This episode debunks the myth of an automatic 'honeymoon period' for new products on Amazon, revealing that Amazon's A9 algorithm no longer provides an inherent boost. Instead, it leverages advanced machine learning, including Bayesian formulas and semantic search (BERT, COSMO), to evaluate new products using historical data and external signals. E-commerce operators launching products on Amazon must focus on high-quality content, accurate attributes, and strategic advertising, rather than relying on a predetermined grace period, to effectively rank and gain visibility.
Key takeaways
The concept of a guaranteed 'honeymoon period' for new product launches on Amazon is outdated; the A9 algorithm no longer provides an automatic ranking boost.
Amazon's algorithm uses sophisticated machine learning models (e.g., ColdGuess, COSMO) and external traffic signals to assess new products, effectively ending the 'gaslight ranking' era.
Successful Amazon product launches now require focused efforts on high-quality product content, precise attribute selection, and strategic advertising campaigns from day one.
Leverage external traffic and targeted on-site advertising, prioritizing relevancy over search volume for keywords, to generate initial engagement and behavioral signals.
Implement structured ranking campaigns and strategically analyze competitors to optimize product exposure and maintain an effective campaign structure.
Danny and Oana return for part two. In this episode, Danny goes more in-depth on Fig. 5 of the patent; this time on how it impacts external traffic and how the system punishes giveaways. Mastering the Cold-Start System and Beyond In this episode, we focus on the other areas cold-start system and how to position new products for long-term success. We explore key strategies for overcoming the challenges of launching a product without historical user data, including the importance of attributes and machine learning to generate early traction (and impact of AI for matching etc). Why Giveaways Always Drop Off Giveaways can give an initial boost in rankings, but they often lead to a drop-off once the influx of free traffic ends. We discuss why this happens and the pitfalls of relying too heavily on giveaways, which can create unsustainable patterns that hurt your long-term performance. <span class= "yt-core-attributed-string yt-core-attributed-string--white-space
Frequently asked about this episode
What does this episode say about amazon & marketplaces?
The concept of a guaranteed 'honeymoon period' for new product launches on Amazon is outdated; the A9 algorithm no longer provides an automatic ranking boost.
What does this episode say about paid acquisition?
Amazon's algorithm uses sophisticated machine learning models (e.g., ColdGuess, COSMO) and external traffic signals to assess new products, effectively ending the 'gaslight ranking' era.
What does this episode say about organic & seo?
Successful Amazon product launches now require focused efforts on high-quality product content, precise attribute selection, and strategic advertising campaigns from day one.
What does this episode say about ai & automation?
Leverage external traffic and targeted on-site advertising, prioritizing relevancy over search volume for keywords, to generate initial engagement and behavioral signals.
What does this episode say about amazon & marketplaces?
Implement structured ranking campaigns and strategically analyze competitors to optimize product exposure and maintain an effective campaign structure.
