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:   Sales Deduplication Knowledge: A solid understanding of deduplicating sales between DSP and PPC, often through merchant tokens, which ensure accurate attribution. Customized Campaign Strategy: Effective DSP managers tailor retargeting windows and budgets based on product price points and sales cycles, avoiding generic settings. 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: sam.lee@triviumco.com   Looking for a Free PPC Audit? https://www.databrill.com/

Ritu Java, CEO and co-founder of PPC Ninja, brings her extensive eCommerce experience to the discussion. She delves into how AI is revolutionizing Amazon sales, emphasizing tools like Canva AI and ChatGPT for creating stunning product images. Ritu also discusses the impact of AI on podcast production and offers insights into maximizing productivity through digital tools. Additionally, she shares strategies for training businesses to integrate AI effectively while maintaining a personal touch, all designed to enhance seller success.

Andrew Bell, former Director of Amazon for Touch of Class and now with the National Fire Protection Association, shares insights on Rufus, an innovative AI tool improving product detail pages. He discusses Rufus's capabilities in text retrieval, allowing for enhanced data analysis from customer feedback and visuals. The conversation highlights the importance of image optimization and machine learning in engaging customers. Andrew emphasizes how Rufus revolutionizes product search, making it more intuitive and contextually relevant for users.

Dive into the evolving landscape of Amazon's A9 algorithm, exploring algorithm improvements from 2022 to 2023. Discover how Bayesian updates and advancements in machine learning are reshaping ranking and visibility. The discussion debunks the 'honeymoon period' myth, emphasizing the significance of personalized product titles and accurate data. Gain insights into launching products effectively and leveraging Amazon's AI recommendations to enhance conversions. Understand the pivotal role of keyword prioritization in improving search relevance and shopper experience.

Nafiseh Razavi, founder of StudyKey, an innovative educational tool for language learners, shares her inspiring journey. She focuses on outdoor learning to minimize screen time, inspired by her own challenges with language acquisition. Nafiseh discusses the importance of personal engagement in brand promotion, balancing quality and quantity in her social media strategy. She emphasizes storytelling and authentic content creation to connect with her audience, revealing the resilience needed to navigate brand management and financial hurdles on Amazon.

Sim Mahon, an eight-figure seller with six private label brands, joins forces with Matt Kostan, who leads consumer insights at ProductPinion, and Peter-Paul Maan from Intellivy. They dive into strategies for optimizing Amazon listings, focusing on main images to boost conversions. The trio discusses the importance of consumer research in product development, effective presentation, and addressing buyer objections. They also explore innovative visuals and the impact of emotional connections in marketing, ultimately revealing how visual elements can significantly enhance sales.

Dr. Ellis Whitehead, a data scientist specializing in AI, discusses the revolutionary custom large language model, Rufus, designed to enhance the shopping experience on Amazon. Oana Padurariu shares insights on optimizing product listings, emphasizing key attributes like gluten-free options. Jeffrey Anderson, a former Amazon seller, highlights challenges in advertising with Rufus, particularly regarding reporting features. All three guests explore the power of reinforcement learning, showing how Rufus continually evolves through customer feedback, dramatically improving personalized shopping.

Seller Sessions - The Man Behind the Honeymoon   In this episode of Seller Sessions, Danny McMillan welcomes Anthony Lee, the innovator behind the term "honeymoon period" in the world of Amazon FBA. Anthony dives into the history of this ranking strategy, clarifying misconceptions and discussing its evolution, while touching on advanced topics related to Amazon algorithms and the role of AI in e-commerce.   The Honeymoon Period Debunked Anthony discusses the origins of the "honeymoon period," a concept he coined around 2015 when data showed unusual ranking activity in Amazon listings around the six-month mark. Initially, it appeared that there was a grace period where rank was closely tied to sales history, leading to faster ranking boosts for new products. However, over the years, as Amazon’s algorithms shifted towards keyword relevance, this phenomenon became outdated. Today, relying on the honeymoon period as a ranking strategy can be risky, as Amazon’s focus is now on more sophisticated factors such as relevance and real-time data.   Understanding Amazon's Cold Start Anthony explains how Amazon's "cold start" period, originally lasting up to seven days, has shortened dramatically. This cold start phase allows the algorithm to gather enough data on a product to understand its relevance, but it is no longer something sellers can easily game. He emphasizes that many outdated strategies, such as manipulating sales velocity during this time, no longer yield the results they once did.   The Importance of Attributes and AI The conversation highlights how attributes—both front-end (keywords, titles) and back-end (image metadata, product details)—are becoming critical to Amazon's ranking engine. Anthony reveals how tools like Amazon's AI-powered Recognition and Comprehend can analyze product images and listings to assess relevancy and performance. Sellers should optimize both their text and images to align with Amazon's ever-evolving search algorithms. Anthony also hints at the future of e-commerce with AI, as more sophisticated machine learning models like Cosmo and AtroBERT help Amazon improve relevance in real-time searches.     Moving Away from Gimmicks Both Danny and Anthony criticize outdated methods like reissuing ASINs to reset rankings or over-relying on past strategies that don’t align with Amazon’s current approach. Instead, they advocate for a focus on product quality and data-driven decisions. As margins become tighter, leveraging tools and understanding Amazon's new algorithmic systems—like knowledge graphs and semantic models—become crucial to winning in a competitive marketplace.   Conclusion Anthony Lee urges sellers to focus on building strong, high-quality products and adopt a data-driven approach to launches, rather than relying on outdated tricks. As Amazon continues to refine its search algorithms, it's essential to stay ahead of the curve by embracing new technologies and methodologies, including AI tools for product optimization.

Product Titles & Descriptions - How Amazon Plan To Take Control   In this episode of Sellers Sessions, Max Sinclair discusses major shifts for sellers, focusing on AI-driven personalization and its impact in the coming months.   Key Topics: Personalized Product Descriptions Amazon will now dynamically change product titles and descriptions to fit individual customer searches. For instance, if a user searches for “gluten-free cereal,” Amazon’s AI may push that keyword to the front of a title. This shift takes some control away from sellers, raising concerns about SEO and content optimization.   Challenges for Sellers Sellers may struggle with these automatic adjustments, as AI-driven changes could remove or rephrase important keywords. While this may feel disruptive, Max suggests that Amazon is implementing these changes because they work better for customers... Only time will tell!   AI-Generated Bullet Points Amazon is also using AI to suggest more concise and standardized product bullets. While these edits aim to create consistency, they don’t necessarily focus on increasing conversion rates, which has caused frustration among sellers.   Future of AI Agents Amazon is rolling out AI assistants like Amelia, which will help sellers with tasks like tracking metrics and escalating support issues. Max believes that, while these AI tools are still in development, they will soon become powerful resources.   The MCM Model Max introduces MCM (Multitask Pre-trained Customer Model), a new AI designed to enhance product recommendations. He predicts it will soon become essential for sellers to understand how it works.   Looking for a Free PPC Audit? Visit https://www.databrill.com