Managing Ecommerce Vendors

Are you prepared for the algorithm-led future of #Amazon's Vendor Management? 🤖 Amazon buyers are becoming a rare breed, and brands must adapt to thrive in an increasingly automated environment. To cut costs, Amazon is pushing ahead with its offshoring initiatives and starting to shape its future without account-specific Vendor Managers: - Layoffs have reduced the VM community by -15% - AVS is getting offshored to Eastern Europe and India - Amazon actively pushes account management tasks to brands - Automation is now driving pricing, listing, and CRAP decisions - Pan-EU and North American regionalisation is here to stay Interestingly, most vendors I talk to ignore this trend altogether. They think their brand is too important for Amazon to neglect. Yet Amazon quietly transfers most of the manual account management tasks to suppliers and automates the rest, leading to a future where algorithms hold the reins. How should brands respond? By focusing on 3 key areas: 𝟭- 𝗟𝗲𝗮𝗻 𝗔𝗰𝗰𝗼𝘂𝗻𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 Amazon has already increased its regional management of European vendor accounts. Instead of a dedicated buyer contact by market, brands mainly navigate their business at pan-European level. Given Amazon's past layoffs, it's unlikely that more VM resources will be deployed on vendor accounts anytime soon. Instead, brands must adapt to this regional focus to ensure they don't duplicate tasks across markets, when only one Vendor Manager sits on the other side. This almost always means that some form of re-organisation has to happen. Whether it's impacting your wider digital commerce unit or not will depend on the existing org structure. 𝟮- 𝗖𝗵𝗮𝗻𝗻𝗲𝗹-𝗦𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 With automation becoming Amazon's NorthStar to develop its retail business, vendors must review and adapt their portfolio strategies with the online retailer. Ensuring your NPD pipeline aligns with a healthy ASP, and Net PPM ambition from Amazon is already and will become even more critical. It is good practice to follow a selective portfolio approach by focusing on listing those items with a healthy RRP to ASP ratio. 𝟯- 𝗢𝗳𝗳𝘀𝗵𝗼𝗿𝗶𝗻𝗴 𝗮𝗻𝗱 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 As Amazon deploys fewer headcount resources per account and offshores more tasks to brands, vendors are faced with higher headcount requirements to manage these processes from their side. Identifying labour-intense tasks should already be high on the priority list of suppliers. Manually downloading data, disputing chargebacks, or listing items should be outsourced at best and eventually automated. --- Amazon's profitability focus will reduce and eventually remove the Vendor Manager function. The question is: Is your business ready for an algorithm-led future? Let me know in the comments! #amazonvendor #amazonstrategy

Running a successful eCommerce sales business is becoming increasingly complicated and expensive. Yet, the growing share of 3P sellers on Amazon highlights the platform's vital role as a diverse and expansive marketplace for small and medium businesses, enhancing consumer choice and driving innovation in eCommerce. ++ 🔢 Key Stats and Facts I Find Fascinating ++ 📍Over 60% of sales on Amazon are generated by small and medium-sized businesses. This underscores the significant role Amazon plays in the eCommerce landscape for these businesses. 📍The Federal Trade Commission (FTC) has filed a lawsuit against Amazon, alleging the use of "monopoly power" to control prices and stifle competition, compelling independent sellers to bear high fulfillment and advertising costs. Despite these challenges, many businesses find Amazon essential for their eCommerce #strategy. 📍Operating on Amazon and other online retail platforms is becoming more expensive and complex. Sellers face significant costs that can amount to nearly half the listing price, including fees for listing, fulfillment, and advertising. Additionally, Amazon’s pricing strategies and the need for expertise in marketplace optimization add to these challenges. 📍Consumers shopped on big marketplaces such as Amazon and Walmart 30% more in 2023 compared with 2022, according to a recent survey by 1WorldSync. 📍Globally, sales from third-party online marketplaces are expected to be the fastest-growing #retail channel over the next five years, making up 60% of all global eCommerce sales growth, according to Edge (fmr. Ascential, now a part of Omnicom). Because the 3P sales model allows for improved margins, better pricing control, favorable payment terms, and reduced reliance on Amazon. ++ 🔭 Short Playbook for Success on Amazon ++ 💡1. Understand Pricing Dynamics: Recognize Amazon's pricing policies, such as the anti-discounting strategy. Maintain competitive pricing on Amazon while balancing relationships with other #eCommerce platforms. 💡2. Manage Costs Effectively: Keep product costs low and optimize the supply chain. Aim for a profit margin of around 10%, accounting for #fulfillment, #advertising, and overhead expenses. 💡3. Leverage Amazon's Reach and Advertising: Utilize Amazon’s vast customer reach and advertising tools. Understand and adapt to Amazon’s algorithms for product listing optimization. 💡4. Embrace Professionalism and Expertise: The era of amateur selling on Amazon has passed. Invest in expertise for search engine optimization and sponsored product placement to enhance visibility and sales. 💡5. Utilize Analytical Tools: Employ analytics to monitor product placement and pricing across various platforms, ensuring competitiveness and market alignment. 💡6. Adapt to eCommerce Evolutions: Be prepared to evolve strategies in response to Amazon’s shifting policies and market trends. #ecommert for eCommerce strategy, #digitalshelf and #retailmedia

AI adoption is failing at most companies. (it's not the technology) You use ChatGPT daily. Your team has random AI tools. No unified strategy. No measurement. Your VP keeps asking: "What's our AI plan?" You need frameworks, not more tools. 9 AI Adoption Frameworks: 1/ Workflow Audit Before Tool Selection → Map your team's top 10 daily tasks first → Flag repetitive work worth automating → Identify judgment calls for AI augmentation 2/ Build vs Buy Decision Matrix → Buy for standard ops (scheduling, emails) → Build only for competitive differentiation → Partner for specialized expertise gaps 3/ Pilot Program That Actually Scales → One department, one use case, 90 days → Define success metrics before you start → Document every lesson for VP presentation 4/ Executive-Ready Training Strategy → VP briefing: ROI projections and risks → Manager training: implementation roadmaps → User training: hands-on, role-specific 5/ ROI Measurement That VPs Care About → Track hours saved per employee per week → Measure quality improvements and accuracy → Calculate revenue impact, not just savings 6/ Data Governance Framework → Audit what data touches AI tools now → Create approval process for new platforms → Set data retention rules before scaling 7/ Change Management for AI Rollouts → Address "will AI replace me?" fears early → Show augmentation wins before automation → Create AI champion roles for career growth 8/ Smart Automation vs Augmentation Rules → Automate: data entry, report generation → Augment: strategy, creative work, decisions → Never automate: customer relationship calls 9/ VP-Level Adoption Mistakes to Avoid → Don't chase every shiny new AI tool → Never skip the governance foundation step → Stop letting AI adoption happen randomly AI adoption isn't a technology problem. It's a leadership strategy problem. Twice a week I send frameworks like this to 15,000+ operators in Tactical Memo. Join free: https://lnkd.in/eFNHsxmh

𝗣𝗿𝗼𝗰𝘂𝗿𝗲𝗺𝗲𝗻𝘁 - 𝗰𝗮𝗻 𝘆𝗼𝘂 𝗮𝘃𝗼𝗶𝗱 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝘃𝗲𝗻𝗱𝗼𝗿 𝗹𝗼𝗰𝗸-𝗶𝗻 𝗼𝗿 𝗶𝘀 𝗶𝘁 𝗮 𝗴𝗶𝘃𝗲𝗻? There is a compelling case for off-the-shelf Procurement solutions. But there are potential downsides to consider. 𝗪𝗵𝗮𝘁 𝗶𝗳: ▪️ new features are tied to hefty price hikes ▪️ evolution to changing business needs is not possible ▪️ architecture options are dictated by vendor upgrade plans ▪️ product roadmap do not align with your specific plans and needs ▪️ the flexibility promised through rich functionality does not materialise Yes, 𝘄𝗵𝗮𝘁 𝗶𝗳, 𝘁𝗵𝗲 𝗮𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲 𝗼𝗳 𝗿𝗮𝗽𝗶𝗱𝗹𝘆 𝗱𝗲𝗽𝗹𝗼𝘆𝗶𝗻𝗴 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 𝘁𝘂𝗿𝗻𝘀 𝗶𝗻𝘁𝗼 𝗮 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗿𝗶𝘀𝗸? Talking to many companies on their Digital Procurement, this major worry is real. Given the long range of investment payback, it would be an illusion to bet on building own solutions. 𝗙𝗹𝗲𝘅𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗶𝘀 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝗮 𝘁𝗼𝗸𝗲𝗻 in this case - 𝗶𝘁'𝘀 𝗰𝗲𝗻𝘁𝗿𝗮𝗹 𝘁𝗼 𝗮 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗮𝗻𝗱 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝗳𝗼𝗿𝗰𝗲 of a company. Find here a few points which come to mind to 𝗮𝘃𝗼𝗶𝗱 𝗮 𝗳𝘂𝗹𝗹 𝘃𝗲𝗻𝗱𝗼𝗿 𝗹𝗼𝗰𝗸-𝗶𝗻 but build a stable Digital Procurement architecture, while keeping flexibility: ✅ Build a 𝗺𝗼𝗱𝘂𝗹𝗮𝗿 𝗣𝗿𝗼𝗰𝘂𝗿𝗲𝗺𝗲𝗻𝘁 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 choosing solutions with an API-first to easily integrate or replace components over time. Modern solutions can be integrated and orchestrated without hard dependencies! ✅ 𝗛𝘆𝗯𝗿𝗶𝗱𝗶𝘀𝗲 𝘆𝗼𝘂𝗿 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵 by buying core capabilities like Source to Contract solutions but build or extend AI plug-ins or custom Automations. Intelligent Automation & Orchestration solutions provide extra flexibility and not just a patch. ✅ 𝗘𝘅𝗶𝘁 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗳𝗿𝗼𝗺 𝗱𝗮𝘆 𝟭, factoring in possible migration paths to prevent costly transitions later. For example ingesting the data of your Spend Analytics provider regularly into your own data lake. ✅ 𝗠𝘂𝗹𝘁𝗶-𝘃𝗲𝗻𝗱𝗼𝗿 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 which does not overcommit to a single provider but uses a mix of best-of-breed tools where flexibility matters most. Rationalising your vendor choice can bite you down the line. Procurement Tech should evolve at pace with your business needs, not lock you into someone else’s roadmap. The best strategy here is: Flexibility as a principle. ❔What's your view on this challenge. Anything missing on the picture? ❔Can vendor lock-in be minimised.

GRC vendors are playing the wrong game. The losing strategy: Be everything for everyone. Compete on dashboards, workflows, integrations, AI features. Result: Feature parity race. Acquisition or death. The winning strategy: Be a lego-block. Own ONE vertical so completely that you're embedded in 80% of companies without competing directly. Examples? Assurance-driven TPRM - Telemetry nobody else captures, insights nobody else offers, integrate to procurement for tracking Workflow orchestration - Focus on getting work done, not reporting work done Data transformation - AI-ready GRC data at scale (framework become only translation layers) Agnostic trust networks - Verification infrastructure making third-party attestations obsolete The protection: Companies use you PLUS whatever else. You're infrastructure, not application. They can vibe-code dashboards. They can't vibe-code your proprietary telemetry or deep vertical capability. Like Stripe for payments. Everyone uses it. Nobody competes with it. Too embedded to replace. If your main USP is "GRC automation platform with AI," you don't have a USP. Own a vertical. Become infrastructure. Be a lego-block. Which vertical is most defensible? #GRCEngineering #VendorStrategy

Amazon starts rewriting non-compliant product titles on 27 July. Character limits per category, promotional language stripped, repeated words and stray symbols removed. If your title breaks the rules, Amazon edits it for you, and its version is built for tidy catalogue data rather than your conversion rate or your keyword ranking. For a few hero products that is an afternoon of tidying. For a catalogue of thousands of ASINs it is a month of work you did not plan for. So I built a system to do it properly, and here is how it fits together. I start with a Project in Claude that holds Amazon's title rules for my categories: the character limits, the banned terms, the formatting requirements. That becomes the compliance layer, so every rewrite is checked against the rules by default instead of me policing thousands of titles by hand. Then I feed it the keyword research from Helium 10, the search volumes and current ranks, so it knows which terms are worth protecting inside the limit and which are dead weight. The instruction becomes "keep the highest-volume relevant keywords before the truncation point" rather than "make it shorter." Then the part that actually matters: performance context. A rewrite made blind to how a product sells is just a guess. I connect my live Seller Central data into Claude through Windsor.ai, so the rewrite is grounded in what each ASIN is really doing: what converts, what ranks, what drives the sales. You scope exactly which metrics and SKUs to share. Three tools doing three jobs. Helium 10 for the keywords, Windsor for the live performance, Claude for the rules and the rewriting. They do not merge into one button. The system is wiring those inputs into one workspace and then working through your A products first, three variants each, with your judgement on every hero SKU. The sellers who treat 27 July as a compliance chore will let Amazon rewrite their catalogue. The ones who build the system turn a forced deadline into the listing optimisation they had been putting off. I break the whole build down, plus the EU's new import duty that went live this week, in this week's Signal Over Noise.

For AI to be truly adopted at work, we need more than technology — we need transformation. New tools are launching every week. Investors are flooding the space. Budgets are open up for experimentation. That’s all exciting. But no AI vendor will succeed in the workplace by simply delivering an out-of-the-box solution — unless they also: Deeply understand their customers' existing business processes and design use cases into the platform where AI agents can drive real value. Lead internal change management, helping teams adopt, experiment, and evolve their workflows and culture. Build community, creating space for shared learning and new playbooks that extend beyond one customer’s walls. The vendors who win won’t just sell software — they’ll position themselves as partners in AI transformation, guiding customers from the old way of working to the new.

The most valuable AI asset isn't a wildly intelligent model. It's the capability you build to use it. After observing dozens of AI implementations, a pattern emerges that mirrors another domain near to my heart: trading. The most successful trading desks don't just subscribe to external data feeds—they build proprietary analysis capabilities that transform common information into uncommon insights. Similarly, leading firms in AI adoption aren't merely licensing algorithms; they're developing institutional knowledge that turns vendor solutions into competitive advantage. This capability-building happens across three critical layers: 1️⃣ At the strategic level, cross-functional AI steering committees ensure alignment between technical possibilities and business realities—particularly important in regulated financial environments. 2️⃣ For technical depth, structured upskilling creates "T-shaped" AI professionals who understand both financial context and technical implementation. 3️⃣ On the operations front, internal AI champions translate between quants, technologists, and business stakeholders—bridging the communication gaps that derail most implementations. In capital markets, sustainable AI advantage requires institutional knowledge that can't be purchased off-the-shelf. The most effective vendor engagements deliberately build this knowledge with: → Pilot-as-a-Service projects where your team shadows vendor experts, creating internal runbooks → Hybrid Pod structures pairing vendor technical leads with your domain specialists → Capacity-Ramp Engagements that financially incentivize knowledge transfer by shifting payment from vendor MSAs to internal headcount For executive teams and boards, this approach demands different oversight questions. Does the vendor own integration outcomes with SLA-backed timelines? Is there contractual clarity on explainability and audit trails that satisfy regulators? Does indemnity cover third-party models and user prompts? How many internal staff will shadow the vendor, and for how long? At what capability threshold do we insource or dual-source? Each successful implementation should leave your organization more capable than before — lowering the cost and time required for the next project. This transforms vendor selection from a procurement exercise into a talent strategy that acknowledges the real source of lasting value: not just what the system does, but what your organization learns. Sustainable advantage in financial technology is fundamentally about capability development, not vendor selection. #governance #fintech #ai #startups

𝗧𝗼𝗼 𝗺𝗮𝗻𝘆 𝘃𝗲𝗻𝗱𝗼𝗿 𝗿𝗲𝘃𝗶𝗲𝘄𝘀 𝗶𝗻 𝗳𝗮𝗰𝗶𝗹𝗶𝘁𝘆 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝘀𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗼𝗻𝗲 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻: 𝗗𝗶𝗱 𝘁𝗵𝗲 𝗦𝗟𝗔 𝘀𝗰𝗼𝗿𝗲 𝗽𝗮𝘀𝘀 𝘁𝗵𝗶𝘀 𝗺𝗼𝗻𝘁𝗵? That matters, but it is rarely enough. A green scorecard can still hide growing operational risk if the real signals are sitting underneath it: 👉aging backlog 👉repeat failures 👉inconsistent response quality 👉rising safety observations 👉recurring complaints from users or site teams Strong IFM contract management is not just about reporting outcomes. It is about spotting drift early enough to correct it. In practice, the most useful vendor reviews often combine lagging indicators with a few leading ones: 👉backlog age by priority 👉number of repeat callouts on critical assets 👉percentage of reactive jobs needing rework 👉safety non-conformances or near misses 👉quality of supervisor follow-up and closeout This changes the conversation. Instead of asking, “Why did performance fall?” we start asking, “What is changing in the operation that could affect performance next?” That is where vendor governance becomes more valuable to the business: less surprise, faster correction, and better service stability for occupants. In IFM, good reviews do not just confirm service levels. They help protect them. Takeaway: Measure what helps you intervene early, not just what helps you report later. #kpi #SLA #review #IFM #learnwithvishal