Ecommerce

Replenishment isn’t a side feature, it’s a force multiplier. This is a big mistake. We’ve seen replenishment flows outperform promos and win-back emails combined. They convert better every time with the right timing and zero customer effort. Brands overspend on ads to win new customers, then forget to win them again. They need to predict exactly when a customer needs to repurchase and trigger the message at the perfect moment. Not too soon, not too late. Just right. ++ 𝗪𝗵𝘆 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀 𝗗𝗼𝗻’𝘁 𝗥𝗲𝗼𝗿𝗱𝗲𝗿 – 𝗔𝗻𝗱 𝗛𝗼𝘄 𝘁𝗼 𝗙𝗶𝘅 𝗜𝘁 ++  𝗧𝗵𝗲𝘆 𝗙𝗼𝗿𝗴𝗲𝘁 ✅ Fix: Replenit’s AI triggers proactive reminders across channels exactly when customers are likely to run out, via the brand's own marketing automation vendors, without any migration. 𝗣𝗼𝗼𝗿 𝗧𝗶𝗺𝗶𝗻𝗴 𝗼𝗿 𝗖𝗵𝗮𝗻𝗻𝗲𝗹 ✅ Fix: Multichannel orchestration (SMS, push, email) with personalized timing based on consumption behavior. 𝗡𝗼 𝗖𝗹𝗲𝗮𝗿 𝗜𝗻𝗰𝗲𝗻𝘁𝗶𝘃𝗲 ✅ Fix: Smart upsell bundles, urgency messages (“running low?”), and loyalty integration improve reorder ROI.   • Food & Beverage, pet food and treats, wellness & beauty products hold the highest repeat purchase potential, being very high due to frequent, perishable-driven consumption patterns. • Online groceries and FMCG rank high in habitual/impulsive behavior, presenting a strong fit for mobile push and SMS-driven replenishment campaigns. Brands like Glosel turned a leaky bucket into a revenue engine with Replenit’s AI-powered multichannel replenishment flows. 🚀 53.75% more automation revenue 🛒 +28% higher AOV 📲 100% of the Multichannel approach, email, SMS & Push channel revenue -12X Higher Engagement Rate Why does it work? Because Replenit activates timely, no-effort reorders across email, SMS, push, and more. Most brands forget to remind customers. ++ 𝟯 𝗧𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗥𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝗥𝗲𝘁𝗮𝗶𝗹𝗲𝗿𝘀 ++ 1️⃣ Make Replenishment an Always-On Growth Engine Don’t treat it as a postscript. Integrate replenishment flows as a core revenue pillar in your retention strategy. 2️⃣ Automate Across Channels With Smart Triggers Use AI-powered solutions to trigger SMS, email, and push notifications based on usage cycles, not guesswork. 3️⃣ Track and Optimize With First-Party Data Loops Leverage Replenit’s dashboards to identify top retention products, run experiments on timing, and iterate continuously. 𝗧𝗼 𝗮𝗰𝗰𝗲𝘀𝘀 𝗮𝗹𝗹 𝗼𝘂𝗿 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗼𝗹𝗹𝗼𝘄 ecommert® 𝗮𝗻𝗱 𝗷𝗼𝗶𝗻 𝟭𝟰,𝟮𝟬𝟬+ 𝗖𝗣𝗚, 𝗿𝗲𝘁𝗮𝗶𝗹, 𝗮𝗻𝗱 𝗠𝗮𝗿𝗧𝗲𝗰𝗵 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲𝘀 𝘄𝗵𝗼 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲𝗱 𝘁𝗼 𝗲𝗰𝗼𝗺𝗺𝗲𝗿𝘁® : 𝗖𝗣𝗚 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗚𝗿𝗼𝘄𝘁𝗵 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿. About ecommert We partner with CPG businesses and leading technology companies of all sizes to accelerate growth through AI-driven digital commerce solutions. #CPG #ecommerce #Replenishment #AI #FMCG

Who are the biggest fintechs globally? And what are the success factors behind their rise? When PayPal (1998), Ant Group (2004) and Stripe (2010) launched, the financial world ran on monolithic core-banking systems, clunky gateways and brick-and-mortar branches. Smartphones were in their infancy, APIs weren’t standard, and regulators tightly guarded payment rails. These first pioneers tore down barriers - packaging payments, lending and banking into nimble, digital-first services. 𝗠𝗮𝗶𝗻 𝘁𝗿𝗶𝗴𝗴𝗲𝗿𝘀 𝗯𝗲𝗵𝗶𝗻𝗱 𝘁𝗵𝗲 𝗿𝗶𝘀𝗲 𝗼𝗳 𝗳𝗶𝗻𝘁𝗲𝗰𝗵𝘀: 1. The 2007-2008 financial crisis The 2007–2008 banking collapse eroded consumer trust and triggered massive layoffs. Venture capital saw nimble startups as an alternative, and displaced finance experts brought operational know-how to new ventures. 2. The decoupling Startups unbundled the user experience from core banking infrastructure. With cloud-based services and APIs, they built slick front-ends while outsourcing processing, payments, and compliance. 3. The mobile revolution Apple’s 2007 iPhone debut put full web browsers and app ecosystems into everyone’s pocket. Overnight, fintechs could launch banking, investing and payments interfaces without a single branch. 4. API-first architecture APIs turned complex financial functions into plug-and-play components. Businesses outside of finance could now offer payments, credit, or wallets as part of their core experience. 5. Regulation New e-money and payments licenses lowered entry barriers for non-banks. In Europe, Open Banking forced banks to open up customer information, while in the US  fintechs expanded nationally by securing state-by-state money transmitter licenses. 𝗟𝗲𝘀𝘀𝗼𝗻𝘀 𝗹𝗲𝗮𝗿𝗻𝗲𝗱: 1. Payments remain fintech’s backbone - high frequency, large volume, and predictable revenue at scale. 2. Coinbase and Binance reached billion-dollar status quickly, driven by retail crypto demand. However, market swings and regulatory uncertainty are key topics. 3. Mobile-first, low-cost digital banking models with instant onboarding scaled rapidly and efficiently across regions. 4. Stripe and Adyen highlight the value of infrastructure models - plug-and-play B2B financial tools driving sticky revenue at scale. 5. Ant Group and Nubank illustrate how bundling payments, credit, and savings into a single app works best in markets underserved by traditional banks. 6. Early unicorns may have grown fast, but long-term success hinges on balancing growth with profitability - especially in sectors like lending and crypto that face tighter regulation. 7. Resilient fintechs diversify revenue - blending interchange, subscriptions, lending, and trading.   Opinions and graphics: Panagiotis Kriaris 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐦𝐲 𝐧𝐞𝐰𝐬𝐥𝐞𝐭𝐭𝐞𝐫: https://lnkd.in/dkqhnxdg

🛟 The New European Accessibility Act (EAA) Is Coming 2025. Useful pointers for small and large companies to be prepared — and comply with a new EU directive ↓ ✅ EEA is a new EU directive to standardize accessibility. ✅ It mandates accessibility for most “essential” products. ✅ It includes digital products, banking, transport, eCommerce. ✅ Applies to all private companies globally that sell to EU customers. ✅ EEA doesn’t reference WCAG, but implies WCAG 2.2 AA compliance. 🤔 Grace period: 5 years for “unaltered” services/products. 🚫 Accessibility overlays aren’t accepted as-is under EAA. ✅ In B2C, companies providing a service must comply with EAA. ✅ B2B vendors → B2C companies must do their due diligence. ✅ B2C companies carry responsibility for compliance, not B2B. ✅ Review and update contracts with B2B partners to cover EAA. ✅ EU operations → your products must meet EAA’s requirements. ✅ Accessibility statements → incl. overview, mechanism for feedback. ✅ Must comply even if you don’t have a shop, but support (“service”). ✅ Exceptions: micro enterprises, <10 people, turnover < EUR 2 Mio. ⏰ The deadline to comply with EAA is 28 June 2025. As Craig Abbott writes, the European Accessibility Act is probably one of the most ambitious directives in accessibility regulation. It’s extensive and universally applicable. It covers a lot of ground to help everyone get a better, more accessible digital experience. However, WCAG 2.2 AA isn’t the ultimate checklist. National laws and local regulations might complement EAA with additional requirements — e.g. localization, biometric systems etc. You must also provide an accessibility statement with an overview of compliance, mechanism for feedback, non-compliant elements. It’s not quite clear how exactly the new directive will be enforced. But it’s probably a good time for companies to launch accessibility efforts to explore just what exactly needs to be done to improve accessibility ahead of the upcoming deadline. While on it, perhaps we could also run a round of accessibility testing with actual people. Automated tools are helpful, but accessibility isn’t a checklist, and compliance doesn’t guarantee a good experience. Bring in a wide range of users on board, and you’ll be surprised how quickly you’ll discover actual UX challenges that actual people experience every day. Useful resources: How To Get Started With EAA Compliance, by Marli Ritter 👍 https://lnkd.in/e2vfY93C European Accessibility Act: What You Need To Know, by Craig Abbott https://lnkd.in/ezu9Vyh9 EAA: Everything You Need To Know, by SiteImprove https://lnkd.in/eGH4EUvA How To Explain Accessibility To Stakeholders, by yours truly https://lnkd.in/eY2Ty7FG [continues in comments ↓] #ux #accessibility

So, here’s a quick story about how I managed to take our app ratings at airtel from a 3.2 to a solid 4.3 in just 30 days. I was on a call with our account executive at MoEngage where we were discussing the RFM model. If you’re not familiar, RFM stands for Recency, Frequency, Monetization—it’s basically a way to understand customer behavior based on how often they use the app, how recently they’ve been active, and if they’ve made any purchases. After the call, I started thinking—how can we use this data beyond just targeting users for offers or notifications? And then it clicked: we could use this to improve our app ratings. Here’s what I did next: instead of showing the app rating prompt to everyone (which was clearly not working), I decided to get more specific. I created a segment of users who were really engaged—people who were listening music for at least 20-30 minutes a day and opening the app 5-6 times daily. These were our power users, the ones who were already loving the app. But I didn’t just stop there. I made sure the rating prompt would only pop up after an “aha moment,” like after they listened to five songs or changed their hello tune. I wanted to catch them at a high point when they were already feeling good about their experience. Plus, we capped the prompt to only show up once a week, so we weren’t bombarding them. And guess what? It worked! By focusing on the users who were most likely to give us positive feedback, we managed to take our ratings from 3.2 to 4.3 in just a month. It was all about understanding who to ask, when to ask, and how to make that moment feel seamless.

𝗪𝗵𝗮𝘁 𝗶𝘀 𝗶𝗻𝘁𝗲𝗿𝗰𝗵𝗮𝗻𝗴𝗲, 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝗳𝗮𝗰𝘁𝗼𝗿𝘀 𝗶𝗺𝗽𝗮𝗰𝘁 𝘁𝗵𝗲 𝗶𝗻𝘁𝗲𝗿𝗰𝗵𝗮𝗻𝗴𝗲 𝗿𝗮𝘁𝗲? Let’s dive in: Every time a consumer swipes a card to make a purchase, the merchant pays an interchange fee. Revenue from the fee gets divided among parties that facilitated the transaction: the banks that send and receive the payment, the card network, the payment processor, and—more recently—fintechs and businesses that embed payments. When you take the bird-eye view diagram below 👇 as an example: If a user swipes a card issued by a Neobank, $1.70 (interchange fee) goes to the issuing bank and the card network, $0.50 (acquiring fee) goes to the acquiring bank. Interchange fees are not always the same though. 𝗪𝗵𝗮𝘁 𝗳𝗮𝗰𝘁𝗼𝗿𝘀 𝗶𝗺𝗽𝗮𝗰𝘁 𝗶𝗻𝘁𝗲𝗿𝗰𝗵𝗮𝗻𝗴𝗲 𝗿𝗮𝘁𝗲? ► Credit vs. Debit Interchange rates on credit cards are significantly higher than those on debit cards. ► Rewards programs These benefits are financed through higher interchange rates, and have proven to be very popular with consumers. ► Online vs. Offline Online purchases are less secure than in-person purchases. ► Consumer vs. Commercial Cards associated with business or corporate accounts carry higher interchange rates than consumer cards. ► Merchant Category Code (MCC) Every merchant is categorized by the major card networks according to a Merchant Category Code (MCC). This means that there are different interchange rates depending on whether someone uses a card in a supermarket, a retail store, a gas station, or with some other form of merchant. ► The Card Network Different card networks charge different rates. Visa and Mastercard are known for charging lower rates. Other networks like AMEX are known for charging higher rates. ► Network partner programs Visa and Mastercard’s partner programs like VPP (Visa Partner Program) and MPP (Mastercard Partner Program) often give specific retailers interchange rates that are much lower than the networks’ published interchange rates. ► Size of the issuing bank (in the US 🇺🇸) Larger banks are subject to a regulation called the Durbin Amendment that caps interchange rates on consumer debit transactions. Smaller banks are exempt. As a result, these smaller banks can earn more revenue from interchange rates—and that benefits the FinTechs and embedded finance businesses that partner with them. Find this helpful? [ 𝗿𝗲𝗽𝗼𝘀𝘁 ] Anything to add about this subject? [ 𝗶𝗻𝘃𝗶𝘁𝗲𝗱 𝘁𝗼 𝗰𝗼𝗺𝗺𝗲𝗻𝘁 ] Nice story, Marcel. Next! [ 𝗹𝗶𝗸𝗲 ]

Do you sometimes feel frustration, as you are building a product to get the management off your back, rather than address the users? Here are 6 ways to become user-centric again: 1) Prioritize in a transparent way This is a great place to start. If your backlog is prioritized based on data and potential opportunity, risk, and cost, it will be easier to put forth user-centric initiatives ahead of those that came from upstairs. At the very least, you will have a good basis for an educated discussion. 2) Utilize users' perspective using user stories and personas If your team understands the users and their problems, it will be easier to craft something great that will later appeal to the same users. Just keep up the empathy of creating something by people for other people, and not get some metric magically go up! 3) Make user feedback public If everyone in the company can see the themes that come from user feedback, it will be way harder to ignore it in favor of some corporate nonsense. Let those voices be heard by everyone! 4) Have the NPS and user ratings at the forefront The same goes for a single metric representing the general product sentiment. If the number is low or, worse, is going down and everyone can see that, the responsible Product Manager has to react. 5) Focus on your product goals Now, upstairs mandates might not be the only distraction you face when trying to improve your product. To survive them all, focus on one thing: your product goals. This will allow you to demonstrate you are doing what you are asked for and you can use user feedback and points 1-4 to pursue those goals. Thus, it's like killing 2 birds with 1 stone. However, you can also simply: 6) Have the confidence to say "No" Not all company/legal/management requests can be ignored. Sometimes changing the law or a wider company initiative will require you to comply and that is OK! However, there will also be times when someone will try to force your compliance. This is where you need to be confident, and exercise your Product Manager's independence, especially when there is no data to support a specific request. There you go! My 6 ways you can become a user-centric Product Manager. How about you? Do you address your users or your management first and foremost when developing your product? Sound off in the comments! #productmanagement #productmanager #usercentricity

Last week, I shared how Gen AI is moving us from the age of information to the age of intelligence. Technology is changing rapidly and the way customers shop and buy is changing, too. We need to understand how the customer journey is evolving in order to drive customer connection today. That is our bread and butter at HubSpot - we’re deeply curious about customer behavior! So I want to share one important shift we’re seeing and what go-to-market teams can do to adapt. Traditionally, when a customer wants to learn more about your product or service, what have they done? They go to your website and explore. They click on different pages, filter for information that’s relevant to them, and sort through pages to find what they need. But today, even if your website is user-friendly and beautiful, all that clicking is becoming too much work. We now live in the era of ChatGPT, where customers can find exactly what they need without ever having to leave a simple chat box. Plus, they can use natural language to easily have a conversation. It's no surprise that 55% of businesses predict that by 2024, most people will turn to chatbots over search engines for answers (HubSpot Research). That’s why now, when customers land on your website, they don’t want to click, filter, and sort. They want to have an easy, 1:1, helpful conversation. That means as customers consider new products they are moving from clicks to conversations. So, what should you do? It's time to embrace bots. To get started, experiment with a marketing bot for your website. Train your bot on all of your website content and whitepapers so it can quickly answer questions about products, pricing, and case studies—specific to your customer's needs. At HubSpot, we introduced a Gen AI-powered chatbot to our website earlier this year and the results have been promising: 78% of chatters' questions have been fully answered by our bot, and these customers have higher satisfaction scores. Once you have your marketing bot in place, consider adding a support bot. The goal is to answer repetitive questions and connect customers with knowledge base content automatically. A bot will not only free up your support reps to focus on more complex problems, but it will delight your customers to get fast, personalized help. In the age of AI, customers don’t want to convert on your website, they want to converse with you. How has your GTM team experimented with chatbots? What are you learning? #ConversationalAI #HubSpot #HubSpotAI

Demystifying Cloud Strategies: Public, Private, Hybrid, and Multi-Cloud As cloud adoption accelerates, understanding the core cloud computing models is key for technology professionals. In this post, I'll explain the major approaches and examples of how organizations leverage them. ☁️ Public Cloud Services are hosted on shared infrastructure by providers like AWS, Azure, GCP. Scalable, pay-as-you-go pricing. Examples: - AWS EC2 for scalable virtual servers   - S3 for cloud object storage - Azure Cognitive Services for AI capabilities - GCP Bigtable for large-scale NoSQL databases ☁️ Private Cloud Private cloud refers to dedicated infrastructure for a single organization, enabling increased customization and control. Examples:  - On-prem VMware private cloud - Internal Openstack private architecture - Managed private platforms like Azure Stack - Banks running private clouds for security ☁️ Hybrid Cloud Hybrid combines private cloud and public cloud. Sensitive data stays on-prem while leveraging public cloud benefits. Examples: - Storage on AWS S3, rest of app on-prem - Bursting to AWS for seasonal capacity - Data lakes on Azure with internal analytics ☁️ Multi-Cloud Multi-cloud utilizes multiple public clouds to mitigate vendor lock-in risks. Examples:  - Microservices across AWS and Azure  - Backup and DR across AWS, Azure, GCP - Media encoding on GCP, web app on Azure ☁️ Hybrid Multi-Cloud The emerging model - combining private infrastructure with multiple public clouds for ultimate flexibility. Examples: - Core private, additional capabilities leveraged from multiple public clouds - Compliance data kept private, rest in AWS and Azure  - VMware private cloud extended via AWS Outposts and Azure Stack Let me know if you have any other questions!

If you want to know where the money is in Machine Learning, look no further than Recommender Systems! Recommender systems are usually a set of Machine Learning models that rank items and recommend them to users. We tend to care primarily about the top-ranked items, the rest being less critical. If we want to assess the quality of a specific recommendation, typical ML metrics may be less relevant. Let’s take the search results of a Google search query, for example. All the results are somewhat relevant, but we need to make sure that the most relevant items are at the top of the list. To capture the level of relevance, it is common to hire human labelers to rate the search results. It is a very expensive process and can be quite subjective since it involves humans. For example, we know that Google performed 757,583 search quality tests in 2021 using human raters: https://lnkd.in/gYqmmT2S. Normalized Discounted Cumulative Gain (NDCG) is a common metric to exploit relevance measured on a continuous spectrum. Let’s break that metric down. Using the relevance labels we can compute diverse metrics to measure the quality of the recommendation. The cumulative gain (CG) metric answers the question: How much relevance is contained in the recommended list? To get a quantitative answer to that question, we simply add the relevance scores provided by the labeler: CG = relevance 1 + relevance 2 + ... The problem with cumulative gain is that it doesn’t take into account the position of the search results. Any order would give the same value however we want the most relevant items at the top. Discounted cumulative gain (DCG) discounts relevance scores based on their position in the list. The discount is usually done with a log function, but other monotonic functions could be used: DCG = relevance 1 / log(position 1) + relevance 2 / log(position 2) + ... DCG is quite dependent on the specific values used to describe relevance. Even with strict guidelines, some labelers may use high numbers and others low numbers. To put those different DCG values on the same level, we normalize them by the highest value DCG can take. The highest value corresponds to the ideal ordering of the recommended items. We call the DCG for ideal ordering the Ideal Discounted Cumulative Gain (IDCG). The Normalized Discounted Cumulative Gain (NDCG) is the normalized DCG NDCG = DCG / IDCG If the relevance scores are all positive, then NDCG is contained in the range [0, 1], where 1 is the ideal ordering of the recommendation. #MachineLearning #DataScience #ArtificialIntelligence

Introducing the web's first market map of the Product Analytics Market: I was floored when I couldn't find one of these online. Surely, Gartner or CBInsights or A16Z would have created one? It turns out not. So I spent the past 3 months: • Talking with 25 buyers • Researching the space myself • Interviewing 5 product leaders at key players This is what I learned about the most significant players in each space: (that PMs and product people need to know) 1. Core Product Analytics Platforms     The foundational tools for tracking user behavior and product performance Amplitude : The leader, an all-in-one platform for PMs to master their data Mixpanel : The leader in easy UX and pioneer in event-based analytics Heap | by Contentsquare: The automatic event tracking and real-time insights leader 2. A/B Testing & Experimentation     Platforms for analysis Optimizely : The premier tool for sophisticated A/B and multivariate testing VWO : The best for combining A/B testing with heatmaps and session recordings AB Tasty: The all-in-one solution for testing, personalization, and AI-driven insights 3. Feedback & Session Recording     Capture qualitative insights and visualize user interactions Medallia: The top choice for comprehensive experience management Hotjar | by Contentsquare: The go-to for visual feedback and user behavior insights Fullstory: The best for detailed session replay and user interaction analysis 4. Open-Source Solutions     Customizable, free analytics platforms for data sovereignty Matomo: The robust, privacy-focused open-source analytics platform Plausible Analytics: The lightweight, privacy-first analytics solution PostHog: The versatile, open source product analytics tool 5. Mobile & App Analytics     Specialized tools for mobile and app performance analysis UXCam: The best for in-depth mobile user interaction insights Localytics: The leader in user engagement and lifecycle management Flurry Analytics: The comprehensive, free mobile analytics platform 6. Data Collection & Integration     Gather and unify data across platforms Segment: The top choice for effortless customer data unification Informatica: The enterprise-grade solution for data integration and governance Talend: The flexible, open-source data integration tool 7. General BI & Data Viz     Non-product specific tools for data analysis and visualization Tableau: The leader in interactive, rich data visualization Power BI: The best for deep integration with Microsoft tools Looker: The modern BI tool for customizable, real-time insights 8. Decision Automation & AI     Systems for automated insights and decisions Databricks: The unified platform for data and AI collaboration DataRobot: The leader in automated machine learning and AI Alteryx: The comprehensive solution for analytics automation Check out the full infographic to see where your favorite tools fit and discover new platforms to enhance your product analytics stack.