- Tips & Tricks
- Dec 22, 2021
Computer Vision and the Future of Digital Marketing
A subfield of artificial intelligence – Computer Vision has seen immense developments with the improved accuracy of object identification and classification. It has only been around for the last couple of decades but has drastically transformed and transcended our world. Its supremacy in this internet-loving era integrates itself in web development, social media, eCommerce, and currently in the exciting sphere of Digital Marketing.
Computer Vision (CV) is a significant part of AI and machine learning that focuses on creating a digital system that can enable computers to process, identify and comprehend visuals in the same way that humans do. Just like we use our eyes to capture and process a scene, CV scans images and translates their content into metadata. This data can then be utilized by marketers to enhance their marketing strategies and understand the preferences of customers.
In such a short interval, CV has taken a huge leap and has managed to have a considerable influence on both the professional and personal lives of humankind, and is expected to reach $48.6 billion by 2022, making it an extremely favourable UX technology.
Google uses this technology and has invented an image recognition app that interprets photos taken by mobile phones. If you visit social media, you may have seen images interpreted by vision algorithms. For example, when Facebook recognizes your friend in a picture and asks – “Do you want to tag (name)?” – that was computer vision doing its thing. This implementation provides developers with more insight on how to integrate CVs into various platforms to improve web user experience and help digital marketing.
The technology is modifying marketing and providing cutting-edge opportunities to the marketplace. In social media, online publishing, and marketing, visual content is poised to surpass text as the most valuable asset.
There are many exciting applications of computer vision we expect to see in the near future and it will demonstrate just how useful and versatile this technology is for marketers.
AI and The Evolution of Computer Vision
When we think of AI, self-driving cars immediately pop up in our minds, but in reality, AI is everywhere around us in the smallest of things making our lives simpler by automating tasks and improving efficiencies every day. Today, the participation of AI in digital marketing has opened up exciting opportunities for marketers to showcase their products in the world. In this day and age, digital marketers have shifted their attention from written content to visual content like photos and videos to get more engagement and conversions from their audience. No wonder platforms like Instagram, Facebook, and YouTube are the top social networks worldwide where your visual content can go viral and entice people to know your brand better.
Marketers have already started to apply one or more AI technologies to lead their company towards profit. CV is a huge part of artificial intelligence and can compute bulk data and analyze a complex string of queries in an instant to provide solutions to businesses. CV’s marketing possibilities are becoming increasingly apparent. Artificial intelligence and computer vision are now more feasible than ever to use.
But Computer Vision is not a new concept; the first known use of CV was around the year 1950 where it was used to interpret typewritten and handwritten texts. The process back then was simple but required a lot of manual work; also the computational power was not as good as it is now and was prone to errors. Fortunately, today we have access to plenty of computer power. When combined with robust algorithms, cloud computing can assist in solving even the most complicated hurdle.
So how does Computer Vision work?
CV follows three successive processes that are implemented one after another. These three steps namely are
- Image Acquisition
In this process, the computer translates an analog image into a digital one. Simply put, it remodels a normal image into binary data (a combination of zeroes and ones). These datasets can be built with the help of advanced tools like webcam, digital compact cameras, embedded cameras, etc., and later this data is used to attain more competence for the next steps. - Image Processing
Applied mathematics algorithms are used to perform low-level processing on digital images during this step. The computer processes all the geometrical elements of the object in an image, detecting its edges and matching its features, segmenting and classifying it to understand it better. - Image Interpretation
This is the final step of CV where sophisticated algorithms are applied to the processed images to perform actual data analysis. Object recognition, 3D scene mapping, object tracking, etc. are all done in this step.
A lot of people think that computer vision is a technology from the future. This is not the case. Many aspects of daily life are already impacted by computer vision. We use this technology in a variety of ways today, and here we will specifically discuss how it’s used by different platforms for digital marketing.
Computer Vision Changing the field of Digital Marketing
Product Discovery Through Image Search
Browsing through thousands of products on a website to find a specific item can be tedious and a complex process for the users. Consumers, hence, depend on the search bar or filter function to discover products. On the back end, this filtered search requires loads of work and extensive ‘tagging’ that are assigned to the products manually and is subjective to the seller. Manual tagging, however, can also be deceptive and discordant especially if it is encouraged by illusive practises.
Thanks to image search management, this process has become effortless and has shortened the path from search to conversion. By providing an alternative to out-of-stock products, visual search has also decreased the shopping cart abandonment rate.
With visual search, users can discover products more quickly, increase conversions, and enjoy a rich media experience.
One of the best examples of image search implementation is perhaps Pinterest’s Visual Lens. It’s like shazam but for the visual world where the audience can browse, compare and narrow down their options through image-generated similarities. The customer can use an image of a shirt they like and find similar options. This minimizes the need for customers to know brand jargon and simplifies the product search process.
The image recognition market is expected to grow to $53 billion in 2025 and since the human brain processes and responds to images 60,000 times faster than texts, more and more brands are getting on board and investing in image recognition softwares to make their users’ experience plain sailing.
Image editing and Optimization
We know visual content is such an indispensable tool of digital marketing. Customer-focused and prominent images and videos on social media and websites help in grabbing the attention of the audience and attracting them to our products. And creating compelling visual content can take time, and effective marketing requires a lot of it. AI can help in generating visual content for you- without requiring countless hours on your part for editing and optimizing the images.
Image editing is mainly done to improve the quality of the image by the application of different algorithms. It is also important to prepare images for Computer Vision models, such as segmenting or labelling recognizable features.
Earlier heavy softwares like Photoshop and Illustrator were used for editing images and making graphics, but now with AI-generated tools such as automatic online background removal, marketers can edit their photos without breaking a sweat.
However, we cannot ignore the fact that images affect the loading speed of a website, especially if there are thousands of products on an eCommerce store. The slow loading speed can discourage new customers and diminish the conversion rate.
Making a transparent image background can help overcome this obstacle, and you will not have to worry about hiring designers to remove backgrounds or manually photoshoot products with a white background. This all can be done by an online bg remover software that uses computer vision for effective object detection and background removal.
Using computer vision technology, it becomes easier for eCommerce business owners and individuals to edit images and optimize them on their own.
Improves Customer Experience
The current pandemic conditions have forced people to stay indoors and not go out unnecessarily in crowded places. This has driven many eCommerce platforms to embrace Artificial Intelligence and more personalized services to help consumers find, compare, and test the products online. It has drastically changed customer behaviour and their purchase expectations. According to the Digital IQ2020 report, 82% of top companies put a high emphasis on digital customer service.
CV has an extraordinary impact on all aspects of the customer journey, including sales, retail, marketing, and customer service. Through automation, manual processes can be streamlined, customers can be retained, and costs can be decreased. It enables camera tracking of customer movements throughout the store, spotting attractions and inefficiencies, and giving marketers more insight into the performance of their store layout. It is also a force multiplier that adds further substantial insights into customer purchase patterns and cross-sales.
Sephora’s Virtual Artist is the best example to market different products through computer vision. Virtual Artist uses augmented reality and artificial intelligence where the buyers can try on makeup products on their image and see how it will look on them, helping them to simplify their purchases.
Sephora’s AI also helps the consumer to discover the perfect product that matches their skin tone. This technology has helped Sephora to increase its sales and repeat purchases.
This kind of tool encourages engagement and increases customer satisfaction. Consumers can experience a whole new world of efficiency, less work, and a whole new experience.
Visual Listening
There is an old saying that ‘ A picture is worth a thousand words ’, but in social media, it’s true. Visual listening is the same as social listening or social media listening where instead of analyzing written continent, computers analyze visual mentions shared by people like images, GIFs, and videos containing the logo or product of the brand. Today over 3.2 billion images are shared online every day, which is roughly around 80% of all social media posts. Among these visuals, about 88% of logos that appear in images do not contain any mention of the brand in the text following the image. If it wasn’t for visual listening, companies would have had a hard time understanding the preferences and attitudes of their customers towards their brand.
This visual data can be used for creating different campaigns and strategies in return for better sales and engagements. Using visual listening, you can also figure out if your brand is being used in an offensive or negative way. To maintain a clean brand image and influence, you need to sustain such a factor.
The Future of Marketing through AI and Computer Vision
AI has become a valuable part of our lives and influences almost every aspect of it in some way. AI along with computer vision has been around for quite some time, but there’s still a lot to learn about integrating them into various business models.
Even though computer vision systems still have a long way to go in terms of accuracy, trustability, adoption, and privacy concerns, the way it processes large amounts of data to generate results is enough to warrant marketers’ attention. Marketers can leverage this technology to accomplish all the above, and much more. We can expect to see many new and exciting creative uses of computer vision as the technology matures and people’s dependence on visual communication grows.
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