It didn’t take long after Google first went live in 1998 for the company name to become a verb. For more than two decades, “Google it” has been shorthand for looking up information on the internet and getting to the bottom of any question. But as any communications professional can tell you, “Googling it” isn’t always a direct path to guaranteed results. Search engines like Google, as well as traditional media monitoring tools, rely on Boolean search logic. Yet a Boolean search is tedious, manual, and faulty.
Here's why PR and comms professionals should move beyond Boolean search and consider AI-powered media monitoring tools.
AI-powered search is precise, with less margin for error
For most consumers, Boolean search works just fine. But for PR and comms professionals tasked with generating an exhaustive scan of the media landscape, Boolean logic demands complex search strings. Boolean search requires the use of “and,” “or,” and “not” operators to get pertinent results. Navigating the hundreds of billions of websites indexed by Google requires the ability to craft precise search queries.
With Boolean search comes a lot of opportunity for margin of error because there could be typos in your Boolean string, knowing how long and complex they could be. You could miss a key surface form for how a topic or organization is being mentioned. And so it could potentially put pretty significant gaps in the data that you end up working with.
Writing Boolean string queries is a complex and time-consuming process that's easy to get wrong and almost impossible to scale. In even the best situations, organizing Google search results into useful data that tells a story requires hours of manual tagging, sifting through endless false positives, and filtering out irrelevant stories. “Googling it” no longer cuts it.
AI-powered search saves you time
The biggest issue with Boolean search is the significant time investment. You have to generate a really long Boolean string or search string, and you have to know exactly what you're looking for to create that string. This requires a lot of manual research, and due to time constraints, Boolean is often only used in crisis or to understand where your brand is being mentioned, leaving less time for strategic applications of the information and only time to react to crisis management.
Companies today need to understand the media landscape around their brand quickly, easily, and precisely. That’s where AI excels. Each day, Signal AI’s sophisticated AI-powered search engine, known as AIQ, ingests more than five million documents across the sources that matter - traditional news, podcasts, blogs, broadcast, social media, and more - in 75+ languages from more than 226 markets.
AIQ search then analyzes and organizes content into searchable, editable concepts. Our proprietary AI then turns unstructured data into Topics like Sustainability or Automation and by Entities (e.g., Organizations, People, or Locations) to provide an immediate and thorough overview of the media landscape.
Letting AI do this work gives PR and comms professionals a way to instantly grasp how their brands are being discussed, freeing them from the drudgery of all that manual labor and enabling them to get to work putting that data to action — a much better way to spend time and resources.
AI search has context & nuance built in
One of the greatest advantages of AIQ-powered search over keyword-based Boolean search is its ability to offer precise analysis that takes into account the context of a passage or piece of content. Boolean search, on the other hand, is binary. Either the searched phrase exists, or it doesn’t. There’s no concept of context; all words are treated equally and individually, as if in a silo. This means the way things are said is not taken into account. Colloquialisms are not understood, and things like emotions, slang, and sarcasm cannot be considered.
This is particularly true when it comes to measuring brand sentiment. Most sentiment models available today use simplistic techniques that analyze individual words of a sentence in isolation, averaging them to categorize the overall sentiment of a piece of content as positive, neutral, or negative. But this is flawed. Imagine if a review of a new smartphone said the brand “absolutely killed it with its new design!” In isolation, “killed” could be read as a negative word. Signal AI’s sentiment engine, on the other hand, is “entity-based,” meaning it looks at an entire section of text and understands the target of the statement before labeling its sentiment.
Example: Square, the company or shape
Media monitoring is another example of the importance of context-aware search. Consider the payment processing company Square and how a Boolean search isn’t able to distinguish between content discussing "Square" the business from content discussing square as a shape. Signal AI can, and it’s even able to stay up to date. When Square changed its name to Block, the platform didn’t skip a beat.
Below, Signal AI's SVP of AI, Alexandre Pinto, explains why AI beats the traditional Boolean search query:
https://youtu.be/ZUXuTDq-DbE
Go beyond Boolean: Narrative white spacing with AI
Getting your brand on the radar of the right people for the right reasons is an increasingly difficult task. Signal AI’s tools can help identify “reputational white spaces.” Reputational white spaces are topic areas with positive brand associations yet little competition, affording brands ample opportunity to capture that attention and reputational gain. This increases the odds that related brand coverage is positive and quick to accelerate.
The Signal AI 500, a global reputation ranking of 500 of the world’s most talked-about companies, is a powerful tool to help businesses identify these white space opportunities before competitors.
AI can also help brands understand and quantify how sentiment and perception evolve over time. Signal AI’s tools can identify shifts and patterns in public perception and present the information in easy-to-digest graphs and charts. Providing stakeholders with an easy way to understand the evolution of their brand’s perception year over year or month over month helps enable strategic, data-informed decision-making.