The Data Behind Your Perfect Fit
Data is the buzzword of 2020, but it’s only as good as the analytics it drives. When deployed correctly, data can support the retail experience by providing insights into customer behavior. Brands that can use these learnings to create personalized shopper journeys are likely to see big rewards, from increased customer loyalty to greater brand engagement and, ultimately, to more sales.
FN spoke with Sebastian Schulze, co-founder and managing director at Fit Analytics, about the value of personalization and why the right fit for retail is Fit Analytics.
FN: Why is personalization worth the investment in 2020?
Sebastian Schulze: Customers crave a hyper-personalized experience. As shopping shifts online, retailers must keep up with their customers’ wants and needs to maintain a competitive edge. Customers may only spend a short amount of time browsing online; if they don’t immediately find something tailored to them, they will shop elsewhere.
In an A/B test of our Product Suggestions feature with partner Simons, we found that showing more personalized products led to an increase of 10% in net revenue; 5% in average order value; and a 2% higher conversion rate for shoppers who interfaced with the feature. In order to keep shoppers engaged within massive product catalogues, retailers must personalize the online experience with items that are relevant to them.
FN: What gives Fit Analytics an edge in the marketplace right now?
SS: Fit Analytics recommendations are based on real people – not just numbers. We take into account body modeling data and size charts, in addition to purchase and returns data, for truly accurate recommendations. When Fit Analytics launched a decade ago, we offered a webcam-based body modeling service that captured over 100,000 3D scans of real people. While the original technology proved a bit too laborious, we were able to gain irreplaceable insights, which served as the foundation for our AI-powered sizing platform. Our algorithms are constantly improving with every recommendation given; we provide over 1 billion size recommendations a month.
We are also continuously adding to our suite of solutions: We want to ensure that we are meeting retailers and their customers every step of the way. With the ongoing innovation within our platform, we perform regular user testing, learning from shoppers first-hand.
FN: How would you define a superior user experience in e-commerce?
SS: A superior user experience is one that keeps customers coming back. We’ve found that customers are not afraid of a longer journey, if it leads to a trustworthy recommendation. When presented with personalized products that are relevant to their likes and interests, customers are more likely to come back and shop again.
Recent A/B tests showed that Fit Finder users were more confident to check out when experiencing a medium-length user journey. Those shoppers had a 14.4% higher conversion rate on mobile (+6.5% on desktop) than those who received a size recommendation after just a few questions. Shoppers who completed the longer questionnaire were more confident with the provided recommendation and therefore more likely to convert. This type of user experience creates brand trust and leads to customer satisfaction and loyalty.
FN: How can machine learning and AI help strengthen existing retail strategies?
SS: These technologies take data points that don’t have much meaning when isolated and translate them into actionable insights. Through AI and machine learning, retailers can truly get to know their customers and create a completely custom experience for every shopper. Retailers can also leverage this data to improve initiatives around merchandising, inventory planning, product development, and marketing. This is essential when considering the expectations shoppers currently have to get what they want right now.
For more information, visit fitanalytics.com
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