The talk focused on what was then the current state of the Semantic Web, as well as upcoming formats/technologies in development and the research being done in the field of semantic search.

The idea that the Semantic Web would be central to search within just a few years was met with some skepticism at the time — back then, all most folks were tracking was the adoption of Semantic Web technologies and semantic search using primarily RDFa, embedded metadata, or semantic markup.

Search Becomes Semantic & Graph-Based

The prediction that search would become increasingly semantic and graph-based has certainly proven to be more than true. Not only have the search engines since adopted schema.org as a standard along with microdata as a syntax (Facebook RDFa and Open Graph are examples), but things are now elevated to the next level in this process of adoption.

Graph Search on consumed/verified/validated information, which is a core component of the Semantic Web, is now considered key for the future of search in both search and social engines.

Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch.

Google Knowledge Graph

Google originally started their rich snippets program in 2009 and finally announced their Knowledge Graph just a couple of days before Facebook’s IPO in 2012. For their “rich snippets,” Google initially utilized their own ontology/vocabulary, then switched to schema.org standards along with the three other major search engines (now four, as Yandex recently joined the mix).

Google’s Knowledge Graph is effectively using their version of graph search; however, it is largely focused on information and facts rather than searcher interests and social interactions. While there are some social signals provided via Google+, Knowledge Graph search is primarily based on Wikipedia-type information and other related/verified sources.

Excitingly, this is constantly being extended. (Those of you interested in further details and additional information may want to check out wikidata.)

Search Is Fast Moving

As an example of the incredible pace at which the industry moves, look at image recognition. Image recognition was a research problem in the 1990s. Several years ago, facial recognition made its way into everyday applications like i-photo, Facebook, Picasa and even your camera. Computer vision techniques are now being applied all over the Web — especially in retail to find “visually similar” items (e.g., Google Shopping and Amazon).

Google Play also now leverages facial recognition to enhance its Knowledge Graph capabilities. It’s pretty remarkable, but we are only seeing the beginning. 

The reality is that intelligent systems are driving e-commerce and sales. We now live in an era where forward deployed inventory calendars can leverage predictive stocking, making same-day delivery of online purchases possible. Technologies like graph search, graph analytics, predictive analytics and big data are par for the course, being leveraged under the hood every time a user makes a purchase online. Google itself is a master at these technologies.

Bing Tiles

Clearly, the other search engines have adopted similar data tracking techniques and are working to leverage them as well. Bing “tiles” are their version of rich snippets; their version of the knowledge graph, or graph search, is probably Bing snapshots. (Feel free to read more on Microsoft’s graph-based repository, Satori.)

From what I can garner looking at this in the context of Bing Tags, they are taking a more social approach to Semantic and graph-based search (presumably in line with their relationships with social media sites like Facebook, LinkedIn, etc), even going so far as to allow users to control their appearance in SERPs to some extent. Bing clearly needs this strongly differentiated angle to help increase its share of the search market.

Facebook Graph Search

Finally, enter Facebook Graph Search, which utilizes Facebook’s wealth of easily filterable user data to provide searchers with results that are tailored specifically for them. As my colleague, Gerald Burnand, writes in his piece about Facebook’s Graph Search, there are numerous possibilities for how this filtered data might be used.

Recruiters might look to Facebook to narrow down potential candidates by industry (although it has a long way to go if it wants to compete with LinkedIn). Similarly, Facebook marketers could use Graph Search to learn more about common Likes/interests among their fans, making it easy to create highly-targeted micro campaigns aimed at relevant demographics. 

Understanding & Using Search Data

Today, even the end consumer has access to these resources in the form of their mobile phones, tablets and computers. Remember, every time a consumer picks up a mobile device, uses an app, or performs a search, those devices are essentially acting as sensors, tracking search queries, geolocations, and many other consumer behaviors — all of which ultimately turn into information to be used to increase conversions.

Google’s status as a market leader in search grants them access to copious amounts of invaluable user data and product information. This information — obtaining it, understanding it and applying it — is the key to their success.

There is clearly a race among these companies to better aggregate and understand huge amounts of data. The Semantic Web has come far since 2010, but improvements and new technologies are continuously being developed. What new developments do you see on the horizon?

“What are the biggest search marketing challenges you expect to face in the next two years?”

Today, I’m convinced the findings were not only accurate, they are especially relevant for retailers. Below are the top three challenges the report uncovered:

1. Integrating search marketing with other marketing
2. Understanding new search engine developments
3. Measuring how search impacts your business’ bottom line

First, let’s briefly review what’s happened in search over the past year or so.

Facebook Open Graph Search

On January 15, 2013, Facebook announced its Open Graph Search, and Danny wrote Up Close With Facebook Graph Search, introducing the search box and its initial capabilities.

Initially, I didn’t make the connection between search engine marketing, other retail marketing (couponing, loyalty programs, etc.), search engine developments/innovations and a retailer’s bottom line.

However, I’ve recently learned from Rio SEO that Facebook has been testing a few hidden, unknown features behind-the scenes. I’ll get into the details of these tests after a brief review of what led up to the situation we face in search today.

The New Meta Data That Matters

In June 2011, Google Webmaster Blog introduced Schema.org to create and support a common set of schemas for structured data markup on webpages.

In November 2011, Matt Cutts, referring to Rich Snippets (structured data), announced in  Google’s Inside Search blog:

“This change helps us choose more relevant text to use in snippets. As we improve our understanding of webpage structure, we are now more likely to pick text from the actual page content, and less likely to use text that is part of a header or menu.”

In November 2011, Webnodes released a test showing “a 30% increase in organic search engine traffic to websites using microdata (structured markup) or similar technologies.” And, around the same time, Google announced its support for GoodRelations, which revealed “there is preliminary evidence that search results with respective extensions (structured markup) get a 30% higher click-through-rate (CTR).”

In October 2012, SMX East NYC established the first Semantic SEO Track in the Agenda.

Within its first year, Schema.org had quintupled in size, exposing a labyrinth of detailed structured markup for Events, Health, Organizations, Persons, Places, Products and Reviews. The organization of schemas, a set of ‘types,’ each associated with a set of properties, are arranged in a hierarchy that only the most experienced semantic strategists could begin to figure out and properly implement (IMHO).

To give you an idea of the level of detail provided, and required, to properly label and code a “Place,” here’s a small portion of a screenshot showing how a “Place” gets filtered-down by “type” to be labeled a “City Hall” vs. a “Church.”

Vocabularies Vs. Syntax

Then came the education curve. We all started learning the difference between vocabularies like Open Graph protocol and GoodRelations vs. syntax like RDFa and Schema.org (microdata). My good friend and colleague Barbara Starr enlightened me with the fact that “they’re all basically a type of semantic markup.”

Say no more; I’m calling it “semantic markup” from now on — if that’s okay with you! Remember, I’m a marketing guy who likes to drive growth in organic search traffic and revenue, not a semantic strategist. A lot of SEOs were not initially getting it right at the time, as there were a lot of unknowns; however, for those who stuck with it –payday is right around the corner.

SEO For Facebook Open Graph Search

Here’s where it gets interesting for me, and hopefully, you’ll see the value-add once we expose the bigger picture that we believe Facebook has in-mind and has been testing for the last two years.

As a consultant, I’m in touch with a lot of agencies and SEOs on a daily basis. One of my favorite (there are too many to list here), is an unknown, brilliant sales engineer and SEO Tyler Ludwig, who hangs his ultra-white-hat with Rio SEO, the software division of Covario.

I mention Tyler because he is the one who first told me about the test Rio is doing with Facebook , and he deserves the recognition, not me.

What you are about to see requires a little imagination because this is visionary work; it hasn’t been released or exposed yet. Based on what I’ve seen, I am certain Facebook will crush-it once they drop the other shoe.

Facebook Testing On Open Graph

You’re familiar with Facebook’s Open Graph Search, right? Here’s a screen shot from my Facebook OG search on a national sporting goods retailer. The search box is in the upper left corner of your Facebook page and it drops-down as list of search results.

In this case, the retailer has been taking part in a test where Bing is powering Facebook location map pages. Here’s how it works along with some of the preliminary findings.

The Open Graph protocol (new meta data) looks like this for a location landing page (this is what a retail store location landing page should look). I hope all top retailers have one, as many I’ve surveyed use sub-standard store locators and SEO gimmicks which will eventually go away.

When users “Like” a retailer’s store location landing page, Facebook automatically creates a Local “Child Page” using structured data from the Open Graph protocol. Note the “About” and “Locations” Tabs near the bottom of page.

Take a look at the “About” page; Facebook and the retailer now have a method for using and updating business data, e.g., address, phone number, description, longitude/latitude, etc. Note Facebook links to the retailer’s store location landing page.

Take a look at the “Locations” page powered by Bing/Facebook; the retailer now has a method for using and updating business data, e.g., logo, address, phone number, longitude/latitude, etc. Note Facebook links directly to the retailer’s store location landing page.

The Takeaways

The white-hat SEO best practice checklist for retailers has grown significantly in the last few months as a result of tests like the above with Facebook and other tests conducted on sites properly coded with structured data and semantic markup.

And, guess what? Google likes it! The preliminary findings show a remarkable lift in organic traffic. Structured data is now accessible to machine-reading search engines, Web applications, in-car navigation systems, tablets, mobile devices, Apple maps, SIRI, Yelp maps, Linked Open Data and all computers!

Structured markup presents your verified business data to a Search Engine as if it were chocolate; they love it and eat it up! Search engines understand it and know how to aggregate it for a better user experience, and display it for a boost in your CTR.

Structured business data includes rich media video content, product reviews and ratings, location and contact information, business specialty details, special offers, product information, medical data… and the list goes on.

Getting back to the Forrester findings, the following appear to be critical path for retailers wanting to improve their bottom line in 2013:

1. Integrate search marketing with other retail marketing activities
2. Understand new search engine developments
3. Measure how search impacts your bottom line

Since Google has a history of applying successful strategies to solve different problems, the use of Paid Inclusion was not unexpected when it introduced Google Shopping and Product Listing Ads (PLA).

Retailers advertising in Google Shopping should know the definition of Google Product Rank is a combination of “relevance and bid price” (similar to Ad Rank, also a combination of “relevance and bid price”). What this means is merchants must use clean data in their data feeds.

What Does Clean Data Mean?

Both Product Rank in Google Shopping and Ad Rank in Google AdWords are specified as relevance multiplied by bid price. Relevance, in both cases, is defined as quality.

In the case of Google Shopping, it means all data you give Google in the data feeds must be accurate, reliable and not conflict with any other informational sources or “signals” being sent out. The more your data is verified from multiple sources, the stronger signal it will give the search engines.

Products are listed on Google Shopping based on a paid component and a quality or relevancy component. A typical example of paid ranking results for HD TVs is depicted below. You must pay to rank, but having better data quality and richer data will, in many cases, offset the bid price in terms of ranking in Google Shopping.

What End Users See In The SERPs

Google also makes the above explicit (sort of), to the end users, should they think of clicking on the unobtrusive blue button (tiny info boxes on the “Google Everything Page,” where the ads are prominent with enhanced displays such as the one below, or on the just-as-unobtrusive blue statement, “Why these products,” in Google Shopping itself).

However, the user experience is far more engaging than before, and many of the enhanced displays are stunning.

The Importance Of Data Quality

What does this mean for SEO folks wanting to appear high in SERPs while minimizing their bid price? You must adhere to the Quality Score guidelines as well as Product Quality. It is crucial to adhere to quality standards — the richer your data and the better your Quality Scores and Product Scores, the less you pay for your ad. Data quality is key!

From a search engine perspective, this makes complete sense. Search engines do not like to be spammed, and neither do users. No-one likes being deceived in Google shopping when they select an item with one price on Google Shopping and then find the price is different or out of stock when they go to the item on the page! No-one likes dirty, noisy data — from the search engines themselves right down to the end users. 

Google Shopping Clean Data Mandate

None of this is news, but if you put yourself in a search engines shoes, you can see they clearly must provide relevant results and create a great customer experience. If they can’t do this, they will lose market share. Clean, rich, well-attributed and relevant data is a must!

Paid inclusion is now a fact of life in Google Shopping, and Google made a simultaneous mandate requiring clean, relevant, rich data inputs to product feeds, data that is verifiable by cross referencing other sources. That means the following data must all be the same and in sync:

• Data on the webpage visible to humans
• On-page semantic markup
• Data in the product feed

Google’s mandate is strongly reminiscent of Semantic Web philosophy for dealing with data quality and provenance. As is the fact that Google uses some forms of Rich Snippets to expand its Knowledge Graph.

Paid Inclusion Good For Google & Merchants

Paid Inclusion has been a very effective strategy for Google, which had a strong fourth quarter, and was good for merchants as well. An example is listed below and derived from this report from Marin Software, published January 29, 2013. There are also many other reports indicating much better ROAS (Return on Ad Spend) with PLAs.

Marin Report Summary:

“… in the last year the click share of PLAs as a percent of total search clicks increased 210% as consumers increased their engagement with the ads, which appear as image results in Google Search and as product listings within Google Shopping. Google transitioned Google Shopping to a commercial model in October 2012, and the bet appears to be paying off with Marin finding that advertisers increased their share of search budgets directed towards PLAs by nearly 600% in the last quarter of 2012.”

What Does Rich Data Imply?

The easiest way to obtain a realistic understanding of data quality is to put yourself in your customers’ shoes. Customers want easily findable items and want to be able to quickly filter out SERPs they don’t need. They aren’t able to study the complexity of Data Quality, Provenance, Trust, Object Authority and the complexities of the Semantic Web .

However, adopting a basic KISS approach from a user’s perspective makes it very understandable. Think about your customers looking for black pumps.

If they go into Google Shopping, they can enter search queries that are filtered by the attribute, color property and also the size and silhouette. If merchants do not populate those fields in their data feed to Google, no matter how “clean” the data is, the items cannot possibly display in the SERPs.

Hence, it is essential to have rich, well-attributed information. Rich Data sends Rich Signals to the engines. Clearly illustrated in the image below are the filters on the left hand side. If merchants do not populate the color and other attributes, their items cannot show up in filtered search results. 

You can markup many of these attributes with properties from schema.org on your product page. It is key, however, to put them in the product feed submitted to the major CSE’s (Comparison Shopping Engines). Richly-attributed data, therefore, increases your visibility in Search Engines.

Google Merchant Data Quality Specifications

Here, we can take a look at what Google stipulates as Data Quality. The first item in the diagram below states the submitted price on the feed must match the price on the webpage. We discussed the necessity for that already.

You also must absolutely have a valid UPC or other identifier. Another important requirement is that the Product Category must be accurate, or else the product will be suspended. There are several such examples that may or may not appear obvious to a naïve user.

Looking at the Google Merchant Data Quality issues depicted below, several things stand out. Clearly, no user wants to be frustrated with a price mismatch, or items suddenly unavailable, or going to a non-existent or off-the-subject landing page when making a purchase decision on the buy URL (destination page). You can read more about the importance of this and other details on the Google webmaster site.

Key Information

To summarize the takeaways from the information above:

• Ensure your data is clean and scrubbed, with the same signals being sent from all sources
• Ensure all necessary fields are populated, like UPC, brand, product category correct
• Ensure your landing pages are valid and product availability accurately specified
• Ensure that your data is fresh and frequently updated with new changes
• Ensure that your images are properly enhanced
• Ensure that you mark up the appropriate information for rich snippets, including product videos and product images, to send stronger signals
• Ensure markup of specific items in schema such as reviews and any other items that drive traffic and increase “trust” or authority (providing you have that information visible to human readers, too)
• Ensure that your sitemap is optimized and easy for the search engines to crawl, making use of the last mod feature in your site map

Special Note: The three universities: UCSD, UCI, UCLA, are coming together to create a Semantic Computer Consortium that leverages both academic and industrial resources. They will be at the next San Diego Semantic Web Meetup (no cost to attend) I am hosting on Thursday, February 21, at 6:00 pm at the UCSD Supercomputer Center. Anyone interested in hearing Prof Shlomo Dubnov and Prof Phillip Sheu elaborate on these plans is welcome to attend.

In this article, I am going to focus on recent developments that are search engine and/or Google specific, then take a further look back in search engine history with the assumption (for you history and strategy lovers,) that a successful strategy used once, may well be used again in similar circumstances.

Image via presentermedia.com under license

Google & The Semantic Web

In the interim, since September of 2011, Google has taken increasingly more steps in becoming more semantic-web like in nature and in the migration of its SERPs, resembling those of an answer engine.

For example, Google has added explanations to the Knowledge Graph. Subsequent to that, on November 9^th, Google adopted GoodRelations as part of schema.org. Aaron Bradley wrote an excellent article about that, and you can read more details here if you missed it.

The Knowledge Graph was rolled out globally, on December 4, 2011, and various interesting changes were made to flight search and associated activities. All very interesting; however, not exactly earth shattering, but well worth noting.

Google Knowledge Graph

Google Data Highlighter

On December 12, 2012, Google rolled out a new tool, called the Google Data Highlighter for event data. Upon a cursory read, it seems to be a tagging tool, where a human trains the Data Highlighter using a few pages on their website, until Google can pick up enough of a pattern to do the remainder of the site itself.

Better yet, you can see all of these results in the structured data dashboard. It appears as if event data is marked up and is compatible with schema.org. However, there is a caveat here that some folks may not notice.

No actual markup is placed on the page, meaning that none of the semantic markup using this Data Highlighter tool is consumable by Bing, Yahoo or any other crawler on the Web; only Google can use it!

Google is essentially hi-jacking semantic markup so only Google can take advantage of it. Google has the global touch and the ability to execute well-thought-out and brilliantly strategic plans.

Image via presentermedia.com under license

You may think, how odd, why would Google go to all that effort to create standards for schema.org for all three search engines, (actually four at this point: Bing, Yahoo, Yandex and Google), and then create a tool useful only for Google? It appears such a charitable gesture on their part.

A Little Google History On Sitemaps & Schema.Org

Perhaps some history can help us understand this better. Schema.org was first announced on June 2, 2011 as the first time the three major search engines came together to produce a standard since sitemaps.org. Depicted below are the home pages of both schema.org and sitemaps.org. Note the striking similarity.

Sitemap.org & Schema.org Homepages

Even the terms and conditions appear similar, as can be seen in the figure below. Ok, so now you may be thinking, so what, they used the same template. It is much more interesting than that. As you know, Google is brilliant at applying successful strategies, and if it succeeds once, it is very likely to pursue that same tactic again. (All you historians out there know how history tends to repeat itself). Perhaps a look at where Google is going with the structured markup can be determined by what they did with sitemaps.org.

Robots.Txt File

On a similar note, www.example.com/robots.txt is a standard location for a sitemap to reside; however, Google provides the option to submit the sitemap directly. (Most search engines do, actually). Given that the search engines allow direct sitemap submission (and may even prefer it), it’s interesting in that there is no longer an actual need for a robots.txt file. Many sites do not have them.

In some sense, however, this is strongly reminiscent of the action being taken by Google with regard to the Data Highlighter for events. Net effect: it makes this information available to Google, but not necessarily to others.

Although up and down in certain situations, there seems to be a lot of controversy around this fact, you can see a recent example noted in Matt McGee’s article. (For the rest, I will leave it to historians to make the projections.)

Historically, it also seems, for both search engines and websites/webmasters alike, best practice is to adhere to standards, as that is what best practices ultimately boil down to and why standards are so important! Ultimately, they should be enforceable (one would assume).

How Small Merchants Can Remain Competitive In Google Shopping

Small Merchants can remain competitive in Google Shopping by adding rich snippets and more. Below are some mechanisms worth looking into, as it appears Google is facilitating the small merchant in Google Shopping to retain inventories that include more eclectic and interesting product offerings!

If you understand the big picture, it is easier to predict not only what to do, but how and why with regard to getting good SERP visibility. For example, I am embedding a portion of Google’s webmaster advice on rich snippets and products. You can find the link here. I checked the last update, which was December 3, 2012.

Considering how fast things are changing with schema.org microdata, there is a need for automation to remain both competitive and compliant.

On Google’s part, this would offer an ideal mechanism for small business owners with unique, boutique-style products or eclectic products to enter Google Shopping. This would enrich and augment the current inventory on Google Shopping. On the part of the small merchant, it would now be necessary to strongly consider this option.

On a similar note, I also found this link to be of interest on Google, posted the same day! It is a great guide on best practices. Note the sitemap and robots.txt stipulations as well as those on rich snippets. It also was last updated December 3, 2012.

It may be, that much of this falls on the heels of Microsoft’s Scroogled campaign, where in addition to much else, they comment on PageRank being replaced by Pay to Rank as an algorithm. It may have elements of truth, but PageRank is now reduced to only 1 of a couple of hundred signals Google uses to rank pages.

Essential & Professional Practices For 2013

Based on the information above, it seems to me that retailers can profit by following my suggestions below.

• Stick to industry standards and standards recommended by the search engines.
• Always provide unique and interesting content on your pages.
• Keep your information fresh and relevant.
• Provide clean sitemaps and make use of the <lastmod> feature.
• Mark up your pages for rich snippets, if you are not sure how to do that, engage experts to help you or look for automated software that can provide the same!

Lastly, in terms of 2013 predictions, count on the Data Highlighter being expanded to more than just events!

Before getting into some results and the impact on marketing efficiency, I received several comments in response to my last post surrounding how consumers behave within PLAs. Personally, I do not think of PLAs as ad placements like paid search because, unlike the ads in paid search, shopping results are not clearly labeled as ads.

On a normal Google search engine results page, ads are clearly labeled as ads.

But, within Shopping, there is no clear notification that results are paid placements.

Unless you click on the ‘Why these products?’ link in the upper right, in which case you will see:

If there has been no significant visible transition to users, we can assume that user behavior within Shopping / PLAs has not changed, either. After all, the nature of the product, comparison shopping, remains the same. However, looking at the distribution of traffic and transactions over time for a composite of several long-time PLA/Shopping e-tailers suggests there have been some interesting things going on:

Both tables provide insights, and I’ve highlighted the most significant metrics. Distribution of traffic tells us that the visitor inflection point occurred in July — meaning that August was the first month that Google pushed the majority of traffic into PLAs.

However, distribution of transactions shows that traffic driven by PLAs in Q3 was highly unqualified, resulting in very few relative transactions despite more volume being pushed via PLAs.

The disruptive switch could be due to a multitude of reasons, including extensive testing in the layout or the evolution of auction settings such as bid floors, both of which could have significant ramifications as the market settled into place.

Whatever the cause, it had a massively negative impact on PLA conversion rates relative to Google Shopping in Q3:

That said, by October, things appeared to settle down as PLAs steadily took over nearly 100% of traffic.

So, if we ignore the rocky Q3 and look at the aggregate shift from beginning to end of 2012, general user experience within Google Shopping has not changed too much. So what has changed?

To get some answers I reached back out to my good friend, Erick Barney, VP of Marketing at Motorcycle Superstore (MCSS) to get his thoughts and insights on the last few months of PLAs and their underlying market currents. The following is a summary of our conversation along with some quotes from Erick.

It’s worth noting that MCSS has been in the Google Shopping space for many years and had a highly optimized merchant center feed prior to the PLA switch. Since the completion of the paid click transition, they have observed a ROI via Shopping that exceeds paid search.

Despite all of the product-level details in the merchant center feed not being present in the new placement formats, Erick believes that as a category leader with an optimized feed, MCSS is benefitting from the new auction based model.

“PLAs represent Google’s effort to reduce the clutter in the Shopping experience. By charging for clicks, companies need to have a strategy surrounding product submission, feed optimization, and bid management,” says Barney.

Even with the new associated costs, PLAs have been good to MCSS. “There’s no point in getting hung up on the cost of PLAs. It is what it is. We should be grateful we got all those free clicks for so many years,” says Barney.

Category leaders and price competitive e-tailers stand to benefit the most from the paid shopping experience as consumers purchase either from trustworthy or cheap stores. In response, Google has also recently introduced the Google Trusted Store guarantee, which highlights category leaders and incentivizes users to purchase from trusted retailers.

All said and done, the biggest change in PLAs remains to be the absence of Amazon from the listings. If you search long and hard enough you will find some Amazon listings in the results but only at a fraction of the company’s former visibility.

Just a few months ago, Amazon dominated Google Shopping. They ranked #1 for virtually all results. Now, other brands can take up those top positions. But, is that a good thing? It depends.

“Amazon used to drive huge volume but you don’t own the customer from an Amazon transaction so lifetime value is relatively low. Removing Amazon from Shopping has freed up the top spot on most product searches but has also led to reduced overall conversion volume,” says Barney.

While category leaders have picked up traffic through increased exposure and increased lifetime value through ownership of the customer, they have lost net revenue. As a result, smaller retailers who were not previously subsidized by Amazon stand to benefit the most from increased exposure.

“Amazon faces a very interesting predicament. It was historically easy for them to submit their entire catalog to Google Merchant Center and rest on their laurels as the unrivaled e-commerce leader to generate massive amounts of traffic. Now they need to worry about bidding and margins like the rest of us, which appears to have taken them out of the shopping results,” says Barney.

Maybe it’s temporary, but the results have impacted businesses in a big way.

The Bottom Line

The process of shifting traffic from fee to paid clicks was by no means smooth, but it appears to have normalized leaving opportunity for price-competitive businesses to stake a claim to placements that have previously been reserved for Amazon.

That said, because there is now a cost of engagement, businesses need to be strategic in which products are submitted to the Google merchant center, continue to focus on feed optimization, and consider margins in determining an ROI goal for PLAs.

If you gave PLAs a shot back in Q3 2012, it’s worth going back to see if you may have been victimized by the rocky transition. Compare relative conversion rates and distribution of traffic to get a better picture of the impact of the transition and you might find it’s worthwhile to put together a test budget for Q1 2013.

If you are an online retailer and your business is significantly dependent on product level keywords and ads, you might be interested in hearing about a couple of ways those processes can be improved – your priority being to offer the most relevant results for product-related queries.

Controlling Product Level Keywords/Ads In Traditional Paid Search

There are many ways to build out paid search campaigns based off a product-level feed; however, most of them are not scalable with many feed updates. One is to use MS Excel to generate keyword strings and ad copy based off product-level information. I have personally utilizes this strategy before and ran into three main challenges:

• Generate relevant keywords and ad copies. There are so many scenarios you need to anticipate, such as, some product names are too long for the ad coy headline, or even too long for the ad copy description. In case you’re using trademarked terms you might need a back-up ad copy, etc.

• Create new ad groups for new products each time a new product becomes available.

• Pause existing ad groups for out-of-stock products.

There are a couple of technologies out there. If you search for [inventory driven search], [dynamic inventory management paid search] or similar queries in Google or Bing you’ll definitely get to know more about tools that can help you address those challenges.

As I mentioned in a previous post, my company’s solution does a pretty amazing job at tackling those issues. We recently launched 40 fully-automated campaigns based off a 200,000+ SKU feed; hence, 200,000 ultra-targeted ad groups with unique keyword combinations, relevant ad copy, and deep landing pages.

Without getting into more details, I believe it is fair to say this is the ultimate solution to automatically manage an inventory of that scale, especially since ad groups get created and paused daily based on feed updates. This solution is also the best set-up in terms of control since one can have 10-20 exact keywords for each individual product.

Controlling Product Listing Ads Campaigns

A strong PLA strategy is critical for retailers… especially since Google Shopping is no longer free! Etailers definitely need to allocate more resources to this channel and explore new ways to maximize the channel.

A couple of interesting articles about PLAs do mention that, ideally, you want to build one ad group by product category. In other terms, a catch-all ad group which covers all auto-targets (=all products) is not optimal.

More specifically, advertisers can even go further than the product-category level and actually build out one ad group by product (=unique auto-target). Most advertisers will find that this way requires too many ad groups to create and effectively manage. Product-level ad groups definitely are more time-consuming without a tech solution; however you can then:

• Break down your top products in separate campaigns for budget allocation purposes

• Set specific bids (or CPA targets, depending on the pricing model you’re using) for each individual product

• Customize ad copies by product

Also, while the challenges of successful PLAs are similar to traditional product-level paid search campaigns, it is worth noting that:

• Like traditional campaigns, product-level PLAs require 1) new ad groups and auto target for new products and 2) ad groups to be paused when out of stock

• Unlike traditional paid search campaigns, PLAs don’t require keywords; that’s one less challenge

• Unlike traditional paid search campaigns, URLs are set within the Google Merchant Center feed and not in AdWords

On a side note, it also makes sense to collect all traffic and revenue data for both non-PLA and PLA campaigns in one place; so, search marketers can potentially leverage any transition paths from non-PLAs to PLAs campaigns and vice versa.

Long story short, online etailers should definitely look into technologies which can help build out both product-level traditional paid search and PLA campaigns. Not only is the methodology to get the most control over your PLA campaigns similar to traditional paid search, but the technology required to generate highly-targeted ad groups for traditional paid search and PLA is very similar as well.

A couple weeks ago in E-Commerce SEO Using Schema.org Just Got A Lot More Granular, Aaron Bradley wrote that Google had integrated e-commerce schemas from GoodRelations into schema.org, explaining how that impacts and benefits retailers by significantly increasing “the number of schema.org classes and properties available for e-commerce websites.”

Importance Of Structured Data

That means now, more than ever before, it’s important for retailers to put a renewed focus on the vocabularies and syntax for structured data. As explained, above, structured data sends detailed information about the meaning of your webpage content to the search engines and other data consumers in a way that can be easily processed by computers.

While there are dozens of vocabularies available, there are only two that predominantly affect retail. The most important is the GoodRelations ontology, followed by the Open Graph protocol. Henceforth, we will focus on GoodRelations.

The two most popular syntaxes are RDFa, used in the GoodRelations ontology, and Microdata, used in the Schema.org hierarchy.

Vocabularies and syntax for structured data can be summed up as semantic markup.

Semantic SEO involves the injection of semantic markup into webpages to enhance the meaning of your domain and your pages so they can be easily processed by machines, e.g., computers. As a result, these machines permit search engines to provide users with better answers to their queries.

I’ve been recommending GoodRelations ontology for retail since early 2010 and believe it’s now mandatory to include Semantic SEO as an SEO best practice, and here’s why.

All Your Business Data & Digital Content Become Easily Accessible

Your business data include: rich-media video content, product reviews and ratings, location and contact information, business specialty details, special offers, product information, medical data, and the list goes on. These data are:

• Accessible to machine-readable search engines, web applications, in car navigation systems, tablets, mobile devices, Apple maps, SIRI, Yelp maps and all computers, as well as being consumed by Linked Open Data
• Semantic markup presents your business data like chocolate to Search Engines; they love it and eat it up!
• Search engines understand it and know how to aggregate it for a better User Experience
• Search Engines use structured data to display and boost CTR in the SERPs

Google Supports GoodRelations Vocabulary

Back in July 2011, Google announced support for GoodRelations. There has been preliminary evidence that search results with respective extensions get a 30% higher click-through-rate (CTR). When you add semantic markup, search engines will use that information to enhance the rendering of your page directly in the SERP.

GoodRelations is a powerful vocabulary for publishing all the details of your products and services in a way that is friendly to search engines, mobile apps, and browsers. By adding RDFa to your Web content, you provide potential customers all the features, services and benefits of doing business with you; then, your customer’s computers extract and present your information with ease. Search engines love to aggregate this information.

GoodRelations makes it easy for restaurants, hotels, rental car companies, and retailers to send daily offers, operating hours, and menu cards directly to a huge number of different smartphone applications. There’s no need to prepare individual feeds for each application that you want to support.

Where Do You Start?

It is well known by now that Mobile is crucial to Web retail. Mobile is Local, and Local is one of the most misunderstood and under-rated search tactic. Looking under the hood, most retailers fall short in several areas of Local and Semantic SEO.

The first place to start is to change your Store Locator and Mobile Store Locator. Next steps include the injection of Local SEO best practices, Maps Optimization and IYP Link Optimization.

Bill Connard, Rio SEO Senior Director Product Development says, “When applying these important automated local SEO techniques to local search focus, you can expect to dominate and achieve multiple page-one positions in the SERPs.” To get top results for clients, Bill is injecting Schema.org [Place] and [Local Business] Microdata on all client websites.

Optimize First, Then Markup Your Data

Once you have collected, optimized and marked-up all of your local business data, you’re ready to markup your products with GoodRelations. It’s only logical that we first organize and optimize local business data first, bridging the online digital and in-store physical storefronts.

Once all the digital and physical storefronts are synched-up, search engines and Information Services are consuming the same data, known as “data fidelity.” Data fidelity creates trust and authority, which is rewarded with multiple page-one positions in the SERPs.

Adding GoodRelations semantic markup to your pages will enhance your page-one listings with increased click-through rates (CTR). For more details about using RDFa, go to the GoodRelations Community Wiki, where you can find information about the GoodRelations vocabulary, an ontology for publishing e-commerce information that computers, mobile applications, and search engines can understand.

Retail Takeaways

Using semantic markup can get you better visibility in the SERPs and a 30% higher CTR. The GoodRelations vocabulary is an accepted Web standard for publishing e-commerce information that computers, mobile apps and search engines understand. Organizing and optimizing for Local and Mobile is a first-step must. Adding structured markup after optimizing for Local can lead to higher visibility and profits for Web retailers.

Put another way, this means that webmasters can now provide Google, Bing, Yahoo and Yandex with much more granular information about products and offers on e-commerce sites in a manner that is officially sanctioned by these search engines. In terms of e-commerce SEO, this is potentially a pretty big deal:  it is a means of providing very exact e-commerce information to the search engines in exactly the form they want it.

The Integration Of GoodRelations Into Schema.org

As Barbara Starr emphasized in a previous article, GoodRelations and schema.org are both vocabularies, and should not be confused with the syntaxes available to encode these vocabularies. Both of these vocabularies can be marked up in HTML using either the RDFa or microdata syntax (although initially, schema.org was very much geared toward microdata).

Until now, these were related but separate vocabularies. With this integration, however, the bulk of the GoodRelations vocabulary has now been made available in schema.org.

For those who had been using RDFa to markup GoodRelations, the situation is unchanged. The developer of GoodRelations, Martin Hepp, said in a post on the technical background of the integration with schema.org that “GoodRelations will remain an independent vocabulary, and usable in RDFa and other RDF syntaxes.”

The majority of sites using schema.org, however, have been employing microdata, including e-commerce giants like WalMart, Overstock and eBay. For these sites, the type of e-commerce information that can be marked with microdata has been vastly extended.

New Product Types & Offer Properties In Schema.org

While the integration of GoodRelations has resulted in a multitude of new types and properties being made available in schema.org, the types most obviously extended by GoodRelations are schema.org/Product and schema.org/Offer.

The schema.org Product (left) and Offer (right) classes before and after integration with GoodRelations

The number of Product-specific properties have grown from eight to 25, and three product-specific types have been added where previously there were none.

The additions now make it possible to describe physical properties of a product, allows relationships between products to be defined, and extends the number of product identifiers (like SKUs and part numbers) that can be associated with an item.

• Product physical properties color, depth, height, weight, width, itemCondition These properties now make it possible to provide structured data about the physical dimensions, appearance and condition of products.
• Product relationships isAccessoryOrSparePartFor, isConsumableFor, isRelatedTo, isSimilarTo These properties now make it possible to formally link related products. Previously, there was no way in schema.org to indicate which camera a lens cap fit onto, or what battery that camera required: now, these relationships can be explicitly declared.
• Product numbers gtin13, gtin14, gtin8, mpn, sku These properties allow much more specific product identifiers to be declared than the previously all-purpose property productID.
• Specific product types IndividualProduct, ProductModel, SomeProducts Allows everything from very specific products (such as a laptop with a specific serial number) to very general products (such as multiple, similar laptops) to be described with Product properties.

The number of Offer-specific properties have grown from ten to 37. These additions make it possible to provide much more detailed information about prices and offer conditions, as well as allowing vendors to describe e-commerce offers other than the sale of physical products.

• Offer conditions acceptedPaymentMethod, addon, eligibleQuantity, eligibleRegion and other properties These properties facilitate the description of all types of offer conditions, such as the payment types that the vendor accepts, or the geographical regions for which that offer is valid.
• Offer availability availabilityEnds, availabilityStarts, availableAtOrFrom, availableAtOrFrom, availableDeliveryMethod These properties make it possible to state the length of time an offer is available, where the offer (usually, but not always, a product) can be obtained, and what delivery method is available for the offer (which can be everything from a specific delivery service to a computer download).
• Offer business function The property businessFunction and its expected type, BusinessFunction This allows markup of offers besides simple offers to sell, representing a huge extension of types of transactions supported by schema.org. Apartment rentals, car leases, maintenance agreements and even offers to buy can now be marked up.
• Offer warranties The property warranty and its expected type, WarrantyPromise The duration of a warranty and the precise scope of services a warranty covers can now be explicitly stated with schema.org.

These are cursory descriptions of just the main additions to Product and Offer in schema.org as a result of GoodRelations integration.  E-commerce site owners would to well to examine the Product and Offer classes in detail to determine which specific properties might apply to their products and services (keeping in mind that what constitutes an e-commerce site, from a schema.org perspective, has been vastly extended).

As well, the impact of GoodRelations integration extends well past these two e-commerce-specific classes. OwnershipInfo, for example, provides a way of formally describing the ownership history of a product.

OpeningHoursSpecification supports a more granular way of specifying hours of operations, as well as making hours of operation applicable to more things, such as places. And so on… Again, there’s a lot to discover for anyone not previously familiar with the GoodRelations vocabulary.

Rich Snippet Benefits

Functionally, the chief benefit of the integration is that it extends the number of things that you’re able to describe to the search engines using structured data markup, especially things in the realm of e-commerce.

Currently, the chief SEO benefit of providing such markup is increased visibility in the SERPs when search engines generate rich snippets for a product in the search results. Here’s a rich snippet in Google for a camera on eBay, which uses schema.org Product, Offer and Review markup.

It’s still far too early to see if the search engines will produce rich snippets for any of the GoodRelations types and properties newly integrated into schema.org (things like “For rent from $623 per week” or “1 year warranty”). However, I’d be very surprised if we don’t see more product and offer information appearing in the SERPs based on the extension of the schema.org vocabulary.

Certainly, e-commerce sites desirous of a competitive advantage won’t want to wait until new rich snippets start appearing before adding new markup to their pages. And businesses whose offers weren’t adequately addressed by the original schema.org vocabulary will certainly want to add markup if that now allows them to provide detailed structured data to the search engines about their products and services.

Rich Snippet & Structured Data Testing

As reported by schema.org’s Dan Brickley, Google’s Structured Data Testing Tool “was updated before we pushed the new site.”

Accordingly, the Testing Tool should now validate or provide error messages for code using the extended schema.org vocabulary.

The Testing Tool certainly didn’t have any problems validating an example snippet contained in Martin Hepp’s post detailing the integration, although testing hiccups are likely to occur during the initial phases of the roll-out.

SEO Benefits Beyond Rich Snippets… & Benefits Beyond SEO

Will supplying even more specific e-commerce information to the search engines result boost a site’s organic rankings? However, it stands to reason that sites capable of correctly offering more extensive e-commerce information will by necessity be well-structured, rich in detail and technically sound — attributes generally regarded as favoring higher rankings.

Outside of the realm of SEO, this integration has potential repercussions for the entire ecosystem of commercial transactions on the Web. Search engines are, after all, not the only data consumers that can digest and make use of semantic markup.

OpenLink founder Kingsley Idehen openly pondered the implications of the integration and said, first of all, that “structured data driven e-commerce at Web-scale is now a reality — think Amazon.com on steroids as a natural feature of the Web.” It also facilitates serendipitous discovery of offers, wish-lists, products etc., and does all of this without compromising privacy.

In short, it brings us closer to the realization of the linked open commerce model that he, Martin Hepp and Aldo Bucchi have been working on since 2009. The model envisions the interlinking of e-commerce platforms, big Web retailers, product catalogs and ontologies — with GoodRelations providing the universal e-commerce model.

Pretty geeky, right? Fortunately, the site also provides a very concrete example of where linked open commerce might take us.

Even if this integration doesn’t usher in a brave new world of intelligently linked e-commerce information, it certainly does allow e-commerce webmasters using schema.org the opportunity to provide more detailed information to the search engines about what’s offered on their websites.

Linked open commerce images above used under a Creative Commons license from linkedopencommerce.com.

It is also clear that some of these verticals derive far higher revenues for Google than other verticals. Google Shopping, with its new pay-to-play model, looks like it is going to do just that.

According to a recent study, Google’s new PLA’s (Paid Listing Ads) have been found to significantly outperform text ads in click-through rates by 47% and conversion rates by 38%, resulting in a 25% ROAS (return-on-ad-spend). Take into account that the deadline for merchants to comply, October 17, just kicked in, and that we are only just hitting Q4, and this becomes even more significant.

Advantages Of Vertical Search

From a search engine perspective, one of the advantages of vertical search is the ability to far more easily determine user intent. When a user clicks on “Shopping” in Google, there is a limited number of actions they intend to take.

In Google shopping, that intent would either be to purchase a product or research the purchase of a product. Add something like location to the mix, and the context is even further refined. And oh yes, all your past search history, information from your Google plus account and anything else Google knows about you can be added to further refine the search and determine user intent.

This further refines the context, enabling and facilitating a search engine’s capability to provide the searcher with the information they are seeking. The other clear benefit from a search engine point of view is how it narrows down the search space for them,  as they can  ”activate” appropriate contexts within. This can indirectly improve performance as well as directly improve accuracy of results served.

Overall effect: this can provide the searcher as much as possible with what they are seeking,  facilitating the knowledge graph or “knowledge graph” type displays seen above.

The large online retailers, such as Wal-Mart or Amazon, have the funding and capability to provide internal semantic search.

From a search-engine perspective, another thing to note is that within a vertical search engine, the internal taxonomy with reference to relevant search may differ from generic search, e.g., chlorine tablets may pull up pool equipment; whereas, in a standard taxonomy or vocabulary, this would not be the case.

A typical example would be Walmart’s Polaris ,which incidentally caused a 10 -15 % lift in sales,  and began with the formation of @walmartlabs and the initial acquisition of Kosmix amongst other later acquisitions. Astronomically expensive and beyond the reach of smaller e-tailers with relevant products, wishing to contend and have their products findable in the major Comparison Shopping Engines such as Google Shopping itself.

The Future Of Vertical Search & E-Commerce

Getting back to vertical search and e-commerce, let’s take a look at some current reality. After Google’s earnings disaster last Thursday, one of the questions Larry Page was asked about was the future of vertical search.

In his response, he referred to the large investment Google made in the Knowledge Graph and the critical need for the search engines to get the correct information to the user at the appropriate moment in time. He also cited airline flights (presumably referencing their recent flight search capability) as an example  of a  recent vertical other than Google shopping (YouTube and others are clearly significant verticals, but Google’s flight search is more recent).

Given that Google’s Knowledge Graph, in essence, is a play at adopting semantic search and semantic-Web-like technology, Google has thus far put this to brilliant use with respect to anticipating the answer and leveraging the results − along with its extremely visually-pleasing Knowledge Graph displays.

A successful guess at the answer within that display provides the searcher with no further need to click through to another site. Google is even making a stab at providing explanations via associated relationships, doing a rather good job of it too, for a nascent technology, and perhaps better than the innovators of that technology, themselves!

However, Google is not the only player in the vertical search game. There are many other contenders, and it is possibly still a hot topic in the race for the end game (or even a piece of it).

Vertical Search Works is the name of a company based in New York, London, Vienna and Carlsbad that offers users an alternative to Google paid search ads. They also offer several niche vertical search engines of their own, such as wedding, food, woodwork and more.

An image from that site is selected below (in terms of its vertical searches) and does not include its semantically targeted advertising capability.

There are also many other vertical search, or semantic search engines, that have phenomenal underlying technology, and have either been overlooked for some reason, and which could, with a little capital and innovation, be players in the future.

Perhaps not included in this list, are other new and innovative startups in this arena. Feel free to add those not mentioned in the comments below. I believe the list is extensive. To name but a few off the top of my head, I would cite:

• Hakia.com
• Evri.com
• Blekko.com
• Dogpile.com
• Sindice.com – the Semantic Web index

I also received this, in a Google Alert from a pearltree user: Some great semantic search engines!

In addition, to state the evident, there are the other major CSE’s (comparison shopping engines) such as Amazon, E-bay, etc., or some great online retailers such as Overstock.com, which are pure e-commerce. As previously mentioned, feel free to add to the list in the comments below.

Key Takeaways & Action Items

In summary, online merchants should focus on the following during the holiday season:

• It is always worth submitting your feed to Google Shopping and any other major CSEs or niche search engines.
• Make sure you have excellent data quality; if not, you may be suspended.
• Make sure your site maps are readable and of good quality.
• Use semantic markup on your pages where possible, and ensure on-page content, on-page markup and the data you submit in your feed all match.
• Make sure you frequently update your product feed (at least once a day or more).
• Keep your eyes open for novel vertical search engines and recommendation systems (typically semantic in nature). They may just get you that unexpected lift.
• Last, but not least, here’s an unexpected takeaway from e-Bay: keep your images crystal clear, with an unfussy background!

We will look at an alternative vocabulary, namely GoodRelations, and take a deeper dive into the specifics of generating semantic markup for the e-commerce domain using GoodRelations.

To clarify, GoodRelations is a vocabulary for e-commerce. Microdata and RDFa are syntaxes. Schema.org for product is an alternate vocabulary for e-commerce.

The diagram below from Linked Open Commerce gives an excellent graphical depiction of how all entities are interlinked and related with the Web of commerce. It also gives an indication of what other novel applications can arise that can leverage proliferation of structured markup that adheres to standards. In this case, showing specifically those that pertain to e-commerce.

First off, we can take a look at the GoodRelations toolset. There are many. Even converters from RDFa to microdata. The easiest way may be to use a plugin if you use a tool like WordPress, but we will do a manual walk-through of the entire process, as it is more illustrative of precisely what is occurring.

What can it do for you, anyway? How does it make your website or business findable? An example is listed below.

How do you generate markup for this using GoodRelations? For our first step, take a look at the figure below. As you can see, there are many tools available, even crawlers and code that will consume GoodRelations on page markup. 

For the scope of this walk-through, we are going to select the GoodRelations Rich Snippet Generator. Part 1, depicted below, will generate a snippet for your organization. I went ahead and filled in the information for a store.

After entering the information above (it is not a real store), I got the result displayed below. You can see all of the generated RDFa once you fill in all the details for your store and select Generate Code Snippet.

It also tests the validity of your inputs, e.g., what opening times and closing times are valid, and will generate a specific error with suggestions for correct input formats. Pretty cool, right?

When it is happy with your input you get a green checkmark along with the validated semantic markup! (The markup is illustrated in the grayish box below”. It begins and ends with a <div>.

I now have the markup needed to add to my page for organization information. In our next step, we are going to get all the structured markup for the store, including opening hours!

Make sure to select “Copy address information from company” and then also select “Determine geo position from address,” in that order.

A lot of code gleaned from above will be populated, and you do not have to worry about deriving your longitude and latitude. It will be filled in for you!

After filling in the rest, we are now ready to select the Generate Code Snippet. Upon doing so, the necessary markup is generated as shown in the figure below.

Assuming you keep following the steps shown on the Rich Snippet Generator page, you can add product information and offers as well.

You now have the correct format to place on all your product pages, as well as the correct formats for local and business. You can continue to follow the next steps (two more) on the GoodRelations Rich Snippet Generator page until you have all the markup you need to edit your webpage.

Let us look at a store that is an actual user of GoodRelations. An example would be Peek and Cloppenberg. Below is a snapshot of one of its product pages:

I took the URL for the product, and then ran it through the Sindice Web Data Inspector tool. It extracted all the RDF or “triples” as you can see!

The graph view can make it a lot easier to see the inheritance hierachy. You can even use it for debugging to ensure your divs are correctly nested.

As you can see, if you want your site to be “Findable” in the Web of commerce, it is wise to add semantic markup.

GoodRelations is great for e-commerce. Schema.org is used by all three major engines and is less complex in some ways, but less rich in others. You may have to walk through it once. But there are many tools provided to get you on your way.

Just a caveat here. Some training may be required. But if you want to be findable in the Web of commerce, make sure you add semantic markup to your site and follow other best practice guidelines (like clean, verified and validated data feeds).

And one more thing! When making your decision, “rdfa vs microdata” the below chart is definitely something to keep in mind! For more background, also read this GoodRelations Primer.

Make sure your markup matches your data feeds and that you also do not “stuff” your pages with RDFa information or microdata that is visible to bots only and not to humans. (The latter is considered cloaking). But you do want to be findable in the Web of commerce. So, delve into it. But make sure you tread smartly.

A few final stats on the usage of markup in the current online landscape:

Below is more data on the above chart:

In closing, using GoodRelations and related markup is something all retailers should investigate using to ensure their online store is findable in the global graph of commerce!