Auto-tagging: an essential ally for fashion retail to improve conversion rates

Artificial vision (or computer vision) can be used to automate the process of tagging, thereby enriching the existing data. That allows us to create a richer, more descriptive database that improves filters, enables search engines to easily find products, and gives customers a boost to help find products.

Fashion companies often handle catalogue data manually. Such data, describing each product, is used in the brand’s website and in others, as well as feeding the company’s database. That’s how it has always been done…but the manual workload with such data leads to various kinds of inefficiency.

1

Greater workload

Manually managing data implies increasing the workload for some of the products’ departments that are already overloaded, which ends up causing bottlenecks.
2

Delays in updating products

Another undesirable effect of manually handling such data is a delay in uploading new products to the website, which leads to a loss of sales for the brand.
3

Inaccuracies and errors

Of course, inaccuracies and human errors also arise, making the customers’ browsing experience more difficult. As we know, that has a direct impact on the conversion rate of e-commerce.

According to the study Re-imagining e-commerce carried out by Episerver, 21% of unfinished purchasing transactions are due to incomplete content and 14% to inaccurate content.

Taking into account the growth of the online channel over the last year, this is even more relevant. While the fashion sector in Spain was being hit by a fall in sales of 25% in 2020, online sales over the same period grew 43.6% compared to the previous year.

Even Forbes reflected on the contribution of efficient data management to growth in e-commerce, considering it to be a necessary part of operations today.

Automating data in fashion

Artificial vision (or computer vision) can be used to automate the process of tagging, thereby enriching the existing data. That allows us to create a richer, more descriptive database that improves filters, enables search engines to easily find products, and gives customers a boost to help find products.

And it goes much further! It is possible to describe products exactly as clients are searching for them (e.g. “a red, sleeveless mini-dress with a round neck, pockets and patterned with fruit”), which will improve conversions without a doubt.

Furthermore, unlike the expensive, repetitive manual process, artificial vision algorithms can process hundreds of images per second. This saves time and costs, making the management of the catalogue’s products much more efficient.

Is there any doubt the time has come to introduce auto-tagging to the fashion sector?

Sciling

Author Sciling

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