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 However, over the years, as Amazon’s algorithms shifted towards keyword relevance, this phenomenon became outdated.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He emphasizes that many outdated strategies, such as manipulating sales velocity during this time, no longer yield the results they once did.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Both Danny and Anthony criticize outdated methods like reissuing ASINs to reset rankings or over-relying on past strategies that don’t align with Amazon’s current approach.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.
