Fit Analytics is turning 10 this year! In this article, we take you back in history to better understand how we started, why we pivoted, and where we are today.
As we approach Fit Analytics’ 10-year anniversary, we would like to reflect on our journey with you and share our learnings along the way.
It all started in 2010 with the launch of UPcload, a webcam-based body modeling service that allowed users to create a personalized fit profile using a laptop and a CD as a reference item. While the idea behind UPcload was extremely relevant to online shoppers – fit recommendations based on physical photo scans – ultimately the technology was a bit too laborious for consumer adaptation at scale.
Fit Finder’s Multiple Size Alert feature is proven to reduce multi-size orders for retailers.
As apparel shopping continues to shift from offline to online, more and more customers are forced to adjust to a new way of shopping. During the last 5 months alone, COVID-19 has shortened the timeline for evolution from brick-and-mortar to online from 5-10 years to a matter of months. While consumers are concerned with fitting room accessibility and the fear of shopping in stores, they are now looking to e-commerce to fulfill their shopping needs.
This isn’t a new trend for the retail industry. In fact, according to Statista 59% of U.S. shoppers purchased clothing online between Q2 2018 and Q2 2019. We expect to see that number rise in 2020.
Fit Analytics’ North American Senior Client Partner, Irina Sulejmanovic, shares her view on common characteristics of companies partnered with Fit Analytics.
Our partners are diverse and varied, but one thing they all have in common is an innovative ethos. In today’s interview, Irina reflects on our journey from ambitious start-up to the industry leader in sizing and fit, and how cutting-edge thinking continues to inform decisions in an ever-changing apparel landscape for Fit Analytics and our partners.
Looking for ways to improve your retail sales? Adopting our size advisor tool, Fit Finder, has been proven to convert browsers to buyers.
Apparel retailers are always looking for new ways to increase sales in retail without losing out on profit. In this article, we share some key tips on ways to drive revenue while protecting your margin.
Machine learning is a key way for e-commerce businesses to drive sales, meet their customer’s needs, and differentiate themselves from the competition. In this article, we explain the benefits of adopting machine learning technology.
Machine learning technology is much sought-after by apparel retailers but what is it?
In this article, we explain what user experience is and the three key ways machine learning can enhance user experience in apparel e-tail.
Machine learning allows companies to get to know their users in an intimate and non-evasive manner. This technology is being utilized by various industries – the healthcare system, security and of course, apparel retail.
Fit Analytics has an intuitive solution that reduces the need for shoppers to deliberately over-order.
The Multi-Size Purchase Issue
Poor fit is one of the leading reasons shoppers return items they purchase online. Sizing varies across different apparel brands and even within a single retailer’s store. This has led shoppers to mistrust online sizing information and taking matters into their own hands by multi-size ordering.
In this article, we cover how to reduce returns in e-commerce by focusing on how Fit Analytics is using the power of machine learning to combat the apparel industry’s multi-size purchase problem.
Fit Analytics’ Head of Product, Dan Mayer, explains how Fit Finder takes the guesswork out of finding the perfect size online.
Launched in 2013, Fit Finder was created to help find your fit. The intuitive size advisor tool is powered by the most advanced machine learning algorithms in the business and the industry’s biggest data set.
Bridging the gap between in-store and online shopping, Fit Finder provides customers with the requisite confidence needed to find the right size and fit every time, without ever stepping foot inside a store.
Fit Analytics’ Head of Product, Dan Mayer, gives a deeper insight into Fit Finder and reveals how the tool can evolve in the future.
A two-month A/B test with Fit Finder showed positive results for Swedish outdoor retailer Ridestore. Customers using Fit Finder showed a 9% increase in value per visitor and a 4% increase in conversion rate.
Everyday explorers need extraordinary gear – Ridestore is where they go to get it. Founded in Sweden over 10 years ago, Ridestore is known for its impressive selection of outdoor brands. With customers at the heart of its operational strategy, the company offers regional online shops in Sweden, Finland, Norway, Denmark, Poland, Germany, Austria, Switzerland, Italy, France, Spain, The Netherlands, and the UK.
Fit Intelligence is an advanced insights platform offering deep analyses of customer data. Fit Intelligence harnesses the power of machine learning to make Fit Analytics’ partners more profitable.
Prevent Profit Loss with Fit Intelligence
The interactive Fit Intelligence portal translates Fit Finder data into actionable analyses, allowing fashion companies to identify new revenue opportunities. Fit Intelligence unlocks customer demographics and reveals areas for commercial optimization.