Ecommerce

We studied 2 lakh+ Indian threat indicators in 2025. And here’s what 2026 regulators now demand (but most companies still don’t do.) 2025 changed the game. We tracked threats across every state in India, from Maharashtra to Manipur. The scale of activity is no longer random. It’s strategic, coordinated, and sector-targeted. And now, so are the regulators. Here’s what 2026-ready companies are expected to do (but 90% still haven’t): 01. State-wise Risk Mapping is now a compliance expectation. 82% of malware volume came from just 6 Indian states. But the fastest-growing threat zones were Tier-2: Punjab, Odisha, Assam. Regulators now want geo-behavioral segmentation, and not just IP logs. 02. Proof of real-time detection, not just dashboards. In sectors like BFSI and energy, response time is now being scrutinised. Can you prove your system reacts in seconds, not hours? 2026 audits will ask: “Show me what your XDR did the last time your East zone flagged an anomaly.” 03. Sector-specific threat coverage: not optional anymore. Pharma, power grids, BFSI, healthcare, they’re all being hit differently. A generic firewall rule isn’t compliance. Mapping sector threat intel to your stack is now a regulatory demand, not a suggestion. 04. The death of checkbox compliance. 68% of compromised orgs in 2025 were “fully compliant”. But only 12% had active breach simulations in place You can have 100 tools. But, if nobody’s testing them in real-world breach drills, it won’t save you in 2026. 05. From centralised to hybrid monitoring Work-from-anywhere isn’t new. But regulators now want user behavior-based controls that adapt to geolocation, risk context, and device intelligence. 2026 audits will go beyond log files. They’ll ask: “How does your system behave when a user travels from Pune to Patna?” Regulatory audits in 2026 will feel more like red-team simulations. What are you seeing across sectors? Seqrite Quick Heal #CyberSecurity #ThreatIntelligence #XDR #RegTech #CISO #Compliance #CyberRisk #IndiaCyber #BFSISecurity #CriticalInfrastructure #SecurityLeadership

The disconnect between sales managers and reps in 2025 is wild. Manager: "Just pick up the phone!" Rep: *sends 47 emails, 12 texts, 3 LinkedIn messages, and a carrier pigeon* Sound familiar? 😅 After 20+ years in sales, I've watched this communication gap grow wider every year. But here's what both sides are missing: It's not about choosing ONE channel. It's about understanding WHICH channel works WHEN. The most successful reps I've seen? They've cracked the code: **First 24 hours:** • Email → Sets professional tone • LinkedIn → Shows you've done homework • Text → Only if they've given permission **Days 2-5:** • Phone call → NOW it's time (they know who you are) • Voice note → Personal touch that stands out • Video message → Shows real effort **The truth?** Your manager's right - calls DO convert better. You're also right - cold calling blind is dead. The magic happens when you warm them up FIRST. Think of it like dating: You wouldn't propose on the first date. So why are we calling strangers without context? **My top 3 strategies that actually work:** 1. The "Permission Play" End every email with: "Would a quick call tomorrow at 2pm work to discuss?" (They expect it now = higher answer rate) 2. The "Multi-Touch Warm-Up" Email → LinkedIn view → Call within 48 hours (They recognize your name = 3x more likely to answer) 3. The "Context Creator" Reference their LinkedIn post before calling "Saw your post about X, had a thought..." (You're not a stranger = conversation not pitch) Here's the brutal truth: Managers: Your reps aren't lazy. They're adapting to how buyers ACTUALLY buy in 2025. Reps: Your manager isn't wrong. The phone still closes more deals than any other channel. Bridge the gap. Use both. Win more. What's your take - Team Phone or Team Omnichannel? P.S I'm running a FREE 6-week LinkedIn Social Selling Bootcamp starting Monday 15th Sept, grab a free spot here https://lnkd.in/eVmxsMbM

"Is $20/month too much for our product?" Instead of guessing, we used the Van Westendorp method to find our pricing sweet spot. 4 questions revealed exactly what users would pay (and we haven't touched our pricing since). Here's the framework any founder can steal: 1. Send a survey to actual users, not prospects We surveyed people already using Gamma. They understood the real value of our product, not hypothetical value. Too many founders survey their waitlist or randomly select people who have never used their product. That's like asking someone who's never driven about car prices. 2. Ask these 4 specific questions - At what price would this be too expensive for you to consider it? - At what price is it expensive but still delivering value? - At what price does it feel like a bargain? - At what price is it so cheap you'd question if it's reliable? These create bookends for perceived value. You're mapping the entire spectrum of price psychology, not just asking "what would you pay?" 3. Plot the responses and find where the lines intersect Graph responses from lots of users. Where "too expensive" and "too cheap" lines cross: that's your acceptable range. Where "expensive but fair" meets "bargain": this is your optimal price point. 4. Test within the range, don't just pick the middle The intersection gives you a range, not a number. We ran pricing experiments within that range to see actual conversion rates. A survey shows willingness to pay; testing reveals actual behavior. 5. Lean towards generous (especially for product-led growth) We chose to be more generous with AI usage than our "optimal" price suggested. Word-of-mouth growth matters more than maximizing initial revenue. Not everything shows up in the numbers. 6. Lock it in and stop tinkering Once you find the sweet spot through data, stick with it. We haven't changed pricing in 2 years. Every month debating pricing is a month not improving product. Remember: pricing is a signal, not just a number (Image: First Principles)

Website traffic was a valuable metric correlated to growth. Now it may be a vanity metric, not correlated to growth. Search has been disrupted. Visits to your website are declining. So, marketers - what now? The search landscape was already shifting (I talked about this at INBOUND last year). Now, the change is accelerating dramatically: - AI Overviews appear in 43% of Google searches – when they do, organic CTR drops by nearly 35%. - Google’s AI Mode and audio AI overviews are coming – they will cause clicks to collapse further. - More buyers are using LLMs to find information, ChatGPT search in Europe grew 3.7x in six months. So, what should marketers do? And how can AI help? 1. Be everywhere and diversify your channels The days of relying solely on Google search are way over. You need to show up on YouTube, LinkedIn, Instagram, podcasts, and in niche communities. The good news? AI makes multi-channel, multi-format content creation scalable – even for small teams. 2. Be specific with context In the past, broad informational content was the way to rank in Google. Today, buyers expect results deeply relevant to them, whether they’re on Google, LLMs, or Reddit. You need specific content that reflects your expertise and resonates with your buyers. 3. Optimize for conversion, not clicks Traffic was once the lever you could pull. Now, conversion is where the opportunity lies. AI enables you to deliver personal messages that drive better conversion. Don’t ask, “How do we get more blog visits?” Ask, “How do we convert more prospects into customers across all channels?” The changes in search are sending shockwaves across marketing teams and media companies everywhere. The era of traffic-based marketing is ending. But a new era full of opportunity is just beginning. Super exciting times for marketers to reinvent the playbook!

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

Most brands spend a lot on media, but treat landing pages as an afterthought If you’re running ads and sending traffic to a homepage or a poorly built landing page, its almost criminal. Specially when gen AI has reduced the cost and time for content creation drastically Here’s how to get landing pages right. Consistently. 1. Match Intent, Not Just Aesthetics The #1 job of a landing page? Continue the conversation you started with your ad •If your ad says “energy efficient fans”, the landing page should show highlight this feature front and center •If your Google ad targets “Mixer Grinders under ₹5000,” don’t show ₹8000 models on the page. Message match > Visual design 2. Keep the Hero Section Clean & Focused Above-the-fold matters. You need to have •Clear headline – Say what the product is and why it’s special. •Key benefits – 3 crisp points max. •Visuals – High-quality product image or demo video. •CTA – One action. Not three. Buy Now,” “Book a Demo,” or “Know More”—but pick ONE 3. Product Benefits, Not Just Features Nobody cares that your mixer uses XYZ motor tech. I mean they do care but only if they care how it helps them They care a lot more that the mixer has a coarse mode which enables silbatta like texture resulting in great taste And that BLDC or intelligent motor tech enables it 4. Solve for Trust People are skeptical by default. Give them reasons to believe •Ratings & Reviews – Show real customer ratings (4.5 stars? Flaunt it). •Media Mentions – “As seen on The Hindu / NDTV” works. •Certifications – BEE 5-Star? BIS approved? Display badges. •Guarantees – Free returns? Warranty? Mention clearly 5. Speed & Mobile Optimization Today at least 80 percent of your traffic is mobile. If your landing page loads in 4 seconds, you’ve lost half. Aim for <2s load time. Avoid fancy animations that slow things down. Test your page on Mobile (3G/4G) and in all browsers Chrome, Safari etc 6. Minimize Distractions A landing page is not your website. •No top nav bars with 7 menu items. •No footer clutter. •No exit doors—except the CTA you want. Keep it focused. Keep them moving toward action 7. Strong CTA (Call to Action) •Make it obvious. One clear button. •Use actionable language: “Get My Free Sample,” “Book a Demo,” “Shop Now.” •Repeat CTA 2-3 times as they scroll, especially after key benefit sections. 8. A/B Test, but with caution: Gen AI makes it very easy to do so. Test •Headlines •CTA text and colors •Images vs Videos •Long-form vs Short-form copy But get the fundamentals of A/B testing right. You need statistically significant sample sizes for each test A good landing page doesn’t sell the product by itself. But It removes friction so the product has a better chance of selling And when done right, your CAC drops, your ROAS climbs, and your ads finally start working to their fullest potential

COULD TRUMP'S TARIFFS SPELL THE END OF CHEAP FASHION? Analysis by Apparel Insider suggests President Trump's tariff hikes on key garment hubs could translate to retail price increases of between 10 to 25 per cent. The effect is expected to be particularly pronounced for fast fashion retailers who operate on tight margins. We spoke to manufacturers as well as a couple of well known brands. Our analysis suggests: - A pair of Nike running shoes currently retailing at U.S.$100 and manufactured in Vietnam may soon be priced between U.S.$115 and U.S.$125, as import costs climb from U.S.$40 to over U.S.$58 - A U.S.$30 H&M summer dress made in Cambodia could soon cost upwards of U.S.$35, reflecting an almost 50 per cent rise in import duty - Jeans from Bangladesh, sold at U.S.$40 by brands like Old Navy, may increase to around U.S.$50 - A U.S.$60 sports bra produced in Sri Lanka by brands like Under Armour could retail for closer to U.S.$75 Brands also have a choice of absorbing some costs or passing price increases on to suppliers by asking for discounts. History tells us the former us unlikely, while the latter may prove tricky with many suppliers already working on very low margins. Brands have always been extremely loathe to increase prices for clothing where deflation (in real terms) has been a trend in recent decades. The scale of the current tariff hikes might leave them with no choice. Comments and thoughts welcome.

🤔Understanding 𝗣𝗮𝘆𝗺𝗲𝗻𝘁 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 Let me break it down for you: In the 𝟭𝟵𝟵𝟬𝘀, with the rise of Ecommerce, the first payment gateways came into existence. However, they lacked today's advanced collection and reconciliation tools. The 𝟮𝟬𝟬𝟬𝘀 saw integrations between developers and gateways due to limitations in serving all customers through one gateway. By the 𝟮𝟬𝟭𝟬𝘀, PSPs transformed, introducing alternative payment methods, fraud prevention, and global payments in local currencies. The 𝟮𝟬𝟮𝟬𝘀 witnessed a shift, with over 60% of retailers using multiple payment providers and payment orchestration becoming essential for businesses. What is 𝗣𝗮𝘆𝗺𝗲𝗻𝘁 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻? Drawing from the world of music, payment orchestration functions similarly to a maestro harmonizing an orchestra🎼 This system blends multiple payment processes, offering an efficient and streamlined transaction route. It centralizes various gateways, ensuring a smooth consumer checkout. Integrated reporting provides a unified data view, and "smart routing" auto-directs transactions through the best route. Europe's e-commerce data shows that roughly a quarter of Mastercard's payment authentications in early 2021 failed. Smart routing in payment orchestration aims to combat such issues. Business Research Insights predicts that by 2027, the payment orchestration market will be valued at nearly $5 billion. Key advantages of payment orchestration include: 1️⃣ Cost and Time Efficiency: Merchants can choose lower transaction fees from a range of providers. 2️⃣ Increased Conversion: Improved customer experience boosts conversion rates. Factors like smart routing, diverse payment methods, and local currency support play significant roles. 3️⃣ Transaction Success: With the rise in digital payments, ensuring transaction success becomes vital. Payment orchestration can notably reduce decline rates. 4️⃣ Customer Loyalty: Offering preferred payment methods enhances the buying experience, fostering customer loyalty. 5️⃣ Global Expansion: For businesses aiming globally, understanding regional payment preferences is crucial. 6️⃣ Rapid Scaling: Merchants can swiftly integrate solutions supporting business growth. 7️⃣ Fraud Reduction: A consolidated platform with multiple payment methods aids in fraud prevention. 8️⃣ Automatic Reconciliation: This feature minimizes errors, saving internal resources and enhancing efficiency. 9️⃣ Real-time Ledgers (RTLs): RTLs provide almost instant financial data visibility, ensuring transactional integrity. Source: Axerve Find this helpful? [ 𝗿𝗲𝗽𝗼𝘀𝘁 ] Anything to add about this subject? [ 𝗶𝗻𝘃𝗶𝘁𝗲𝗱 𝘁𝗼 𝗰𝗼𝗺𝗺𝗲𝗻𝘁 ] Nice story, Marcel. Next! [ 𝗹𝗶𝗸𝗲 ]

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.

The real challenge in AI today isn’t just building an agent—it’s scaling it reliably in production. An AI agent that works in a demo often breaks when handling large, real-world workloads. Why? Because scaling requires a layered architecture with multiple interdependent components. Here’s a breakdown of the 8 essential building blocks for scalable AI agents: 𝟭. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 Frameworks like LangGraph (scalable task graphs), CrewAI (role-based agents), and Autogen (multi-agent workflows) provide the backbone for orchestrating complex tasks. ADK and LlamaIndex help stitch together knowledge and actions. 𝟮. 𝗧𝗼𝗼𝗹 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 Agents don’t operate in isolation. They must plug into the real world:  • Third-party APIs for search, code, databases.  • OpenAI Functions & Tool Calling for structured execution.  • MCP (Model Context Protocol) for chaining tools consistently. 𝟯. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 Memory is what turns a chatbot into an evolving agent.  • Short-term memory: Zep, MemGPT.  • Long-term memory: Vector DBs (Pinecone, Weaviate), Letta.  • Hybrid memory: Combined recall + contextual reasoning.  • This ensures agents “remember” past interactions while scaling across sessions. 𝟰. 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 Raw LLM outputs aren’t enough. Reasoning structures enable planning and self-correction:  • ReAct (reason + act)  • Reflexion (self-feedback)  • Plan-and-Solve / Tree of Thought These frameworks help agents adapt to dynamic tasks instead of producing static responses. 𝟱. 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗕𝗮𝘀𝗲 Scalable agents need a grounding knowledge system:  • Vector DBs: Pinecone, Weaviate.  • Knowledge Graphs: Neo4j.  • Hybrid search models that blend semantic retrieval with structured reasoning. 𝟲. 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 𝗘𝗻𝗴𝗶𝗻𝗲 This is the “operations layer” of an agent:  • Task control, retries, async ops.  • Latency optimization and parallel execution.  • Scaling and monitoring with platforms like Helicone. 𝟳. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 & 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 No enterprise system is complete without observability:  • Langfuse, Helicone for token tracking, error monitoring, and usage analytics.  • Permissions, filters, and compliance to meet enterprise-grade requirements. 𝟴. 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 & 𝗜𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲𝘀 Agents must meet users where they work:  • Interfaces: Chat UI, Slack, dashboards.  • Cloud-native deployment: Docker + Kubernetes for resilience and scalability. Takeaway: Scaling AI agents is not about picking the “best LLM.” It’s about assembling the right stack of frameworks, memory, governance, and deployment pipelines—each acting as a building block in a larger system. As enterprises adopt agentic AI, the winners will be those who build with scalability in mind from day one. Question for you: When you think about scaling AI agents in your org, which area feels like the hardest gap—Memory Systems, Governance, or Execution Engines?