Pricing and Data Science

More regarding pricing.  Large amount of data included is interesting. 

Using Data Science to Avoid Global Pricing Chaos    By Andrea Marron
What every industry can learn from luxury fashion.

 Luxury Fashion Global Pricing Business Omniretail

Technology and e-commerce have revolutionized the way consumers buy everyday products. While this often benefits consumers, many industries face challenges that never existed before. Take “showrooming,” for example. A customer wants to buy something — a piece of furniture, perhaps. It looks good online, but he wants to try it out first. So, the customer finds a nearby store that carries the item, looks at it in person, and decides he’d like to purchase it. Then he picks up his phone, and in a few clicks, finds that same model available at a cheaper price from an e-commerce site in another country, even with shipping costs.

This problem pervades many industries, and while organizations have learned to watch out for showrooming and other new obstacles, many still struggle with substantial price variations across markets.

One powerful example of this problem is the luxury and contemporary fashion industry, in which pricing variations create operational problems for retailers and e-commerce sites in some markets.

My startup, Ragtrades, used big data to explore just how big a problem this is. We aggregated figures from 20 major luxury and contemporary fashion brands and 50 retailers. We assessed prices across 12 countries, using local versions of each website and each country’s currency. This analysis included more than 300,000 data points, using our proprietary algorithms to identify exact matches. Our analysis found that prices in Russia and East Asia are the most misaligned, leaving the same item available at a wide range of prices. Western Europe, meanwhile, had the most aligned prices.  .... "