This episode dives deep into the Amazon A9 algorithm, explaining how it uses behavioral features like popularity and customer preferences, alongside probabilistic context-free grammar and logistic regression models, to deliver highly relevant search results. It provides a practical framework—the 5-point relevance system—to optimize product listings for profitable launches by focusing on keyword relevance, helping Amazon sellers reduce PPC spend and make informed product decisions.
Key takeaways
Understand that Amazon's A9 algorithm prioritizes behavioral features (conversion rate, popularity, Prime status, sales velocity) over static product data, so optimize for customer behavior in addition to keywords.
Utilize a 5-point relevance system for keywords, categorizing them from irrelevent to highly-relevant product types to ensure PPC campaigns and product launches target genuinely interested customers.
Differentiate between product type and modifiers in search queries; optimize product titles and descriptions to align with how Amazon's A9 algorithm interprets these distinctions for improved search ranking.
Avoid using broadly related or indirectly relevant keywords to boost volume for new product launches, as this often leads to failed campaigns due to low conversion rates.
Leverage Amazon's internal logic, including probabilistic context-free grammar (PCFG) and logistic regression models, to refine keyword targeting and product categorization, even if it's not explicitly visible in the seller interface.
On the episode I bring in Dan Ashburn and Adam Heist to discuss some of our views and observations on the changes over the last few years. A lot of seller at the top of search have sold, whilst a good chunk come back as sellers, and some as service providers over time... And with the higher walls built around selling on Amazon, is there a shift in volume; of new people coming through? Sometimes, in our community we forget we are in an echo chamber. Of course, there will be plenty of sellers that do not engage directly within and some do not even know it is there as they do not listen to podcasts, Facebook groups or attend networking events. Adam Heist Adam "Heist" Runquist first discovered the Amazon marketplace 4 years ago after being on the executive team of a leading outdoor products brand. After taking that business from 8 to 9 figures - and discovering the power of marketplace channels and physical products brands in the process - Adam has since started, sold and invested in multiple Amazon native brands. In addition to leading and launching his own brands, Adam runs the YouTube Channel "Adam Heist" and is an investor and advisor to Amazon aggregator D1 Brands (who just raised $123M). Dan Ashburn Dan and his industry-leading team are responsible for delivering 8-figures in annual sales on Amazon. Operating in online marketing for 17 years, Dan is an international speaker, advisor and co-founder of Titan Network.
What does this episode say about amazon & marketplaces?
Understand that Amazon's A9 algorithm prioritizes behavioral features (conversion rate, popularity, Prime status, sales velocity) over static product data, so optimize for customer behavior in addition to keywords.
What does this episode say about paid acquisition?
Utilize a 5-point relevance system for keywords, categorizing them from irrelevent to highly-relevant product types to ensure PPC campaigns and product launches target genuinely interested customers.
What does this episode say about analytics & attribution?
Differentiate between product type and modifiers in search queries; optimize product titles and descriptions to align with how Amazon's A9 algorithm interprets these distinctions for improved search ranking.
What does this episode say about conversion & cro?
Avoid using broadly related or indirectly relevant keywords to boost volume for new product launches, as this often leads to failed campaigns due to low conversion rates.
What does this episode say about amazon & marketplaces?
Leverage Amazon's internal logic, including probabilistic context-free grammar (PCFG) and logistic regression models, to refine keyword targeting and product categorization, even if it's not explicitly visible in the seller interface.
