Top 10 coolest AI trends in fashion eCommerce (II): improving operations

In our latest blog post, we presented the top 5 AI solutions for fashion eCommerce to help improve online merchandising and customer experience.

However, AI solutions for online retail go far beyond that domain. Many of them target operational efficiency. Therefore, to complete the list of our top 10 coolest AI trends in fashion eCommerce, we’d like to present the top 5 AI solutions focusing on business efficiency.

 

6. Market intelligence and trend prediction

Social media is one of the main leverages in the fashion industry. It influences purchases and creates new trends. Therefore, all major players in fashion retail have started targeting potential shoppers and engaging with them in social media platforms such as Instagram.

These networks provide tons of data, both structured and unstructured that can be analyzed to predict trends. These analysis made by AI-based algorithms enable better decisions in operational departments. For instance, it can help make decisions regarding designs, or plan merchandising strategies of certain products even before they would reach the peak of their popularity.

 

7. Automatic image product tagging

Boosting productivity of employees has always been any employer’s goal. Automatic product tagging solutions based on AI can help accomplish and speed up certain tasks that have usually been carried out manually.

Using computer vision to recognize product images uploaded to an eCommerce platform or a PIM system, this solution automatically assigns the relevant categories and attributes to every catalog item. Moreover, translation of the attributes can be automated and product updates can be fetched several times a day in order to automatically inject the attributes and categories.

Besides productivity, this also enhances the SEO value of the product, better positioning it for search results.

 

 

8. Customer purchase prediction

This AI-based solution allows retailers to tailor specific discounts based on a ranking that takes into account their likelihood to buy.

It leverages different data from different customers and analyzes behaviors, such as  newsletter open rates or product page views by a particular customer.

On the one hand, by offering small discounts to customers who are already willing to buy, retailers can increase their margin. On the other, with bigger discounts offered to those whose likelihood to buy is smaller, the chances that they make a purchase are increased, and so is revenue.

9. Ideal price recommendations

A similar solution consists in ideal price recommendations. Using deep learning models and AI, it is possible to automatically and constantly keep an eye on competitors’ pricing strategies and remain competitive when faced to changes in external factors, like the retailers own inventory, or competitors specific situations where, for example, they run out of stock.

It is widely used, for instance, to compete with giants like Amazon and always offer more competitive prices for every reference.

 

10. Counterfeit detection in fashion

The large fashion offering that eCommerce has made available to the masses comes with great risks for brands. Nowadays, counterfeits account for around 5% of all goods imported into the EU. Even major companies like Amazon and eBay have suffered from the issue. Luckily, Artificial Intelligence yet again offers a tech-enabled solution to this particular problem. Using image recognition, companies like Red Points are able to automatically detect counterfeit items in online webshops.