Marketing strategy

How Computer Vision is Powering Marketing Strategies in 2022

How Computer Vision is Powering Marketing Strategies in 2022

Illustration: © IoT for all

The field of marketing has evolved remarkably in recent years. Businesses have found better ways to reach customers, collect relevant data, and process information to gain valuable insights using new technologies. Some of the most common technologies we see companies using include big dataartificial intelligence, machine learning and computer vision. When used intelligently, the combination of marketing and technology can improve your brand’s productivity and effectiveness.

Similar to how we use various senses to better sense our surroundings, these technologies act as the eyes and ears of a business. Brands can start creating better strategies using the data collected by these technologies.

Five Ways Computer Vision Helps the Marketing World

Today, with the advent of better mobile devices and connectivity infrastructure, computer vision is helping brands transform and improve their marketing practices. The goal is to provide a better and more personalized brand experience to consumers, ensuring that they will come back.

Let’s look at five remarkable ways businesses are using computer vision to combine marketing and technology.

Brand logo tracking through object recognition

Consumers leave reviews on various online platforms. Companies hire entire teams to scour social media platforms or individual websites to find posts that might harm or help the brand. It’s not only time-consuming and expensive, but it’s not painstaking either. Here, computer vision comes into play.

Gumgum is a company that provides, in its own words, “social listening services”. The company uses computer vision to find and identify individual brand logos and each accompanying notice they have online. While this is an efficient solution, images tagged with consumer-relevant captions help make this process faster and more accurate. By marketing with this technology, brands can find out how customers interact with their product and gather valuable feedback on what may need improvement.

Visual product discovery and reverse image search

With the rise of e-commerce and online businesses, understanding how consumers are searching for your products is critical. Most of the online shopping sites use a search bar allowing consumers to search for the required products using text phrases. However, this type of search system requires that all products be tagged using appropriate descriptors. But these tags are not consistent across platforms and instead rely on individual retailers or outlets. Therefore, this process may not be reliable. Some platforms, like Pinterest, use specialized tools to allow consumers to search through images. This removes the need for manual tagging entirely and can be used as a great search and filter tool.

Recording Consumer Emotions Using Computer Vision Camera Technology

Better processing power, improved camera technology and the advent of powerful image processing algorithms are enabling brands to use computer vision. This computer vision technology is used to analyze customer reactions to better understand the emotions aroused by your products.

Disney designed a system using infrared cameras, which record and record their audience’s reactions to their films. The accompanying algorithm was designed to process subtle facial signals and gestures to predict how the viewer feels at each part of the film. The system, Variational Factorized Autoencoders, was a huge success.

Identifying and understanding the emotions that your products evoke in consumers is necessary. Using this knowledge in computer vision, businesses can now predict how their target market will react to a potential result, as well as the approximate sales it will generate if launched. These projections help brands plan their strategies accordingly.

Key aspect of marketing: personalized and original content

In business marketing, original content is the currency. With various platforms and formats to pursue, brands are racing to leave their competitors behind by being the first to deliver new and unique content.

By using a deep learning process called Generative Adversarial Networks (GAN), brands can speed up the process when it comes to visual content. GANs can help create high-quality, realistic images, videos, and even three-dimensional images. A GANs tool created by a Japanese firm, DataGrid, allows its users to create realistic images of mannequins without the need for live models. Therefore, instead of scheduling and planning new shoots with actual models, brands can use the tool to quickly produce new fashion shoots at a fraction of the cost. Not only that, but it also guarantees originality.

Likewise, many online logo design tools use AI and GANs to create unique and highly personalized designs that match their brands. Oftentimes, amateur comic artists use online tools to generate super hero logos for their characters. Taking visual cues from established characters, these tools create engaging and unique usability options.

Photo rating to classify images

Platforms like Yelp often rank both restaurants and reviews that contain images versus those that don’t. The goal is for the platform to try to select images based on the likelihood of them attracting views. However, rather than using a primitive system based on likes, the platform judges these images based on characteristics that are important in photography. Parameters such as contrast, lighting, angle, and image depth are used to rank photos against each other. Yelp’s photo rating model is based on a convolutional neural network, which assigns a higher weight to DSLR images and a lower weight to non-DSLR images. In this way, they effectively filter mediocre images from impressive ones.

Market reach of computer vision

With the market becoming more sharing-friendly, collecting and shaping visual data into usable datasets is now essential for marketing teams. By using computer vision and combining marketing with technology, a brand can reduce costs and time by maximizing their return on investment through better campaign planning, strategy, and more. Today, computer vision and related technologies are still being improved. Adopting them as soon as possible will allow you to take full advantage of the benefits these systems offer businesses today.