Seller Sessions Amazon FBA and Private Label

Pushing the Limits of Main Image Optimization for Amazon Sellers Join Danny and an all-star panel of e-commerce experts in this episode of Seller Sessions as they dissect the science of main image optimization for Amazon sellers. Using insights from their Main Image Monthly feature, the panel takes a deep dive into improving click-through rates (CTR) and driving conversions through data-driven design changes. Featured Product: A standout wooden ride-on toy, modeled after a London double-decker bus, is at the center of the discussion. Designed for kids aged 1-3, this unique product undergoes a real-time transformation guided by the panel's expert feedback. Key Takeaways:

• Customer Objections Turned into Strengths: By analyzing video reviews and surveys, Simon, a featured brand owner, tailors his listing to address common buyer concerns effectively.
• A/B Testing for Visual Excellence: The panel explores how image quality, badges, and strategic callouts influence customer perception and improve listing performance.
• Leveraging Data for Results: Discover how measuring CTR changes and tracking conversion feedback can revolutionize your main image strategy.

Your Expert Panel:

• Sim Mahon: A seasoned seller managing six private label brands and driving eight-figure revenues.
• Matt Kostan: Founder of ProductPinion, with over a decade of experience in e-commerce growth strategies.
• Peter-Paul Maan: Head of Sales and Partnerships at Intellivy, connecting brands with their target audience through strategic alignment.
• Hannah Lyss Tampioc: Founder of Mad Cat Creatives, specializing in high-impact visual strategies for Amazon listings.

Get ready to unlock practical tips and actionable insights for optimizing your product's main images and driving sales success on Amazon!   Reaching the Guests https://www.linkedin.com/in/sim-mahon-46a30123a/ https://www.linkedin.com/in/hannahlysstampioc/ https://www.linkedin.com/in/mattkostan/ https://www.linkedin.com/in/pmaan/   Reaching the Guests Sim Mahon Hannah Lyss Tampioc Matt Kostan Peter-Paul Maan   Out Now on SellerSessions.com 'The Cold Reality Of The Honeymoon Period And External Traffic' 🔗 https://sellersessions.com/the-cold-reality-of-the-honeymoon-period-and-external-traffic/ 💬 Your opinion matters! Drop us a comment 📣 and join the conversation. Remember, sharing is caring—so hit the like button 👍❤️, give us some love, or share this post with someone you think will enjoy it! 🔄 🎟 Seller Sessions Live, 2025. Grab tickets now: https://sellersessions.com/seller-sessions-live-2025/ 📺 Watch this podcast in its full glory. Out now on YouTube: https://www.youtube.com/@SellerSessions

Advanced Ranking on Amazon in Q4: Garfield Coore In this episode of Seller Sessions, Danny McMillan welcomes back Garfield Coore, a top-ranking strategist, to break down Amazon’s algorithm. Garfield shares insights for sellers to maximize organic rank, external traffic, and achieve ranking stability as Q4 peaks., Garfield shares essential techniques for sellers looking to stay competitive and profitable. Key Takeaways The Three-Stage Ranking System:

1. Primary Ranking Events:
   • Roughly 90% of ranking impact comes from core behaviours on Amazon.
   • Key actions include searches leading to a product page, add-to-cart from a search click, and purchases from a search pathway.
   • Garfield stresses avoiding variations that interrupt the ranking pathway.
2. Keyword Cohorts:
   • Leveraging keyword "families" (using Amazon’s Opportunity Explorer) helps products gain ranking bleed-over.
   • Start ranking with smaller keywords, allowing spillover to higher competition terms.
3. Territorial Influence:
   • Minor ranking factors include BSR and inventory location, affecting regional rank. BSR reflects category sales ranking relative to competitors.

Conversion Events Beyond Sales

• Clicks, page visits, and add-to-cart actions all serve as ranking events, which Garfield calls “conversion events.”
• Focus on generating quality traffic to accumulate these events, which can boost rank without immediate sales.

PPC Strategy: Maximizing Click-Through Rate

• PPC placement relies on expected revenue from clicks, not relevance. Garfield explains that a history of clicks improves PPC placement probability.

  Timing Event-Driven Ranking

• Garfield advises starting campaigns for seasonal events early to establish a low-cost rank before high-demand periods.
• For example, healthcare and weight loss products should begin ranking efforts before New Year’s resolutions in January.

Reach Garfield - https://www.facebook.com/garfield.coore

Out Now on SellerSessions.com The Cold Reality of the Honeymoon Period And External Traffic 👉 https://sellersessions.com/the-cold-reality-of-the-honeymoon-period-and-external-traffic/ If you have problems with the links, check the link in our bio! Engage with us! Your opinion matters! Drop us a comment 📣 and join the conversation. Remember, sharing is caring—so hit the like button 👍❤️, give us some love, or share this post with someone you think will enjoy it! 🔄 Seller Sessions Live, 2025 Grab tickets now https://sellersessions.com/seller-sessions-live-2025/ Watch this podcast in its full glory Out now on YouTube - https://www.youtube.com/@SellerSessions

Advanced: Master Amazon Ranking: Bite-Sized Insights from the Whiteboard Episode Summary

In this episode of Seller Sessions, hosts Dan and Oana take a deep dive into Amazon's ranking mechanism, focusing on the Bayesian update process and its impact on product visibility. Inspired by their previous series on the complexities of the "cold start," Dan and Oana aim to simplify the algorithm’s operations, allowing sellers to apply these insights to common Amazon business challenges, from managing stockouts to ASIN resets.

The Bayesian update plays a crucial role in Amazon's ranking formula, guiding the platform's initial "guess" for each new product’s rank and continuously refining it as user interaction data accrues. They explain the difference between prior and posterior predictions:

• Initial Prior Prediction: When a new product launches, Amazon evaluates similar products based on shared attributes and performance data, assigning a starting rank that’s essentially a best guess.
• Posterior Prediction: As users engage with the product (clicks, scrolls, purchases), this real-time behavior helps Amazon fine-tune its ranking, transitioning from a speculative ranking to a data-informed position.

The duo also references two pivotal Amazon patents from 2022 and 2023, which document how real-time interaction data (e.g., clicks and conversions) informs ranking recalculations every 2-24 hours, depending on available data. This Bayesian cycle is fundamental to Amazon's dynamic ranking shifts, especially in crowded categories where initial guesses are quickly updated with interaction-driven insights.

Key Takeaways

• The Role of Bayesian Updates: Sellers learn how the Bayesian update transforms initial ranking predictions by integrating real-time user data, continuously recalculating product rankings.
• Exploration vs. Exploitation: Amazon prioritizes real user data over hypothetical scenarios, relying on actual behavior to shape ranking results.
• New Products vs. Returning Products: Newly listed items start from scratch, but if a product goes out of stock and returns, it resumes with past data, allowing quicker integration of new engagement data.
• Ranking Frequency: Ranking updates may occur every 2-24 hours, creating a near-real-time feedback loop that adjusts based on ongoing user interactions.

Dan and Oana emphasize that traditional concepts like the "honeymoon period" are less relevant due to Amazon’s continuous ranking adjustments. As technology advances, rankings are now recalculated frequently, meaning sellers should focus more on engagement metrics than waiting for prolonged ranking boosts.

This episode demystifies complex Bayesian methods in Amazon’s ranking algorithm, offering insights that will help sellers understand how to strategically navigate the platform’s data-driven system.

Out Now on SellerSessions.com - "The Cold Reality Of The Honeymoon Period And External Traffic"

https://sellersessions.com/the-cold-reality-of-the-honeymoon-period-and-external-traffic/

If you have problems with the links, check the link in our bio!

Your opinion matters! Drop us a comment 📣 and join the conversation. Remember, sharing is caring—so hit the like button 👍❤️, give us some love, or share this post with someone you think will enjoy it! 🔄

Seller Sessions Live, 2025. Grab tickets now: https://sellersessions.com/seller-sessions-live-2025/

Watch this podcast in its full glory. Out now on YouTube - https://www.youtube.com/@SellerSessions

Building a Full-Funnel DSP Strategy For Amazon Sellers   Danny welcomes Sam Lee, an Amazon DSP expert with years of experience at companies like Thrasio. Sam provides insights into the Amazon DSP (Demand Side Platform), a less accessible yet powerful tool compared to Amazon’s PPC. DSP allows for advanced targeting using Amazon’s first-party data, perfect for those ready to expand beyond traditional ad methods. Danny and Sam dive into the essentials of DSP, covering campaign structures, targeting methods, and common pitfalls that many brands face when venturing into DSP.   What is Amazon DSP? Sam explains that Amazon DSP is different from traditional Amazon PPC in accessibility and functionality:  

• Barrier to Entry: DSP isn’t as easy to access as Seller Central; it requires Amazon-approved agencies or meeting certain spend thresholds.
• Initial Challenges: Early misuse led to its reputation issues, as many advertisers applied blanket strategies, not optimizing DSP for unique brand/product needs.

  Building the Full Funnel Sam emphasizes a strategic approach to DSP that adapts to product price points and buying cycles, avoiding a one-size-fits-all approach:  

• Understanding Customer Journey: Higher-priced products require longer consideration windows, so retargeting timelines should vary.
• Tailoring Campaigns by Product Type: A $10 product doesn’t need a 30-day retargeting window, while a $200 product may need up to 45 days to properly engage the audience.

  Key Metrics for Success in DSP To evaluate DSP campaign effectiveness, Sam discusses focusing on core metrics:  

• Return on Ad Spend (ROAS) and Total ROAS as primary performance indicators.
• Effective Cost Per Detail Page View: Lower costs (below $1) signal efficient DSP campaigns, with top performers achieving $0.50 or less.
• Percent of Purchases New-to-Brand: Indicates how well DSP attracts fresh customers, avoiding retargeting those already inclined to purchase.

  Sam highlights Amazon Marketing Cloud (AMC) as a tool to monitor customer touchpoints in the purchase path, offering more transparency into DSP’s role in converting new users.   DSP Budgeting Insights One misconception Sam dispels is that DSP requires excessive budgets to yield results:  

• Optimal Spend Range: While larger budgets provide more data for refinement, DSP can still be tested effectively at lower levels if PPC campaigns are fully maximized first.
• Synergy Between PPC and DSP: He advises investing as much as possible into PPC until returns diminish, then strategically layering DSP to further boost conversions.

  Evaluating DSP Managers When hiring or assessing a DSP manager, Sam recommends looking for these critical skills:  

1. Sales Deduplication Knowledge: A solid understanding of deduplicating sales between DSP and PPC, often through merchant tokens, which ensure accurate attribution.
2. Customized Campaign Strategy: Effective DSP managers tailor retargeting windows and budgets based on product price points and sales cycles, avoiding generic settings.
3. Expertise with Streaming and Video Ads: Familiarity with OLV (Online Video) and Streaming TV (OTT) can add value to campaigns, especially for brand awareness.

  Streaming TV and Online Video (OLV) Advertising Sam and Danny discuss the advantages of Streaming TV (OTT) and Online Video (OLV) as part of DSP’s offerings:  

• OTT vs. OLV: OTT, or Over-the-Top Media, is a more premium option, placing ads on streaming platforms like Hulu and Prime Video, while OLV covers a broader online space (e.g., ads between games or online content).
• Use Cases: Streaming ads are highly effective for certain brands but come with higher costs, while OLV offers a budget-friendly alternative for brands targeting broader, online-savvy audiences.

  DSP for Non-Amazon Sellers One of the most forward-thinking DSP strategies involves leveraging Amazon’s first-party data for external brands:  

• Application for Non-Amazon Sellers: Brands not selling on Amazon, like car companies or public services, can still use DSP to target potential customers based on Amazon’s deep data insights.
• Geotargeting and Demographics: For example, public transit services like LA Metro have used DSP to target specific areas, showing the versatile applications of DSP data.

  The Role of DSP in Amazon’s Search and Ranking Algorithm Sam shares advanced insights on how DSP impacts Amazon’s ranking system through behavioral targeting:  

• Bayesian Update System: Amazon’s algorithm adapts based on live data (clicks, conversions), helping high-performing products “win” visibility quickly while demoting less successful items.
• Behavior-Driven Launch Strategy: For launches, a well-optimized DSP campaign can create significant early traction, contributing to better search rankings.

  Common Pitfalls and Misconceptions in DSP Sam addresses frequent DSP errors that agencies and brands make:  

• Misleading Attribution: Lack of merchant tokens can lead to inflated success metrics, misleading clients on actual DSP effectiveness.
• Uniform Strategy Application: Applying the same retargeting window or budget across all campaigns, regardless of product type or target audience, can dilute DSP’s impact.

  Amazon as a Search Engine First Both Sam and Danny agree that Amazon’s primary goal is search relevancy, driven by conversion rates and user experience:  

• SEO Principles on Amazon: Amazon prioritizes high-conversion products to ensure users find relevant, desirable items. Successful DSP campaigns enhance this by generating high-quality traffic.
• Cold Start Problem: New products face Amazon’s cold-start challenges, where initial performance metrics determine future visibility. DSP’s behavioral targeting can boost early sales velocity, easing this process.

  Closing Thoughts Danny and Sam conclude by reinforcing Amazon’s profit-centric nature, encouraging sellers to align with Amazon’s goals to maximize DSP benefits. For sellers looking to experiment with DSP, Sam advises working with knowledgeable agencies or managers to avoid wasted spend and achieve incremental gains over PPC alone.   Reach Out to Sam Lee:  

• Company: Trivium Co.
• Contact: [email protected]

  Looking for a Free PPC Audit? https://www.databrill.com/

Real-World AI For Amazon Sellers : How We Use It to Drive Business Success Introduction   Ritu Java is the CEO and Co-founder of PPC Ninja, a company that offers Amazon PPC Software to agencies and brands, along with PPC Management Services. She leads a team that manages Amazon Sponsored and DSP Advertising for sellers with 6, 7, and 8-figure revenues. With over a decade of experience in eCommerce, Ritu has guided hundreds of Amazon sellers through the complexities of Amazon Advertising.   AI for E-Commerce Ritu also runs the AI for E-Commerce Newsletter, which has been active for 18 weeks and has 1,900 subscribers.   Out Now on SellerSessions.com: The Cold Reality Of The Honeymoon Period And External Traffic   If you have problems with the links, check the link in our bio! Your opinion matters! Drop us a comment and join the conversation. Remember, sharing is caring—so hit the like button , give us some love, or share this post with someone you think will enjoy it!

Testing 100 Amazon Product Listings with Rufus: My Findings   Capabilities of Rufus on a Product Detail Page with Andrew In this episode, Andrew, a former Director of Amazon for Touch of Class and current Amazon Lead for the National Fire Protection Association, dives into the powerful features of Rufus and how it transforms the way customers interact with product detail pages.   Andrew's Background:

• Former Director of Amazon for a luxury home brand, Touch of Class (8 eight figure brand)
• Created top-rated Amazon Custom GPTs
• Amazon Lead at the National Fire Protection Association
• Self-taught in SEO, SGE, and Generative AI applications
• Holds a black belt in traditional Taekwondo and enjoys pickleball

  Rufus' Core Capability: Text Retrieval Rufus uses Optical Character Recognition (OCR) to extract text from product information, customer reviews, and visuals. This technology allows for a comprehensive data analysis that can enhance the accuracy of product details and reviews.   Rufus in Action:

• Extracts relevant insights from text, images, and customer feedback
• Moves beyond basic search terms, offering a more intuitive search experience for users
• Delivers highly relevant product information by utilizing advanced AI techniques

  Conclusion: Andrew explains how Rufus represents the future of product search and engagement, making customer interactions with product detail pages more insightful, efficient, and responsive to user needs.   Watch the full Version on Youtube

Bayesain Updates - Changing the Game of Ranking   In this episode of Seller Sessions, Danny and Oana unveil their latest collaborative article, which delves into Amazon's patents and algorithms, particularly focusing on the evolution from 2022 to 2023. This monumental piece—over 10,000 words—aims to be the most extensive public resource on Amazon's A9 algorithm, tracking its history and impact.   Article Origins and Team Effort -Danny and Oana teamed up for several papers, each expanding in scope. Their latest collaboration incorporates insights from two patents and 15-16 additional scientific papers. -The goal: Analyze the algorithm changes between 2022 and 2023, highlighting key differences and their implications for sellers.   Key Themes Covered -BERT, Cosmo, and External Traffic: Deep dive into these technologies and how they impact ranking, visibility, and traffic management. -Sales Velocity and Cold Start Mechanisms: The duo explores how Amazon’s cold start problem has evolved, driven by Bayesian updates and machine learning. -Honeymoon Period Myth: A thorough debunking of the concept, explaining why it no longer holds true after algorithm changes in 2022.   Data-Driven Approach This project digs into how Amazon now processes data, with updates to ranking and product visibility happening every 2-24 hours. The emphasis is on personalization, driven by Amazon’s focus on conversion likelihood, making an optimized launch strategy critical.   Amazon’s Shift Towards Personalization -Amazon’s increasing focus on tailored customer experiences, from personalized search results to dynamically adjusted product titles. -Concerns about how machine learning models, like Cosmo and Rufus, will continue to evolve and potentially override manual optimizations sellers make.   Tune in to gain the edge on launching your products and mastering Amazon's constantly evolving system.   READ THE ARTICLE HERE

Building a Brand from Scratch Why Nafiseh Razavi Is the Face of Her Growing Brand   In this episode of Seller Sessions, we explore a fresh approach to selling with guest Nafiseh Razavi, founder of StudyKey, an educational tool for language learners.   Key Takeaways:

• StudyKey is a compact educational tool aimed at helping language learners study away from digital distractions.
• Nafiseh’s journey is pretty unique because she is both the creator and the face of her brand (most sellers prefer to stay in the background) , promoting a personal connection with her customers. She emphasizes how being hands-on has helped her keep costs down while also ensuring that the product is represented exactly as she envisions.

Building a Brand from Scratch:

• The motivation behind StudyKey came from Nafiseh’s personal experience learning Spanish and recognizing what was missing in her own language learning journey. This insight shaped the product, which is designed to reduce screen time and encourage outdoor studying.

Challenges & Strategies:

• Financial constraints led Nafiseh to take charge of her social media and content creation, ensuring authentic brand representation. She discusses the importance of balancing quality with quantity in social media posts, preferring a hands-on approach to engaging with her audience.

Product Promotion & Social Media:

• Nafiseh shares her process for creating content, using real-life scenarios to integrate her product naturally into daily activities.
• She emphasizes consistency in posting, aiming for daily content while focusing on quality over volume.

Advice for Aspiring Entrepreneurs:

• For those looking to take a similar approach, Nafiseh advises stepping out of your comfort zone and embracing the role of being the face of your brand. She highlights the importance of persistence, learning from mistakes, and continuing to improve with every step.

Future Plans:

• Nafiseh is focused on scaling her brand, expanding beyond Amazon, and creating more products to support her community of language learners.

Amazon Sellers: Boost Conversions with Main Image Strategies – Part 3   Image Teardowns for Better Conversion   Welcome to our monthly show on all things images and conversion, where we bring in some of the world's best Amazon conversion optimizers. Each month, we will take an ASIN and run tests using all our technology, then bring it back to the table and break down our findings, showing you how easy it is to test, how to test properly, and how to use your imagination. This month, we work on a SnoozeShade Stroller Cover, innovated and decorated by brand owner Cara Sayer.   Your Takedown Team Sim Mahon (8-figure Seller) Matt Kostan (ProductPinion, Multiple 7-figure brands) Peter-Paul Maan (Intellivy)   About Our Guest Panelists   Sim Mahon runs an eight-figure business with six private label brands spanning various categories. Over the past eight years, he has navigated the highs and lows of e-commerce, from eBay to Amazon, and from Vendor Central to Seller Central. His journey has equipped him with a wealth of experience and insight into the dynamic world of Amazon.   Matt Kostan (ProductPinion, multiple seven-figure brands) has over a decade of experience in selling on Amazon, Kickstarter, and retail. He has built multiple seven-figure brands from the ground up. At ProductPinion, Matt leads a team dedicated to helping Amazon sellers grow their sales through real consumer insights from hundreds of thousands of shoppers.   Andri Sadlak (ProductPinion / 8-figure Seller) is not a serial k*ller but a serial immigrant and entrepreneur. Now running an eight-figure brand, Andri started his journey by launching his first FBA business in 2017 and selling it three years later before co-founding ProductPinion, a leading conversion optimization tool for Amazon sellers.   Peter-Paul Maan is the Head of Sales and Partnerships at Intellivy. Peter-Paul stands at the forefront of e-commerce, transforming sales into journeys. His approach ensures products are perfectly aligned with consumer desires, leading to successful launches and satisfied customers. He is the go-to expert for those aiming to master their Amazon strategy and authentically connect with their audience.

The Science Behind RUFUS - Expert Insights on Amazon's AI Gamechanger   The Science Behind RUFUS   Rufus Revealed: Expert Insights on Amazon's AI Gamechanger On this episode, we do a roundtable featuring Dr. Ellis Whitehead, who used artificial intelligence to enable laboratory robots to autonomously run and analyze scientific experiments before AI was a buzzword. Oana Padurariu, who is the Head of Amazon at Trivium, is also featured. Her stock is rising as she drops groundbreaking knowledge around the science of ranking and is a rising star in the Amazon community. Jeffrey Anderson already has an exit under his belt and is in high demand with software companies and agencies for his technical genius. Rufus Revealed: Expert Insights on Amazon's AI Gamechanger   Key Points Custom Large Language Model (LLM): Rufus uses a custom-built LLM trained with specific shopping data, including the entire Amazon catalog, customer reviews, and community Q&A posts, providing tailored answers to shoppers.   Retrieval-Augmented Generation (RAG): Rufus goes beyond its training data, pulling relevant information from reliable sources like customer reviews, product catalogs, and API data to generate accurate and helpful responses.   Reinforcement Learning: Rufus improves over time by learning from customer feedback, constantly enhancing its ability to provide useful shopping advice . AWS Infrastructure and AI Chips: Amazon's custom AI chips, Trainium and Inferentia, enable Rufus to provide real-time responses at scale, with minimal latency, even during peak shopping hours. Streaming Architecture: Rufus provides real-time, token-by-token responses, ensuring that shoppers don’t experience delays while interacting with the AI assistant.   About Jefferey Jeffery Anderson. He sold his business in 2021 and recently invested in a tea company. Jeffery's expertise lies in technical processes tailored for large sellers and agencies, along with providing software training.   About Oana Oana Padurariu is the Head of Amazon at Trivium, an advertising whiz with a flair for SEO and PPC. From political science dreams to Amazon mastery, she's led brands across the US and EU to success. Now, she channels her passion for the Amazon puzzle into leading her team to innovate and excel in the competitive e-commerce space.   About Dr Ellis A Data Scientist and Algorithm Expert.Ellis has a proven track record in his ability to solve complex problems and turn them into simple solutions through software engineering, mathematics, and data science. He has been deeply involved in the success of the groundbreaking Amazon software tool, Jungle Scout. Ellis became inspired to solve these complex problems after completing his PhD in applied artificial intelligence to enable laboratory robots to autonomously run and analyze scientific experiments.