Summary: This post examines a specific case where a system response—a JSON error message—was misinterpreted as a text extract, revealing a deeper misunderstanding of both content processing and data formats. We’ll break down the situation and explore what it teaches marketers, content editors, and technical teams about parsing, displaying, and communicating machine-generated data in human-readable contexts.
The Mistake: Misreading Format and Meaning
At first glance, someone tried to extract or rewrite a “story” from a block of data. But what they had wasn’t a narrative at all. It wasn’t raw website content. It wasn’t an abstract, article, or landing page copy. It was a JSON (JavaScript Object Notation) response containing a system-level error: “insufficient account balance.”
JSON isn’t written for humans. It’s written for machines to exchange data. Its structure is meant to be parsed, not read. So this isn’t just a small misunderstanding. It indicates a fundamental breakdown in recognizing context and purpose. When you're trying to architect marketing systems that rely on automated content extraction—or even when you're just building feeds between tools—you need to know: Are you dealing with displayable content or backend data?
The Real Message: System Limitation, Not Story Content
So what is this JSON string really telling us? That the system was instructed to process an action—maybe a content request—but the user account didn’t have the balance required to fulfill it. That’s it. No human story behind that string. No editorial tone. Just a functional barrier in the workflow.
Trying to derive narrative from an error message like { "error": "Insufficient balance" } is like trying to mold clay from a shadow. This is an object with a technical purpose. And when taken out of technical context, your marketing, automation, or support workflows will fail silently—or worse, publicly.
Why This Misstep Matters to Marketers
To everyone building systems that rely on machine-readable and machine-generated content—AI tools, scraping scripts, automation pipelines—this is a common pitfall. We tend to treat everything as content. But not everything that shows up in an API or response stream is meant for display.
This points to the need to build logic gates in our content engines that can:
- Detect non-displayable formats like JSON, XML, or system logs
- Trigger fallback content when system data (not narratives) appears
- Map errors to human-understandable messages within front-end experiences
Let me challenge your current setup: Are you validating that what reaches the frontend is actually what your customer should see? Or are you relying on fallible filters that assume every input is human-friendly output? What happens when that assumption breaks?
Empathy for the End User, Clarity for the Builder
An error like “insufficient balance” may seem small—until it reaches your customer, cloaked in confusion. If you show raw JSON to a human, they feel one of two things: frustration or fear that something is broken. Either way, it erodes trust.
On the developer side, these are simply system signals. But on the consumer side, they’re triggers for support requests, abandoned transactions, and negative reviews. So we need to act as translators between machine and mind.
The Real Lesson: Humanizing the Data Layer
This kind of mistake is exactly what Chris Voss would flag in a negotiation: a moment where assumptions are made without testing the true meaning of what's being said. One side is speaking its own language (JSON), and the other is misunderstanding it as a complete story.
What would a better move have been? Mirror the structure of what's received—ask, “What is this string really trying to say?” Then label the situation: “It seems like this is an error message, not content.” This labels the context, clears up assumptions, and re-centers the analysis.
How to Avoid This Again: Build Intelligent Content Filters
Here’s a practical checklist to prevent these missteps in any system that ingests content dynamically:
- Detect format: Use code that recognizes and flags non-HTML or non-plaintext responses like JSON or XML.
- Parse before display: Strip out unrenderable characters or data types before showing anything to users.
- Fallback logic: When system errors show up, substitute a plain-language message, e.g., “Oops, we hit a technical issue. Please check your balance or try again.”
- Error mapping: Maintain an internal dictionary that translates system errors to user-facing messages written in human tone.
- Test contextual assumptions: Ask: Are we feeding actual display content into the system—or raw signals used for infrastructure decisions?
Confirm the Suspicion: You're Not Alone
Let’s not kid ourselves—this kind of error is more common than most systems admit. I’ve seen advanced marketing platforms spit out JSON tokens straight onto social media previews when their thumbnail URLs broke. Entire emails go out with placeholders showing [object Object] instead of usable copy.
This behavior confirms what sharp marketers already suspect: a lot of “smart” systems aren’t as human-aware as they seem. The sophistication isn't always under the hood—it’s patched together at the edges.
No Is Power: When to Reject the Input
Here’s where the power of “No” comes in. When your machine hands you a JSON error instead of usable copy, don’t try to make it work. Say no to garbage in, because you’ll get garbage out. Instead, reroute. Pause the display. Signal a content fault. That ‘no’ protects your brand and buys time to correct the backend.
Closing Thought: Data Isn’t Content Until It Communicates
What looks like content might just be data. And data that fails to speak in narratives isn’t usable until someone—an editor, a converter, a machine-learning model—turns it into a message with meaning. That’s the line we walk in modern content operations. If you cross it blindly, you’ll confuse customers, undermine trust, and flood your queue with support tickets.
But if you recognize the difference—and structure your systems accordingly—you’ll always deliver smarter experiences that talk to humans, not just machines.
#ContentParsing #MarketingAutomation #ErrorHandling #UXMistakes #JSONFail #SystemDesign #DataVsContent #DigitalInfrastructure #HumanCenteredUX
Featured Image courtesy of Unsplash and Joshua Hoehne (vCO1Frox2j4)