How to Transform Visual Marketing using Machine Learning

Marketers spend much time understanding how to use visual media to enjoy the benefits provided by visual marketing. They have to set up a suitable ML solution to solve their marketing problems. 

AI and ML technologies can offer a regularly updated view of the present needs of web users, looking at actions taken by them in the past, to inform the next step of a prospective customer. 

Machine Learning Importance for Marketing

Today, both ML and marketing are inseparable and help marketers understand data deeply and act accordingly.

The powerful marketing departments depend on KPIs and a complex set of analytics to monitor their progress toward customer growth goals.

As machine learning utilizes predictive analytics, it suggests tailored offers to each customer. Through this individual approach, loyalty and average time spent on site increase significantly.

The added advantage of machine learning is the clear targeting of the audience. Moreover, it is impossible to ignore A/B tests.

It is important to figure out which variations work well for a particular content.  

What else can marketers expect from machine learning? Task automation, of course! In the 1990s, marketing automation platforms became familiar with major changes in CRM and email marketing programs.

The major functionalities of automating tedious and simple tasks were rapidly augmented with various complicated tasks led by machine learning.

ML can process a vast amount of collected audience data. The automation platforms started power-wise decision-making with customized audience development and automated lead scoring along the whole audience journey.

So, without the use of machine learning algorithms, it can be very hard to grow and stay ahead of competitors.

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Efficiency of Machine Learning in Visual Marketing

The past ten years have seen the rapid development of visual data such as videos and pictures. Marketing has implemented AI and ML very early in the phase of boosting the customer experience.

NLP-processed text can be used by machine learning models to inspect vast amounts of customer-generated data to find rules and patterns in social media posts, feeds, and interactions on customer support chats, blogs, and product websites.

By deeply inspecting the keywords and sentiments in reviews, comments, and posts, machines can generate reports on how users interact with the brand.     

AI and ML technologies can offer a continuously updated view of the present demands of web users and the actions they took a few months ago to inform marketers what their prospective customers want to do.

Use Cases of Machine Learning in Visual Marketing

Automatic Content Generation

For instance, GPT-3, the language generator of OpenAI, can automatically produce shorter content pieces according to structured data, automating the creation of product descriptions, tweets, short news reports, and customer marketing emails in a matter of seconds.

Personalized Visuals

The growth of visual marketing purely depends on the personalization level, where machine learning algorithms use consumer browsing history, behavior data, and demographics to provide visually impressive content to consumers.

In visual marketing with machine learning, this translates into highly personalized visuals based on the interests and preferences of consumers.

The goal of using ML and AI-based computer vision technology is to develop a model that can perform tasks independently without human support.

Computer vision technology is used, especially for deep learning, to inspect numerous datasets via annotated images displaying the objects of interest in the images. 

Improved advertising

Today, the majority of advertising platforms have combined AI and ML technology to enable advertisers and marketers to target a larger audience with fewer efforts.

The use of automation in optimization can reduce major manual tasks when developing an advertising campaign and enhance performance concurrently.

Visual Search

An integration of machine learning and computer vision is used for the consistent working of the visual search feature.

In addition to letting devices look at the image, computer vision helps the system interpret what is in the image. Later, devices can determine how that information can be used. 

In short, it depends on ML to find the object that is highlighted within the image. 

The functionalities of visual search are different across big platforms. Users wish to use every platform with multiple mindsets, which range from shopping to data recovery, among others.

For instance, Bing and Google’s visual search are mainly used for data; moreover, users can still employ this feature for shopping as well.

Predictive Analytics

Predictive analytics is another very important feature of machine learning.

Predictive analytics goes through the past performance of data to determine the efficiency of multiple visuals. 

Further, predictive analytics can be used to forecast churn, identify cross-selling or upselling opportunities, forecast CLV, point out the relevant marketing messages and channels, and forecast client behavior that is triggered by specific events.

As companies generate a significant portion of revenue through repeat customers, identifying and addressing churn earlier is a major revenue driver.

Businesses can use ML algorithms to point out customers who are likely to churn within a particular period and intervene with targeted offers, incentives, and messages to at-risk customers. 

Marketing Opportunities

Both basic and modern machine learning models provide limitless opportunities to strengthen visual marketing campaigns, save money and time, and make data-led decisions.

Most of the latest and freshest marketing opportunities can go unnoticed.


Last but not least use case of machine learning in visual marketing is if you’re thinking about time management.

All of the cases mentioned above will help save a lot of time. By automating different tasks, such as content creation, video editing, or image tagging, there will be much free time saved.

The free time can be used to do very important manual tasks that will improve your marketing efforts. 

So, using machine learning algorithms, you can create highly effective visual strategies and images and take an individual approach to each website, app, or social media channel visitor.

Automated data visualization

Images are more powerful than words.

AI can more proficiently convert data into visual format than humans.

Analysts normally rely on Tableau or Excel tools to manually produce visualizations, but automated corporate analytics solutions like Qlik can centralize data origins and produce useful reports and dashboards for your sales and marketing teams.

Various platforms make use of data analytics and modern machine learning algorithms to filter market trends, audiences’ behavioral patterns, and other data that are invisible from plain view. 

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The Future of Machine Learning in Visual Marketing

A lot of people are sure that artificial intelligence and machine learning are here to replace people.

The future of machine learning in visual marketing cannot be without human creativity.

The visual search technique is still being developed. Thorough research is needed to perform visual searches in different domains, the relevancy of which will depend on the design of the product structure. 

With the creative features of ML algorithms, brands can predict how an end user feels about visual content.

By perfectly pointing out scenes, emotions, and objects described in images, and GIFs marketing professionals need to ensure that their visual elements are consistent with their brand messaging and reputation. 


Machine learning in marketing allows businesses to stay active in the competition by considering market, customer, and product trends as they are growing, addressing complicated problems before they completely materialize, and forecasting upcoming value with complicated analytics obtainable only through ML-based queries of massive volumes of data.

Visual marketing with machine learning allows customers to search for products they want to locate in the catalog with similar designs and styles. eCommerce brands use this solution to convert their lifestyle images into showrooms.

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