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Making Visual sense with Correlated Deep Tagging

Updated: Nov 3

Connecting the dots to perfect the online experience


Andra L. Rubinstein

November 2, 2020





Online retailers have only just begun to realize the potential that deep tagging can bring to optimizing their businesses.

Correlated deep tagging hyper-personalizes the shopping experience - making products more discoverable and improving the tagging accuracy with computer vision AI.


Describing an item without using a visual example is an impossible task. The general description might be close – big or small, bright or dark but what about textures? Primary function or matching various products? Design is visual so words can only take you so far.

As the architect Mies van der Rohe said "God is in the details", so in order to create something exceptional all details must be taken into account.



The Treasure Map

Finding the right path to a happy shopper heart and mind is tricky and complex and deep tagging helps draw simple lines for smooth navigation, literally.

Every product of an e-commerce store is made up of several tags that are set to describe its characteristics, features, and the category it is part of. Every product has different tags, so every product is unique, as are the shoppers. Tags play a great part in influencing shoppers buying decision.


Correlated deep tagging improves filtering based on the categories they want to explore by adding in context when creating a match. Breaking down each element within a product image, assigning detailed and accurate attribute tags, extracting consistent features and synchronizing it with design best practices and terminology delivers the most accurate results both for small vendors and marketplaces with large inventory sources.



Back - office Innovation

Correlated deep tagging transforms a manual task for retailers into a high-end computer vision and AI technology solution, enriching the product data and better the website's SEO infrastructure.

Revealing detailed style-based insights into buying trends and preferences gives retailer a better understanding of their shoppers and perfect inventory and promotion planning. It is an important supportive tool for business predictions with the power to minimize warehouses and address accurate demand.



A new approach to Deep Tagging

Allowing e-commerce retailers to update their search based on visual product attributes and design methodology is a game – changer. Correlated deep tagging empowers both online shoppers and retailers to connect better by mapping, extracting and synergizing rich and accurate product attributes that does not exist in generic tagging systems while combining previous website interactions, a 360° shopper profile approach and decades of design rules into consideration.




Correlated deep tagging works on multiple visual discovery cues shoppers can select, combine or remove until they find exactly what they want. In a way, in turns product catalog to a tailor-made one.

Filtering out items that do not fit the exact criteria for a more accurate search alleviates shopper frustration and focuses solely on effective product discovery and personal style, enabling personalized style-based recommendations like similar items and completing a full look that will surely improve conversion and increase AOV.


Correlated deep tagging takes standard tagging to the next level by translating industry affinity to AI algorithms that communicate with tagging cues. Decades of design best practices and methodology have been condensed and continuously evolve to craft a complete setting.


When shopping for furniture, visual context is key for the final purchase decision. Correlated deep tagging connects the dots between design styles features and logic and accurate product tags to create a perfect personalized interaction, sampling all materials, shapes and textures as if you were meeting a real-life interior designer.


Correlated deep tagging deciphers design rules and provides a perfected mechanism for retailers to create an unbeatable online experience for shoppers.


An All – Around Player

Using automated correlated deep tagging has various applications and can serve multiple touch points beyond the website itself.


Here is a quick example.

Suppose an e-commerce catalog contains the image of a 'small pink sofa'. Correlated deep tagging technology will label it into ‘Preppy Style', ‘Lawson’, ‘Soft’, 'High back', 'Romantic' and further subsets. A hover feature can also display the product in its contextual setting on site and generate a lifestyle image fitted for social channels. All these components will be generated automatically through a single click.



Main benefits of using AI correlated tagging in images:

  • Drive accurate search results.

  • Capture shopper’s attention with relevant products.

  • Leverage social media for instant fashion updates and spontaneous purchases.

  • Optimize remarketing content product display.

  • Improve customer service experience with tag-based search bots and online assistants.


By offering the right products to shoppers looking for them, retailers will bridge the gap between product discovery and purchase.



The Next Frontier

Automated Correlated Deep tagging is a MUST for a well-performing online retail store and should be address as the core of a well-functioning value chain.

Automated workflows cut costs and increase profits leaving more time to focus on a better customer experience.

Incorporating the correlation between tags will allow learning more about customers and their specific needs and desires while exponentially growing the business.




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