SEO
How To Use Amazon Hidden Keywords To Increase Product Discoverability
Amazon is the biggest name in the game for selling products online. But with 1.9 million selling partners, the competition is fierce.
To claim your market share, you need to show up in the searches people are performing.
And that starts with keywords.
Just as if you were optimizing a webpage for Google, you need to include the right words and phrases on your product page to ensure you’re showing up in relevant searches.
Your titles, features, and descriptions must be optimized, but that’s not enough.
There’s another factor Amazon takes into account when delivering results to queries, something many third-party sellers aren’t aware of: hidden keywords.
If you’re completely unaware of what these are and how they work, or you want to discover the secret to using them to your advantage, you’re in the right place.
In this piece, we’ll take an in-depth look at Amazon’s hidden keywords and show you everything you need to know to put them to work for your Amazon store.
What Are Amazon’s Hidden Keywords?
Hidden keywords, sometimes called backend keywords, are any words related to your product that aren’t included in the title or description.
For example, they’re the terms Amazon returns after a sponsored product ad shows for an item, the ad is clicked on, and the product is purchased, or the queries a customer entered into an Amazon Search.
They’re any words related to your product that aren’t in the product title or description. These could be generic terms or synonyms for your product.
And they should be included in the 250 characters per field (up to five fields) that you supply to Amazon to help boost discoverability in their search results pages.
So, search terms, right? Not exactly. I know this isn’t very clear, so we’ll try clarifying things.
(And yes, the fields in which you enter these hidden keywords in the Amazon UI are also called “Search Terms” to make things a bit more confusing.)
Plus, the report you download from Amazon for reporting on these search terms for advertising efforts is called a “Search Term Report.”
In Google paid search, we’d call it a “Search Query Report,” which seems to make more sense, but apparently, Amazon disagrees.
Calling hidden keywords just “keywords” also gets confusing, especially when choosing keywords for sponsored product campaigns.
And while I hate the name with every search bone in my body, Amazon calls them hidden keywords, which does help to differentiate.
How To Think About Hidden Keywords
Here’s the best way I can think of to help you determine how to think about these terms, how you might best use them, and where you might source future terms from:
Remember the party game Taboo? You broke into teams and pulled a card with a word or object.
Your goal was to get your partner or team to guess the word without saying it or several other restricted-related terms before time ran out.
If you said a forbidden word, the other team would “buzz” you, and your turn was over.
For example, the main word is “football,” but you can’t say football, touchdown, end zone, pigskin, or NFL – what words would you say to get your team to say “football”? Those would be your hidden keywords.
Let’s use a product example.
If the item were a Michael Kors shoe, you’d include the brand name, type of shoe, size, and attributes like a color or pattern all in the title.
And if you couldn’t get all that in the title, it should be in the product description, alongside additional details like material type or comfort.
Those details in the title and description are not what you’d want to use for hidden keywords. Instead, you want to use terms that would help someone find your product if they hadn’t searched for what you visibly provided on the page.
You might, in this case, try to use: slip-on evening closed toe under $100.
Amazon Help gives these examples:
But even those examples wouldn’t begin to fill the first search term box of 250 characters. It can be quite difficult to fill each search term slot without resorting to extreme stuffing, especially if you’re manually inputting.
Choosing Hidden Keywords
Choosing hidden keywords is where I see the most cross-over between search on search engines and search on Amazon. What tools can you use on one for the other?
Many blogs recommend the Google Keyword Planner, Keywordtool.io, Ubersuggest, or search query reports from Shopping Campaigns.
I don’t disagree with those suggestions at all – but only for generating ideas or starting points.
The way people search on Amazon and how Amazon surfaces results differ from Google, so the best way to populate and perpetuate this field is using data from Amazon or your own listings whenever possible.
That means a Search Terms Report, a client-provided list, product details, attributes, or features will work best in compliance and upkeep.
Like search keyword lists or text ads, you can’t include inaccurate or misleading information, promotions (like buy one get one), subjective claims, or profanity.
You use a single space to separate terms and nothing else, which makes the search person in me cringe (I want to add that comma or semicolon so much).
This also means that when you review a bunch of hidden keywords for a product, it looks like a nonsensical line of gobbledygook, even if you follow best practices and use a logical order with your best keywords first.
Also, thanks to the good old “+variants” exercise that Google has put us search folks through, you no longer have that reflex to put in common misspelling, title cases, and pluralization.
The same goes for hidden keywords, making them even more difficult to add, especially by the time you reach search term box four and run out of ideas.
However, one big difference I tend to forget as a search person is that these hidden keywords don’t hold relevancy or scores or rank the same way they do on Google.
That’s why you should change them regularly to keep up with incoming queries and impressions on Amazon.
This keeps your product pages more likely to be listed on a search results page.
This is particularly true if Amazon is finding some of the terms you submitted as not relevant or not using them. If this is the case, replace them and re-submit.
Adding Hidden Keywords
Now that your hidden keywords are selected, it’s time to add them to your product. Here’s how to do that on a per product basis in the Amazon Seller Central UI.
- Log in to Seller Center and click the Inventory tab.
- On the right, look for the “edit” button and click it.
- You will see the “offer” tab; click on “keywords” to open the hidden keywords section.
This can be time-consuming, but if you have an army of content writers or interns to do it for you, have at it. If not, you may want to brew a new pot of coffee because this could take a while.
You may not like the re-assess and adjustment time, though, especially if you have a catalog size of more than a few hundred products.
You might find it worthwhile to investigate your feed capabilities, whether you’re using a feed tool provider or generating your product feeds in-house and sending them to Amazon.
Dynamic generation possibilities can scale this process for sellers with large catalogs depending on the sophistication level.
The output would more closely resemble my earlier gobbledygook comment than if a human were to enter them manually, but again, scale.
How To Check If Your Hidden Keywords Got Added
Unfortunately, the only way to check whether your hidden keywords have been added is through random spot checks. And unfortunately, this is a manual process.
Copy the entire string from a search terms box (wait at least 24 hours after submittal before doing this), and search for it on Amazon.
If the product listing that is supposed to be associated with those search terms appears, it’s working. If not, try another group of terms from another search term box and repeat.
But what if the product listing still doesn’t appear? It may be that not all the terms you provided were used (there may have been an editorial or duplicate error), or you need to continue through all five boxes.
I’ve seen cases where only one of the five boxes is picked up, indexed, and used. As you can see, it’s not a great system in terms of being able to track and adjust.
Fun tidbit: If you remember Yahoo SSP feeds (also known as paid inclusion), this process and indexation may ring a bell. In 2008, you sent content via a feed to supplement Yahoo organic search results meant possibly faster and more frequent information than a crawl.
Final Thoughts On Hidden Keywords
Hopefully, by this point, you’ve developed a working understanding of Amazon’s Hidden Keywords and how to add them on the backend to generate more traffic to your product pages.
Finding the perfect mix for your needs is a bit labor-intensive but is well worth the effort.
Just remember to keep up with it. Keep testing and identifying which keywords are working and which ones aren’t.
Replace the underperformers with new keywords until you find the perfect mix. And then do it all again.
Amazon is an amazing tool for online retailers. And using backend keywords is a great way to ensure you’re receiving the traffic – and making the sales you need.
More Resources:
Featured Image: Krakenimages.com/Shutterstock
SEO
How Compression Can Be Used To Detect Low Quality Pages
The concept of Compressibility as a quality signal is not widely known, but SEOs should be aware of it. Search engines can use web page compressibility to identify duplicate pages, doorway pages with similar content, and pages with repetitive keywords, making it useful knowledge for SEO.
Although the following research paper demonstrates a successful use of on-page features for detecting spam, the deliberate lack of transparency by search engines makes it difficult to say with certainty if search engines are applying this or similar techniques.
What Is Compressibility?
In computing, compressibility refers to how much a file (data) can be reduced in size while retaining essential information, typically to maximize storage space or to allow more data to be transmitted over the Internet.
TL/DR Of Compression
Compression replaces repeated words and phrases with shorter references, reducing the file size by significant margins. Search engines typically compress indexed web pages to maximize storage space, reduce bandwidth, and improve retrieval speed, among other reasons.
This is a simplified explanation of how compression works:
- Identify Patterns:
A compression algorithm scans the text to find repeated words, patterns and phrases - Shorter Codes Take Up Less Space:
The codes and symbols use less storage space then the original words and phrases, which results in a smaller file size. - Shorter References Use Less Bits:
The “code” that essentially symbolizes the replaced words and phrases uses less data than the originals.
A bonus effect of using compression is that it can also be used to identify duplicate pages, doorway pages with similar content, and pages with repetitive keywords.
Research Paper About Detecting Spam
This research paper is significant because it was authored by distinguished computer scientists known for breakthroughs in AI, distributed computing, information retrieval, and other fields.
Marc Najork
One of the co-authors of the research paper is Marc Najork, a prominent research scientist who currently holds the title of Distinguished Research Scientist at Google DeepMind. He’s a co-author of the papers for TW-BERT, has contributed research for increasing the accuracy of using implicit user feedback like clicks, and worked on creating improved AI-based information retrieval (DSI++: Updating Transformer Memory with New Documents), among many other major breakthroughs in information retrieval.
Dennis Fetterly
Another of the co-authors is Dennis Fetterly, currently a software engineer at Google. He is listed as a co-inventor in a patent for a ranking algorithm that uses links, and is known for his research in distributed computing and information retrieval.
Those are just two of the distinguished researchers listed as co-authors of the 2006 Microsoft research paper about identifying spam through on-page content features. Among the several on-page content features the research paper analyzes is compressibility, which they discovered can be used as a classifier for indicating that a web page is spammy.
Detecting Spam Web Pages Through Content Analysis
Although the research paper was authored in 2006, its findings remain relevant to today.
Then, as now, people attempted to rank hundreds or thousands of location-based web pages that were essentially duplicate content aside from city, region, or state names. Then, as now, SEOs often created web pages for search engines by excessively repeating keywords within titles, meta descriptions, headings, internal anchor text, and within the content to improve rankings.
Section 4.6 of the research paper explains:
“Some search engines give higher weight to pages containing the query keywords several times. For example, for a given query term, a page that contains it ten times may be higher ranked than a page that contains it only once. To take advantage of such engines, some spam pages replicate their content several times in an attempt to rank higher.”
The research paper explains that search engines compress web pages and use the compressed version to reference the original web page. They note that excessive amounts of redundant words results in a higher level of compressibility. So they set about testing if there’s a correlation between a high level of compressibility and spam.
They write:
“Our approach in this section to locating redundant content within a page is to compress the page; to save space and disk time, search engines often compress web pages after indexing them, but before adding them to a page cache.
…We measure the redundancy of web pages by the compression ratio, the size of the uncompressed page divided by the size of the compressed page. We used GZIP …to compress pages, a fast and effective compression algorithm.”
High Compressibility Correlates To Spam
The results of the research showed that web pages with at least a compression ratio of 4.0 tended to be low quality web pages, spam. However, the highest rates of compressibility became less consistent because there were fewer data points, making it harder to interpret.
Figure 9: Prevalence of spam relative to compressibility of page.
The researchers concluded:
“70% of all sampled pages with a compression ratio of at least 4.0 were judged to be spam.”
But they also discovered that using the compression ratio by itself still resulted in false positives, where non-spam pages were incorrectly identified as spam:
“The compression ratio heuristic described in Section 4.6 fared best, correctly identifying 660 (27.9%) of the spam pages in our collection, while misidentifying 2, 068 (12.0%) of all judged pages.
Using all of the aforementioned features, the classification accuracy after the ten-fold cross validation process is encouraging:
95.4% of our judged pages were classified correctly, while 4.6% were classified incorrectly.
More specifically, for the spam class 1, 940 out of the 2, 364 pages, were classified correctly. For the non-spam class, 14, 440 out of the 14,804 pages were classified correctly. Consequently, 788 pages were classified incorrectly.”
The next section describes an interesting discovery about how to increase the accuracy of using on-page signals for identifying spam.
Insight Into Quality Rankings
The research paper examined multiple on-page signals, including compressibility. They discovered that each individual signal (classifier) was able to find some spam but that relying on any one signal on its own resulted in flagging non-spam pages for spam, which are commonly referred to as false positive.
The researchers made an important discovery that everyone interested in SEO should know, which is that using multiple classifiers increased the accuracy of detecting spam and decreased the likelihood of false positives. Just as important, the compressibility signal only identifies one kind of spam but not the full range of spam.
The takeaway is that compressibility is a good way to identify one kind of spam but there are other kinds of spam that aren’t caught with this one signal. Other kinds of spam were not caught with the compressibility signal.
This is the part that every SEO and publisher should be aware of:
“In the previous section, we presented a number of heuristics for assaying spam web pages. That is, we measured several characteristics of web pages, and found ranges of those characteristics which correlated with a page being spam. Nevertheless, when used individually, no technique uncovers most of the spam in our data set without flagging many non-spam pages as spam.
For example, considering the compression ratio heuristic described in Section 4.6, one of our most promising methods, the average probability of spam for ratios of 4.2 and higher is 72%. But only about 1.5% of all pages fall in this range. This number is far below the 13.8% of spam pages that we identified in our data set.”
So, even though compressibility was one of the better signals for identifying spam, it still was unable to uncover the full range of spam within the dataset the researchers used to test the signals.
Combining Multiple Signals
The above results indicated that individual signals of low quality are less accurate. So they tested using multiple signals. What they discovered was that combining multiple on-page signals for detecting spam resulted in a better accuracy rate with less pages misclassified as spam.
The researchers explained that they tested the use of multiple signals:
“One way of combining our heuristic methods is to view the spam detection problem as a classification problem. In this case, we want to create a classification model (or classifier) which, given a web page, will use the page’s features jointly in order to (correctly, we hope) classify it in one of two classes: spam and non-spam.”
These are their conclusions about using multiple signals:
“We have studied various aspects of content-based spam on the web using a real-world data set from the MSNSearch crawler. We have presented a number of heuristic methods for detecting content based spam. Some of our spam detection methods are more effective than others, however when used in isolation our methods may not identify all of the spam pages. For this reason, we combined our spam-detection methods to create a highly accurate C4.5 classifier. Our classifier can correctly identify 86.2% of all spam pages, while flagging very few legitimate pages as spam.”
Key Insight:
Misidentifying “very few legitimate pages as spam” was a significant breakthrough. The important insight that everyone involved with SEO should take away from this is that one signal by itself can result in false positives. Using multiple signals increases the accuracy.
What this means is that SEO tests of isolated ranking or quality signals will not yield reliable results that can be trusted for making strategy or business decisions.
Takeaways
We don’t know for certain if compressibility is used at the search engines but it’s an easy to use signal that combined with others could be used to catch simple kinds of spam like thousands of city name doorway pages with similar content. Yet even if the search engines don’t use this signal, it does show how easy it is to catch that kind of search engine manipulation and that it’s something search engines are well able to handle today.
Here are the key points of this article to keep in mind:
- Doorway pages with duplicate content is easy to catch because they compress at a higher ratio than normal web pages.
- Groups of web pages with a compression ratio above 4.0 were predominantly spam.
- Negative quality signals used by themselves to catch spam can lead to false positives.
- In this particular test, they discovered that on-page negative quality signals only catch specific types of spam.
- When used alone, the compressibility signal only catches redundancy-type spam, fails to detect other forms of spam, and leads to false positives.
- Combing quality signals improves spam detection accuracy and reduces false positives.
- Search engines today have a higher accuracy of spam detection with the use of AI like Spam Brain.
Read the research paper, which is linked from the Google Scholar page of Marc Najork:
Detecting spam web pages through content analysis
Featured Image by Shutterstock/pathdoc
SEO
New Google Trends SEO Documentation
Google Search Central published new documentation on Google Trends, explaining how to use it for search marketing. This guide serves as an easy to understand introduction for newcomers and a helpful refresher for experienced search marketers and publishers.
The new guide has six sections:
- About Google Trends
- Tutorial on monitoring trends
- How to do keyword research with the tool
- How to prioritize content with Trends data
- How to use Google Trends for competitor research
- How to use Google Trends for analyzing brand awareness and sentiment
The section about monitoring trends advises there are two kinds of rising trends, general and specific trends, which can be useful for developing content to publish on a site.
Using the Explore tool, you can leave the search box empty and view the current rising trends worldwide or use a drop down menu to focus on trends in a specific country. Users can further filter rising trends by time periods, categories and the type of search. The results show rising trends by topic and by keywords.
To search for specific trends users just need to enter the specific queries and then filter them by country, time, categories and type of search.
The section called Content Calendar describes how to use Google Trends to understand which content topics to prioritize.
Google explains:
“Google Trends can be helpful not only to get ideas on what to write, but also to prioritize when to publish it. To help you better prioritize which topics to focus on, try to find seasonal trends in the data. With that information, you can plan ahead to have high quality content available on your site a little before people are searching for it, so that when they do, your content is ready for them.”
Read the new Google Trends documentation:
Get started with Google Trends
Featured Image by Shutterstock/Luis Molinero
SEO
All the best things about Ahrefs Evolve 2024
Hey all, I’m Rebekah and I am your Chosen One to “do a blog post for Ahrefs Evolve 2024”.
What does that entail exactly? I don’t know. In fact, Sam Oh asked me yesterday what the title of this post would be. “Is it like…Ahrefs Evolve 2024: Recap of day 1 and day 2…?”
Even as I nodded, I couldn’t get over how absolutely boring that sounded. So I’m going to do THIS instead: a curation of all the best things YOU loved about Ahrefs’ first conference, lifted directly from X.
Let’s go!
OUR HUGE SCREEN
The largest presentation screen I’ve ever seen! #ahrefsevolve pic.twitter.com/oboiMFW1TN
— Patrick Stox (@patrickstox) October 24, 2024
This is the biggest presentation screen I ever seen in my life. It’s like iMax for SEO presentations. #ahrefsevolve pic.twitter.com/sAfZ1rtePx
— Suganthan Mohanadasan (@Suganthanmn) October 24, 2024
CONFERENCE VENUE ITSELF
It was recently named the best new skyscraper in the world, by the way.
The Ahrefs conference venue feels like being in inception. #AhrefsEvolve pic.twitter.com/18Yjai1Cej
— Suganthan Mohanadasan (@Suganthanmn) October 24, 2024
I’m in Singapore for @ahrefs Evolve this week. Keen to connect with people doing interesting work on the future of search / AI #ahrefsevolve pic.twitter.com/s00UkIbxpf
— Alex Denning (@AlexDenning) October 23, 2024
OUR AMAZING SPEAKER LINEUP – SUPER INFORMATIVE, USEFUL TALKS!
A super insightful explanation of how Google Search Ranking works #ahrefsevolve pic.twitter.com/Cd1VSET2Aj
— Amanda Walls (@amandajwalls) October 24, 2024
“would I even do this if Google didn’t exist?” – what a great question to assess if you actually have the right focus when creating content amazing presentation from @amandaecking at #AhrefsEvolve pic.twitter.com/a6OKbKxwiS
— Aleyda Solis ️ (@aleyda) October 24, 2024
Attending @CyrusShepard ‘s talk on WTF is Helpful Content in Google’s algorithm at #AhrefsEvolve
“Focus on people first content”
Super relevant for content creators who want to stay ahead of the ever evolving Google search curve! #SEOTalk #SEO pic.twitter.com/KRTL13SB0g
This is the first time I am listening to @aleyda and it is really amazing. Lot of insights and actionable information.
Thank you #aleyda for power packed presentation.#AhrefsEvolve @ahrefs #seo pic.twitter.com/Xe3A9MGfrr
— Jignesh Gohel (@jigneshgohel) October 25, 2024
— Parth Suba (@parthsuba77) October 24, 2024
@thinking_slows thoughts on AI content – “it’s very good if you want to be average”.
We can do a lot better and Ryan explains how. Love it @ahrefs #AhrefsEvolve pic.twitter.com/qFqWs6QBH5
— Andy Chadwick (@digitalquokka) October 24, 2024
A super insightful explanation of how Google Search Ranking works #ahrefsevolve pic.twitter.com/Cd1VSET2Aj
— Amanda Walls (@amandajwalls) October 24, 2024
This is the first time I am listening to @aleyda and it is really amazing. Lot of insights and actionable information.
Thank you #aleyda for power packed presentation.#AhrefsEvolve @ahrefs #seo pic.twitter.com/Xe3A9MGfrr
— Jignesh Gohel (@jigneshgohel) October 25, 2024
GREAT MUSIC
First time I’ve ever Shazam’d a track during SEO conference ambience…. and the track wasn’t even Shazamable! #AhrefsEvolve @ahrefs pic.twitter.com/ZDzJOZMILt
— Lily Ray (@lilyraynyc) October 24, 2024
AMAZING GOODIES
Ahrefs Evolveきました！@ahrefs @AhrefsJP #AhrefsEvolve pic.twitter.com/33EiejQPdX
— さくらぎ (@sakuragi_ksy) October 24, 2024
Aside from the very interesting topics, what makes this conference even cooler are the ton of awesome freebies
Kudos for making all of these happen for #AhrefsEvolve @ahrefs team pic.twitter.com/DGzk5FSTN8
— Krista Melgarejo (@kimelgarejo) October 24, 2024
Content Goblin and SEO alligator party stickers are definitely going on my laptop. @ahrefs #ahrefsevolve pic.twitter.com/QBsBuY5Yix
— Patrick Stox (@patrickstox) October 24, 2024
This is one of the best swag bags I’ve received at any conference!
Either @ahrefs actually cares or the other conference swag bags aren’t up to par w Ahrefs!#AhrefsEvolve pic.twitter.com/Yc9e6wZPHn— Moses Sanchez (@SanchezMoses) October 25, 2024
SELFIE BATTLE
Some background: Tim and Sam have a challenge going on to see who can take the most number of selfies with all of you. Last I heard, Sam was winning – but there is room for a comeback yet!
Got the rare selfie with both @timsoulo and @samsgoh #AhrefsEvolve
— Bernard Huang (@bernardjhuang) October 24, 2024
THAT BELL
Everybody’s just waiting for this one.
@timsoulo @ahrefs #AhrefsEvolve pic.twitter.com/6ypWaTGDDP
— Jinbo Liang (@JinboLiang) October 24, 2024
STICKER WALL
Viva la vida, viva Seo!
Awante Argentina loco!#AhrefsEvolve pic.twitter.com/sfhbI2kWSH
— Gaston Riera. (@GastonRiera) October 24, 2024
AND, OF COURSE…ALL OF YOU!
#AhrefsEvolve let’s goooooooooooo!!! pic.twitter.com/THtdvdtUyB
— Tim Soulo (@timsoulo) October 24, 2024
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There’s a TON more content on LinkedIn – click here – but I have limited time to get this post up and can’t quite figure out how to embed LinkedIn posts so…let’s stop here for now. I’ll keep updating as we go along!
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