This episode debunks the long-held belief of an Amazon 'honeymoon period' for new products. Instead, it introduces the concept of the 'cold start problem' and explains how Amazon's A9 algorithm, specifically through its 2023 patent, uses prior predictions and behavioral data to rank new items. Ecommerce operators will learn how to optimize their listings from day one to ensure initial visibility and gather crucial user interaction data.
Key takeaways
The Amazon 'honeymoon period' is a myth; instead, new products face a 'cold start problem' due to a lack of user data.
Amazon's A9 algorithm uses 'prior predictions' based on similar products' attributes and performance to initially rank new items.
Accurate and complete product attributes, both front-end and back-end, are crucial for optimizing how Amazon's machine learning model perceives and ranks your new product.
The system quickly transitions from prior predictions to actual user interaction data (clicks, purchases) to determine a product's rank, so early engagement is key.
Understanding the cold start mechanism is essential for new product success on Amazon, emphasizing proactive listing optimization over relying on a temporary boost.
The Cold Reality of the Honeymoon Period & External Traffic Part 1 Now let's get to the fun part. Who hasn't heard of the Honeymoon Period? Follow along via video Everyone in the Amazon industry has heard about the so-called "Honeymoon Period," and after years of crafting strategies around an observation (by Anthony Lee in 2015, which he himself, as a very smart man, denounced using scientific literature), it is time for us to try to get closer to the truth. And that is why in the article, you will find references to 15 scientific papers and 2 patents on the A9 Algorithm - the one from 2022 and the updated one from November 2023. Before anyone starts getting concerned about the latest patent update being from almost a year ago, know that semantic matching, BERT, and COSMO will be presented in the article (and Danny's video). The reason is simple: we have the baseline, and what gets added are layers. You will see how the move Using LLMs for processing data and updating rankings in (close to) real-time matches perfectly with the COSMO framework and what RUFUS is currently doing with the results. The scope of the video is to allow you to peek at
What does this episode say about amazon & marketplaces?
The Amazon 'honeymoon period' is a myth; instead, new products face a 'cold start problem' due to a lack of user data.
What does this episode say about organic & seo?
Amazon's A9 algorithm uses 'prior predictions' based on similar products' attributes and performance to initially rank new items.
What does this episode say about product & merchandising?
Accurate and complete product attributes, both front-end and back-end, are crucial for optimizing how Amazon's machine learning model perceives and ranks your new product.
What does this episode say about amazon & marketplaces?
The system quickly transitions from prior predictions to actual user interaction data (clicks, purchases) to determine a product's rank, so early engagement is key.
What does this episode say about amazon & marketplaces?
Understanding the cold start mechanism is essential for new product success on Amazon, emphasizing proactive listing optimization over relying on a temporary boost.
