In modern organizations, AI-powered analytics are incredibly important to Sales and Marketing. And one of these ways is through enabling the recognition of images. With this technology, businesses can capture visual intel about their brand from anywhere online to help them make critical decisions about campaigns -and so much more.
What is Recognition of Images?
Recognition of images (or image recognition) refers to the use of AI-powered methods to detect and analyze topic-specific visual imagery. The technology is equipped to identify people, objects, locations, and other details contained in an image or video and analyze it to understand the story behind the post. And its level of accuracy depends on the capabilities of the tool employed. We’ll discuss this more as we go!
How Recognition of Images Works
Recognition of images can detect, classify, tag, and segment the information revealed depending on what is required. Detection is the identification of a particular element within a visual. Classification involves assigning the image to a category or number of categories. Tagging is the recognition of different concepts within a visual. Finally, segmentation is the breaking down of an image into smaller groups of pixels to reduce its complexity and make it easier to process or analyze.
So, how does it work?
The process is based on Deep Learning, a subcategory of Machine Learning technology. This refers to a class of algorithms that imitate the process through which humans acquire knowledge i.e. learning. And a key element of the deep learning structure is the artificial neural network. This is a layer of mathematical functions, called neurons, designed to mimic the functioning of the human brain. One artificial neural network may have many layers of neurons and the algorithm may contain many networks. It is through these artificial neural networks that an algorithm can detect, classify, tag, and segment an image.
So, that is the architecture and structure of recognition of images. To function, the system has to be trained. A set of visual data is introduced to the algorithm and the latter is taught to recognize the object of interest by being shown many different examples. Finally, the algorithm makes predictions based on its repeated recognition of these images. The classification is cumulative – and this is why a system that’s inaccurate is dangerous to use. Its results may not only miss important imagery, but could classify them wrong and leave a brand with a false sense of the market landscape.
How Recognition of Images is Being Used in Marketing
With recognition of images, many business processes can be automated. Once the algorithm learns, it can be programmed to perform a particular task automatically. Already, in Telecommunications, technicians are using this technology to improve their installations. It is also being used in Pharmaceutical and Construction industries.
In marketing too, recognition of images is being used in a number of ways, specifically with data analytics:
Marketers need to keep a close watch on brand mentions on social media, review sites, forums, and other places on the web. With recognition of images, they can detect the non-explicit mentions that appear as part of a visual. This is an aspect of social listening, where identifying branded objects like products or a company’s logo, is possible. And it captures them wherever they’re posted on the internet.
Personalized Customer Experience
Customer experience is already a great differentiator between businesses. And we already see the desire for hyper-personalization at scale taking hold with consumers. Does recognition of images have a role to play here? Absolutely. Facial recognition can help businesses identify their customers and tailor the perfect experience for them.
Candy seller Lolli & Pops was already doing this four years ago. As soon as a customer steps inside the store, the sales team can identify them by their purchasing history, tastes, and preferences. After that, it is not difficult for Lolli & Pops to deliver personalized recommendations.
Online Shopping Experience
Online shopping is great. It’s convenient, it saves time, and consumers love it. In fact, more people are shopping online today than ever before. However, it used to have one major drawback: Customers couldn’t try out products virtually.
Not anymore. Recognition of images allows sellers to offer their customers the option of trying out products virtually through AI-powered technology offering AR capabilities. For instance, Johnson & Johnson’s Neutrogena has an app that works with a skin-scanning gadget that allows users to analyze their skin health and find relevant products. Another great example, Sephora’s Virtual Artist, lets consumers try out the company’s make up products through a mobile app.
Content generation is at the same time one of the most important and also the most resource-intensive aspects of marketing, especially online marketing. Thankfully, someone went and created neural networks known as Generative Adversarial Networks (GANs) that promise to make the process easier, faster, and cheaper. With GANs, marketers can create realistic visual content including images, videos, and 3D models. ModelingCafe, a Japanese tech company, uses GANs to generate photos of fake fashion models that work just as the real ones.
But how does this apply to your marketing strategy? Some of it doesn’t, but let’s explore the parts that do!
How to Implement Recognition of Images into Your Marketing Strategy
For starters, you don’t have to create your very own recognition of images system fortunately – there are many great tools to choose from. And whichever you choose should offer some solid recognition of images capabilities, including:
Improve Your Targeting
Whether on your website or online store, you can use images to create and display better targeted ads and product recommendations. And we’re not just saying it – 75% of internet users in the United States incorporate visual content into their research during the purchasing process.
By tracking your target audiences’ activity online, you can find out which images they’re interacting with and share, and use this to reveal important insights around their interests and sentiment. And associated imagery included in relevant posts can lead to powerful and unexpected collaborations with other brands.
View this post on Instagram
The success of your marketing campaign depends not only on the aspects of it that you get right but also on your understanding what you need to rethink next time around.
Are consumers sharing still shots of your ads to rave over them or mock them? Maybe they’re meme’ing them! Recognition of images analytics detects these instances and offers insight you’d likely not consider (nor find) otherwise.
More Accurate Social Listening
Text analysis is fantastic to find conversations that may otherwise be hidden from you. However, it is not enough. Without recognition of images, you could still miss tons of insightful data generated by consumers who use images to communicate on platforms such as Instagram.
For instance, a user sharing a nice afternoon selfie outside your shop may not necessarily mention your brand, but they may take a very good shot of your banner. If you don’t have recognition of images to detect that element, this is a good conversation-starter you may never know about.
Identify Potential Influencers
Various recent reports suggest that trust in influencers is waning. One study showed that a measly four percent of people trust online influencers and another revealed that among millennials, the generation that basically nurtured the influencer, more than half no longer trust influencers.
It seems clear that the fault lies in the fact that influencers are paid to promote, which is a bit of a catch-22 since this is part of why it works. The way out of it is for influencers to be genuine about what they say. Therefore, you want to work with those that genuinely love your products. Recognition of images can help you find those who started using your products even before the paid partnership.
AI-powered recognition of images saves you from manually combing the web (as if that were possible!) for images that affect your brand or have the best performance. If you are looking for a tool that will be with you from the beginning stages of your recognition of images understanding through to advanced capabilities that will truly impact your marketing, reach out for a demo!