Ecommerce

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!

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

Having a dominating share on e-commerce marketplaces has been one of the pillars of our growth. 10 pointers for founders to keep in mind while scaling e-com: 1. The fundamental equation of e-com is “Sales= Traffic*Conversion”. Not meeting sales numbers is either a traffic problem or a conversion problem. For every SKU, figure out whether it is a traffic problem or a conversion problem. Do not try to solve traffic problems with conversion levers. And vice versa. 2. Like all performance marketing, e-com media also has diminishing returns. Beyond a point, increasing spends will not increase sales at the same speed. Stop at that point 3. If you want to increase profitability, you need to increase your organic discoverability in the platform. Amazon is a search led platform with search contributing to 60-70% views in most categories. For Flipkart, along with search, merch and reco are equally important. But the fundamentals of organic discoverability is same. Both platforms have an algorithm where SKUs with the best reviews, highest listing quality score, lowest time to delivery and highest conversion rates get pushed. Optimize for these parameters and see organic discoverability skyrocket 4. The other way to reduce dependency on platform ads( and hence increase profitability) is to ensure your branded searches increase. This is directly a function of your off platform marketing activities, word of mouth and repeat customers. So, work on those parameters 5. Category Relationships matter a lot. Understand what the number 1 objective of your category manager is for the year. And help them achieve it. Eg: If they are looking to improve ASP, help them with your premium assortment. If you help them achieve their number 1 KPI, they will ensure you do well on the platform 6. Whatever the ads team tell you, take it with a pinch of salt. Most times they are very helpful. But their number 1 KPI is to sell ads. Not your success. So, sometimes what is good for them might not be good for you 7. All SKUs will not do well. All sub-categories won’t do well. If there is no PPCMF, no amount of good execution will cut it. So, important to cut your losses and stop investing more money on losers. Instead, allocate to your winners in the portfolio 8. Have a E-Commerce dashboard which goes beyond the L0 metrics. Look at your L1 and L2 metrics daily and hold teams accountable for these metrics. Ads driven sales, share of search, organic visits, conversion rates etc are all examples of L1 metrics 9. Sometimes there will be irrational competition and they will bid crazily for keywords. Do not compete with them. They are burning cash and because blind venture money is running out quickly in consumer brands, they will fizzle out. 10. Do not overdo discounts. Discounts are like antibiotics. You use it 2-3 times a year, you see huge spikes. Use it every alternate day, and that becomes your market operating price.

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?

"Can my company sell its product or service as a subscription?" This is the question that I’m most often asked by early stage founders. I wrote previously here on LinkedIn about how important it was for WHOOP to change from a hardware / one-time sale to a subscription. Here are some things to consider: 1) Do your existing customers use your product or service regularly? It’s generally hard to justify a subscription for a low engagement product. Furthermore your business will suffer if you create a business model that has high churn. You need to be intellectually honest with yourself: Are my customers getting high value on a daily or at most weekly basis? This will show up in DAU and WAU engagement data that you need to study. 2) Do you have a product that evolves?  It’s pretty hard to sell a subscription that is static. What is the roadmap for your product or service over the next 6 months? Will it continue to evolve every week? Will your customers tell you that the service is getting better? Services like Netflix, and Spotify are constantly adding new content. Subscriptions like ClassPass, Audible, and AG1 are giving you monthly products or credits. At Whoop, we’ve focused on continuing to add new functionality to the existing hardware that members already use. 3) Can your business survive the cash flow implications of being a subscription? When you go from being a one-time sale to being a subscription, there is a meaningful shift in your day 1 cash flow. At Whoop, we originally sold hardware for $500; we then changed our business model to allow for people to sign up for just $30 but pay monthly overtime as a subscription. This allowed many more people to sign up for Whoop, but it changed our cash flows. You will need to model how dramatically this change affects your business. Beware: If you have an expensive product to make, rapid growth can actually accelerate the rate at which you run out of money. 4) What subscription is right for your business? A subscription that is month to month has a higher churn rate than a subscription that renews annually. You may have a monthly subscription that’s $20 / month (or $240 over the course of the next 12 months) and decide that you should offer an annual plan at a meaningful discount, say $149 / year. The advantage to having annual plans is that they help manage your cash flows. Changing your business model to a subscription is not easy and has meaningful cash implications. But if you can do it, there’s no better way to create true alignment with your customers. If they like what you’re delivering, they’ll keep paying, which increases your long term value. And if they don’t, they’ll churn. Good luck! #subscription #retention #LTV #CAC #startups

I hear from a lot of social media teams that they “ask for forgiveness, not permission” to use songs that they don’t have the rights to on TikTok and Instagram. Turns out forgiveness is expensive. Last week, UMG sued Quince for copyright infringement for including unlicensed music in Instagram and TikTok posts. While I’ve talked about brands being sued by music labels before, this one is interesting because it also holds the brand responsible for sponsored influencer posts that use unlicensed music. UMG has identified a whopping 130 works infringed by Quince. The exposure in statutory damages alone is over $20M. I asked marketing lawyer Rob Freund what brands should take away from this lawsuit: “The Quince case is the latest in a string of cases against brands using unlicensed popular songs on social media, both on brand-owned pages and via influencers. The takeaway is that brands cannot use the general popular music libraries that the platforms provide for any commercial content (which includes any posting on brand-owned pages) and cannot treat influencer content as a copyright safe harbor. The platform licenses do not extend to commercial use, unless you use the designated commercial sound libraries. Any brand running a creator program needs a music licensing strategy and clear contractual guardrails for its influencers.”

🚫 How to Run UX Research Without Access To Users. With practical techniques to avoid guesswork and gather insights if you can’t talk directly to users. Attached cheatsheet (with and without access to users) by Nielsen Norman Group. 🚫 Ask for reasons for no access to users: there might be none. ✅ First, study job openings to map existing workflows/tasks. ✅ Make friends with sales, customer success, support, QA. ✅ Find colleagues who are the closest to your customers. ✅ Convey your questions indirectly via your colleagues. ✅ If you can’t get users to come to you, go where they are. ✅ Ask to observe or shadow customers at their workplace. ✅ Listen in to customer calls and interview call centre staff. ✅ Request access to analytics, CRM reports, call centre logs. ✅ Use Google Trends to find product-related search queries. ✅ Gather insights from search logs, Jira backlog, support tickets. ✅ Explore past/ongoing NPS and Voice-of-Customer programs. ✅ Study reviews, discussions, comments for your product/competitors. ✅ Map key themes and user sentiment on TrustPilot, AppStore etc. ✅ Recruit users via UserTesting, Wynter (B2B), Maze, UserInterviews. ✅ Ask for small but steady commitments: 5 users × 30 mins, 1× month. 🚫 Avoid ad-hoc research: set up regular check-ins and timelines. As H Locke noted, if we shed the light strongly enough from many sources, we might end up getting a glimpse of the truth. Ironically, the stakeholders who can’t give you time or resources to talk to users often are the first to demand evidence to support your initiatives. Sometimes the reason why companies are reluctant to grant access to users is simply the lack of trust. They don’t want to disturb relationships with big clients which is carefully maintained by the customer success team. They might feel that research is merely a technical detail that clients shouldn’t be bothered with. Show that you deeply care about that relationship and that you don’t want to disturb it any way. What you do want though is to reduce costs and risk — the risk of drawing wide-reaching conclusions from very little research, or none at all. Your best shot is to explain research as a powerful risk mitigation tool. And: search for people whose priorities align with yours — people who value and see the impact of UX in their units. They would absolutely love to support your work because it also supports their work — and they will put up a good word for you if they only had known that you existed. ✤ Useful resources: UX Research Cheat Sheet, by Susan Farrell from NN/g (attached) https://lnkd.in/eUTHKWvF What Can You Do When You Have No Access To Users?, by H Locke https://lnkd.in/ewHEKhBS UX Research When You Can’t Talk To Users, by Chris Myhill https://lnkd.in/ez5-b6zf #ux #research

Revenue recognition isn't about when you get paid Most founders mess this up. They see $12,000 hit their bank account and think they just made $12,000 in revenue. Wrong. You made $1,000 in revenue...if it's an annual contract. What is Revenue Recognition? Revenue is earned income from delivering goods or services. Recognition is when it's reported on your income statement. These happen at different times. You collect $12,000 upfront for an annual subscription. But you only earned $1,000 of that in month one. The other $11,000? That's deferred revenue sitting on your balance sheet. The Journal Entries: When the sale happens: Debit Cash $12,000 Credit Deferred Revenue $12,000 Each month as you deliver service: Debit Deferred Revenue $1,000 Credit Revenue $1,000 This moves money from your balance sheet to your P&L as you actually earn it. Daily vs Monthly Methods You can recognize revenue daily or monthly. Daily method: $12,000 ÷ 365 days = $33 per day Monthly method: $12,000 ÷ 12 months = $1,000 per month Both get you to $12,000 over the year. Daily gives more precision but monthly is simpler. The Base Formula Every deferred revenue balance follows this pattern: Beginning Balance + Additions - Subtractions = Ending Balance Additions = new cash collections Subtractions = revenue recognized Track this for every contract and you'll know exactly where you stand. The Manual Nightmare Most founders start tracking this in spreadsheets. Works fine for 10 contracts Gets messy at 50. Completely breaks at 100+. Picture this...you've got 50 active contracts. Each one has different start dates, different terms, different recognition schedules. You're tracking everything in Excel. Every month you need to: Update deferred revenue balances for each contract. Calculate how much revenue to recognize. Create journal entries for each one. Make sure everything ties to your GL. I've seen many people spending 3 full days every month just on revenue recognition. And you know what happened? They'd still find errors weeks later. Daily Method Makes it Worse. Think monthly is bad? Try daily recognition with multiple contracts. $12,000 annual contract = $32.88 per day $24,000 contract = $65.75 per day $6,000 contract = $16.44 per day Now multiply that by 50+ contracts...each starting on different dates. You're calculating different daily amounts for hundreds of line items. Automation Saves Your Sanity Maxio completely eliminates this pain. Set up your revenue recognition rules once. The system automatically applies them across every contract. Daily, monthly, whatever method you choose...it just works. 30 minutes to run reports and review everything. That's it. No more manual calculations, no more formula errors, no more audit trail headaches. Everything's automatically GAAP compliant and audit-ready. === How do you currently track your revenue recognition? #MaxioPartner

Can one employee REALLY drive more impact than the brand itself?  Let’s finally put this one to bed shall we… Everyone on socials saw Kathryn Turner pop up about 3 months ago on her own channels, and everybody got very excited by her overnight virality.  The world instantly fell in love with M&S’s Director of Product Development, aka the CREATOR of M&S deliciousness (all hail Kathryn) But 2 months on, was this just a viral moment, or a long term strategy to be implemented? It’s time to dive into the data*:  - Kathryn’s first introduction to social media happened on May 14th 2026.  So in 2 months her audience has grown from 0 to 100k on TikTok and 80k on Instagram across her personal channels. - But the really interesting part is when you compare it to the brand pages - ie Marks and Spencer’s owned handles. Kathryn’s TikTok content delivers around 60× more views and 200× more engagements per post than the official brand channel, despite having less than half the followers.  - On Instagram, Kathryn’s reels achieve 25× higher engagement rate than @marksandspencerfood - And critically, Kathryn’s audience are UK‑heavy, female‑skewed, and concentrated in the 18–34 segment, aligning closely with M&S Food’s priority shoppers and sharers, which amplifies the strategic value of her role. It doesn’t take an expert analyst to say, they’re onto something here… For anyone still sleeping on their employees, here are my takeaways:  This is concrete proof that employee ambassadors can outperform traditional brand channels by up to 200× per post on key platforms. Confirming what we all deep down know to be true - that algorithms and consumers favour human, expert storytellers over corporate logos.  I actually found in the data that the top performing posts on the M&S own brand channel, were always when they collaborated with creators too! So this isn’t an isolated trend. The playbook is clear: identify credible, charismatic internal experts, invest in them as always‑on creators and characters of your brand.  Objections around risk, control and employee departure are manageable with clear guardrails, multi‑ambassador benches, and content governance, and are outweighed by the performance multipliers seen in the evidence (the data doesn't lie !!) I know I’ve been banging on about it for a while now, but employee ambassadors are your most slept-on asset: you are already paying for their expertise; by turning them into creators you unlock large amounts of incremental, highly trusted attention that your competitors’ logo‑only channels will struggle to match. And it’s SO great to see Marks and Spencer social team really leading the charge here, confirming all of my beliefs, you guys are truly killing it. Keen to hear your thoughts below! 👇🏼 * All data sourced via my absolute favourite tool for creator and brand insights - Universe by Primetag - the only AI tool that actually sources from billions of data points on social rather than guessing.

We analyzed 4 million recruiting emails sent through Gem. Most get opened. But only 22.6% get replies. Half those replies are "thanks, but no thanks." We dug into what actually works. Here are 8 factors that drive REAL responses: 1. Strategic timing beats everything else - 8am gets 68% open rates. 4pm hits 67.3%. 10am lands at 67% - Most recruiters blast at 9am when inboxes are flooded - Avoiding peak times alone can boost your opens by 7-10% 2. Weekend outreach is criminally underused - Saturday/Sunday emails get ≥66% open rates consistently - Why? Empty inboxes. Zero competition. Candidates actually have time - Yet few recruiters send on weekends. Their loss is your gain 3. Keep messages between 101-150 words - Shorter feels spammy. Longer gets skimmed - You need exactly 10 sentences to nail the essentials - Every word beyond 150 drops performance 4. Generic templates kill response rates - Generic templates: 22% reply rate - Personalized outreach: 47% increased response rate - Even adding name + company to subject lines boosts opens by 5% 5. Subject lines need 3-9 words - Include company name + job title for highest opens - "Senior Engineer Role at [Company]" beats clever wordplay - 11+ words can work if genuinely intriguing, but why risk it? 6. The 4-stage sequence is optimal - One-off emails are dead. Send exactly 4 follow-up messages - You'll see 68% higher "interested" rates with proper sequencing - After stage 4, engagement completely flatlines. Stop there 7. Get the hiring manager involved - Having the hiring manager send ONE follow-up boosts reply rates by 50%+ - Yet most recruiters don't use this tactic - Weekend advantage: Minimal competition for attention 8. Leadership involvement is a cheat code - Role-specific timing (tech vs non-tech) matters - Technical roles: 3 of 4 best send times are weekends - Engineers check email differently than salespeople. Adjust accordingly TAKEAWAY: These aren't opinions. This is what 4 million emails tell us. Most recruiting teams are stuck in 2019 playbooks wondering why their reply rates won't budge. Meanwhile, recruiters who implement these 8 factors see dramatically better results. The data is right there. The patterns are clear. The only question is: will you actually change how you operate? Or will you keep sending the same tired emails at 9am on Tuesday? Your call.