Discover the surprising power of analyzing images for better data
Digital images are front of mind in 2025 as artificial intelligence becomes more powerful, with various image analysis tasks trending as a result.
Creating and sharing images is easier than ever, and people are responding – billions of photos are shared on social media each day.
Why such an explosion of visuals? Because images are more impactful and memorable than text. At the same time, they’re easier to track than ever, thanks to advances in AI.
Images catch our attention, evoke emotion, and stick in our minds. Our brains are wired for visuals since we can process images much faster than words. It’s no wonder our feeds are filled with memes, photos, and videos.
For brands and marketers, this shift to visual communication presents both an opportunity and a challenge. Visual posts drive more engagement and are more likely to be shared than text updates, which is why you'll often see the phrase “pic for attention” in Facebook groups.
Including imagery can supercharge content performance. However, it also means consumers talk about brands in ways that traditional text-based social listening might miss. An image can convey a story or sentiment without a single word.
Image-based conversation is changing how brands communicate with consumers. It demands new strategies and new image analysis tools to capture and analyze these visual insights.
In this guide:
Why image analysis matters for brands
As social media and the internet become more visual, brands can’t rely on text alone in their analytics.
Many conversations now happen through pictures rather than comments, mentions, or hashtags. That means if you’re only tracking text, you could be missing the majority of consumer posts about your products.
Without image analysis, companies are blind to a huge chunk of the discussion around their brand, products, and competitors.
Core benefits of image analysis
Incorporating image analysis (also known as computer vision) into your social listening strategy unlocks a richer understanding of your audience. It involves identifying logos, products, and other elements in photos, and it allows brands to discover “visual mentions” that would otherwise fly under the radar.
It can expand your global reach since images don’t require translation, making visual analytics extremely useful in a global strategy. A picture of your product shared in any country carries the same meaning without language barriers.
Conducting both text and image analysis also gives you a fuller dataset, which is better for decision-making. You’ll get some insights that pure text analysis would miss.
Image analysis vs. text analysis
Speaking of insights, image analysis can reveal a completely different story than text mentions.
For example, text analysis of online conversations around a kids’ movie might suggest the audience is mostly adults (since parents are posting the tweets), but image analysis could reveal the movie’s true audience – children – appearing in the photos posted on social media.
Visual data can reveal new demographics, contexts, or usage occasions that text alone obscures.
Photos also capture contextual and emotional details (like scenes, activities, or facial expressions) that text can’t. Analyzing these aspects helps brands understand not just what consumers say, but how they live with and feel about the products.
AI advances
The technology behind image analysis is also advancing quickly, making it increasingly accessible at scale.
Modern AI can conduct image analysis on millions of pictures and spot specific logos or objects in seconds – a capability available in tools like Brandwatch Image Insights, which automatically detects your brand’s appearance across social media and the web.
How image analysis works
Now for the science bit. Image analysis uses artificial intelligence – particularly neural networks and deep learning – to recognize attributes within images.
This includes identifying objects, brand logos, people’s faces, scenes, and even activities depicted in a photo. If you’ve ever used facial recognition or used Google Photos or Apple Photos to search your pictures by keyword (“dog” or “beach,” for example), you’ve seen basic image analysis in action.
But today’s advanced image analysis goes much further.
What happened? (Descriptive analysis)
The foundational capability of image analysis is detecting what is present in the image. Descriptive analytics powered by computer vision can identify multiple elements in a photo – logos, objects, people, settings – and even generate captions describing the scene.
For example, an AI might analyze a picture and output: “Boy riding bicycle outside with dog, wearing a blue t-shirt.”
Brands already use these descriptive techniques to analyze images in their social listening practices. It allows a company to automatically find all social media posts featuring their products or logos, even if the post text never mentions them.
Identifying what is in the image is extremely useful for tracking brand awareness and exposure (as we’ll explore in our use cases below) and getting a baseline understanding of the visual content your audience shares.
Why did it happen? (Diagnostic analysis)
The next level is more complex: understanding the why or the context behind an image.
Diagnostic image analysis aims to explain why something is occurring – essentially, interpreting cause and intent. This is the one area where image analysis is still catching up.
Humans can look at a photo and infer context or motivation, but AI doesn’t have the same common-sense understanding of the world, which makes pure visual diagnosis challenging.
That said, partial solutions exist. We can get contextual hints by combining image analysis with text and metadata.
What will happen? (Predictive Analysis)
The cutting edge of image analysis is using visuals to predict future outcomes or infer what’s next.
Predictive image analysis looks at an image and determines what is likely to happen next.
This technology is still very new, but early research is promising. AI is learning to anticipate the immediate future from a still image.
While the tech isn't quite there yet, these predictive insights could soon help companies prepare for customer needs or responses. It’s already being used in healthcare research and weather forecasting.
For marketers, a predictive system might flag that a product is trending towards a certain usage, helping marketing teams double down on that trend.
Best ways to use image analysis techniques
While image analysis is a relatively new addition to the marketer’s toolkit, it’s already proving useful in various ways.
Here are four ways to make the most of this tech.
1. Measure ROI with logo recognition
One of the most straightforward and powerful applications of image analysis is logo recognition – automatically detecting where and when your brand’s logo appears in online images.
This can help you measure the ROI of offline branding and sponsorships.
Consider a company that sponsors a major sporting event or places its logo prominently in a stadium. Traditionally, the success of such sponsorships was measured by TV viewership or event attendance.
But what about the massive audience on social media? Fans at the game might post thousands of photos of the event, many of which include the sponsor’s logos in the background – on banners, player uniforms, or the scoreboard.
Those photos represent brand impressions and engagement that often go uncounted because users typically don’t tag or mention the sponsor in their captions.
Marketers can count how many times their logo appeared in user-generated photos and even measure the reach of those posts (through the posters’ follower counts). This provides a data-driven estimate of impressions from the sponsorship that would be impossible to get otherwise.
Logo recognition example
Dunkin’ has sponsored Boston sports teams for years, with logos displayed in venues like Fenway Park and Gillette Stadium. By using image analysis, Dunkin’ could analyze social photos from games to see how often their logo was captured in fans’ pictures at each stadium.
They might discover that their logo appears in far more shared photos at Fenway Park than at another venue.
These insights help answer which sponsorships and placements are delivering the most visual exposure.
Dunkin’ could then double down on the high-performing placements – investing more in the spots that generate the most fan photos – and consider scaling back on less photographed signage. In short, logo recognition provides tangible social media ROI metrics for offline marketing spend.
Beyond sports, the same idea applies to any event or campaign where your logo might show up in images. Brands use it to track the impact of event sponsorships, product placement in movies/TV, or even influencer campaigns. If people are sharing images with your brand visible, logo recognition ensures you see it and measure it.
Brandwatch Image Insights is one tool that offers world-leading logo detection capabilities, helping brands quantify these visual impressions at scale.
2. Track visual mentions (share of eye) through image processing
Marketers have long tracked “share of voice” – the portion of online conversation or mentions a brand captures relative to competitors.
Now, in the visual era, “share of eye” is an equally critical metric. Share of eye refers to how often your brand appears visually (usually via logos or products in images) compared to others. Image analysis makes it possible to measure this visual share of the conversation.
Why care about share of eye? Because visual mentions can dramatically boost brand awareness. If your brand has a strong visual presence – say, your logo or products frequently showing up in social media photos – consumers are more likely to remember you.
Share of eye example
A classic example is Geico’s visual dominance in insurance. If someone asks, “What’s the first insurance company that comes to mind?” then many people will answer Geico.
This isn’t just luck – Geico has spent years ensuring a huge share of eye through memorable ads and mascots. Consumers have been exposed to Geico’s visuals (the gecko, the caveman, the logo) so widely that Geico stays top-of-mind.
In fact, their share of eye is larger than any competitor, which translates to higher brand awareness and preference.
With image analysis, brands can benchmark and improve their share of eye. Nike, for example, might use visual analytics or object detection to see how often their swoosh logo or sneakers appear in social images compared to Adidas or Under Armour.
This goes beyond tracking hashtag mentions – it captures the true volume of brand presence. Nike could discover that while their text mentions are on par with Adidas, their logo actually appears in more user photos, giving them a visual edge (or vice versa).
By measuring share of eye, brands ensure they aren’t blindsided by a competitor quietly flooding social feeds with visuals, and they can take action to boost their own visual presence if needed.
In practice, this might mean encouraging more user-generated photos (perhaps via contests), investing in highly visual campaigns, or even analyzing which product packaging designs get shared the most.
3. Identify moments of consumption with image analysis software
Every brand wants to know how their products are being used – especially if it's in ways that they didn't originally expect.
Before image analysis came along, you’d rely on surveys or assume usage based on sales data and text posts. However, image analysis can identify actual moments of consumption captured in photos. This is one of the most exciting applications of the technology for consumer brands.
The idea is simple. Whenever someone posts a photo that includes your product, that image is proof of a consumption moment. It might be a customer wearing your apparel, a person drinking your beverage, or a family using your gadget.
These organic snapshots are incredibly valuable because they show your product in real-life contexts without a marketing campaign prompting it. The challenge is that people rarely caption these images with the brand name.
With image analysis, brands can find those untagged moments (perhaps even a photo of someone wearing a brand's shirt while holding another brand's soda), which would never surface through text search alone.
These insights tell you all about how people actually use your product. Maybe you find that a beer brand’s products appear most in Friday night house party photos versus a competitor that shows up at sporting events. Or a snack food might appear frequently in hiking and outdoor adventure pictures, telling you something about its core audience’s lifestyle.
Moments of consumption example
You can correlate these insights with sales data and other metrics to inform strategy. For instance, let’s say sales of Miller Lite beer spike in Chicago every October. Sales numbers alone can’t tell you why.
But by conducting social media image analysis, you might discover a trend of people posting photos drinking Miller Lite at baseball games during the Chicago Cubs’ playoff run. That visual evidence reveals the context behind the sales lift – fans enjoy the beer while watching the Cubs.
Armed with that knowledge, Miller Lite’s marketing team might increase advertising around Cubs games or launch promotions targeting baseball fans, knowing there’s a natural alignment.
4. Uncover demographic details
Using image analysis software for facial recognition also helps uncover demographic details you might otherwise not spot.
Modern image analysis (including features in Brandwatch’s platform) uses facial and situation recognition to estimate attributes like age and gender (within privacy and ethical boundaries).
Brands can analyze the faces in user photos with their products to get an idea of the demographic profile of their real customer base. Maybe you assumed 20-somethings mostly used your product, but images show a lot of older adults enjoying it as well – that’s strategic gold.
Demographic detection example
Let’s say MillerCoors finds via photos that Miller Lite drinkers skew older than Coors Light drinkers; they can tailor their marketing for each beer accordingly. These kinds of insights were hard to get before – now they’re accessible.
Image analysis lets you see your product through the customer’s eyes. You'll find real-life examples of how and where your products fit into people’s lives. This can either validate your marketing assumptions or reveal new opportunities.
Perhaps you’ll discover unexpected ways in which people are using your product (your baking soda is used for science fair volcanoes, via images, for instance) that open up new positioning angles.
You might also find regional patterns – for example; your apparel is frequently photographed at beach locations, suggesting a coastal style appeal.
By tapping into these visual stories, brands can find new ideas for product development, advertising creatives, and targeting. It’s all about bridging the gap between what consumers say and what consumers actually do – and often, what they do is best captured in pictures.
What’s next for image analysis and image classification?
The ideas above only scratch the surface of what’s possible with image analysis technology. Visual AI is learning new tricks every day, so it's worth keeping an eye on what happens next.
Features like scene recognition (identifying the setting of a photo) and emotion recognition (tracking mood from facial expressions or context) are becoming more sophisticated, which opens up opportunities for brands to measure consumer behavior and sentiment in brand-new ways.
As these capabilities become the norm, marketers are increasingly able to categorize and quantify the visual zeitgeist – from the styles of clothing people prefer to emerging lifestyle trends to the emotional tones that resonate in user-generated content.
While text analytics is still important, the future lies in combining text and image analysis for a truly complete picture. Many insights can only be gleaned from images themselves, while you'll need both text and visuals for others (for example, a positive text post plus a photo of your brand at a party tells a fuller story than either alone).
Ultimately, conducting image analysis of social media posts through both lenses is the only way to achieve consumer insights that are both accurate and actionable. Brands that integrate these data sources will have a far deeper understanding of their audience and market.
Try Brandwatch Image Insights
With all this in mind, it's no surprise that image analysis is fast becoming an essential component of marketing analytics. It unlocks a wealth of visual insights that can sharpen brand strategy, from creative and messaging decisions to sponsorship investments and product development.
Once you're ready to unlock the power of image analysis for your brand, speak to a Brandwatch expert. With Brandwatch Image Insights, you can automatically track your logos and products across millions of images and discover the stories you’ve been missing.
