure I'm not exceeding 12% specialized vocabulary.
5. Second Analysis: I run the revised text through the analyzer again, aiming for specific scores: Flesch-Kincaid below 9.0, average sentence length under 18 words, and passive voice under 12%.

This process added about 20% to my documentation time initially, but as I've internalized the principles, I find my first drafts are naturally becoming clearer. The analyzer now serves more as confirmation than correction.

When Text Analysis Gets It Wrong

It's not all sunshine and readability scores, though. Text analyzers have their limitations, and I've learned when to trust my judgment over the algorithm.

Last year, I was documenting an error handling system with complex conditional logic. The analyzer kept pushing me to simplify a particular explanation, but doing so would have removed critical nuance that developers needed. In that case, I deliberately maintained a higher complexity score for that section, but added clarifying examples to compensate.

The tools aren't perfect. They don't understand context or audience expertise the way a human does. They're guides, not gospel.

Frequently Asked Questions About Text Analysis for Documentation

Isn't simpler writing "dumbing down" technical content?

That's what I initially thought, but experience proved me wrong. Clarity isn't simplification—it's optimization. Einstein reportedly said, "If you can't explain it simply, you don't understand it well enough." I've found that forcing myself to write clearly exposes gaps in my own understanding.

Do text analyzers work for all types of technical content?

They're most effective for user-facing documentation, procedural guides, and error messages. They're less useful for API references and highly technical specifications, though even those benefit from clearer sentence structures.

How much time should I spend optimizing documentation?

I typically allocate 15-20% of my total development time to documentation now, up from about 8% previously. The investment pays dividends in reduced support costs and higher user satisfaction. For every hour I spend improving documentation, we save approximately 3-4 hours of support time.

Do clients really notice the difference?

Absolutely. Beyond the metrics I've mentioned, we've received direct feedback. One client's CTO told me, "For the first time, I can share your documentation with my team without having to translate it first." That was both gratifying and slightly embarrassing.

Beyond Documentation: Unexpected Applications

The principles I've learned for documentation have spilled over into other areas:

• My emails are shorter and generate faster responses
• My code comments have become more helpful to other developers
• My user interface text is more intuitive
• Even my commit messages have improved (my team is particularly grateful for this one)

I've come to believe that clear writing isn't just a nice-to-have skill for developers—it's essential. Code exists for machines, but documentation exists for humans. And humans deserve better than what most of us have been providing.

If you're struggling with documentation like I was, I'd encourage you to try a text analyzer. We've built one that's freely available at Web Utility Labs as part of our commitment to improving the technical communication landscape. Combine it with our Color Palette Generator to create documentation that's not just readable, but visually appealing too.

Your users will thank you—probably by not needing to contact support quite so often.

Do you have a documentation horror story or success? I'd love to hear about it in the comments below.