8/29/2016A core methodology at Stitch Fix is blending recommendations from machines with judgments of expert humans. Our machines produce recommendations via algorithms operating over structured data, while our human stylists curate and modify these recommendations on the basis of unstructured data and knowledge that isn’t yet reflected in our dataset (e.g., new fashion trends). This helps us choose the best 5 items to offer each client in each fix. The success of this strategy within our styling organization prompts consideration of how machines and humans might be brought together in the realm of fashion design. In this post we describe one implementation of such a system. In particular, we explore how the system could be implemented with respect to a target client segment and season. Fashion design is normally achieved by a qualitative process focused on stylistic intent and inspiration. In contrast, our team conceptualizes the design process as an algorithm searching for desirable regions (for example, points that yield maximal positive responses from our clients) within the space of possible blouses. Put another way, designing a new blouse can be thought of as searching a space with dimensions that correspond to attributes of a blouse like color, print, material fabrication, chest diameter or type of neckline.
Few weeks ago, I came across Rocketgraph. This is a new platform that offers custom reports based on cloud data sources. While the concept is not new, what sets this company apart is the reports & dashboards are sold to users in a marketplace. The platform brings the analytics buyers and sellers together and provides the infrastructure. For years, many vendors have promised custom out-of-the-box solutions. In a majority of cases, most businesses require significant customizations. Will a marketplace approach to analytics offer an intermediate solution with significant time & cost savings? I interviewed Rocketgraph co-founder Constantine Nikitiadis to found out. Take a listen.
Data visualization blogosphere is filled with great ideas and inspiration. What is missing is the candid conversations about the limitations of data. Unfortunately, finding quality content on this topic is like finding a needle in a haystack. So, when one of the greatest thought leaders in SaaS data world wrote on this topic, I feel obligated to share it with you. Here is Tomasz Tunguz on the limitations of data.
Self-service has been a buzzword in the analytics industry for the last few years. While the self-service movement has been instrumental in bringing about rapid decision making and empowering business users get answers to their data questions, one has to be aware of the key skills still required. Stephen Few highlights this important foundation of building a data-driven culture.
Subscribing to email newsletters written by experts on growth and analytics is a great way to learn. Here are five newsletters that stand out from the rest. Written by entrepreneurs, data scientists, growth marketers and venture capitalists, each one offers unique insight into the process of using data to make better decisions and build a better company.