# retail Published articles for retail. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Automate replenishment with MMF, Databricks Genie, and Amazon Quick DevFeed: [Automate replenishment with MMF, Databricks Genie, and Amazon Quick]() Original publisher: [Read original article]() Author: Venkatavaradhan Viswanathan Published: 2026-09-14T15:42:06Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [databricks](), [Amazon S3 Tables](), [Amazon S3](), [Automation](), [foundation-models](), [API]() Tags: [advanced-300](), [amazon-quick-suite](), [amazon-s3](), [amazon-s3-tables](), [api](), [automation](), [databricks](), [foundation-models](), [machine-learning](), [retail](), [technical-how-to]() ### AI overview This technical walkthrough presents an unattended replenishment workflow for retail. Databricks Many Model Forecasting uses Chronos-2 to predict seven-day demand for each SKU, Databricks Genie detects demand surges, and Amazon Quick reconciles those surges with supplier availability in Amazon S3 Tables. The workflow places routine purchase orders through a Supplier Order API and escalates cases without a suitable single supplier for human review. ### Source excerpt Foundation models made catalog-wide demand forecasting easy; the hard part is now acting on the forecast. This post builds a closed detect-decide-act loop on Databricks and Amazon Quick that reconciles demand surges against live supplier availability and places replenishment orders unattended, escalating to a human only when no supplier can cover a surge. ## The search multiplier: Driving revenue, productivity, and AI at scale DevFeed: [The search multiplier: Driving revenue, productivity, and AI at scale]() Original publisher: [Read original article]() Author: Nicole Volk Published: 2026-08-20T00:00:00Z Content type: article Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [elasticsearch](), [AI search](), [AI Infrastructure](), [Generative AI](), [idc](), [Resilience]() Tags: [accelerate](), [agentic-ai-cloud-search-context-engineering-customer-experience-deployment-end-user-experience](), [ai](), [ai-infrastructure](), [analysis](), [business-value](), [customer-story-elasticsearch-workplace-search](), [elasticsearch](), [elasticsearch-platform](), [financial-services](), [government](), [healthcare](), [resilience](), [retail](), [retrieval](), [search]() ### AI overview An IDC study of 11 large enterprises reports that the Elasticsearch Platform, used as a search and retrieval foundation, helped organizations deliver AI products faster while improving revenue, productivity, search relevance, and operational resilience. The study reports a 517% three-year ROI, $13.4 million in annual benefits per organization, and an 11-month payback period. ### Source excerpt An independent IDC study found organizations deploying the Elasticsearch Platform for enterprise search and agentic AI deliver AI products faster, drive higher revenue, boost productivity, and strengthen operational resilience across the business. ## Food Delivery & Takeout UX Benchmark: 3,100+ Performance Scores and 1,900+ Best Practice Examples DevFeed: [Food Delivery & Takeout UX Benchmark: 3,100+ Performance Scores and 1,900+ Best Practice Examples]() Original publisher: [Read original article]() Author: Anders Nielsen Published: 2026-08-13T08:01:00Z Content type: article Language: en Sources: [Baymard Institute]() Topics: [Benchmark](), [User experience (UX)](), [App](), [web applications]() Tags: [apps](), [article](), [benchmark](), [case-studies](), [customer](), [ecommerce](), [insights](), [mobile](), [performance](), [research](), [retail](), [user-testing](), [ux]() ### AI overview Baymard presents a Food Delivery & Takeout UX benchmark based on eight mobile sites and apps assessed across more than 390 research-based UX parameters. The benchmark reports over 3,100 weighted performance scores and 1,900 best-practice examples, with overall experiences ranging from poor to mediocre. Key weaknesses include repeat ordering, dietary and customization support, category filters, navigation, fulfillment timing, and tipping details. ### 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.) At Baymard, we've just released a new UX benchmark with 8 "Food Delivery & Takeout" UX case studies. This follows our large-scale user testing on Food Delivery & Takeout UX and adds to our existing ecommerce UX benchmark. In this article, we give you a snapshot of the overall UX performance. 8 Food Delivery & Takeout UX Case Studies and the Overall Performance Just Eat mediocre Food Delivery & Takeout 49 page designs: mobile, app KFC mediocre Food Delivery & Takeout 48 page designs: mobile, app Uber Eats mediocre Food Delivery & Takeout 54 page designs: mobile, app Domino's Pizza poor Food Delivery & Takeout 34 page designs: mobile, app DoorDash poor Food Delivery & Takeout 48 page designs: mobile, app McDonald's poor Food Delivery & Takeout 39 page designs: mobile, app Deliveroo poor Food Delivery & Takeout 53 page designs: mobile, app Burger King broken Food Delivery & Takeout 41 page designs: mobile, app YourSite.com? Want to know how your site performs? Get Premium access to review your own site or have it audited by Baymard researchers. These are the 8 in-depth Food Delivery & Takeout UX case studies. The 8 mobile sites and apps have been manually assessed across 390+ research-based UX parameters relevant to Food Delivery & Takeout, resulting in 3,100+ weighted UX performance scores and 1,900+ best practice examples from these mobile sites and apps. Each of the 3,100+ UX performance scores from the 8 case studies is summarized in the interactive scatterplot below -- showing you how they perform collectively and individually: {{ scatterplot-graph: size=big + habitat=public + base-sites=collection:online-food-delivery + view-structure-id=gemini-st_l9jw5tjp }} The overall UX performance of the Food Delivery & Takeout sites and apps ranges from "poor" to "mediocre". What is missing is a ## How avatarin built a 24/7 retail agent with GPT-Realtime DevFeed: [How avatarin built a 24/7 retail agent with GPT-Realtime]() Original publisher: [Read original article]() Published: 2026-07-30T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [AI Chat](), [AI Bots](), [Retrieval Augmented Generation (RAG)]() Tags: [agent](), [ai](), [ai-agent](), [api](), [customer](), [gpt](), [low-latency](), [openai](), [retail](), [retrieval](), [retrieval-augmented-generation](), [speech](), [voice]() ### AI overview avatarin built a 24/7 multilingual retail shopping agent for Yamada Denki using OpenAI's GPT-Realtime. The agent supports voice conversations and product recommendations; a two-week public campaign reported about 30,000 users and 92% positive survey responses. ### Source excerpt avatarin uses OpenAI's GPT-Realtime to give Yamada Denki shoppers 24/7 multilingual support. In two weeks, 30,000 people used the agent and 92% of survey responses were positive. ## The Special Value Pi 4 was extremely short-lived DevFeed: [The Special Value Pi 4 was extremely short-lived]() Original publisher: [Read original article]() Author: jeff@jeffgeerling.com (Jeff Geerling) Published: 2026-07-08T14:00:00Z Content type: article Language: en Sources: [Jeff Geerling]() Topics: [Hardware](), [benchmarking](), [boot]() Tags: [benchmarking](), [blog](), [boot](), [dram](), [errors](), [hardware](), [kernel](), [memory](), [performance](), [pi-4](), [product](), [raspberry-pi](), [retail](), [shortage](), [speed](), [validation](), [video](), [youtube]() ### AI overview This article examines a short-lived "Special Value" Raspberry Pi 4 edition certified for 1.25 GHz instead of the usual 1.8 GHz. It compares the boards with retail Pi 4s, discusses their different DRAM packages, and reports boot, clock-speed, kernel-panic, and benchmarking observations. ### Source excerpt The 'Special Value' Pi 4 pictured above is probably the rarest Raspberry Pi I own--even rarer than my blue special edition Pi. A Raspberry Pi reseller briefly listed a special 'value edition' Pi 4. But the product page 404's now. While it was up, my curiosity got the better of me, and now I have two 'value' Pi 4s. What makes them a 'value'? They're only certified to run at 1.25 GHz (retail Pi 4s run at 1.8 GHz, and can usually be overclocked). ## ClickHouse achieves AWS Retail Competency DevFeed: [ClickHouse achieves AWS Retail Competency]() Original publisher: [Read original article]() Author: Aditya Chidurala Published: 2026-06-12T21:17:44Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Amazon Web Services](), [real-time](), [data](), [Amazon S3](), [Compression](), [Kafka](), [Microservices](), [observability](), [Traces](), [Cloud](), [networking]() Tags: [amazon-s3](), [aws](), [batch](), [business](), [clickhouse](), [cloud](), [compression](), [data](), [e-commerce](), [kafka](), [microservices](), [observability](), [personalization](), [real-time](), [retail](), [s3](), [storage](), [traces]() ### AI overview ClickHouse has achieved the AWS Retail Competency in the Advanced Data Insights category, recognizing its validated expertise in real-time retail analytics and customer success on AWS. ### Source excerpt ClickHouse has achieved the AWS Retail Competency, joining a select group of AWS Partners recognized for deep expertise in helping retailers turn live operational data into real-time decisions. ## Empowering Carrot Ads with Domain Adaptive Learning DevFeed: [Empowering Carrot Ads with Domain Adaptive Learning]() Original publisher: [Read original article]() Author: Xiyu Wang Published: 2026-05-04T19:11:17Z Content type: article Language: en Sources: [Instacart]() Topics: [recommendation systems](), [Artificial Intelligence](), [data](), [Optimization](), [Website]() Tags: [advertising](), [ai](), [data](), [domain](), [machine-learning](), [optimization](), [ranking](), [retail]() ### AI overview The article explains how Instacart applies Domain Adaptive Learning to Carrot Ads to address the cold-start problem for new retail partner websites. It describes transferring knowledge from Instacart Marketplace data to target domains to improve click-through-rate prediction and ad ranking with limited partner-specific interaction data. ### Source excerpt Authors: Trey Zhong, Xiyu Wang Contributors: Joseph Haraldson, Sharad Gupta, Sarah Lamacchia Introduction Carrot Ads is Instacart's omnichannel retail media solution that allows retailer partners to build and scale their own advertising businesses on either their owned-and-operated (O&O) websites and apps or their whitelabel Storefront hosted by Instacart. Carrot Ads empowers retailers and CPG brands to accelerate revenue, while improving the customer experience, engagement and Ads return on investment. It features enterprise-grade infrastructure, AI-powered optimization, years of proprietary first-party data and flexibility to choose from retailer-sourced Ads demand, Instacart-sourced demand from 7,500+ CPG brands, or both. However, onboarding a new partner onto Carrot Ads introduces a key challenge: the 'cold start' problem, where limited historical interactions make it difficult to predict user behavior accurately. To serve performant ads, our systems rely on predicting a user's Click-Through Rate (CTR) to generate a ranking score. On the Instacart Marketplace, we have billions of historical signals to train a model to do so. But when a partner launches a new ads experience on their O&O e-commerce site, there is often little to no interaction history for that property, so training an accurate model becomes challenging. User behavior can vary dramatically between websites -- for example, browsing patterns on a grocery site differ from those on a pet supply or electronics site. Training a model from scratch for a new domain is data hungry. Conversely, directly deploying Instacart's existing Marketplace model often fails to capture the nuances of the partner's specific inventory and user base. To address this, we developed a Domain Adaptive Learning approach that transfers knowledge from Instacart's data-rich environment to new partner environments. By treating the Instacart Marketplace as a source domain and the partner's website as a target domain, we can transfer ## Partnering with industry leaders to accelerate AI transformation DevFeed: [Partnering with industry leaders to accelerate AI transformation]() Original publisher: [Read original article]() Author: David Thacker Published: 2026-04-21T14:54:15Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [Artificial Intelligence](), [Frontier AI](), [AI Strategy](), [AI Development](), [Google](), [Cloud]() Tags: [agentic-ai](), [ai](), [cloud](), [data](), [enterprise](), [finance](), [frontier-ai](), [global](), [google](), [industry](), [leadership](), [management](), [manufacturing](), [media-and-entertainment](), [models](), [partnerships](), [real-time](), [responsibility-safety](), [retail](), [scale](), [solutions]() ### AI overview Google DeepMind describes partnerships with Accenture, Bain & Company, BCG, Deloitte, and McKinsey to help organizations adopt frontier AI at scale. The initiative focuses on industry-specific AI capabilities, early access to frontier models including Gemini, and leadership support for enterprise transformation across sectors such as finance, manufacturing, retail, and media and entertainment. ### Source excerpt Google DeepMind partners with global consultancies to bring the power of frontier AI to organizations around the world. ## How Eisan made POS analytics faster, cheaper, and more reliable with ClickHouse Cloud DevFeed: [How Eisan made POS analytics faster, cheaper, and more reliable with ClickHouse Cloud]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-04-08T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [real-time](), [data](), [personalization](), [Cloud]() Tags: [analytics](), [clickhouse](), [cloud](), [cost](), [data](), [performance](), [personalization](), [real-time](), [retail]() ### AI overview Eisan replaced a QlikView-based BI stack with ClickHouse Cloud for real-time ID-POS analytics across retail clients. The platform supports basket and cross analysis, personalized recommendations, and hundreds of millions of records per client, while improving reliability and performance and reducing licensing and infrastructure costs. ### Source excerpt Rill uses ClickHouse to power real-time operational BI for 100B+ daily events, enabling instant exploration and conversational analytics directly against live datasets through a declarative, BI-as-code workflow. ## Building AI Security with Our Customers: 5 Lessons from Evo's Design Partner Program DevFeed: [Building AI Security with Our Customers: 5 Lessons from Evo's Design Partner Program]() Original publisher: [Read original article]() Author: Rudy Lai Published: 2026-04-01T04:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Artificial Intelligence](), [Securing AI](), [Generative AI](), [shadow AI](), [ai security](), [Security](), [Automation]() Tags: [agentic](), [agents](), [ai](), [ai-adoption](), [ai-security](), [application-security](), [aspm](), [automation](), [awareness](), [blog](), [ci-cd](), [compliance](), [customer](), [customer-featured](), [developer](), [devops](), [executive](), [finserv](), [generative](), [generative-ai](), [interest](), [pmm](), [policy](), [retail](), [scale](), [security](), [shadow-ai](), [snyk-apprisk](), [snyk-platform](), [tech]() ### AI overview Snyk shares five lessons from its Evo design partner program for securing generative AI. The article emphasizes discovering AI sprawl and shadow AI, understanding custom AI deployments, replacing static spreadsheets, enforcing governance policies, and using actionable risk intelligence to move AI from chaos to controlled production. ### Source excerpt Learn 5 key lessons from Snyk's Evo design partner program. Discover how AI discovery, risk intelligence, and policy automation help teams secure generative AI and govern AI sprawl at scale. ## Building a Magic Mirror: AI retail experiences with Remix DevFeed: [Building a Magic Mirror: AI retail experiences with Remix]() Original publisher: [Read original article]() Author: Nikola Draca Published: 2026-03-19T12:33:46Z Content type: article Language: en Sources: [Shopify Engineering](), [Shopify Engineering - Shopify Engineering]() Topics: [Artificial Intelligence](), [Remix](), [webcam](), [Shopify](), [browser](), [API]() Tags: [ai](), [api](), [browser](), [building](), [code](), [customer](), [generate](), [how-to](), [mirror](), [product](), [retail](), [server](), [shopify](), [webcam]() ### AI overview This article presents Shopify's AI-powered magic mirror: a customizable retail installation that uses a display, hidden webcam, and Remix server to recognize visual signals and deliver personalized messages, animations, product recommendations, challenges, and discount codes. It also outlines a makeup shade-matching use case and the hardware needed to build the experience. ### Source excerpt The technical blueprint for an AI-powered mirror that sees customers, analyzes their appearance, and delivers personalized product recommendations in real-time. ## White Paper on Data Science Technical Program Management DevFeed: [White Paper on Data Science Technical Program Management]() Original publisher: [Read original article]() Author: Sonu Jain Published: 2026-02-27T12:41:46Z Content type: article Language: en Sources: [Walmart Global Tech]() Topics: [Data Science](), [Model Development](), [Development](), [Software Engineering]() Tags: [collaboration](), [coverage](), [data-science](), [experimental](), [leadership](), [machine-learning](), [management](), [paper](), [retail](), [software-engineering](), [technical](), [technical-program-manager](), [training](), [validation](), [white-paper]() ### AI overview This white paper presents a structured approach to managing Data Science programs through technical program management. It discusses business alignment, cross-functional collaboration, data validation, model training and retraining, governance, and phased execution, using an inventory forecasting initiative as a real-world example. ### Source excerpt 1. Abstract Managing Data Science programs requires a structured approach to handle the complexities of data, model development, and business alignment. This whitepaper provides a comprehensive guide on the effective program management of Data Science programs by technical program managers. It highlights the critical role of Technical Program Managers (TPMs) in driving successful execution and outlines the key phases, challenges, and recommended best practices at every stage for effectively managing Data Science programs This white paper is grounded in a real-world inventory forecasting initiative aimed at improving stock availability and reducing overstock across multiple retail categories. The program involved cross-functional collaboration between Data Science, Engineering, Product, and Business teams to build predictive models that could dynamically adjust inventory levels based on demand signals. 2. Introduction Data Science has become a critical pillar of decision-making across industries, but organizations continue to struggle with operationalizing these initiatives. Unlike software development, which follows predictable sprint cycles, Data Science programs are inherently experimental -- requiring repeated cycles of data validation, model training, and retraining before they reach acceptable performance levels. This uncertainty often leads to misaligned expectations, delays in delivery, and inconsistent business impact. The iterative nature of model development makes predictability especially challenging: teams may require multiple iterations to achieve coverage and accuracy thresholds that satisfy business needs. Without structured program management, these efforts risk becoming siloed experiments rather than scalable, value-generating solutions. This whitepaper aims to address this gap by providing a practical framework for Technical Program Managers (TPMs) to manage Data Science programs effectively. It draws on real-world experience from a large-scale inve ## PVH reimagines the future of fashion with OpenAI DevFeed: [PVH reimagines the future of fashion with OpenAI]() Original publisher: [Read original article]() Published: 2026-01-27T06:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [ChatGPT](), [Artificial Intelligence](), [OpenAI](), [data](), [Security & Privacy]() Tags: [accelerate](), [ai](), [chatgpt](), [creativity](), [data](), [design](), [efficiency](), [enterprise](), [global-affairs](), [innovation](), [openai](), [optimization](), [privacy](), [product](), [retail](), [scale](), [security](), [security-privacy](), [supply-chain]() ### AI overview PVH Corp. is adopting ChatGPT Enterprise and OpenAI frontier models across its global fashion operations. The initiative targets product design, demand planning, inventory optimization, supply-chain management, marketing, and consumer engagement, with an emphasis on data-driven decisions, creativity, efficiency, security, privacy, and responsible data governance. ### Source excerpt PVH Corp., parent company of Calvin Klein and Tommy Hilfiger, is adopting ChatGPT Enterprise to bring AI into fashion design, supply chain, and consumer engagement. ## Run for your money: Engineering a treadmill that prints store credit DevFeed: [Run for your money: Engineering a treadmill that prints store credit]() Original publisher: [Read original article]() Author: Nikola Draca Published: 2025-11-25T13:31:13Z Content type: article Language: en Sources: [Shopify Engineering](), [Shopify Engineering - Shopify Engineering]() Topics: [Shopify](), [Extension](), [Barcode](), [real-time](), [Python](), [Socket.IO](), [Hardware]() Tags: [checkout](), [code](), [customer](), [data](), [engineering](), [events](), [extensions](), [hardware](), [python](), [real-time](), [retail](), [sensor](), [shopify](), [ui]() ### AI overview Shopify and Endorphins Running built a gamified manual treadmill for the 2025 NYC marathon weekend. A pace sensor sent Bluetooth FTMS data to a Python script, while Socket.IO and a Shopify POS UI extension connected the running experience to store-credit redemption. Participants earned credit for maintaining a selected pace and received a barcode for applying their discount at checkout. ### Source excerpt We combined FTMS sensors, Socket.IO, and POS UI extensions to turn a manual treadmill into a discount-printing machine. ## Securing a retail AI endpoint from abuse for virtual try on DevFeed: [Securing a retail AI endpoint from abuse for virtual try on]() Original publisher: [Read original article]() Author: Alexander Nohe Published: 2025-11-11T00:00:00Z Content type: tutorial Language: en Sources: [Firebase Blog]() Topics: [Artificial Intelligence](), [Firebase](), [Authentication](), [Back end](), [Code](), [cURL]() Tags: [ai](), [app-check](), [attestation](), [authentication](), [backend](), [code](), [curl](), [devices](), [firebase](), [generation](), [genkit](), [how-to](), [image](), [model](), [rate-limiting](), [retail](), [server](), [token]() ### AI overview A tutorial on protecting a Firebase AI endpoint for a virtual try-on application. It explains using App Check, replay-protected tokens, authentication, and rate limiting to reduce unauthorized access, replayed requests, and excessive generation costs. ### Source excerpt Learn how to protect expensive AI features from abuse using Firebase App Check, Authentication, and rate limiting to ensure only legitimate users can access them. ## How to test the reliability of a Point of Sale (POS) system DevFeed: [How to test the reliability of a Point of Sale (POS) system]() Original publisher: [Read original article]() Author: Gavin Cahill Published: 2025-10-20T00:00:00Z Content type: tutorial Language: en Sources: [Gremlin Blog]() Topics: [Chaos Engineering](), [Resilience](), [autoscaling](), [Microservice](), [Complex Systems](), [systems]() Tags: [autoscaling](), [best-practices](), [chaos-engineering](), [cpu](), [engineering](), [gremlin](), [how-to](), [infrastructure](), [memory](), [microservices](), [outage](), [reliability-management](), [resilience](), [retail](), [testing]() ### AI overview This tutorial explains how to test the reliability of retail Point of Sale systems using Gremlin and Chaos Engineering. It focuses on resilience testing for microservice-based checkout systems, including autoscaling, CPU, memory, and disk I/O capacity, to identify failure conditions and reduce outages. ### Source excerpt Find out how to use Gremlin and Chaos Engineering to make sure your Point of Sale system is reliable. ## The Adaptive Talent Imperative: How CPG and Retail Companies Can Transform With Cross-functional Experts DevFeed: [The Adaptive Talent Imperative: How CPG and Retail Companies Can Transform With Cross-functional Experts]() Original publisher: [Read original article]() Author: CHRIS DANIEL, GM, CONSUMER PRODUCTS & SERVICES @ TOPTAL Published: 2025-06-24T07:00:00Z Content type: opinion Language: en Sources: [Toptal Blog]() Topics: [data](), [real-time](), [genai]() Tags: [analytics](), [aws](), [azure](), [business](), [genai](), [google](), [manufacturing](), [retail](), [technical]() ### AI overview The article argues that consumer packaged goods and retail companies are constrained less by technology or capital than by a shortage of adaptive, cross-functional talent. It emphasizes professionals who can connect business strategy, analytics, data, and technical execution, helping organizations overcome siloed structures and improve decision-making and GenAI adoption. ### Source excerpt The biggest obstacle consumer packaged goods (CPG) and retail companies face isn't technology or capital. It's finding talent that can bridge the gap between business strategy and technical execution in real time. ## 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 ## Zembula fast-tracks its roadmap with Tinybird DevFeed: [Zembula fast-tracks its roadmap with Tinybird]() Original publisher: [Read original article]() Author: Tinybird Published: 2024-11-20T00:00:00Z Content type: article Language: en Sources: [Tinybird]() Topics: [personalization](), [real-time]() Tags: [customer-stories](), [customers](), [personalization](), [real-time](), [retail]() ### AI overview A case study of how Zembula, an email personalization platform, used Tinybird to answer previously unanswerable questions about real-time attribution for its retail customers. ### Source excerpt Learn how a rising email personalization platform used Tinybird to answer previously unanswerable questions about real-time attribution for their retail customers. ## How REI built a DevSecOps culture and how Snyk helped DevFeed: [How REI built a DevSecOps culture and how Snyk helped]() Original publisher: [Read original article]() Author: Brian Piper Published: 2024-02-27T14:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Application Security](), [Vulnerabilities](), [migration]() Tags: [acquisition](), [application-security](), [aws](), [blog](), [code-security](), [customer](), [cybersecurity](), [devsecops](), [megawatt](), [migration](), [open-source-security](), [retail](), [security](), [snyk-code](), [snyk-open-source](), [tooling](), [vulnerabilities](), [workflows]() ### AI overview REI describes building an AppSec and DevSecOps culture during its cloud migration, combining security tooling, processes, and developer collaboration to address vulnerabilities. ### Source excerpt Learn how the REI team built a strong security culture across development units in this AWS Re:Invent chat between Dan Ngo, Lead Security Engineer, Cybersecurity Engineering and Risk Management at REI, and Clinton Herget, Field CTO at Snyk. ## Unify your e-commerce checkout with GraphQL DevFeed: [Unify your e-commerce checkout with GraphQL]() Original publisher: [Read original article]() Author: Shane Myrick Published: 2022-12-12T15:27:26Z Content type: tutorial Language: en Sources: [Apollo Blog]() Topics: [GraphQL](), [GraphOS](), [User experience (UX)](), [Authorization](), [JSON Web Tokens]() Tags: [backend](), [e-commerce](), [graphos](), [graphql](), [how-to](), [payments](), [retail](), [schema](), [ux]() ### AI overview This tutorial explains how GraphQL and Apollo GraphOS can unify e-commerce checkout functionality across applications. It describes using a connected GraphQL schema to combine backend services, standardize authorization, expose cart and payment operations, and help client teams build smoother checkout experiences. ### Source excerpt This post is a part of our "How to power modern retail apps with Apollo GraphOS" series. Also in this series: - Personalizing the e-commerce shopping experience with GraphQL - Creating an omnichannel shopping experience with GraphQL - Manage time-gated product launches with GraphQL - How to aggregate and share data with third parties using GraphOS 17% of online shoppers abandon their carts due to long, complicated checkout experiences (Baymard Institute). ## Digital payments are surging in informal BNPL; Under Rs 200 payments lead the surge DevFeed: [Digital payments are surging in informal BNPL; Under Rs 200 payments lead the surge]() Original publisher: [Read original article]() Author: Team OkCredit Published: 2022-05-23T11:27:01Z Content type: article Language: en Sources: [OkCredit - Medium]() Topics: [digital](), [QR Code](), [data](), [Transactions]() Tags: [bnpl](), [digital](), [digital-payment](), [payments](), [qr-code](), [retail](), [small](), [store](), [upi]() ### AI overview The article examines digital payment adoption among informal businesses using OkCredit data and customer examples. It reports that UPI, especially QR-code payments, is widely used for small transactions, while cash remains significant. ### Source excerpt Photo by David Dvořáček on Unsplash Prior to 2019, the biggest pain in Nitesh Bansal's life was getting customers to pay their dues. Bansal runs a Kirana cum mobile recharge store at Surat in Gujarat. Customers would keep on buying things at his store over and above an existing credit line, inflating the dues. " The typical excuses would be- "Forgot cash at home", "Settle the current bill, will pay the dues later" , "Don't have change" etc. In June 2019, Bansal latched on to one of the UPI platforms and since then things have changed. Digital payments have made his life a lot easier, by making credit settlements fuss-free. His customers now scan the QR code or pay him via Paytm. That doesn't mean he has gone completely digital; 60% of the payments at his store still happen in cash- a reality that has also been reflected in OkCredit's data UPI and within that QR codes have been the heroes of this digital revolution. Be it the neighbourhood vegetable vendor or the street food hawker, it isn't uncommon to find QR codes pasted on their shops and carts. The sheer convenience of scanning a code rather than looking for change or navigating through multiple apps makes QR codes a clear favourite among small and micro businesses. In fact, 90% of online payments happening on OkCredit take place through UPI, giving it a significant lead over all other forms of payments. While convenience is the primary reason, it has also been seen that shops that accept digital payments have a better control on cashflows than those who don't. Three shops away from Bansal's store, Ajay Sirohi's "Beena recharge point" suffers regularly because of non-paying customers. Despite several customers requesting him to add digital payments at the store, Sirohi hasn't done it for fear of cyber frauds. Payments are always an issue at his store, forcing him to resort to borrowing from his friends. Interestingly, for shops accepting digital payments, smaller denomination transactions are mostly happen throu ## Graduates Are Entering India's Unorganised Retail Sector DevFeed: [Graduates Are Entering India's Unorganised Retail Sector]() Original publisher: [Read original article]() Author: Team OkCredit Published: 2022-05-23T09:16:53Z Content type: opinion Language: en Sources: [OkCredit - Medium]() Topics: [data](), [Job]() Tags: [business](), [covid](), [customer-experience](), [digital-bookkeeping](), [india](), [jobs](), [kirana](), [retail](), [technology](), [unemployment]() ### AI overview The article argues that rising unemployment, particularly during Covid-19, is leading more graduates in India to enter unorganised retail. It suggests this shift may increase the adoption of business apps and improve customer experiences in neighbourhood stores. ### Source excerpt Photo by Anmol Ramanujam on Unsplash There's a particular imagery that comes to mind when one thinks of a shopkeeper- sparsely educated, between 20 and 40 years in age, ceaselessly taking orders on phone with one hand and packing stuff with another. For this neighbourhood "bhaiya" or "uncle", only three things matter- customers, inventory and his bahi-khata (the account register). Now, picture a retail store owner in the same age group, but genial, well-informed, inclined to technology, using apps for his business needs. There is a stark difference in customer experience at both these stores. While there are a bunch of factors responsible for this change, one of them is educated folks, particularly graduates getting into unorganised retail. Neighbourhood stores in India have typically been run by semi-literate, less educated individuals, some of who join it as a family owned business. For others, it's the easiest source of livelihood, if they aren't qualified enough for a job.The barriers to entry are so low that anyone with a basic minimum qualification can open a shop. While this is true even now, Covid-19 has altered the scenario, partially. Rising unemployment has meant that graduates who would earlier prefer jobs, are now looking at unorganised retail as a viable source of livelihood. At OkCredit, we have credible data that shows this in reality. In our annual study released this year in March, the demographic data gave a true picture of this change. Even though, across age groups, owners of small businesses lack education, in the 35-45 age group, a decent chunk of business owners are graduates. Covid-19 may have been the forcing factor but this also points to a mindset shift among the educated class who have typically preferred "safe" jobs over business. For an outsider, this shift may seem trivial, but it's significant for two reasons- First, as more of educated class joins unorganised retail, there will be an increase in the usage of business apps. Young and ## Pine Store Community Pricing & Online Retail Stores DevFeed: [Pine Store Community Pricing & Online Retail Stores]() Original publisher: [Read original article]() Published: 2020-12-02T00:00:00Z Content type: opinion Language: en Sources: [Community blog on PINE64]() Topics: [foss](), [Linux](), [Hardware](), [Support](), [Wiki]() Tags: [community](), [developers](), [foss](), [hardware](), [linux](), [pine-store](), [pine64](), [pinebook](), [product](), [retail](), [software](), [store](), [support]() ### AI overview PINE64 explains that new online retail stores planned for 2021 will operate alongside the existing Pine Store, whose community-oriented pricing and device sales will remain unchanged. The strategy is intended to serve general consumers while preserving the existing community-focused model and reducing pressure on support staff from software troubleshooting requests. ### Source excerpt In 2021 you'll see online retail Pine stores open in Europe, North America and possibly also worldwide at a later stage. Let me start by making one thing clear - the current Pine Store isn't going away and the pricing in the Pine Store will remain unchanged. You'll always be able to buy and pre-order your devices from pine64.com at a community-oriented price point. The retail stores will function alongside the Pine Store, not replace it, and offer a different customer experience. In this blog I'll explain the rationale behind this strategy. PINE64 is not a business First things first - PINE64 is a community, not a business, and the Pine Store's sole purpose is to serve this community by providing FOSS development-friendly hardware. Sales numbers and revenue are not, and never were, a driving force behind this project; making the next fun and often experimental device was and still is. Some devices, such as the original Pinebook, were even sold at a loss at times - simply because we knew people wanted one. Seriously. Victims of our own success Our strategy was always clear - work with developers to design a product, have partner projects and community developers work on the software and sell the device at a community-oriented price point. This approach has proven to be a great way forward for us, and one that, dare I say it, ultimately distinguished us from the other FOSS-vendors and manufacturers. That said, we're slowly falling victim to our own success. News of our devices have reached people outside of our target audience, and enthusiast-grade products now frequently end up in the hands of non-technical customers. I have just been looking at the incoming support tickets these past weeks, and the majority of incoming queries are by people who are not comfortable troubleshooting software problems, have little Linux experience, and do not understand the nature of our devices. In a nutshell, they are general tech-consumers unfamiliar with the intricacies of Linux, FO [Next page]()