How AI can improve SEO of product data for retailers

SEO strategies for retailers

Designing efficient SEO strategies for product data has always been essential for retailers. Rich, detailed content is one of the main traffic drivers for eCommerces. Therefore, in order to enhance site searches and increase sales, retailers should consider implementing smart product description strategies.

An image is worth a thousand words

In site searches, results are always as good as site data. This means that whenever the site contains poor data, results will be insufficient and they won’t probably meet visitor expectations.
We know that consumers are 80% more willing to engage with content that includes relevant images. Therefore, mapping images to rich attributes is essential in order to display relevant results within a website. This is a smart way to show products the customer might really be interested in.
This is even more necessary in the case of eCommerce. Especially, if we consider that product overview and appearance are key factors for a purchase. Actually, 93% of consumers state images are the most relevant factor when deciding whether to buy or not.

Product description SEO

The magic of automated, accurate product data

When launching a search to find a specific product, we find it annoying to be taken to misleading results. For instance, when we type “red mini skirt”, we do not expect to see pictures of blue skirts, or long-tailed skirts. Neither do we want other items such as red pants to appear among our results. As of now, search engines rank images taking into account several factors. These include the file name, the text surrounding the image, the images alt tags, how the item is categorized, etc. As we’ve seen, the more specific the description is, the greater the chances are to correctly match a search.

In this context, Artificial Intelligence (AI) solutions based on image recognition have started to make their way into the industry. The automatic image tagging software generates deeper and more accurate attributes from product pictures in an automated way. Simply said, it is like having a troop of product sheet writing specialists in-house.

AI fashion tagging banner2

But what do these AI solutions consist in?

AI solutions based on image recognition and computer vision are designed to recognize objects. For the fashion industry, this means, for instance, identifying:

  • piece of clothing (i.e. shirt, skirt, trousers…)
  • Its colour
  • Its fabric (i.e. leather, velvet, denim…)
  • Its pattern: (i.e. checked, stripes, floral…)
  • Its shape: (i.e turtle-neck, short sleeves…)

The aim is to be able to do so in a similar way a human brain does. We are still not able to fully understand how our minds process and interpret images. However, certain AI-solutions get relatively close to it. They are able to classify and extract very specific information from an image. From a product picture provided by the customer, the image tagging tool uses algorithms to recognize specific patterns and draw conclusions. It is also able to assimilate previous experiences (it sort of “learns” by itself).

catalog product onboarding

Homogenising product attributes and categorization


As previously seen, eCommerces are faced with product onboarding and categorization, which are highly time-consuming. When left to humans, these tasks may be associated with typing errors or duplicity in categorization. These mistakes can strongly affect SEO of product data. Not only Google takes into account linguistic accuracy, but it also values proper classification. Moreover, the more specific the product attributes are, the easier to match long tail searches. This also helps show more relevant products to the users that launch a query. Robust image recognition solutions based on AI help avoid categorization mistakes and provide richer attributes. This streamlines online catalog management and paves the way for search engines to find the appropriate products. And there it is: automated content enhances product discoverability.


Multilingual content for global reach with a local focus

eCommerce sites highly depend on organic search traffic. Optimising images for SEO is one of the actions a retailer can take in order to increase it. However, it can be a critical task when performed in several languages and for huge amounts of images. Machine learning solutions based on image recognition also allow retailers to carry out product onboarding and tagging in several languages. This improves SEO of product data for retailers in the different target countries and reduces time to market.

Catchoom’s Solutions improve SEO of product data

SEO of product data - privalia's fact sheet

Catchoom’s fashion AI solution is designed to enhance catalog management, improve SEO of product data by enriching the content, improve conversion rates and redefine user experience. It uses Artificial Intelligence and Machine Learning technology trained to adapt to the specific requirements of the fashion market.