Imagine if fashion houses knew that teal blue was going to replace orange as the new black. Or if retailers knew that tie dye was going to be the wave to ride when swimsuit season rolls in this summer.

So far, there hasn’t been an efficient way of getting ahead of consumer and market trends like these.

The company’s deep-learning pricing and supply chain systems, powered by CPUs, let organizations quickly respond to changes whether in markets, weather, inventory, customers, or competitor moves by recommending optimal pricing, inventory and promotions in stores.

Now the company’s  discovered new algorithms that could outperform even the most complex and expensive commercial pricing systems in use at the time.

Using a combination of advanced machine learning methods and statistics, the system transforms products into functional attributes, such as type of sleeve or neckline, and into style attributes, such as the color or silhouette.

It works off a database that maps the social media, internet patterns and purchase behaviors of over 1.5 billion consumers, which is a fully representative sample of the entire world population.

Then the system uses multiple algorithms and approaches, including meta-modeling, to process market data that is tagged automatically based on the clients, prices, products and characteristics of a company’s main competitors.

This makes the data directly comparable across different companies and geographies, which is one of the key ingredients required for success.