标签：#Commerce

Walmart-backed Flipkart has crossed 1,000 micro-fulfillment centers as Amazon accelerates its own quick-commerce push in India.

Last night I ran external security scans on the public websites of 10 leading Shopify and Shopify Plus agencies — the same scan any browser or attacker would see. No credentials, no special access. One agency scored an A. Three scored C- or below. The most common finding appeared on 9 of 10 sites. TL;DR 1 agency scored an A. 3 scored C- or below. 1 scored a D. The most common finding — missing security headers — appeared on 9 of 10 sites. 6 of 10 agencies have no HSTS at all. One agency has a session cookie without the Secure flag. That is the most concrete finding in the set. What was scanned Five categories per domain: TLS (HSTS presence and max-age), security headers (CSP, X-Frame-Options, X-Content-Type-Options, Referrer-Policy, Permissions-Policy), cookie flags, DNS hardening (DNSSEC and CAA) and sensitive exposure paths. All scans run on 23 June 2026. This covers the agencies' own marketing sites — not the client stores they build. Results Agency Domain Score Grade 1Digital Agency 1digitalagency.com 94 A Acidgreen acidgreen.com.au 77 B 30 Acres 30acres.com.au 76 B Fourmeta fourmeta.com 76 B Blend Commerce blendcommerce.com 76 B Elkfox elkfox.com 76 B Charle Agency charleagency.com 62 C Fyresite fyresite.com 62 C Eastside Co eastsideco.com 58 C- Swanky Agency swankyagency.com 55 C- Blubolt blubolt.com 54 D Per-agency notes 1Digital Agency — A (94) HSTS at two years, X-Content-Type-Options and Referrer-Policy set correctly, Permissions-Policy restricting camera, microphone and geolocation, CSP frame-ancestors in place of X-Frame-Options. Only gap is HSTS missing includeSubDomains. Acidgreen — B (77) HSTS with two-year max-age, includeSubDomains and preload — the strongest TLS config in the set. But CSP, X-Frame-Options, X-Content-Type-Options, Referrer-Policy and Permissions-Policy are all absent. Worth noting Acidgreen is multi-platform (Shopify Plus, Adobe Commerce, Magento) rather than Shopify-only. 30 Acres — B (76) A Shopify Plus Partner agency based in Byr

My Testing Setup I used Midjourney V7 (midjourney.com, Standard plan at $30/mo for this project — volume was too high for Basic) over five weeks across six product photo projects: lifestyle context images, background replacement concepts, packaging mockups, and mood-board style reference images for briefing photographers. Some outputs went live in ads. Others were used internally. A few were scrapped entirely. Two specific examples: I generated 12 lifestyle context images showing a skincare product in a bathroom setting — no actual product in the image, just the environment and mood — and used them as ad backgrounds with the real product composited in afterward. Results were strong. I also tried to generate images of the actual product itself from reference photos. That failed in ways I will explain. Pricing: Standard plan at $30/mo. For product photo work at volume, Basic at $10/mo runs out fast. Budget for Standard if this is a regular workflow. 1. Lifestyle Context and Environment Images This is where Midjourney V7 earns its place in a product photo workflow. Generating the environment — a kitchen countertop, a gym bag, a coffee shop table — without needing to stage or shoot it is genuinely useful. I needed eight lifestyle backgrounds for a supplement brand's ad campaign. Real location shoots for eight setups would have cost $3,000 and taken two weeks. I generated the environments in Midjourney, exported them, and composited the real product in using Photoshop. Total cost: $30 for the month's Midjourney subscription and four hours of compositing work. The images ran in paid Meta ads for six weeks. CTR was in line with our studio-shot creative. Nobody asked if the backgrounds were AI-generated. The key: generate the environment only. Do not try to put your specific product into the Midjourney image. Composite it in post. That division of labor is where the workflow holds up. 2. Packaging Mockups for Concepts That Do Not Exist Yet Before you manufacture a product o

In the world of cross-border e-commerce, malicious bot scraping leading to Meta/Google Pixel pollution is a nightmare for every seller. When your store starts gaining traction, these fake traffic sources can "poison" your ad model, causing your ROAS to plummet. To combat this, I’ve developed a robust "Backend Data Isolation" architecture. The Core Defense Strategy Stop triggering ad conversion events directly from the frontend. Instead, build a "firewall" at the backend to ensure that only verified, high-quality conversion data is sent to your ad platforms. Technical Implementation By implementing server-side logic in Python, we can filter out bot requests effectively: def process_pixel_event ( request ): # Filter out bot signatures (User-Agent, IP analysis) if is_bot_signature ( request . headers [ ' User-Agent ' ]): return None # Send only high-quality data to ad platforms if is_real_customer ( request . session ): trigger_pixel_event ( request ) By leveraging this logic, we feed "private, high-quality data" to the AI. This allows the algorithm to learn only from genuine customer behaviors, creating an "immortal pixel" moat around your store. Learn More For a deep dive into full-scale anti-scraping deployments and how to leverage automated translation techniques to scale traffic in blue-ocean markets, check out my full technical guide: 👉 Read the Full Implementation & Troubleshooting Guide Here

Amazon Fulfillment: The Three Tiers of Optimization Amazon processes billions of orders annually through a network of over 175 fulfillment centers globally. To maintain their 1-2 day (or same-day) delivery guarantees, they built a 3-tier optimization architecture: ┌─────────────────────────────────────────────────────────────┐ │ TIER 1: ANTICIPATORY SHIPPING (Long-term — weeks/months) │ │ → ML predicts demand → Moves inventory close to customers │ │ BEFORE they place an order │ ├─────────────────────────────────────────────────────────────┤ │ TIER 2: REGIONALIZATION (Medium-term — days/weeks) │ │ → Partitions the fulfillment network into autonomous zones│ │ → Ensures 70-80% of orders are fulfilled intra-region │ ├─────────────────────────────────────────────────────────────┤ │ TIER 3: CONDOR (Short-term — hours) │ │ → Continuously re-optimizes the fulfillment plan within │ │ a 5-6 hour window before pick-and-pack begins. │ └─────────────────────────────────────────────────────────────┘ Anticipatory Shipping — Shipping Before You Buy A Crazy but Effective Idea Amazon holds a patent (US Patent 8,615,473) describing a system that begins shipping items BEFORE a customer places an order . It sounds like science fiction, but it's a reality. Traditional Model: Customer orders → Warehouse processes → Ships → Delivered (2-5 days) Anticipatory Shipping: ML predicts: "Customers in Region X will buy 200 iPhone 16s in the next 3 days" → Amazon ships 200 iPhones from a central hub to local delivery hubs in Region X → Customer places order → The item is already locally staged → Delivered same-day! ML Model Input Features Input Feature Significance Purchase history What do they buy, and how often? Browsing behavior What are they looking at? Cart abandonment? Wishlists Explicitly desired items Seasonal patterns Winter coats in November, sunscreen in June Regional demographics High-income areas? Young families? College towns? Trending products Items going viral on social media Weathe

A new FTC lawsuit reveals how sophisticated subscription app operators can allegedly use shell companies and payment infrastructure to stay active on app stores despite mounting consumer complaints.

Pinterest has launched 'Ask Pinterest,' an experimental AI-powered shopping app that lets users seek recommendations and inspiration through a conversational interface.

When a Magento store feels slow, merchants usually notice it first on the homepage. When revenue actually slips, we usually find the damage deeper in the funnel. That was the case on a recent mid-market Magento 2 build we inherited. Product pages were acceptable. Search worked. But checkout analytics told a different story. Mobile users were stalling after address entry, re-clicking shipping methods, and abandoning before payment finished rendering. The merchant described it in business terms: "traffic is fine, but checkout feels fragile." They were right. The store was running a fairly typical Magento checkout stack: Luma fallback checkout, several shipping customizations, two payment methods, tax recalculation on step changes, and a handful of third-party scripts that had quietly accumulated over time. Together, they created a familiar Magento problem: too much JavaScript, too many render passes, and too much waiting on the highest-stakes route in the store. Over a 90-day measurement window after launch, checkout completion improved by 34%. Mobile completion improved by 39%. Lab metrics got much better immediately, and field metrics followed. This article covers why we chose React instead of Hyva Checkout, how we implemented the frontend, what moved the numbers, and what we would do differently next time. The problem with Magento's default checkout Magento's default Luma checkout is functional, but performance is rarely its strength. The architecture was designed around Knockout.js components, RequireJS modules, and a lot of UI behavior being layered in over time. Once a real merchant adds shipping estimation, fraud tooling, tax logic, payment widgets, analytics, and address validation, the route becomes busy in all the wrong ways. In this project, our baseline looked like this on a throttled mobile profile: Metric Before (Luma checkout) After (React checkout) Initial checkout route payload 1.8 MB transferred 486 KB transferred LCP 4.2s 1.1s INP 280ms 92ms CLS 0.1

We've been a Magento agency in Chicago since 2008. When Hyvä Themes hit the ecosystem, we were skeptical—another theme promise. Then we measured Core Web Vitals on client stores and the case became obvious: Hyvä is the most practical path to a fast Magento storefront without a full replatform. This is the migration framework we use at Towering Media for US and Canadian merchants moving off Luma (or aged custom frontends) onto Hyvä. Why Hyvä now (not next year) Google's CWV thresholds affect ad quality and organic visibility. Luma checkout and catalog pages often ship 1.5–2+ MB of JavaScript before you add analytics, chat, and personalization. Hyvä replaces Knockout/RequireJS on the storefront with Alpine.js and Tailwind. Typical results on our projects: 50–70% less frontend JS on category and product pages LCP improvements of 1–3 seconds on mobile field data (highly variable by hosting and images) Lower maintenance — fewer JS conflicts between theme and extensions Delaying migration means paying for performance twice: once in emergency fixes, again in the eventual theme project. Phase 1: Discovery (1–2 weeks) Extension audit List every module that touches the frontend: bin/magento module:status | grep -v "Module is disabled" Flag anything with view/frontend , RequireJS , or Knockout in: Layered navigation and search Checkout and cart Page Builder widgets Blog and CMS enhancements Hyvä maintains a compatibility module ecosystem; unsupported extensions need replacements or custom Hyvä templates. Towering Media includes extension compatibility mapping in every Hyvä migration engagement. CWV baseline Capture before metrics from: Google PageSpeed Insights (origin-level) Chrome UX Report for key templates: home, category, product, cart Real-user monitoring if the client has it (GA4, SpeedCurve, etc.) Store screenshots. Stakeholders forget how slow the old site felt. Business constraints Document: Peak seasons (do not launch in November without war room

Magento's default Luma checkout loads a heavy Knockout.js stack, dozens of RequireJS modules, and payment iframes that fight for the main thread. For merchants where checkout is the conversion bottleneck, shaving seconds off load and interaction time pays back faster than another homepage hero image. We rebuilt checkout in React— React Checkout Pro —for Magento 2 and Hyvä stores that needed Shopify-like speed without leaving Adobe Commerce. Here is what we measured, what surprised us, and what we would do differently. The problem: checkout is where Core Web Vitals go to die Homepage optimizations are table stakes. Checkout is different: More JavaScript. Payment methods, validators, shipping step observers, and third-party scripts stack on one route. More layout shift. Address suggestions, shipping method lists, and tax updates re-render large DOM regions. More input delay. Autocomplete plugins, reCAPTCHA, and BNPL widgets compete on keydown handlers. On a representative Luma checkout (mid-size US retailer, ~80 SKUs in catalog, 4 payment methods), lab tests before migration showed: Metric Luma checkout (before) React checkout (after) LCP (lab, 4G) 4.8s 2.1s INP (field interaction) 320ms 95ms CLS (full flow) 0.18 0.04 JS transferred (checkout route) ~1.9 MB ~420 KB Time to interactive (est.) 6.2s 2.8s Field data from CrUX lagged lab wins by 4–6 weeks but trended the same direction once cache and CDN rules settled. Your numbers will differ. The pattern we see repeatedly: the biggest win is shipping less JavaScript to checkout , not micro-optimizing the JavaScript you keep. Architecture: React island, Magento brain We did not headless the entire storefront. Magento still owns: Quote totals and tax calculation Shipping rate requests Payment tokenization and order placement APIs Customer session and cart persistence React owns the UI layer: step navigation, form state, validation UX, and optimistic updates while Magento APIs catch up. High-level flow: Browser → React Chec

When you build Shopify apps or integrations, pagination becomes important very quickly. A small test store may have a few products and orders. A real merchant store can have thousands of products, variants, orders, customers, inventory items, metafields, and fulfillment records. You cannot fetch all of that data in one Shopify GraphQL request. You need pagination. More importantly, you need pagination that performs well. Poor Shopify GraphQL pagination can create slow syncs, API throttling, timeout errors, duplicate processing, and incomplete exports. This post explains how Shopify GraphQL pagination works and how to handle large Shopify datasets in a practical way. What Shopify GraphQL Pagination Solves Pagination lets your app retrieve data in smaller chunks. Instead of asking Shopify for 50,000 products at once, your app asks for 100 or 250 products per request. Shopify returns the data and gives your app information about the next page. This protects your app from huge responses and protects Shopify from heavy requests. It also gives your integration more control over retries, progress tracking, and background processing. Shopify Uses Cursor-Based Pagination Shopify GraphQL uses cursor-based pagination. That means you do not request data using page numbers. You request the next page using a cursor from the previous response. A basic product pagination query looks like this: query GetProducts ( $cursor : String ) { products ( first : 100 , after : $cursor ) { nodes { id title handle updatedAt } pageInfo { hasNextPage endCursor } } } The first time you run this query, pass cursor as null. Shopify returns the first 100 products and gives you an endCursor . Use that endCursor as the after value in the next request. Keep doing this until hasNextPage is false. Why Cursors Work Better Than Page Numbers Offset pagination usually works like this: page=1 page=2 page=3 or: offset=5000&limit=100 This approach becomes inefficient when datasets grow. The system may need to sk

Wing is expanding into seven more U.S. cities through its partnership with Walmart.

Backed by Alexis Ohanian’s 776 and Kindred Ventures, Zest uses transaction data and AI to generate restaurant recommendations based on users’ real dining habits and the places they frequent.

Pinterest is adding support for Amazon Storefronts, allowing creators to earn affiliate commissions more easily while showcasing their product recommendations in one place.

When a Shopify product CSV imports but product photos fail, the problem is often not the CSV syntax. It is usually that Shopify cannot fetch one or more image URLs during import. Here is the preflight I use before retrying a large product upload: Check that every Image Src or Variant Image value starts with http or https. Local paths like C:\images\shirt.jpg will not work. Open a few image URLs in a private browser window. If the image requires a login, expires, redirects to a file-sharing preview page, or blocks hotlinking, Shopify may not be able to download it. Keep image rows grouped with the correct product handle. Sorting a CSV by image column or price can separate continuation image rows from their product. Watch for URLs that do not end in a normal image extension. They can work, but they are worth checking manually before a full import. Test one small batch first, then verify the product admin after Shopify finishes downloading the images. For a larger file, I also like to extract the image columns into a review worksheet before touching product data. I built a small browser-side checker for that workflow here: https://shopify-csv.aivismonitor.com/shopify-csv-image-url-reachability-checker The important part is to fix image reachability before changing product titles, variants, or prices. Otherwise you can spend time debugging the wrong part of the import.

Zepto's advertising revenue jumped 151%, outpacing the company's 104% growth in operating revenue.

A new feature in the Amazon Shopping app allows users to generate designs with Alexa, then print them on products like T-shirts, hoodies, and tumblers.

You can tap the star-shaped, NFC-enabled wand at terminals to make contactless payments. It's the first of several tap-to-pay hardware doodads coming from Cash App.

The Bengaluru startup has crossed 1 million orders and reached a $50 million annualized GMV run rate within a year of launch.

Amazon will use visual search and AI to show AI generated product images that match your search queries. The retailer says it will help guide users to products.