Fashion is one most rapidly growing industries. However, although its enormous implications on different levels (social, economic, etc.), fashion retail has remained quite traditional over decades.
The emergence of modern technologies has provided huge amounts of data that have started to disrupt the industry. Some of these technology areas are Artificial Intelligence and Machine Learning.
We’ll list the top 10 AI trends in fashion eCommerce in two separate blog posts, starting by the top 5 focusing on online merchandising and customer experience.
1. Similar product recommendations
Webshop visitors often abandon the site when an item is out of stock, not in their size or it is not exactly what they were looking for. This is when AI comes to the rescue.
Through computer vision and algorithms, it is possible to automatically suggest similar items when a customer is taking a look at a specific garment or accessory in a brand’s website or online marketplace. It is also a useful feature when customers can’t find the exact item in the right size, reducing the chances of abandonment.
The advantage of this kind of recommendations is that, unlike most AI-based solutions, it doesn’t rely on behavioral or customer data, as it focuses on the visual similarities of the item the person is looking at. The clothing recognition is carried out using computer vision, and visual features are extracted thanks to an automatic product image tagging tool.
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2. Recommendation engines
It is widely known that traditional brick-and-mortar businesses used to (and still do) display products following certain rules. These are meant to attract customers to the items they want to sell the most.
In a similar way, with digital transformation and the advent of eCommerce, retailers’ goal has become to personalize merchandising, this time depending on their customers’ taste. This is the main reason why AI-powered recommendation engines are quickly gaining ground in the eCommerce field. They provide personalized product recommendations based on user behavioral data and are often presented in the form of “You may also like…” or “Customers also bought…”. However, they rely heavily on this data and when there’s a lack of it, its recommendations can be really disappointing.
Companies like Nordstrom have already integrated such engines and provide recommendations based on other users’ behavior. It is the same kind of recommendations we can find in media business such as Netflix. The more the system is fed with data, the easier it can predict what the customer might be interested in. This translates into higher conversion and profit. It can also be combined with visually similar recommendations, which are based on product data.
3. Virtual assistants and chatbots
One of the researched fields in AI for fashion has been chatbots or virtual assistants. These are virtual machines that recommend garments and accessories that best suit a specific customer via chat as if they were actual shopping assistants working around-the-clock.
These services are fed with data and learn from each customer interaction. Data enrichment improves the algorithms the system is based on in order to provide more relevant recommendations and therefore increase conversion rates.
4. Visual Search
Ever tried to find something online that you really liked but didn’t really know how to exactly describe with words? It can be really frustrating and time-consuming.
Visual search aims at eliminating such hassle by enabling consumers to take a picture of a product in order to search for it online. With the use of computer vision and image recognition, visual search solutions match the image uploaded by the consumer and with the closest image in the retailer’s catalog.
This increases the chances of providing relevant results and driving conversions. Asos recently integrated visual search in its mobile app. Users can find a camera icon beside the search bar, letting them snap any piece of clothing or accessory and find the most similar piece in Asos catalog.
5. Virtual personal stylists
The same piece of clothing usually fits certain body types differently. This is the main reason why consumers find it hard to be confident that the apparel they buy online will suit them. In fact, retailers in the US report a return rate of between 20% and 40% for online sales, poor fit being the number one reason.
That’s why some companies like Stitch Fix are starting to use algorithms with humans in the loop as virtual personal stylists. With the use of AI, these solutions learn what suits each person best according to their body type. Customers only need to fill out their profile. Then, the system recommends the best items for them, and a human stylist picks the final suggested products. The more data the solution gathers about different body types and what customers keep and return, the better the recommendations are.
Many brands and marketplaces have already adopted different AI solutions to boost their eCommerce performance and meet e-consumers’ evolving expectations by predicting behaviors, forecasting trends, optimizing operations, etc.—all thanks to AI.