AI Styling engines work by using tens of thousands of style rules. These are often curated by human stylists themselves and can generate millions of outfits in a day. In a grim retail landscape, could this actually become a tool to drive conversions for the retailer? Does it have any benefits for the customer? Let's find out.
The Covid-19 pandemic has brought an unprecedented disruption to businesses and individuals far and wide, causing a constant stream of bankruptcies and furloughs, with retail being one of the hardest hit sectors.
Certain types of stores, such as supermarkets and essential shops, saw a surge in demand during the months of lockdown. However, it has had the opposite effect on fashion and apparel retailers who have felt the devastating effects of the health crisis, causing sales in the UK to fall by 50% in May (YOY) and by 37% in June (YOY).
The way consumers shop in our “new normal” has fundamentally changed too. Even though non-essential retail in the UK was allowed to reopen on 15 June, people are not exactly in a rush to get a Zara top in store and put their health at risk.
A silver lining in all of this is the rise of ecommerce, which has proved essential for retailers forced to shut physical stores. Online sales during Covid-19 grew by 52.20%. We expect this trend to remain strong and well into the second quarter of 2020, solidifying it as the new normal.
AI styling automates the process of outfit recommendations. At Streamoid, we’ve programmed the intelligence of world class stylists into the system by incorporating over 60,000 style rules. This means that, to create an outfit, the AI Styling tool considers over 2,300 relevant data points including specific brand style, product attributes, current trends and colour palettes, among others.
Thanks to machine learning models, Streamoid’s AI Styling tool can “understand” what is considered good style and what isn’t, and make recommendations based on this. For example, it wouldn’t pair a polka dot top with a stripy jacket. The system is also self-learning, meaning that it improves based on how shoppers interact with its recommendations.
In addition to giving retailers a vast amount of merchandising choices (Streamoid’s AI styling service can create thousands of looks in seconds), our AI Stylist help brands and retailers by:
Curating Trending Outfits: Similar to the experience with a store associate, the system creates styles and suggests complementary outfits in accordance to current trends in real-time, and will adapt to changes within the industry.
Stock level adaption: Outfits will adapt to stock levels and will never recommend out of stock products.
Curation control: While Streamoid’s recommendations are automatically optimised for a brand’s style, retailers have access to a dashboard where they can edit or delete outfits created by the AI Styling tool. This lets retailers push a certain product more, if needed, and also ask for specific brands to be matched together (eg. combine Topshop skirts with Zara sandals).
Geo-local styling: Multinational retailers can style their catalog differently based on the specific markets they operate in and local fashion preferences.
Context: Outfits are created based on themes, styles and occasions. For example, shoppers can browse for “Work wear” and see outfits that would be appropriate for the occasion.
Instead of holding a sale, dumping inventory or off-loading it to discount partners, AI styling can help make recommendations that are six times more likely to convert.
When customers see the versatility of the product (being able to style it for different occasions and to create a number of looks), the purchase through the funnel becomes much easier.
In addition to higher conversion, AI Styling can increase revenue and the average order of shoppers’ basket size.
When ABFRL - one of India’s largest retail conglomerates - implemented Streamoid’s Outfitter it reported a 31% increase in basket size and 38% increase in revenue.
With the uncertainty that comes with events like the coronavirus pandemic, it’s imperative for retailers to have the right tools to optimise their current processes.
AI Styling makes the online shopping experience better for customers too.
When product recommendations are implemented correctly, they can strengthen customer loyalty and create a positive brand experience. In fact, a study by Accenture found that 91% of consumers are more likely to buy from companies who remember them and provide relevant offers. 83% of them are also willing to share their data in exchange for a personalised experience.
AI styling engine personalizes curation at scale, eliminating the choice paralysis shoppers face when they land on a retailer’s site without having FOMO (fear of missing out).
While we’re yet to see the full effect of the pandemic on the retail sector, now is the time for businesses to understand new consumer behaviours, embrace digital solutions and provide a seamless curated online experience to rise above the chaos and come out more efficiently and stronger than ever before.