Week 5 – Product Strategies

In week 5 of my project I continued my research on the product strategies of major retailers for their private label/in-house brands. Amazon seemed to be a leader in this area, as their unrivaled capital and leading technologies allow it to market their items in a manner unique to all the other companies. Using Alexa, customers can have a personal shopping guide right at home through its “Echo Look” device; this device will use a camera and machine learning to create actual stylist responses to what you are wearing and suggest clothing, often Amazon’s own in-house brands. Alongside this, they offer an augmented reality (AR) mirror to try on clothes virtually from the comfort of your home before purchasing them. And if you order that item and it still doesn’t fit you, that isn’t a problem either, as Prime Wardrobe enables users to try on the physical clothes at home and return them free of charge if they don’t fit.

I also continued to spend some time at the office shadowing data scientists and learned more about their work process and applications of different algorithms to optimize the shopping experience.

Alongside this research work I began reading the book Big Data by Bernard Marr. It’s opened my mind to the endless applications of data science in our world, from video games and sports to retail optimization and even homeland security. I am also interested in how high fashion companies use data science, and how their strategies differ from more mainstream retailers; other than that, I would love to learn about the intersections of big data analytics with ethics and society, especially in todays context of large corporations amassing immense amounts of data (even private information) from consumers and using them in whichever ways to fit their needs.

My next goal is to find out how blockchain can be employed in the fashion and clothing industries to display transparency and maintain ethics, as well as preserve authenticity of garments in an age riddled with cheap fakes and bootlegs.