# online-shopping Published articles for online-shopping. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Apparel & Accessories Quantitative UX: 3 High-Level Takeaways from 40+ Charts DevFeed: [Apparel & Accessories Quantitative UX: 3 High-Level Takeaways from 40+ Charts]() Original publisher: [Read original article]() Author: Niel Gan Published: 2026-06-12T08:02:00Z Content type: article Language: en Sources: [Baymard Institute]() Topics: [User experience (UX)](), [data](), [benchmarking]() Tags: [article](), [benchmarking](), [customer](), [data](), [ecommerce](), [online-shopping](), [research](), [reviews](), [shopping](), [survey](), [ux]() ### AI overview This quantitative UX study examines apparel and accessories shoppers' habits and preferences using survey data from 1,922 US online shoppers. It presents more than 40 insights on topics including sizing and fit confidence, product-page evaluation, reviews, returns, loyalty programs, and delivery expectations. The article highlights uncertainty throughout the shopping journey, including mismatches between shoppers' self-reported sizes and industry definitions, infrequent shopping as a barrier to loyalty enrollment, and reviews serving as a proxy for fit evaluation. ### Source excerpt (Note: Unfortunately, e-mail and RSS don't support advanced layouts and features. If the graphics in this article look strange, you may want to read the article in your web browser.) Key Stats & Takeaways 40+ new insights on Apparel & Accessories shopper habits and preferences 1,922 US online shoppers surveyed in this quantitative UX study Apparel and accessories shoppers face uncertainty at every stage of their journey, from how their self-identification corresponds to retailer categories to how they evaluate fit on the product page We've released new Quantitative Insights into people who shop on "Apparel & Accessories" sites, adding to our growing body of data on the habits and preferences of online shoppers across key ecommerce categories. These insights are survey-based data visualizations that complement and deepen our large-scale UX research findings and benchmarking of the Apparel & Accessories industry. The 40+ insights cover the Apparel & Accessories online shopping experience across a wide range of topics: online trip drivers, size and fit confidence, product page evaluation, reviews usage, returns, loyalty programs, and delivery expectations. Apparel and accessories shoppers make decisions with incomplete information at every stage of their online journey. They can't feel a fabric, try out a fit, or know whether a size label will translate to their body. Beyond that, they may not know whether the items they buy will ultimately feel "worth it" to them, or whether they'll shop frequently enough to make a rewards program worth joining. Each of these uncertainties plays out at a different stage of the journey, and each has distinct implications for how Apparel & Accessories sites should be designed. In this article, we'll highlight 3 high-level findings that reflect how apparel and accessories shoppers navigate that uncertainty: Shoppers' self-reported size categories often diverge from industry definitions Infrequent shopping, not program design, is the top ## Size Recommendation System at Myntra DevFeed: [Size Recommendation System at Myntra]() Original publisher: [Read original article]() Author: Aayushi Das Published: 2025-01-30T06:25:43Z Content type: article Language: en Sources: [Myntra]() Topics: [Data Science](), [implementation](), [real-time](), [dashboards](), [Database]() Tags: [blog](), [dashboards](), [data-science](), [e-commerce](), [engineering](), [fit-and-size](), [implementation](), [models](), [online-shopping](), [performance](), [pipelines](), [real-time](), [recommendations](), [retail]() ### AI overview This article describes Myntra's Size Recommendation System for personalized clothing size and fit recommendations. It explains how the system uses users' past purchases and Try Size Finder inputs, provides real-time recommendations, monitors performance with dashboards, and refreshes models and vectors through offline pipelines. ### Source excerpt In recent years, online shopping has surged, revolutionizing how people purchase products and services. E-commerce's convenience has reshaped consumer behaviour and the retail landscape. Unlike traditional stores, online shoppers often face sizing challenges, leading to hesitancy and missed sales. Myntra has been a pioneer in addressing size and fit challenges in India, leading the way with innovative solutions that have significantly enhanced the shopping experience. Building on its leadership in this space, Myntra's latest initiatives take these solutions to the next level, offering even sharper and more effective recommendations. Solving this complex problem requires a combination of various features addressing size and fit issues. This blog details Myntra's approach to size and fit recommendations, including our solution, implementation, offline pipelines, online services, handling size recommendation leakages, A/B analysis and more, providing a comprehensive overview of our strategies and outcomes. What is the solution being used at Myntra? Personalized recommendations are generated using data science models, which rely on two main types of inputs - Past purchases of the user Size and fit inputs provided through the "Try Size Finder" questionnaire for users without purchase history Image 1: Recommendation based on past purchasesImage 2: Recommendation based on user inputsHow have we implemented this solution? We have implemented a Size Recommendation System (SRS) to personalize size and fit recommendations for Myntra users, enhancing their shopping experience. This end-to-end service provides real-time size recommendations based on user profiles, purchase history and inputs, ensuring a seamless and engaging buying process. System performance is monitored via dashboards with appropriate alerts. Offline pipelines The models tend to degrade over time due to drifts. To address this issue, we have established refresh cycles at suitable frequencies to maintain high c