E-Commerce Technology Platforms

Payments have evolved from paper and plastic to APIs and orchestration - giving rise to a new breed of players that simplify the complexity and connect the dots behind the scenes. Here's how we got here. 𝟭. 𝗜𝗻 𝘁𝗵𝗲 𝗽𝗿𝗲-𝟭𝟵𝟵𝟬𝘀 𝗲𝗿𝗮, banks owned the entire payments value chain -acquiring, processing, settlement. Merchant onboarding was complex, and domestic clearing systems ruled. 𝟮. 𝗧𝗵𝗲 𝗿𝗶𝘀𝗲 𝗼𝗳 𝗲-𝗰𝗼𝗺𝗺𝗲𝗿𝗰𝗲 in the late 1990s changed everything. Players like PayPal and Authorize made online payments possible, while banks began exiting the acquiring space or partnering with processors to keep up with demand. 𝟯. 𝗕𝗲𝘁𝘄𝗲𝗲𝗻 𝟮𝟬𝟬𝟬 𝗮𝗻𝗱 𝟮𝟬𝟭𝟬, specialized gateways and regional wallets began to scale, offering merchants greater flexibility and control. The launch of SEPA in Europe marked a push toward payment harmonization, while non-bank players started building infrastructure that bypassed traditional acquiring models altogether. 𝟰. 𝗧𝗵𝗲 𝘀𝗵𝗶𝗳𝘁 𝘁𝗼 𝗔𝗣𝗜-𝗱𝗿𝗶𝘃𝗲𝗻 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 transformed payments from siloed systems into modular, developer-friendly tools. Merchant onboarding became faster, integrations simpler, and innovation more scalable. Open Banking regulations enabled direct access to bank data, while new credit models redefined consumer behavior. Payments evolved into a flexible, programmable layer of the digital economy. 𝟱. 𝗧𝗼𝗱𝗮𝘆, we’re in the age of seamless integration. Payments are embedded in everything - from ride-hailing apps to SuperApps. Real-time rails like SEPA Instant, UPI and PIX are live. CBDCs are in pilot. However, as payment ecosystems grow more fragmented - with new methods, regional schemes, compliance layers, and fraud risks -complexity has become a major bottleneck for merchants, fintechs, and even banks. Integrating multiple providers, maintaining uptime across systems, and ensuring regulatory compliance isn't just costly - it's unsustainable without the right foundation. This is where a new breed of infrastructure players like 𝗔𝗸𝘂𝗿𝗮𝘁𝗲𝗰𝗼 fit in - offering the tools to simplify complexity and still retain control. • 𝗪𝗵𝗶𝘁𝗲-𝗹𝗮𝗯𝗲𝗹 𝗽𝗮𝘆𝗺𝗲𝗻𝘁 𝗴𝗮𝘁𝗲𝘄𝗮𝘆𝘀 let banks, PSPs, and fintechs launch their own branded platforms fast - without building from scratch. • 𝗣𝗮𝘆𝗺𝗲𝗻𝘁 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 enables merchants to route transactions dynamically across multiple acquirers, reducing costs and failed payments while improving UX. • 𝗕𝗮𝗻𝗸𝘀 can embed API-driven acquiring services into their offerings without the burden of a full-scale tech overhaul. In a world where growth brings fragmentation, the real challenge isn’t enabling payments - it’s managing them. The advantage will lie with infrastructure that can unify complexity, adapt in real time, and scale across borders without adding friction. Opinions: my own, Graphic source: Akurateco Payment Hub Subscribe to my newsletter: https://lnkd.in/dkqhnxdg

Marketing Automation & Customer Service is no longer just about sending emails or filling out contact forms. With AI these flows can become journeys: interactive and truly personalized - unlocking new levels of engagement and conversion in Whatsapp or Chat. But where to start? Here’s a breakdown of the top journeys most e-commerce brands have implemented and how I rank their AI potential and impact: 1️⃣ Product Recommendations | AI Potential: High Helping your customer to make a choice and find the product that fits their needs. > Move beyond static scripts! AI can find best fitting products with LLM powered semantic search, resolve blockers, compare products and provide tailored suggestions. 2️⃣ Welcome Flow | High You offer an incentive, collect and opt-in and further into > With AI, this flow can become interactive: No form like answering all extrated from a normal informal conversation. Enrich their profiles for future personalization (email, birthday, ...) 3️⃣ Customer Service | High Taking care when your customers have a problem: > AI Agents will provide 24/7 multilingual support. Collect the info you need before handing over to a human if the certain problems still need the human insight, access, or touch. Save costs while enhancing customer experience. 4️⃣ FAQ Automation | Medium Make it easy for customers to find answers. > AI ensures responses are nuanced and personalized. 5️⃣ Abandoned Cart | Medium Customer is (almost) ready to buy, but got interrupted or needs a little nudge > Send a(i) personalized message based on the exact product they have in their cart. Highlight how it fits their preferences or past purchases. 6️⃣ Cross-Sell / Up-Sell | Medium Encourage customers to buy complementary products. > AI can craft compelling arguments for upgrades, bundles or next product to buy. 7️⃣ Birthday or Special Day Campaigns | Medium Send wishes and a little gift > Let AI create a personalized message, image, or video and send it via WhatsApp. 8️⃣ Winback / Replenishment | Low Remind customers to repurchase or return. > Personalization helps, but the core is timing. 9️⃣ Review Collection | Low Gather feedback and build trust with REVIEWS.io or alike > AI can personalize requests and handle negative feedback gracefully avoiding bad reviews. 🔟 Back-In-Stock | Low Notify customers when the product they wanted to buy is available again. > AI can add a personalized touch to the reminder [don't want to get out of stock? Talk to VOIDS] 1️⃣1️⃣Referral Programs | Low Encourage word-of-mouth with incentives for sharing. > AI can personalize referral messages for higher trust and conversion. 1️⃣2️⃣Fulfilment Updates | Low Keep customers informed about their orders. > Let AI add a personal touch related to the product shipped. [Want to turn into an upsell opportunity: Karla is doing a great job here] The future of e-commerce is about conversations, not campaigns. Which flow or journey are you excited to tackle first? #conversationalai

From day one in #b2becommerce, this has been one of the biggest traps I’ve seen. Distributors: please don’t get pulled down the primrose path just because a platform says it has an “out-of-the-box” integration with your ERP. The truth no one talks about? There's really no such thing as an 'out-of-the-box' integration. Everyone uses ERP and eCom differently. So, at the least, there will be configuration. But there are several platforms still running this gig - old tech, proprietary, no ecosystem. They tempt you with a seemingly low-risk way to launch quickly. The bigger issue: What will you be left with in the end? Let’s be honest: integration sucks. But it happens successfully every day. And many ERP platforms have connectors (or other tools) to simplify this process. So chances are, you’re not starting from scratch regardless of which direction you go. But here’s the catch: That “easy” integration comes with old technology, clunky experiences, and heavy customization. And by the time you realize how much you’ve spent just to get it halfway usable, the golden handcuffs are on. You could easily be stuck with a platform your customers hate with no clear path forward. Don’t do that to your eCommerce business - or, more importantly, your customers. They deserve a great customer experience- modern, API-first, composable platforms that let you build the experience they actually want. At the very least, do an honest, thorough evaluation against other B2B platforms, specific to your requirements. Because in the end, it’s not about what it costs. It’s about what you can make. #B2BEcommerce #B2BPlatforms #ERPIntegration #DigitalTransformation #CustomerExperience #MidMarketDistributors #ComposableCommerce #PlatformSelection

In this deep dive edition of Fintech Wrap Up, I explored how AWS is enabling businesses to build modern credit card payment processing platforms and payment gateways with its powerful cloud infrastructure. As payments become increasingly digital, AWS provides a secure, scalable, and resilient solution to handle credit card transactions efficiently and in real-time. By using services like API Gateway, DynamoDB, Elastic Kubernetes Service (EKS), and Amazon Managed Streaming for Apache Kafka, businesses can meet high availability and low latency requirements while adhering to compliance standards like PCI DSS. The article delves into the lifecycle of credit card transactions, from authorization to clearing and settlement, offering detailed reference architectures for both the acquiring and issuing processes. It highlights AWS’s capabilities to support global expansion, manage compliance in different regions, and protect sensitive data through tools like AWS Payment Cryptography and ElastiCache. Key features include the ability to scale operations during seasonal spikes, maintain stringent security protocols, and automate monitoring for real-time issue detection. Whether businesses are enhancing their fraud prevention mechanisms, optimizing tokenization processes, or ensuring compliance with industry regulations, AWS’s cloud infrastructure provides the flexibility and reliability needed to succeed in today’s fast-evolving payments ecosystem. If you’re looking to future-proof your payment systems, this deep dive is packed with essential insights! #fintech #payments #aws #cardprocessing Prasanna Thomas Richard Panagiotis Tony Nicolas Arjun Dr Ritesh Sandra

Think you've left it too late to get the Shopify store performing better before peak? Well, you're right. Sort of. Ideally by now, you'd be into your A/B testing for optimisations, but if not, here's how i'd spend the next 8 weeks: 𝗪𝗲𝗲𝗸 𝟭 • Conduct a speed audit and deploy short term fixes • Install heat mapping software onsite. 𝗪𝗲𝗲𝗸 𝟮 • Analyse email marketing flows and create missing core flows. • Setup SMS with create a sign-up onsite. 𝗪𝗲𝗲𝗸 𝟯 • Implement and test A/B testing to email flows • Plan email campaigns for BFCM build up. 𝗪𝗲𝗲𝗸 𝟰 • Analyse heatmaps • Complete UI/UX Audit 𝗪𝗲𝗲𝗸 𝟱 • Prioritise implementation and A/B tests derived from heatmaps and UI/UX Audits • Begin implementation 𝗪𝗲𝗲𝗸 𝟲 • Implementing 𝗪𝗲𝗲𝗸 𝟳 • Complete implementation • Pick winning tests and complete implementation 𝗪𝗲𝗲𝗸 𝟴 • Test customer service/chat macros • No more site changes. #cro #ecommerce #shopifyplus

Building a Data Analytics Team for a Mid-Sized Fashion & Beauty E-Commerce Brand! Continuing from my previous post on building a data analytics team, I received many DMs asking for real-world examples. So, in this post, I’ll try to wear the hat of a mid-sized fashion & beauty e-commerce brand and build their data team from scratch. -> Challenge? Scaling an analytics team that drives growth, retention, and profitability while solving key business problems First, What Problems Do We Need to Solve? Before hiring, let’s define the top challenges a data team should tackle: 1) Marketing Attribution & ROI – Are our paid ads actually bringing new customers? 2) Customer Segmentation & Retention – Who are our high-value customers? How do we keep them engaged? 3) Demand Forecasting & Inventory Planning – What should we stock, and when, to minimize dead inventory? 4) Personalization & Conversion Optimization – Can we recommend the right products at the right time? 5) Fraud Detection & Order Cancellations – Are we losing money due to fake COD orders or excessive returns? #Year 1: How to Build the Right Data Team & Solve These Problems? A) Phase 1 (0-3 Months) – Laying the Foundation ->Key Hires: 🔹 1 Data Analyst – To track key KPIs, build dashboards, and analyze marketing performance 🔹 1 Data Engineer – To set up ETL pipelines and connect multiple data sources 🔹 1 BI Developer – To automate reporting and create self-serve dashboards -> Quick Wins: ✔️ Centralize data in a data warehouse (Snowflake, BigQuery, or Redshift) ✔️ Automate daily sales & marketing reports for better decision-making ✔️ Implement UTM tracking for paid ads & influencer campaigns B) Phase 2 (3-6 Months) – Scaling Insights & Retention Strategies ->Next Hires: 🔹 1 Data Scientist – To build customer segmentation models & predict churn 🔹 1 CRM Analyst – To optimize retention campaigns, loyalty programs & lifecycle marketing -> Key Initiatives: ✔️ Identify high-value customers vs. those likely to churn ✔️ Optimize ad spend & ROAS – Cut waste, double down on high-performing channels ✔️ A/B test pricing & discounts – Find the sweet spot for conversions C) Phase 3 (6-12 Months) – AI-Driven Decisions & Advanced Analytics -> Final Hires: 🔹 1 Demand Forecasting Analyst – To predict inventory needs & optimize supply chain 🔹 1 AI/ML Engineer – To implement recommendation engines & dynamic pricing -> Big Impact Areas: ✔️ Build AI-powered product recommendations to increase AOV (Average Order Value) ✔️ Implement predictive demand forecasting to reduce stockouts & excess inventory ✔️ Set up fraud detection models to minimize return abuse & fake COD orders What challenges have you faced in scaling data teams for e-commerce? Let’s discuss! #Ecommerce #DataAnalytics #AI #CustomerRetention #FashionTech #MarketingOptimization

I gave this problem to 30 candidates in mock system design interviews to help them think better when it comes to backend and distributed systems. Only 13 could answer it well. Here’s what I asked: You’re designing an order service for a large e-commerce platform. Whenever a user places an order, your system needs to: – Save the order in an SQL database (first) – Then, trigger one or more side effects, such as: – Publishing an event to Kafka – Invalidating a cache – Sending an HTTP request to another service – Calling a webhook But here’s the catch: – If your service crashes after saving the order (but before notifying other systems), downstream services are out of sync. – If you try to “coordinate” both steps, you realize these writes (to DB and to Kafka/cache/HTTP/etc.) aren’t part of the same transaction. – This is the classic dual writes problem, it’s not just about Kafka. Any time you’re trying to write to two (or more) systems that can fail independently, you risk ending up in an inconsistent state. Question: How would you design this flow so that either all side effects happen reliably after a DB write (no matter if you’re sending to Kafka, invalidating cache, or calling a webhook). or none do? – What pattern would you use? – How would you structure your DB and system? – How would you handle failures and retries, and make sure your solution is robust at scale? Walk through your solution step by step. Here’s a video I made, breaking down the entire solution: (https://lnkd.in/gFCsh58T) If you’re prepping for any serious backend role, be ready to go deep, not just naming patterns, but explaining the tradeoffs, implementation, and why it works.

Ever wondered what happens after you click “Checkout”? Let me try to explain the core building blocks of an E-Commerce Architecture. Here’s a breakdown of the journey of an online order using a microservices-based architecture - where each step, from cart to shipping, is handled by an independent service. The process kicks off when a customer places an order, which is managed by the Shopping Cart microservice via a REST API. The order then flows into the Order Placement service, which records and broadcasts the order details through an event stream. Next, the Inventory service checks stock levels and interacts with the Supplier backorder system if needed. The Payment microservice integrates with third-party providers (via SOAP or REST) to process payments securely. Once payment is confirmed, the Shipping service prepares the consignment, updates order status, and notifies the Operations team for dispatch. Meanwhile, reporting tools consume order and inventory events and store them in an OLAP database for analytics and dashboards. Don’t forget to save this for later !

Gartner has just released its 2024 Magic Quadrant for Digital Commerce Platforms—a must-have resource for evaluating the top solutions in the market. This report categorizes vendors into Leaders, Challengers, Visionaries, and Niche Players, based on their ability to execute and their strategic vision. Why is it worth considering? The Magic Quadrant helps businesses identify innovative and reliable platforms, reducing risks and maximizing ROI in digital commerce strategies. Here are five platforms from the report that caught my attention: Salesforce: A leader known for its global reach and native integrations, perfect for large B2C and B2B enterprises. Robust capabilities, but its monolithic architecture can be a challenge. Shopify: Great for quick launches with easy setup and omnichannel support. However, its advanced features and pricing may limit scalability for large companies. commercetools: A modular and scalable option, ideal for mature, high-volume businesses. While highly innovative, its implementation can be complex. VTEX: A strong choice for businesses combining B2B and B2C. Its composable, modular architecture enables flexibility, though native customization is limited. BigCommerce: Perfect for mid-market companies needing flexibility. Its cloud-native architecture is modern and composable, but its global reach is still developing. There’s no one-size-fits-all solution in digital commerce. Digital leaders need to conduct a thorough analysis to select the platform that best aligns with their unique business needs.

I put together a step-by-step guide to help you implement LLM Optimisation the right way for E-commerce. In this carousel, you’ll find the high-level overview. The full version includes: → How to generate a structured product feed (CSV, JSON, XML) with the right attributes: title, GTIN, MPN, price, availability, and product URLs → How to apply Schema . org Product markup using JSON-LD, with full code examples → How to configure your robots.txt to allow GPTBot, OAI-SearchBot and PerplexityBot access to your PDPs and feeds → How to prerender JS-heavy product pages using static snapshots or SSR → Where and how to submit your feed to Perplexity’s Merchant Program and OpenAI’s early access initiative → How to debug AI visibility using server logs, schema validators, and live prompt testing inside LLM tools If you’re working on e-commerce, SEO, or growth, this is where things are moving. 💬 Comment or DM me, and I’ll send you the full guide. 📌 Save this post if you're not ready yet but know you'll need it.