Seller Sessions Amazon FBA and Private Label

Welcome back to Conversion Monthly! Danny McMillan is joined by the full team — Sim, Matt Kostan, and Dorian — plus special guest Anna, who launched a reusable microfiber cleaning cloth roll on Amazon UK. Anna's product is clever — an eco-friendly alternative to paper towels that's washable and comes in multiple colors. But the listing isn't converting. The team digs into exactly why and what to fix. What's Covered in This Episode: Main image problems — Why Anna's current main image fails to communicate what the product actually is, and how showing roll thickness, sheet count, and color options can dramatically increase click-through rate Baseline click share testing — Matt runs a 100-person UK shopper poll revealing Anna's listing captures only 10% of clicks against competitors The power of color — Shoppers gravitate toward listings showing multiple color options, and the team discusses how bolder, richer colors (not pastels) pop on search results Secondary image ordering — Dorian's favorite test reveals that simply reordering existing images based on what shoppers care about most (reusability and washability) can boost conversions without changing a single image Title strategy — Why keyword stuffing kills readability and how "speed bump keywords" like "washable" or "tear-away" differentiate you from competitors Cost-saving as a conversion lever — Framing the product as a money-saver versus annual paper towel spend gives instant price justification Before-and-after imagery — "Show me, don't tell me" — why context-driven images outperform generic product shots German marketplace analysis — What the German Amazon sellers are doing right with richer colors, dynamic angles, and clean layouts New main image results — After updating the main image with Dorian's mockup, click share jumped 60-70% in testing Key Takeaways: If shoppers can't tell what your product is from the main image, nothing else matters The order of your secondary images matters as much as the images themselves — answer objections early Titles need to balance keyword volume with readability — a title nobody understands won't convert regardless of search volume Look at what competitors in other marketplaces (especially Germany) are doing for image inspiration Test before you invest in final designs — quick mockups and polling save time and money Connect with the Team: Matt Kostan — [email protected] Sim — LinkedIn (also recruiting brand managers) Dorian — LinkedIn Anna — LinkedIn

Danny McMillan and Shubhash Sharma are back with another Claude Sessions episode covering both the back end and front end of building your Amazon business infrastructure with AI. Shubhash walks through exactly how to register for Amazon's Seller Partner API — your free, direct access to your own sales, inventory, pricing, and order data — no third-party subscriptions required. Danny then breaks down the 165-feature design system he built to eliminate AI slop from websites, landing pages, and app interfaces. Part 1: Amazon SP-API Setup (Shubhash) What SP-API is — Amazon giving you a key to your own data warehouse: live inventory, real-time orders, pricing, catalog data, and sales reports 5-step registration process — Register as developer, create an app, select permissions, self-authorize, and connect to Claude Code to build dashboards Common rejection reasons — Usually a missed checkbox or vague answer about data usage. Keep answers focused on personal brand development and safe data storage Advertising API is separate — Different credentials, different registration, different refresh token. You cannot reuse SP-API tokens for ads What you can build once connected — Custom dashboards, forecasting engines, inventory alerts, automated reporting — all built by Claude Code without knowing Python Danny's guardrails — Hire a $50 Upwork specialist to help with paperwork submission, keep them on retainer for when APIs go down (especially Q4, Black Friday, Prime Day) Part 2: The 165-Feature Design System (Danny) The AI slop problem — Default fonts (Roboto, Arial), purple-blue gradients, three-column card layouts, floating animated orbs, oversized border radius — all telltale signs of generic AI output 15 anti-patterns cataloged — The system actively fights against common AI design defaults Four-phase pipeline — Decide, Design, Build, Refine — with 15 databases and components extracted from 11 repos Gap analysis scoring — Rates output out of 60 points. Seller Sessions Live went from 33 to 50; Databrill went from 48 to 55 Psychology of design baked in — Hick's Law (limit choices to 5-7), Miller's Law (chunk information in groups), Jacob's Law — all running automatically in the background "Pretty doesn't convert" is a cop-out — Apple, Ralph Lauren, Sony all prove that quality design builds trust. The real issue was budget — now AI removes that barrier Design is about subtraction — Cut 69% of animations in one project. Overcooking destroys user experience 25 quality gate techniques — Color tokens, typography rules, contrast ratios, accessibility (100+ rules), spacing, and composition patterns Claude Loom workflow — Record feedback via Cmd+Shift+L, Claude extracts screenshots and browser URLs, and the system pushes back if changes violate the design system Key Takeaways: SP-API is free and gives you direct access to your Amazon data — do it tonight The Advertising API requires a completely separate registration process Have a backup developer on standby for API downtime, especially during peak sales periods AI-generated interfaces all look the same because they default to the same fonts, colors, and layouts A design system isn't about making things pretty — it's about trust, conversion, and consistent user experience across all devices Before your customer reads a single word of copy, your page load time and visual quality have already made an impression Coming Next: Shubhash experiments with running AI models locally on an old MacBook using Ollama — cutting token costs to zero. Connect: Shubhash Sharma — LinkedIn Danny McMillan — sellersessions.com

In this third installment of Claude Sessions, Danny is joined by Subash from Not A Square, who helps e-commerce brands scaling past seven figures implement AI without scaling headcount. Subash walks through real client case studies -- including a TikTok brand that boosted its customer satisfaction score from 4.2 to 4.5 in four weeks using a customer support agent built in Claude. Danny then breaks down OpenClaw, the open-source personal AI agent that exploded in popularity, explains why he chose not to use it despite the temptation, and reveals Claude Flow -- his custom operating system built inside Claude Code with 11 engines, 300+ features, and a persistent memory layer powered by ChromaDB. The episode drives home one core message: document your operations first, pick one platform, go deep, and stop chasing every new tool. Key Topics Documenting operations before automation -- Why you cannot automate what is not documented TikTok customer support case study -- Building an AI agent that raised satisfaction scores in four weeks OpenClaw overview and security risks -- What it does, why it blew up, and why Danny built his own alternative Claude Flow -- Danny's custom operating system inside Claude Code with persistent memory The amnesia loop -- How context loss between sessions kills productivity and how ChromaDB solves it Pixel-less environment -- The shift from structured prompts to contextual AI interaction Go deep on one platform -- Why chasing multiple AI tools guarantees you build nothing Timestamps [00:00] Introduction -- Claude Sessions Week 3, delayed from the road [01:03] Subash introduces himself and Not A Square [02:01] Overview of three client projects and the problem founders face [04:30] Why operational truth is the moat in AI commerce [06:48] Three pillars: reduce costs, better governance, scale without headcount [07:30] TikTok case study -- customer support agent boosting store score from 4.2 to 4.5 [09:04] OpenClaw -- history, capabilities, and the security nightmare [15:30] Six core capabilities of OpenClaw (local-first, universal messaging, persistent memory, browser automation, system access, self-extending skills) [18:00] Why OpenClaw matters -- moving from dumb LLMs to personal AI agents [20:00] Security trade-offs -- 1.5M API keys exposed, malware in skills, Cisco tests [22:00] Claude Flow -- Danny's 11-engine operating system built inside Claude Code [24:26] The amnesia loop -- how sessions lose context and how ChromaDB fixes it [28:19] Why Claude MD, agents, and skills are not enough without hooks and triggers [32:40] Go deep on one platform -- stop chasing every new tool [35:35] Subash on helping sellers adopt Claude Code fundamentals (Claude MD, skills) [39:51] Wrap-up and contact info Key Takeaways Document before you automate -- If your business operations live in the founder's head and not on paper, any AI tool will amplify the chaos rather than fix it. Operational truth is the moat -- Clean inventory, accurate catalogs, honest cashflow reporting. Get these right before touching AI. One AI agent moved the needle -- A single customer support agent on TikTok raised a brand's satisfaction score from 4.2 to 4.5 in four weeks, directly improving store visibility. Persistent memory changes everything -- ChromaDB captures decisions, patterns, and project context across sessions so Claude compounds in usefulness over time (zero entries in session one, 1,700+ by session 25). Scaffolding beats raw building -- Danny's Claude Flow system means a project that took five days six months ago now takes 40 minutes. The investment in infrastructure pays exponential returns. OpenClaw is proof of concept, not production-ready -- Broad permissions, prompt injection vulnerabilities, exposed API keys. Wait for the open-source community to patch the holes before diving in. Pick one platform and go all the way in -- Chasing multiple AI tools means you learn none of them deeply and build nothing of value.

In this Conversion Monthly episode, the team takes on a real-world Amazon fashion listing -- a ladies jumper from Adam Jagger's clothing brand. Adam launched this knitwear product last year with strong initial sales, but the relaunch has stalled despite refreshed images and PPC data. Matt Kostan shares consumer feedback from Product Opinion videos revealing that shoppers found the secondary images too text-heavy and "fast fashion" looking, with inconsistent color rendering across photos. Dorian then presents a complete visual overhaul inspired by Zara's photography style -- low-angle shots, model-camera interaction, and stripped-back secondary images that let the product speak for itself. The result: a 45% improvement in click share during simulated testing, jumping from 11% to 16% against competitors. The episode is packed with actionable advice on fashion-specific listing optimization, the power of "less is more" in secondary images, and why pre-launch polling is essential in design-led categories. Key Topics Live listing teardown -- Adam Jagger's ladies jumper analyzed by the full Conversion Monthly panel Consumer video feedback -- What real shoppers said about the listing (color inconsistency, text-heavy images, fast fashion feel) Main image testing -- Baseline vs. Zara-inspired low-angle photography concepts Secondary image overhaul -- Stripping back text and adopting a premium, warm aesthetic Pre-launch polling -- Why design-led fashion products should be tested before manufacturing Selling through the female lens -- Understanding emotional and aspirational buying in women's fashion Timestamps [00:00] Introduction and welcome to Adam Jagger [01:24] Adam explains the product -- ladies jumper relaunch that stalled [02:28] Category rules -- how much creative freedom do clothing listings have? [03:45] Matt shares Product Opinion video feedback from real shoppers [07:25] Dorian's approach -- studying Zara and H&M for photography inspiration [10:30] The "less is more" philosophy for fashion secondary images [14:02] Matt reveals test results -- 11% to 16% click share improvement [19:22] Sim's take on AI-generated images and warmth in the image stack [20:39] AI-generated video concepts for sponsored brand ads [22:50] Adam's reaction and takeaways [26:17] Danny's deep dive -- cognitive overload, decision paralysis, and the female lens [37:31] Adam's final thoughts and next steps [38:30] Sim on pre-launch polling for design-led niches [40:47] Final roundup and upcoming Seller Sessions event announcement Key Takeaways Strip back your fashion images -- Text-heavy, icon-filled secondary images can make clothing look like fast fashion. Clean, warm, product-focused images convert better. Study leading brands for photography direction -- Dorian reverse-engineered Zara's low-angle, model-interaction photography style and applied it to the Amazon listing with a 45% click share improvement. Consumer video feedback reveals what data cannot -- Ten 4-minute shopper videos uncovered issues like inconsistent pink shading across images that no one on the team had noticed. Pricing inconsistency kills trust -- Having one color variation priced higher than others confused shoppers and reduced confidence in the listing. Poll before you launch in fashion -- For design-led categories, spending $100 on pre-launch testing can steer you away from a bad product or nail the positioning first time. Sell the transformation, not the features -- Women's fashion is an emotional, aspirational purchase. The listing should make the shopper feel "this could be me" rather than listing fabric specs. Beware of over-optimizing click-through rate -- Pushing CTR too hard can lead to image fatigue and diminishing returns. Balance scroll-stopping visuals with long-term brand consistency.

Broad Match - Danny and Adam break down Amazon's financial trajectory ahead of the Q4 2025 earnings call, exploring why Prime has effectively tapped out, where the retail business is heading, and why Rufus may be Amazon's most important bet for the future of e-commerce. Host: Danny McMillan Co-Host: Adam "Heist" Runquist Episode Summary With Amazon's Q4 2025 earnings call on the horizon, Adam digs into the historical financials of Amazon's retail business to understand where the company has been and where it is heading. The picture is clear: Prime membership has reached over 200 million Americans, covering roughly 75% of the adult population, and growth has slowed to just 3-4% annually. The remaining unsubscribed population is largely economically unfeasible to convert. The numbers tell a compelling story across Amazon's retail business units. First-party retail has matured and is effectively flat or declining. Third-party seller fees have grown 190% since 2019, far outpacing the 75% growth in Amazon's own retail — but sellers are now squeezed to single-digit net margins with little room for further extraction. Advertising remains the standout at 56 billion dollars in 2024 with 300% growth over five years, yet its long-term sustainability depends on healthy seller participation. This sets up what Adam describes as Amazon's innovators dilemma. Danny and Adam agree that Rufus represents Amazon's play to shift from a purchase destination to a product discovery and research platform, effectively competing with Google, YouTube, and Reddit for the consideration phase. The episode closes with a rallying call for sellers to focus on extreme efficiency, leveraging AI tools to optimise listings at a level of sophistication that was impossible even a year ago, and to prepare for a market where fewer sellers will survive but those who do will be significantly rewarded. Key Takeaways Amazon Prime has effectively saturated the US market at over 200 million members, with the remaining population largely economically unfeasible to convert, signalling the end of Amazon's biggest historical growth engine. Third-party seller fees have grown 190% since 2019 compared to 75% growth in Amazon's own retail, but sellers operating on single-digit margins means Amazon has limited room to extract further on a per-unit basis. Amazon's advertising business pulled in 56 billion dollars in 2024 with 300% five-year growth, but its future depends on whether enough healthy sellers remain to sustain ad spend. Rufus is positioned as Amazon's answer to the innovators dilemma — shifting from a purchase-only platform to a product discovery and research destination to drive more visits, higher conversion, and larger basket sizes. AI tools now allow sellers to accomplish listing optimisation work in hours that previously took weeks, making sophisticated conversion optimisation accessible to small teams without additional headcount. The market is entering a consolidation phase where fewer sellers will survive, but those who maintain cash reserves, optimise ruthlessly, and adapt to the changing landscape will benefit as competitors exit. Chapter Markers 00:00 - Introduction 00:40 - Why Amazon earnings matter for sellers 03:30 - Prime membership growth and saturation 06:22 - First-party retail maturity and decline 09:30 - Third-party seller fees hitting the ceiling 11:10 - Advertising as Amazon's growth engine 13:28 - Rufus and the discovery play 15:47 - The debate around Rufus and objectivity 19:07 - AI efficiency and listing optimisation 22:16 - Beyond keywords and single-dimension thinking 33:24 - Market consolidation and survival strategy 37:19 - Practical steps for sellers right now Resources Seller Sessions Website Seller Sessions YouTube Adam "Heist" Runquist on LinkedIn Adam Heist YouTube Channel ```