This is part six in our series introducing you to the New York Fashion Tech Lab 2016 class. Genostyle uses big data and algorithms to help companies get important insights into their customer’s fashion needs. We talked with Veronica Cabezas, Genostyle’s CMO/CRO, who filled us in.
Genostyle is an algorithm-driven style analytics platform that centralizes and augments customer insights to quickly deconstruct and recommend revenue-driving activities across a fashion retail business. Our solutions and data sets infuse and amplify existing customer data analytics, providing not-seen before style insights to broaden and deepen understanding of a business’s customers, the styles that they consume and how it drives margins. From forecasting to managing returns, to making better business decisions on merchandising, price markdowns and distribution, we help merchants and planners gain a quantifiable competitive advantage.
What was your inspiration behind Genostyle?
Genostyle was ‘pivoted’ from a previous venture on fashion + styling social networking: Best Fashion Friend (2011-2014). Through our affiliate networks we realized that there was a need to prioritize product recommendations based on each buyer’s own style preferences, and not based on “what other people like”; effectively filtering BEST options among thousands of designers and millions of products. The need to understand buyers’ style preferences is equally important for large and small retailers. Matching users and brands is an enabler of smoother digital interactions. The need was clear.
What value does Genostyle have that makes it stand out against other startups in the space?
What is unique about our approach is the nature of the algorithms that we have developed: Traditional product recommended engines look into other people’s likes as input into multivariable logistic regressions in trying to predict what the user will like. Our approach is different: we break style down into quantifiable data, and utilize that data to build a “style genome” of a person or a brand (along our proprietary fashion style taxonomy).
That genome then powers the relationship between the retailer and the consumer, creating a common, quantifiable language of style that guides the consumer towards the products they love, and the retailers towards selling those products. Our algorithms look into both the ‘supply side’ and the ‘demand side’ in determining what are the brands and / or products that match a consumer’s own style. It sounds simple, but it is not. It is a true ‘big data exercise’ that pushes the limits of ‘predictive analytics’ applied to fashion and styling.
Tell us about your Style-Matching Product Search API.
We are working on two B2B solutions. StilSight TM is an innovative style market intelligence and customer analytics platform that centralizes and augments a business’s customer insights to quickly deconstruct style and recommend revenue-driving activities. Powered by our algorithms and paired with our data sets, it infuses and amplifies an existing customer data analytics, providing not-seen before style insights to broaden and deepen understanding of a company’s customers, styles that they consume and how that is positively or negatively driving the bottom line. In addition to categorizing brands by the historical style patterns, we quantify and rank each so they know whether certain customer profiles and styles are up-and-coming, waning or even about to be “the next big thing.”
The second solution is our Style APIs,which allow retailers to optimize their digital channels across marketing and e-commerce functions. Over 50% of retailers’ sales come from their loyal customers. Yet, retailers are failing to deliver on customization and personalization. Research by Forrester (2015) shows that 45% of consumers have chosen, recommended or paid more for a brand providing personalized experience. So style matters!
Our style-matching APIs allow retailers to profile their customers style-wise, and provide style-matching brands and products, bringing customization to a level of intimacy not seen before. It puts the customer at the center of the digital experience.
What has been the hardest obstacle for you in starting Genostyle? What has been your favorite part?
The hardest part as with any new technology is adoption. The challenge is to cross the chasm stage and proof viability of technology in order to accelerate mainstream engagement. My favorite part is trying to solve complex problems with limited times and resources. Sounds like a contradiction but the truth is, it makes your more mentally agile, a better team member and a heck of a lot more resourceful. You also learn a great deal in the process which often results in taking a few steps in order back to make a bigger leap forward. It is humbling undertaking don’t let anyone tell you otherwise but one that lends to more intentional actions and leadership.
Tell us about your journey to get to the New York Fashion Tech Lab Class of 2016?
Our journey to New York Fashion Tech Lab started about five months ago when we felt confident that our technology was ready for real-time business applications. We knew that we could significantly shorten our learning curve and the amount of time that it would take us to get in front of the right people such as angel investors and potential clients by participating in a Start Up Accelerator. It is definitely an investment of time and resources at a critical juncture of your early stage company so you need to carefully assess the opportunity and make sure that you can make the most of it.
New York Fashion Tech Lab was a perfect fit for so we applied for it. We waited impatiently until they called us to present in front of a group of industry insiders, investors and program mentors. Only 8 companies would make the cut and we were beyond delighted when we received an email confirming that we were officially one of the eight companies.
The 12 week program was intense in a great way. We were assigned one of the top retailers in the country as a partner for the duration of the program and this helped us gain insights into the challenges and opportunities for the industry as a whole which allowed us to continue to enhance our business solutions. We were also paired with mentors based on our goals and needs. Their guidance was invaluable and we genuinely felt their commitment to our success.
The experience has been one of the highlights of our professional careers. Kay Koplovitz and Amy Millman from Springboard Enterprises have created a platform that provides critical tools and access to very important contacts to help startups advance their goals in a short amount of time. They have also built an important bridge to bring innovation to one of the pillars of the NYC economy, The Fashion and Retail Industry. But the best thing is the community. New York Fashion Tech Labs have created a truly unique and supportive community of founders and mentors. Jackie Trebilcock is probably one of the most effective people I know and she is just amazing at getting you from A to Z in 12 weeks. We are deeply grateful for the experience.
What can we expect from Genostyle in the future?
The fashion retail industry is going through a major transformation as most industries are due to technology and the empowered/overly informed consumer of the 21st Century. Companies that know what their customers want before they do and can mine data effectively to deliver best product recommendations at the right time in the right channel will come ahead of competitors. Simplicity matters for today’s consumers bombarded with information and simplicity can only be achieved if you understand what are the unique characteristic of your customers as well as your brands, “their style DNA”, so that you can match them in a transparent, seamless manner.
Genostyle aims to be the platform that powers these highly customized and meaningful interactions between retailers and consumers. Moreover, we aim to democratize the personalized shopping and styling experience for consumers of all backgrounds via means of technology.
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