You’ve built the Boolean string. You’ve tweaked the keywords. You’ve added exclusions, wildcards, and proximity operators and you have a paragraph of “OR”s and “NOT”s. But your media monitoring feed is still full of irrelevant mentions, missed context, or worse: you’re not capturing the stories that matter most, making effective media monitoring feel out of reach.
For Communications leaders, Boolean logic is often the foundation of media monitoring software. But in a world of overloaded news cycles, homonyms, acronyms, and increasingly nuanced brand narratives, Boolean-only media monitoring tools fall short, especially for those tasked with protecting and promoting complex brands.
The Limits of Boolean: When Logic Meets Reality
Boolean logic assumes that words alone are enough to define your brand coverage. But the reality of language and by extension, media, is far messier.
Take Amazon. Are we talking about the company? The rainforest? A person referring to a delivery from “an Amazon seller”? A Boolean string can try to rule these out, but it’s a never-ending game of whack-a-mole.
Or consider Apple. A recipe blog for that mentions “Delicious Apple Pie ” might sneak into your media coverage report, altering your idea of coverage count. Even for a company with an unmistakable name like Disney, not accounting for a nickname or shorthand like “The Mouse House” could mean a deep-dive profile of the company’s executive in Fast Company goes uncaptured because it wasn’t accounted for in the string.
And of course, a Boolean string can’t always distinguish between a brand name and an adjective. Brand names like Chewy pose challenges. Booleans may bring in pet food reviews of “Chewy Treats” that may or may not be relevant depending on the context.
These are everyday examples of why Boolean-only tools struggle to deliver the accurate media monitoring that modern communicators need.
Why It Matters: Measurement Starts with Monitoring
You can’t measure what you can’t reliably monitor.
When irrelevant mentions slip into your reporting—or critical stories are missed—it not only skews volume, it could send you in the entirely wrong direction, distorting sentiment, misrepresenting key message pull-through, and undermining your ability to show impact. You end up with incomplete dashboards, flawed benchmarks, and possibly even lost credibility with leadership.
Put simply: bad media monitoring leads to bad media measurement.
AI-Powered Human Intelligence
A human-supported AI approach saves you the time and resources your team would have to spend cleaning up after imperfect Boolean-only tools, applying human judgment to quickly and accurately filter media coverage, identifying the most relevant stories and surfacing the insights behind them.
This means you can:
- Confidently capture every relevant mention—even the ones Boolean logic would miss
- Eliminate false positives that pollute your reporting
- Identify and measure message pull-through with precision
- Understand tone and context that automation often overlooks
- Feed accurate data into your dashboards, attribution models, and strategic decisions
With a human + AI model, you don’t have to choose between scale and accuracy. You get both.
Want to Get Beyond Boolean Strings?
If you’re tired of building Boolean strings that never quite get it right or are frustrated by media reports that don’t reflect your reality, then it’s time for a better solution.
PublicRelay can help you monitor with confidence, measure with precision, and connect communications to business outcomes. Contact us.
