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Images are taking over social media. Whether it’s a meme, artsy photo, selfie, or link to an article, our social feeds are increasingly filled with more images and less text. Why? Images are more impactful than text. More memorable. More engaging. More likely to be shared and reshared. It makes sense that they’re everywhere.
But what does the growth of image sharing mean for social media analytics? How can brands track and leverage the growing number of images posted to social media? This post covers emerging image analysis technology and what it means for brands.
As social media and the web as a whole become more visual, brands can’t rely on text alone when analyzing social media data to better understand their audiences.
Here’s Gartner on the importance of image analytics:
“We do expect multimedia posts to become the predominant type of post on social media. Even the text that accompanies those posts is getting shorter and shorter…It becomes increasingly important for companies to be able to understand what’s going on in those images.”
– Jenny Sussin,VP of Research at Gartner
Over three billion photos are shared daily on social media according to Mary Meeker. Many of those photos contain brands’ products and logos, but 85% of them don’t include a text reference to the brand (LogoGrab). Without image analytics, brands are missing out on a huge chunk of the social conversations about their brand, products, customers, and competitors.
For example, take a look at this Instagram post from Drake:
Drake’s Instagram post prominently features the Bentley logo, but contains no tag or text mention of the brand. If this is an organic mention of the brand, Bentley would surely want to know about it as soon as possible. If Bentley is paying or sponsoring Drake to post about the brand, image analysis technology can help them track exactly when and how Drake is representing the brand.
This is just one example of the power of this new technology and why it matters for brands. The ability to identify brand logos within images may seem like futuristic technology, but it’s actually one of the most basic functions of image analysis.
Image analysis (also known as “computer vision” or image recognition) is the ability of computers to recognize attributes within an image.
Do you use Google Photos or Apple’s Photos app on your smartphone? They both use some basic image analysis features to recognize faces and categorize them in your photos so you can look at all of your photos of a particular person. Type “dog” into the search function within either app to quickly locate your collection of puppy photos or type “beach” to find your tropical vacation pics.
Social media analytics started with, and continues to be based on, text analysis. But image analysis is becoming increasingly important. When applied to social media analytics, image analysis is an extension of text analysis features applied to visual content.
The same methods of categorization apply to image analysis. Instead of looking at all of the posts that contain the word “computer,” object recognition can show you all of the posts that contain photos of a computer.
Image analytics can also identify faces within photos to determine sentiment, gender, age, and more. It can recognize multiple elements within a photo at the same time, including logos, faces, activities, objects, and scenes. The technology can automatically caption images “man and woman standing outside wearing Patagonia shirts with bike and mountains in the background.” And that’s just the basic details.
But that’s just the beginning. As social media image analysis technology evolves, it will be able to provide even more context around photos. For a deeper look at the future of image analytics, download the Altimeter report: Image Intelligence: Making Visual Content Predictive.
The fact that social media is becoming more image-focused doesn’t mean text analysis should take a back seat. The only way to get the complete picture of what’s consumers are saying on social media is to look at text and images together.
There are some big advantages to looking at both text and images when analyzing social media data:
The technology behind image analysis is advancing quickly—making it much easier to scale. Images show contextual, environmental, and emotional factors that you can’t get with just text.
While image analytics is a new addition to the social media analysis technology, it already has a wide number of valuable use cases for brands.
The simplest application of computer vision technology is to more accurately measure brand mentions. Companies use social media analysis tools to track and analyze how people are talking about their brand. In the past, the only way to find mentions of your brand or product within social posts was to look for text-based mentions or direct tags of your brand.
When a big brand like Nike wants to see how many people are talking about their products, one of their first steps may be to analyze their share of voice on social. But, as we’ve discussed, looking at these direct mentions is only one piece of the puzzle. What about the conversation that doesn’t involve text? This is where “share of eye” is important.
Nike would miss posts like this if they are only measuring text-based social conversation about their brand. Image analysis solves this problem by extending social media analysis to visual content, allowing them to identify anything from their logo to any image of containing a particular type of product (for example, running shoes).
Many companies use social media analytics to track how people feel about their brand or products. If you’re only looking at text you aren’t seeing the full picture.
Here, Gatorade’s Summit Storm flavor is “gross”:
Both of these posts are great examples of the need to look at text and images together. Just looking at one or the other won’t give the full context. In these posts, text analysis identifies the sentiment while image analysis identifies the brand and product.
How can you determine if paying to place your brand’s logo in a sports stadium is worth the investment? Was it worth it to sponsor that big event? These types of questions have been raised in marketing discussions for years, but logo recognition technology can finally start to give some answers to the “ROI of offline advertising” question.
Logo recognition technology allows you to quantify the number of impressions and exposure that your brand is getting from something like a red carpet sponsorship. Otherwise, you have no way to track those visual-only mentions.
Similar to the Drake/Bentley example, this Instagram post from Skrillex showcases the Adidas logo. Whether or not this is an organic or paid plug for Adidas, they would want to keep tabs on how influencers like Skrillex are promoting their brand.
One of the most interesting and useful applications of image recognition technology is identifying moments of consumption. Every company wants know as much as possible about how, when, and where people are using their products, but the limits of text analysis can’t provide a complete picture of actual product use.
Unlike text, images provide visual validation of the who, where, and how people are using your product. Without image analysis, your fans and customers have to specifically mention your product, which is less likely to happen.
Visual evidence of product usage in the wild provides a much more powerful metric for brands than just measuring mentions. To take it a step further, you can start to correlate sales data with the number of times your product is appears up in social photos. Crimson Hexagon’s research has shown a strong correlation between sales numbers and the volume of social photos that show use of your products.
For more on how images fit into social media analytics, download our free guide: The Fundamentals of Image Analytics.
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