One of the most common frustrations for new Brandwatch users is being overwhelmed by irrelevant data. You run a query intending to capture consumer thoughts about your brand's new product line – and instead, you find yourself wading through mentions of unrelated events, off-topic slang, or foreign language results with no connection to your business at all.

This “noise” not only makes your data harder to analyze, but can also distract your team from identifying the real opportunities and pain points in the market. So why does it happen?

Lack of Specificity in Search Terms

Brandwatch uses Boolean logic to help you capture mentions of your brand, products, competitors, or industry terms. But the broader your terms are, the more likely you’ll attract unrelated content. For example, using the keyword "apple" without qualifiers can pull in results about the fruit, the tech company, or even a baby name discussion – all in one feed.

Over-Reliance on Single Keywords

Relying on a brand name or product word alone can be misleading. Take the term "Orbit." Without context, Brandwatch won't know if you're looking for a chewing gum brand, a satellite path, or a fitness class. Without surrounding terms or exclusions, you’ll get it all.

Missing Exclusions or Irrelevant Mentions

Say you want to monitor conversations about your sparkling water brand. Without excluding terms like “sparkling personality,” you might find irrelevant tweets that skew your sentiment analysis. This is where exclusions in Brandwatch become essential.

Unstructured Concept Grouping

If your query groups multiple product names or consumer phrases randomly, Brandwatch won’t interpret your intended logic accurately. This often leads to unexpected combinations and poor categorization of posts. Concept group confusion is a top cause of mixed or noisy results – especially for DIY research teams still learning the ropes.

Examples of Noisy Query Pitfalls:

• Using generic terms like “clean” or “smart” without modifiers or exclusions
• Failing to specify your brand when it's used across many categories (e.g., "Delta" for airlines vs. faucets)
• Not filtering out countries, languages, or regions outside your target market

Recognizing these common query pitfalls is the first step toward more accurate, high-signal social listening. With better setup, Brandwatch can surface truly relevant conversations that align to your research objectives – and save your team hours of manual clean-up.

If these issues sound familiar, you're not alone. Many insights teams face these challenges, especially when learning DIY social listening tools under tight timelines. That’s where flexible solutions like SIVO’s On Demand Talent come in, connecting you with experienced researchers who know how to build smart, signal-rich queries from the start – and teach your team how to do it as well.

Once you’ve identified that your Brandwatch results are too noisy or irrelevant, the next step is to refine your query building – and that starts with mastering three essential tools: inclusions, exclusions, and concept groups. When used strategically, these features can dramatically improve the quality and relevance of the social insights you gather.

Inclusions: Telling Brandwatch What Matters

Inclusions are your way of telling Brandwatch what terms or phrases you do want to track. These include:

• Brand names and product lines
• Relevant hashtags or campaign slogans
• Problem-related keywords (e.g., “won’t start” for appliances)

For example, if you're analyzing sentiment around a new snack product, your inclusion terms might be: “BrandName chips,” “BrandName crunchy,” or “BrandName new flavor.” Adding variations and colloquialisms helps you capture how consumers talk in real-world conversations.

Exclusions: Removing the Irrelevant or Off-Topic

Exclusion terms help prevent false positives – irrelevant mentions that make your results messy. Common exclusions in Brandwatch include:

• Words used in other contexts (e.g., excluding “orbit” when it refers to astronomy rather than a mint brand)
• Geographic filters for international brands focusing on specific markets
• Negative or misleading terms unrelated to business goals

Taking the earlier Apple example – excluding “fruit,” “pie,” or “orchard” helps refine the social listening query when you're only interested in the tech company.

Concept Groups: Structuring Queries for Context

Concept groups let you cluster related terms together so Brandwatch understands how to relate different ideas. Think of them as buckets – one for product names, another for customer needs, another for competitors. This makes it easier to build cleaner “AND” / “OR” logic across multiple variables.

For example, you might create a group for:

• Products: “Sparkling water,” “Flavored seltzer,” “Low-calorie soda”
• Occasions: “Summer picnic,” “Beach day,” “Healthy lifestyle”

By telling Brandwatch to look for conversations where these groups overlap, you increase precision and reduce irrelevant noise.

Putting It All Together

The most successful social listening queries don’t rely on a single keyword. They lean on a blend of inclusive search terms, clearly filtered exclusions, and well-thought-out concept groups. The result: cleaner data, stronger themes, and faster insights for your research needs.

Struggling to get the balance right? This is where SIVO’s On Demand Talent can make a real difference. These experienced professionals can step into your team temporarily, structure smarter queries inside Brandwatch, and train your internal team to use DIY research tools more effectively over time. It's a time-saving, skill-building solution that helps you maximize your investment in market research tools – without the need for long hiring timelines or guesswork.

Getting started with Brandwatch or any DIY research tool can be exciting, especially when you're eager to surface real consumer opinions. But one of the most common pain points for beginners is ending up with too much irrelevant data or, worse, missing key insights entirely. The root of the issue often comes down to how queries are built.

Here are some of the most frequent mistakes teams make when creating Brandwatch queries – and how to avoid them:

1. Overly Broad Search Terms

It’s tempting to cast a wide net, but general terms like “coffee” or “fitness” will likely flood your results with noise. Without narrowing the scope, it’s nearly impossible to extract meaningful social media insights.

Instead, think specifically: Is it a certain brand, product name, or conversation trend you're tracking? Refine your queries accordingly.

2. Ignoring Exclusions

Not using exclusions in Brandwatch is like trying to read a book in a room full of static. Exclusions help filter out unrelated chatter – such as company names that share a name with common words (e.g., “Apple” the device vs. “apple” the fruit). Skipping this step is a surefire way to reduce signal quality.

3. Failing to Group Concepts

Beginner users often treat a group of related terms as separate keywords rather than using concept groups. Brandwatch concept groups help you categorize and control logic – for example, grouping all synonyms or product variations into one logical unit for easier parsing and reporting.

4. Not Considering Punctuation and Spacing

A surprising source of error, Brandwatch's system can interpret slightly different spellings or punctuation as separate entries. “e-commerce” might yield different results than “ecommerce.” Always test small versions of your queries to ensure you're not missing out because of formatting nuances.

5. Assuming Brandwatch Thinks Like a Human

Brandwatch is a powerful AI-driven tool, but it’s not human. It doesn't intuitively group words by meaning unless you tell it to. A lack of linguistic precision can quickly lead to irrelevant mentions or poorly classified data.

Start by reviewing each component of your query – what you're including, what you're excluding, and how terms relate to each other. A few small tweaks can dramatically improve the quality of your consumer listening data.

Words matter – and in social listening, the way you use them in query frameworks makes all the difference between high-signal results and overwhelming noise. Brandwatch operates on logic, syntax, and a rich mix of linguistic rules designed to mimic human interpretation. But it still needs clear guidance.

Linguistic nuance is often the missing ingredient in well-intentioned but underperforming Brandwatch queries. Here’s how paying close attention to language can lead to more focused and actionable social media insights:

Synonyms and Slang Are Essential

Consumers don’t all talk the same way. What one person calls “soda,” another may call “pop” or “soft drink.” If you only track one version, you risk missing out on key mentions. Including regional slang, abbreviations, and spelling variations strengthens your listening framework.

Connotation and Context Matter

Words can change meaning based on context. The word “sick” might signal illness in one post – or high praise (“that product is sick!”) in another. Carefully building inclusive search terms that accommodate these variations prevents you from misclassifying valuable data.

Negations and Misleading Phrasing

A classic mistake is picking up every mention of a brand – including those that say “not this brand” or “I wish I didn’t buy [product name].” Adding exclusion keywords or negative sentiment filters helps ensure higher relevance. This is especially important in DIY social listening tools, where automated logic can’t always distinguish intent on its own.

Hashtags, Emojis, and Tone

Brandwatch allows for emoji tracking and hashtag parsing – both often overlooked when building queries. Emojis provide emotional context. Hashtags group conversations. Both should be considered part of your query strategy for well-rounded consumer insights.

Imagine you're tracking opinions on plant-based meat brands. A fictional example: someone says “This isn’t meat – and that’s the best part! 🌱💚” You’ll only capture that sentiment if you're tagging language like “plant protein,” “vegan burgers,” or even leaf emojis as part of your inclusive query.

High-signal social listening strategies require digging deep into how consumers actually communicate – spelling quirks, cultural expressions, and tone all play a role. By building linguistically aware queries, you allow your data to reflect not just what was said, but how and why it matters.

Even with powerful tools like Brandwatch at your fingertips, getting high-quality insights requires more than platform access – it demands skilled setup, linguistic precision, and experienced interpretation. That’s where SIVO’s On Demand Talent steps in.

Whether you’re just beginning to build a social listening program or trying to get more out of your current DIY research tools, partnering with seasoned insights professionals can save time, upskill your team, and ensure your queries actually deliver business value.

What On Demand Talent Brings to the Table

• Expert Query Building: Our researchers know how to structure advanced Brandwatch queries that reduce noise, include key terms, and filter out what's not useful – improving data relevance from day one.
• Concept Group Creation: They help your team understand how to structure Brandwatch concept groups effectively and use them for smarter tagging and reporting.
• Hands-On Coaching: On Demand Talent works side-by-side with your team – not just doing the work, but mentoring along the way so internal capabilities grow over time.
• Platform-Specific Expertise: Unlike general consultants or freelancers, these are trained insights professionals familiar with Brandwatch and other market research tools – and they’re ready to make an impact from day one.

Let’s say a CEO wants to track shifts in brand perception after a product recall. Instead of pulling weeks of noisy results, your On Demand Talent expert ensures the query accurately captures sentiment, product mentions, and brand name variations – without getting hijacked by unrelated conversations. This leads to faster reactions, clearer presentations, and ultimately better decisions.

With budget pressures growing and talent gaps increasing, SIVO’s On Demand Talent offers flexible access to high-caliber professionals who strengthen your team without expanding headcount. Whether you need short-term surge capacity or a longer-term capability builder, you’ll get faster ROI from your social media listening tools – and your people will get better at using them.