Artificial intelligence, among other things, is helping retailers zero in on popular consumer demand as well as find the right price point to boost sales.
“Artificial intelligence (AI) would be the ultimate version of Google. The ultimate search engine that would understand everything on the web. It would understand exactly what you wanted, and it would give you the right thing.” – Larry Page
AI grants us the power to understand exactly what our consumers want, and this power, when leveraged correctly, can be the quickest path to success. AI, compared to the “Demon” by Elon Musk and to “God” by Alan Perlis, is omnipresent today.
It would be hard to name an industry that isn’t growing increasingly dependent on AI, and fashion retail is no exception.
To understand the impact of AI on fashion retail, let’s first divide the industry into its two primary channels — ecommerce and brick-and-mortar stores. While physical stores have a unique set of usage for AI, it’s the e-commerce retailers that are the chief worshippers.
The mind reader
Today, brands like ZARA and Shein churn out clothes that fit into the fad within a turnover period of 15 days. The million dollar (literally) question is, how exactly do they predict trends so quickly?
Well, this is where AI enters the scene. AI facilitates immediate recognition of changing trends that help these brands to stay on top of their games, and deliver instant gratification to their consumers.
Beyond prompt fulfillment, personalisation is another factor that consumers look forward to, and this is where AI-driven visual recognition comes into play. We’re all familiar with the product recommendations offered by sites like Amazon.
Ecommerce fashion retailers of the day unanimously use visual recognition to recommend similar looking products to their consumers, based on their respective search histories. This makes the shopping experience convenient for the buyers and helps brands to score more conversions.
Conversely, visual recognition plays an equally important role at the sourcing end for retailers. Based on past sales data, it can help to identify categories, designs, and colors that are experiencing high demand and also the ones that aren’t.
The said knowledge can be used by purchasing departments to determine the volume of products to be stocked. Data driven decisions like these are great tools for combatting overstocking, and lead to better inventory turnover.
adapted by Your Story
