You might be wondering how is it possible to apply computer vision technology to the marketing world. You may have witnessed computer vision in Snapchat filters or have heard of computer vision technology being used in futuristic machines like self-driving cars and more. But, computer vision technology is not limited to these applications. It is also applicable in the world of marketing.

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Let us first throw some light on the concept of computer vision for a better understanding of the topic. Computer vision is a subfield of artificial intelligence that deals with providing computers with a visual understanding of the world. Today, computer vision is used in a variety of applications like video tracking, motion detection, face recognition, number plate identification, indexing and image restoration, etc.

Computer Vision in the World of Marketing

As discussed earlier, computer vision technology is not limited to self-driving cars only. In this article, we will discuss applications of computer vision technology in the marketing world. These applications are more prominent in retail and B2C marketing. Let’s start.

1. Relevant Advertisement

Admit it. Marketers can’t survive the competition without showing contextual advertisement. Online advertisement networks like Google Adsense displays advertisements on the website in the form of text or images which is relevant to the text on that page OR advertisements based on the previous searches and interest of the user.

But what about images? How to identify images and show advertisements based on those images. Computer vision technology displays advertisements over images by identifying the context of the images. For example, if the image features playing puppies, it might be a good place to advertise a dog food brand, or an image featuring a beach might be a good place to advertise the holiday packages.

This is a challenging task and this hasn’t been possible without the help of computer vision. The recent developments in computer vision have made it feasible to achieve this goal. The recent development in computer vision has enabled computers to get a human-level understanding of pictures. This was not possible until the data scientists built machine learning models that could be trained on a massive amount of datasets.

These algorithms are so powerful that they can correctly identify the images to the next level. Like it can identify ‘Honda City’ and not just ‘car’, ‘Doberman’ and not just ‘dog’. This can help the marketers to place their ads on the targeted brands and not on random images of the same category.

2. Generating the Customer Segment

Computer vision technology can also help in segmenting the target audience. For example, if a wristwatch brand has over 100 images of its newest designs, how does the company get to know which of those images should be targeted to get the clicks from users on Facebook, Twitter, and other social media platforms. Computer vision applications can parse through a variety of images and get the right audience at the right time.

As there is a change in the click-through the behavior of the audience depending on the demographic location and other factors, the artificial intelligence system can be trained to adjust the advertisement based on these factors.

3. Facial Recognition for Customer Feedback

Marketers are well aware of the importance of feedback from the customers. One of the best things about online advertising is that it is trackable. Advertisers can know the number of session, clicks, view, and conversions that happen in a given day or time of the day. They can also customize their ads to specific users during specific days of the week.

This is not possible through the outdoor advertisement campaigns. Because it is difficult to keep track of the visitors who view your billboards and physical advertisements.

Computer vision technology is working to solve this limitation too. With the help of computer vision technology, advertisers can track the engagement of offline ads based on the response of the people who pass by. Facial recognition can help to identify the emotions and attention. Marketers can take this feedback into account and customize the advertisement.

4. Retail Analytics

Computer vision technology can also help in tracking the movements of the people as they pass by the store. This data is useful not only for the security purpose but to track how busy a section of the store is or to know the waiting time of the queue. Computer vision technology can help in knowing customer behavior and identifying solutions to problems like:

Where do shoppers go to the store and where they do not go very often.
Where do the shoppers stop and engage with the customer staff?

5. Image Search

Computer vision is also helpful in the automated tagging of images. This eliminates the need for manual tagging of the images. This also makes image recognition on a larger scale quicker and more accurate. Computer vision technology when applied to videos can be mind-boggling and can fundamentally change the way we access and archive the video content. Take an example of a Google image search that is trained to recognize and search thousands of images.


In a nutshell, we can say that computer vision technology has plenty of prospective uses in marketing. In the upcoming future, online advertisements can gather details about the attention and emotions through facial recognition too. Marketers can take this feedback to determine which ads should be shown to get the maximum response from the customers.