With the advent of Google moving to a paid inclusion model, many folks must be wondering what they can do to optimize their data or product feeds to make themselves findable. Whether you are in a paid vertical or free one, Google has to serve up relevant information.

So, how about putting yourself in a search engine’s shoes?

No one wants to be spammed or fed invalid data, so all information needs to be clean, verified and preferably from more than one source (all semantic Web or named entity extraction type philosophies).

From a search engine’s perspective, it wants (and needs) to serve up not just relevant information, but also information that is valid and verified from their end.

Search engines need to ensure that the user has a good experience on the site.

Details for products, and types of products, are essential to optimize the user experience and make your products appear in listings. After all, even if you have to “pay to play” in the game, you still have to know the rules of play, and have your items exist in the indexes and data sets that search engines maintain!

This may sound somewhat esoteric, but let me now jump to an example in Google Shopping.

Upon a search for “red pumps” in Google Shopping, I received the following display:

 

What you see is a very attractive display of “visually similar” items, and it helps create a great shopping experience. You may be asking yourself, “How does Google do this?”

Image recognition is still a hard problem to solve. Facial recognition was a research problem in the artificial intelligence arena in the 1990’s. Now, it is in everyday items like iphoto. Does this sound boring? Not if you can benefit from it by understanding it.

A search on Amazon provides a similar type of result, illustrated directly below.

 

Scrolling further down on the Amazon results page for “red pumps,” one then gets the option for “see visually similar items.”

Selecting a specific “visually similar” option leads to more of them, and a mechanism for honing in on the selection from a visual perspective.

 

Going back to Google shopping, I then did a search for “black dress,” which yielded the following result:

 

This time in the screenshot above, I included the search options on the left hand side. The very top item, BTW, is a check box under show only “in stock nearby.” This would be indicative of an edge over say, Amazon, or other e-tailers, were they going to directly compete with them. (I would actually believe that is a valid assumption.)

As a further incremental example in that line of reasoning, I had the luxury recently of obtaining a Nexus 7 tablet and playing with it. I did notice the absence of Amazon Prime for movies, etc., and could not even find it in the app store. (However, there was a Google version as part of the default OS and UI).

Back to going down our list on the left hand side, the other items listed are fields required for data feeds for apparel, ranging from color to brand and more. Ensuring accurate details are filled in and populated will make your items findable and appear in these eye-catching presentations.

I selected the first item on the top left hand side (the picture of the dress, not a search option) and received the following:

 

My assumption here is that Google is using results from the deprecated boutiques.com which was actually the result of the like.com acquisition by Google. It was an image recognition engine that turned to a focus on apparel such as handbags and other items with great success.

Under the “visually similar items,” I actually had a couple of pages of results. Scrolling down from the above screen capture, I have depicted the remainder of the results below:

 

Of note, is the line at the bottom of the page, which I will reiterate here in case the image is too small to read:

“Google is compensated by some of these merchants. Payment is one of several factors used to rank these results. Tax and shipping costs are estimates.”

Moving away from apparel and to electronics, I tried a search for a 60 inch LED TV.  This was a generic search, as I neglected to select the “Shopping” option.

 

The results on the right hand side yield a knowledge graph type display. Selecting the shopping option gives the typical Google shopping results, with many relevant searchable fields on the right hand side.

 

In this category, however, selecting an item does not give “visually similar” results as they are not in the apparel category.

However, the moral of the story is as follows: Supply as much accurate information as possible in any data feed sent to Google or other search or shopping engines. Ensure you add structured/semantic markup to your webpages and that it matches the data supplied in your feeds. Also, make sure you have good clean images and they are also marked up on your webpages.

For Google image search alone, it states they are using a combination of both computer vision techniques as well as text and semantic markup.

Google’s recent post, “On Web Semantics,” made it very clear that adding Semantic Markup is the professional thing to do. Add as much valid markup as possible, ensuring the information is displayed to both bots and users alike! It is leveraged by all the major search and social engines.


Opinions expressed in this article are those of the guest author and not necessarily Search Engine Land. Staff authors are listed here.


About The Author

Barbara Starr of SemanticFuse is a semantic strategist and software engineer, providing semantic SEO and other related consulting services. Starr is a technology expert and software designer specifically in the semantic search and semantic Web arenas. She worked as Principal Investigator for SAIC on the ARDA ACQUAINT program, which was the genesis for Watson at IBM. She also worked on the DARPA HPKB program, which was one of the precursors to the Semantic Web. She is the founder of the Semantic Web Meetup in San Diego, CA, as well as several other meetup groups. She is a governing board member of the Semantic Computing Consortium and is industry chair for IEEE International Conference on Semantic Computing.

 

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