By Robert Ahlborg, the CPO and co-founder of Looklet – a fashion tech scale up that provides automated on-model fashion imagery for e-commerce. Based in Stockholm but with offices in NYC and Paris, Looklet has worked with retailers such as Saks Fifth Avenue and H&M.

For a long time, there existed apprehensions about the applicability of Artificial Intelligence in the world of fashion, a field largely guided by creative ability and expression.  But technological interventions are transforming the way businesses function today, and fashion is no exception. From design and production to marketing and sales, Artificial Intelligence has found its way into the many aspects of fashion and retail. This stems from the understanding that these technologies are not deterrents to creativity, but rather powerful tools to augment creative capabilities and customer appeal.

To thrive in an environment as dynamic and fast paced as fashion retail, businesses need to stay agile and be prepared to make the most out of any potential opportunity. Artificial Intelligence enables businesses to understand consumer behavior and act on it. Hundreds of data points are factored in to arrive at valuable insights that can guide retailers to strategize better and enhance customer experiences.

Revolutionizing retail

Traditional approaches to performance analysis involved a retrospective look at product performance at the end of each season. With AI, it is possible to access real time data and observe shifting trends and stock performance as they happen. This enables retailers to tailor proactive strategies to tap into the consumer sentiment and address their demand without missing out on key opportunities. To stay on top of the game, brands always need to stay relevant and ride the wave. What’s more, technologies like automated product tagging allow retailers to analyze market performance on an attribute level that considers detailed attributes like colors, prints, sleeves, necklines, and more.

AI powered tools can help retailers identify their best and worst selling items to efficiently optimize inventory. Learning from prevailing customer behavior and planning inventory stocks accordingly can reduce wastage and unsold inventory costs significantly. By identifying high-demand areas and emerging sales trends in real time, predictive analytics can take the guesswork out of inventory management, saving brands the trouble of overstocking or understocking an item. This puts them in a better position to understand how well a product is likely to be sold and how quickly it needs to be restocked. The technology takes into account seasonal factors, popular fashion trends, geography and the age of the customer group to forecast the demand for items.

Predictive analytics can also help make marketing campaigns more effective. One simple application would be the use of natural language processing to understand the lingo of a certain target audience to tailor marketing campaigns and advertisements directed towards that demographic.

AI helps retailers to optimize their pricing strategy by allowing them to foresee how customers would react to price changes based on data from sales history. The predictions may not always be cent percent accurate but are immensely useful in giving a sense of how customers are likely to respond. Retailers can gain a competitive advantage by making use of  optimal price points  recommended by AI after monitoring competitor pricing. They can also identify the best timings of the seasons to keep the prices low with minimal margins and when to slightly increase prices to maximize profitability. This makes it easier to plan markdowns and promotional strategies to appeal to the right customers at the right time.

Placing customer experience at the heart of retail

While AI opens a world of opportunities for businesses to up their game, customers are also presented with compelling shopping experiences. Take the case of visual search for instance. AI-enabled visual search allows customers to easily search for products by simply using pictures of clothing they wish to purchase or celebrity styles they would like to emulate. After identifying all the products in the image, AI comes back with the closest match for each of the products from the retailer’s stock. This way, customers can find what they want, even when they cannot put into words what they are looking for. Retailers, on the other hand, gain a deeper understanding of consumer taste and preferences.

When customers are faced with products that go out of stock or size, they are redirected to a selection of relevant product recommendations to help them find what they are looking for. These recommendations based on product similarities boosts customer engagement and reduces the number of sales opportunities lost to competitors.

Personalized recommendations make browsing experiences more satisfying for online shoppers as they are presented with a curation of what they are most likely to purchase, thereby saving time and effort. This is made possible by AI – carefully analyzing previous orders to arrive at customer preferences in color, style, size and so on.

Speaking of personalization, virtual try-on solutions are steadily gaining popularity among shoppers. Virtual trial rooms enable customers to try on any garment in an online store from the comfort of their homes. As they are enabled to make more informed purchase decisions, happy customers are likely to buy more in the future, also reducing the chances of returns and exchanges. For instance, the Looklet Dressing Room allows customers to style garments across a broad variety of body shapes to visualize how items of clothing would look on them. By leveraging Looklet’s core rendering technology, along with their AI and 3D systems, high quality and color-accurate imagery is created to offer a photorealistic virtual dressing room experience. Shoppers can freely try on what they like and style multiple items together to create looks they would like to see themselves in.

An exciting future for fashion tech

It is interesting to note how AI is helping retailers bridge the gap between in-store experiences and online shopping experiences, in the process building customer trust to a remarkable extent. From a time where shoppers were hesitant to make online purchases for clothes, to shopping regularly from pure play ecommerce brands, we have certainly come a long way. Shopping experiences, both online and offline have increasingly become more convenient, personalized and enjoyable. Thanks to the contributions of Artificial Intelligence and Machine Learning, the future of fashion and retail is indeed full of possibilities.