Bulletproof Your Top of Search Strategy By Understanding Amazon’s A9

Posted: May 10

The Latest Updates on Amazon’s A9 Algorithm and How To Take Advantage of Them

Klaidas Siuipys

Let’s talk Amazon Strategy | $50M+ in Amazon Sales Growth | Founder @ AMZ bees | Amazon Expert for 7+ Years


Let’s face it – It’s good to be on top. (Get your head out of the gutter; we’re talking about selling on Amazon. 


When saying the words “Top of Search” *(ToS) to anyone that sells on Amazon, there’s an immediate reaction in their mind. Essentially, it’s where everyone wants to be – whether organically or with their ads.


That makes sense since it’s been said that 80% of all clicks come from the first page of search. In fact, some surveys indicate that 70% of shoppers don’t go past the first page. This is obviously disconcerting as a merchant if you’re listing isn’t ranking well. 


The top five positions for most keywords often capture 75% or more of the clicks from search. And with top positions competing with “Highly Rated” and “Amazon’s Choice” sections, they become all the more important.

For this reason, we believe ToS ad campaigns should be an essential part of an overall strategy. This is greatly improved by using the ToS Impression Share metric. It works simply; the metric shows your product’s total percentage of TоS impressions based on the total ToS it was eligible for. 


If your impression share is low, you adjust your TоS placements by increasing the bid modifier. The magic, however, is in knowing which keywords you should be in top-of-search for

A Brief Primer On The History Of Amazon’s A9

Sellers have attempted to “manipulate” the actions of Amazon’s A9 algorithm in their favor since the platform allowed third-party selling. This, coupled with a determination to create an atmosphere that hooks customers and makes them buy more, has led to several shifts in its evolution.


Once upon a time, Amazon determined the worthiness of a rank position through conversion rate. This made the most sense; the best way to rank “popularity” would be from how often a product was found for a keyword and purchased as a shopping platform.


And sellers who caught onto this were handsomely rewarded when they used it to their advantage. In those days, you could take a very competitive product, identify the most common and largest keyword for it (think “garlic press” or “yoga mat”), sell 50 to 100 units in one day with a 100% off coupon code, and rank top ten for months to come.


Over time, however, as the platform grew – even without the blatant manipulation – it became clear that the “conversion-rate-forward” model was not the most efficient. Amazon could do better when serving customers their browse results.


That’s when the marketplace started experimenting with the back-end search terms. It typically had five spaces limited to a hundred or so characters in the early days. Then Amazon changed it to a 2,000-character limit for a time.


During that period, Amazon might have been doing one of two things:

  1. Allowing sellers to determine their product’s relevance by giving them a maximum allocation of indexable keywords.
  2. Collecting data on what sellers believed were relevant keywords, knowing they would revert back to less character space soon.


Regardless of the case, it wasn’t long before search terms were restricted to the 249-byte limit we see today.


These experiments and changes were because Amazon was slowly shifting to a more “relevance-forward-model” for the A9 algorithm. Taking a page out of Google’s book, Amazon was determined to make search query results more relevant to the consumer.

Relevance Becomes King

Since those early days, “keyword relevance” has become the primary driving factor in keyword rank. In simple terms, keyword relevance is how matched a keyword or key phrase is to the products on Amazon’s catalog.


See, whereas before, the goal was just to show people what the most popular products were for a searched term, now it’s specifically focused on how matched the term is to that listing.


But why would Amazon do this? If people are on the platform to purchase products, how does this shift help?


In their infinite wisdom (i.e., customer data and behavior points), Amazon has determined that presenting product listings by relevance leads to more sales over time. To illustrate, let’s search for a “diamond bracelet.”

Outside of the non-organic placements (sponsored products, “from our brands,” and “top-rated”), the top results are mostly genuine, expensive, authentic diamond bracelets. And yet they have BSRs that range from 250k to 900k+. However, you’ll find fake diamond bracelets farther down the page, with thousands of reviews and BSRs in the 5k to 10k range.


If the algorithm was still all about conversions and only focused on buyer intent, the top results would be the fake bracelets that get way more sales. However, that wouldn’t be totally relevant, and many shoppers would simply assume Amazon doesn’t carry real diamond jewelry.


This is the basis for the “relevance engine” that continues to evolve.

Adding Signals

Relevance has been updated over time to include different “signals.” These on and off-page actions show Amazon that a product is relevant for a key phrase. These are things like:


  • Click through from browse page
  • Click through from competitor listing
  • Referring URL
  • Wishlisting
  • Shares
  • Add to carts
  • Purchases
  • Reviews
  • Review votes
  • Relevant keywords in association with the browse node/sub-category you are currently in


And likely many more. We can also expect these signals to continue to be added to and evolve in their weighting. Essentially, on-page and off-page activity that can be attributed to a keyword search gives Amazon the needed data to determine if a product listing most closely matches the search intentions of the shopper.


Not the purchase intention but the search intention. 


Maybe the potential buyer is just doing research. They might be considering a gift or comparing in-store prices with online prices. Regardless of the reason, the relevance engine uses their behavior to determine what order products that match the assumed intention should be in.


All of the signals have their own “weighting,” otherwise, they all impact rank in varying significance levels. And that weighting is impacted by a time delay. That means that the more time that passes between activities, or from one activity to the next, will lessen the increased relevance effects.


If a product hardly gets clicked on from browsing pages, or hardly gets added to the cart, or hardly gets purchased relative to the other metrics, then actions that may have boosted keyword relevance will do so to a lesser degree.


In addition to these relevance boosting activities, Amazon has also added negative relevance modifiers. Amazon pays attention to how often products are not clicked on or chosen in relation to other products on the browse page.


Furthermore, the algorithm has a “hunger score.” It determines what listings and in what order should show up from searches on the home page. Since a category hadn’t been chosen yet, there was little for Amazon to go on with generic search phrases.


And finally, Amazon does, in fact, have what we’ve referred to as a “honeymoon period” baked into the relevance engine, which their developers and data scientists refer to as the “cold start period.” This is wherein, in the absence of relevant data (new listing or new listing content), the algorithm has to take what content is available and combine that with similar historical references and best guesses.

Making Sense Of The Algorithm Confusion

As you can clearly see, the A9 algorithm is complex. But that doesn’t mean it can’t be understood. Essentially, everything that impacts rank can be impacted by one very simple factor: customer buying behavior.


All relevance signals involve customer activity. Where did they come from? What were they looking for? What did they find? Did they seem to like what they found? Did they find something they liked more? Did they tell anyone about it? Did they stay on the page to learn more? Did they realize they were actually looking for something else?


When you view the search algorithm through this lens, you can see how it colors everything you do, how you optimize your listings, how you conduct keyword research, and how you run ads.


We’ve made it а full circle to the beginning point, which is why a top-of-search strategy is essential and how the ToS IS metric can help.


Use tools like the Brand Analytics Search Terms tool, Search Analytics dashboard, or, if you have it, the Opportunity Explorer to determine which keywords are the most relevant and important to shoppers from your target audience.


Then use ToS IS to determine your current placement ratio in relation to competitors on top-of-search ads for those terms. Finally, run campaigns that place your listings on the top of the browse page for those terms. 


Don’t hesitate to contact AMZ Bees, too. We’ll help you develop the best top-of-search strategy for your products. Ideally, your organic positions will rise over time to bolster your ad positions, and your listings can dominate your competitors.

Interested in conquering Amazon together?


AMZ Bees is an e-commerce agency that grows and manages brands on Amazon with advanced PPC strategy, listing conversion optimisation and problem solving.


Email Hello@amzbees.com
Phone number 1-231-825-3486



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