So, what does this mean in terms of displaying dates on pages as I earlier explored? Does the recent algo tweaking change my earlier recommendation that displaying dates on webpages may help rankings?

As you may recall, Michael Gray and I differed on this point — he suggested that one should opt out of having dates on pages because Google displays them willy-nilly in snippets, and they may frequently prejudice users from clicking through if other content with more recent dates is available in the same search results page. In contrast, I argued that Google’s usability testing apparently found that users often prefer to see the dates in the SERP listing snippets, and that factoid makes it an element that Google’s algorithm might prefer slightly for ranking purposes. Even if the algorithm didn’t give advantage to pages with dates, their research indicates that it might still increase user CTR to the webpage, which can indirectly improve rankings over time. Both Michael and I provided caveats, however, and acknowledged that their are exception cases.

In that earlier post, I provided a decision matrix which I believe supports my general stance that having the dates is likely beneficial in more cases than not. In it, the green check marks are cases where having the date is probably advantageous, while the red exxes indicate cases where it might not be helpful:

Now that Google has apparently bumped up the “freshness factor” in ranking signals as slightly more important than before, I believe it further supports my stance that having the date on pages is beneficial for most types of content. By making this change to the algorithm (and announcing it publicly), Google has signaled that this is particularly important. Therefore, having the date in the snippet is likely even more important to searchers.

Now, you might be thinking at this point that you have older or even stale content, so you will opt out of the date in order to avoid some perceived penalty of the application of the freshness factor. I’ll tell you flat out that this particular reasoning is illogical: Google has a separate factor which they independently derive to decide a document’s initial publication date. It’s probably based upon the date when they index the webpage for the very first time. This document inception date is *not* affected by the date printed on the webpage, so you can’t fool the algo by declining to display the date or lying about the publication date! (Search Engine Land further verified this in chatting with Google about their freshness ranking announcement.) In fact, if you frequently change the date you print on the page without actually changing the page’s content, you could get dinged in your quality score — I would not try it.

One thing you could do is periodically review your content and update it with postscripts or revision notes at the end, and THEN change the “last updated” date you display on the page to reflect the day of the change. This method is, of course, costlier, and requires some actual investment in ongoing quality improvement. But, I’d say that it’s the valid way to optimize in terms of date and freshness, and there’s likely no good shortcut for this signal.

Finally, if you’re thinking about abruptly changing all of the URLs of your article pages to periodically start the clock ticking once more for the document inception dates, you should know that one of the prime ranking factors that’s been in play for years now is an advantage given to pages with a longer legacy. Pages which have been around for a longer period of time on stable URLs have had some ranking advantage in Google SERPs. The question now is, how much advantage? Does the advantage of recency now outweigh the advantage of longevity?

If you dump all your legacy URLs you might find out — but the answer may not be to your liking.

In truth, I suspect that this freshness factor tweak is likely somewhat subtle and the actual impact may be fairly light for most sites. While the figure of 35% of searches being changed sounds impressive and impactful, it could be as simple as one page out of 10,000 getting bumped up to the first page. It doesn’t mean that 35% of the first page of SERPs will alter. Listings that are bumping in and out of positions ten and eleven likely wouldn’t notice a change at all.

While this particular Google Places information accuracy widget is new, Google has long been leveraging similar user-generated content to try to enhance and grow map information. They have been actively crowd-sourcing map accuracy work for a while now, but it’s not without significant issues.

Obviously, one of the more serious issues involved is the fact that people will lie and cheat.

So, it’s no surprise that Google Maps help groups have instances reported where people suspect that their locations have been compromised some by malicious competitors, disgruntled former employees, or randomly psychotic customers. I’ve had clients and colleagues approach me with similar reports, and Mike Blumenthal has reported these types of stories as well.

Not only can some of the general public be expected to purposefully try to cause mischief, well-meaning people can also ignorantly make mistakes in commenting or reporting on data accuracy — just think of all the stories throughout popular culture of stereotyped representations of men who can’t find addresses while driving (and refuse to ask directions) or spatially-challenged women who can’t read maps. I’m not suggesting that these stereotypes are accurate representations of the sexes, but that the stories likely come from the fact that many people, regardless of sex, find navigation and map interpretation highly challenging.

So, there are some inherent problems with attempting to base a large percentage of location accuracy upon crowd sourced information.

What’s particularly concerning about Google’s methodology is that they’ve recently declared that they’ll sometimes use this data to override business owners’ disclosed information, or call into question accuracy in consumers’ minds. Blumenthal hilariously communicated the issue in his brother-in-law’s open letter response to the matter. An actively-engaged business owner may have gone in and verified that their address and map are correct in Google Places, but if a small handful of users claim the address is wrong, it can get incorrectly flagged as being a closed location, or that the address may be wrong — something which would clearly discourage potential customers from going to the business.

Mike organized a really humorous experiment to illustrate this issue when he asked a handful of us to go in and flag Google’s own corporate headquarters as “closed”. For a brief while, the Mountain View location’s Place Page listing carried the flag, “Reported to be closed.”:

Auditors (and perhaps an activity pattern detection algorithm) twigged to the fact that a bunch of users had declared the location closed, and someone at Google corrected the defacement. But, the point was made — users can falsely get a listing flagged as closed. (This was done to illustrate the issue from the perspective of small local businesses, and not with any intent or expectation of causing harm. So, hopefully those of us who participated were not blackballed by Google Places!)

If Google headquarters was a local business that relied upon having potential walk-in customers referred by Google Places, they would have almost certainly lost business during the time that the alert appeared on their Places page. You should ask yourself: is that fair? Should a mean little gang be allowed to abruptly paint a “closed” sign over the door of a viable business?

My concern with the testing of these new crowd sourced accuracy widgets is that the same sorts of stuff can and will happen across the millions of businesses listed in the United States. Having a place flagged as closed is potentially damaging to these businesses, and so is having a location of a business tagged as possibly erroneous — or having the address changed outright to an erroneous place.

There are numerous methods for assessing address correctness which can be handled with algorithms and which do not involve trusting humans. For instance:

• if the street address is outside of the ZIP code polygon;
• if the ZIP is not associated with the City name;
• if an odd-numbered address is geolocated to the even side of a street (and vice-versa);
• if the phone area code is outside of the City or ZIP;
• if the address number seems impossibly high or low;
• if the street address is determined to be less likely to be located in the city block where it’s geolocated (if neighboring addresses are pinpointed elsewhere);
• if the street name is not recognized in the ZIP area;
• if the other directory data sources are in reasonable agreement on the geolocation pinpoints;

…And, there are more. (For a primer on basic causes for online mapping errors, see my article, “Top Causes of Errors in Online Mapping Systems“.)

Google is undoubtedly using some of the algorithmic methods for detecting and correcting map errors, but are they using all of them, and couldn’t they do more?!? For long-established businesses, there perhaps shouldn’t even be an option for users to suggest that a location is incorrect.

Owner-verified listings should be particularly trusted above user-generated content. In fact, there should be some safeguards in place for owner-verified listings such that “this location may be closed” and “the address location may be incorrect” messages perhaps shouldn’t be displayed at all without first alerting the contact email address for the listing, and even giving it some sort of time delay such as a month before the general public would see such a message.

Some of the top yellow pages sites which dealt with these types of data issues for many decades came up with business rules to help mitigate discrepancies in data sources. For instance, for a certain number of months after a business has been contacted and has updated/verified its own info, any other data source is set to a lower priority. And, this is the way it should be in Google Maps as well. The business owner has much more invested in insuring that location information is correct than the general public does. They have skin in the game! This is why data which comes directly from the owner is correlated with a higher degree of correctness than that of the general public (at least when that data is fresh or recently verified).

Google’s user-centered product development is great in many ways, but the ongoing refusal to properly incorporate and represent the business owner in the overall local business ecosystem is problematic. They are a type of user themselves, and they are a highly important demographic which should be given proper precedence in some areas of the communication and presentation of business information. This is why we undertook the experiment with the Google headquarters listing — to illustrate the disparity.

One suspects that Google employees overrode the crowd-sourced edits we submitted stating the location was closed — not only should the flag not have appeared, but the same sort of listing alerts and controls should be extended to small businesses who’ve verified their listings.


Panda photo by J. Patrick Fischer, CC BY-SA 3.0

For those of us who have been following Google’s evolution over time, the Panda updates actually weren’t all that surprising. For me, the emergence of Panda seemed very familiar, harkening back to perhaps as far back as 2006 when Google clamped down on affiliate sites. At that same time, Henk van Ess revealed how Google was hiring on temporary quality evaluation staff to rate search results. In Google internal documents which van Ess exposed, the evaluators were instructed to give poor ratings to spam content, porn ranking on inappropriate keyword phrases, and “thin affiliate content”. It became clear very quickly that the negative human ratings for “thin affiliate content” were related quite closely to the virtual penalization that many affiliate sites experienced at that time.

What Google was focusing upon in reducing the ratings of “thin affiliates” were instances where a search results page would be filled up with links to pages which all had virtually the same content, and where those pages often weren’t the final destinations of the people who landed upon them (obviously, with most affiliate sites one clicks-through to the actual retailer’s site where more information could be found and orders could be placed). From Google’s perspective, it was a poor user experience for there to be millions of pages indexed which had all essentially identical content and which often edged out other more-worthwhile pages which consumers might prefer.

From all of the information around the “Panda” Updates, it seems highly likely to me that Google is continuing to leverage their human quality evaluator staff, along with a number of other automated metrics which they could also incorporate in determining quality of pages. For instance, the numbers of people clicking back out of a page they found in the search results in order to select another page — this sort of a bounce rate metric could indicate which a page is of very poor quality for a particular keyword term.

Naturally, there are far more pages on the internet than what Google may reasonably have evaluators visit and assess, but there have also been developments in methods for using small numbers of ratings to be algorithmically applied across larger numbers of websites and pages. The TrustRank analysis technique is just one of these methods, and the research paper describing it shows how one could use a small sample set of rated webpages in combination with an automated analysis of the link graph associated with those pages in order to broadly apply ranking decisions to good content or poor content.


TrustRank Diagram

The combination of human interaction metrics is likely used by Google to determine a sort of “quality score” for pages, and some sort of mechanism similar to the TrustRank method is used to apply the quality score values across a broad swath of a site’s sections and pages.

While the Panda Updates were initially targeting “content farms”, or sites which specifically generated large numbers of pages to target user search queries, the criteria used to ding them could easily wreck many other types as well. Poorly constructed sites where users are confused about where to find what they were searching-for, or sites which make a bad impression by being too crammed full of ads, tricky links, or unsophisticated layouts might also fall under the treads of Panda.

The leaked evaluator documents from Google gave a few ideas of the sorts of things which could decide between a “thin affiliate” that got bad ratings versus sites which happened to contain affiliate content but which might otherwise get good ratings. Having additional content on the pages, particularly “value-added” content such as maps or user ratings or price comparisons could make a difference.

Here in 2011, I’d say the bar is even higher, though. You want your site to make a good impression when a searcher lands upon it, and you want them to have trust in your content. You need the site to be usable so that it doesn’t frustrate users, and you need to seriously consider removing impediments which annoy or hamper users in getting what they’re seeking. That gigantic interstitial ad that blocks them from the page, or all the cluttered fineprint and links may result in higher bounce rates which will translate into lower rankings for you.

In the past year, Google introduced Page Speed as a ranking factor — a major element which impacts consumer satisfaction with webpages. In their blog posts about Google they mention improving a number of elements affecting quality of a sites, including spelling and grammar (we previously highlighted how Google could use spelling and grammar in quality determinations and rankings). With the increasing attention to user experience factors in ranking determinations, it has become clear that if you’re not doing usability analysis, you may not be doing SEO at all.

Search engine optimization based purely upon clever technical tricks really appears to be on the wane with the Panda Updates. SEO may really decline in favor of much more sustainable longterm attention to User Experience and Usability factors. Unfortunately, I don’t see a whole lot of websites or companies positioned to take advantage of the trends. Most of the companies I’ve consulted with continue to base site design decisions more heavily upon arbitrary egos, expediency, and mere immitation of their competition rather than upon informed UX testing.

Quite a number of years ago, AOL got sued by some of their subscribers for not upholding service representations, and as a result, AOL began to automatically cache images and webpages from across the internet, keeping them in their servers in Reston, Virginia. This helped them to cut one leg off of the circuit for delivering content for their subscribers. As part of their caching process, they’d recompress images, knocking percentages of the images’ filesize off, reducing how much data needed to be transferred. This actually used to cause problems for some types of images, since their compression algorithm had one or two errors in it, causing big honkin’ artifacts to be left unattractively in the image.

(It’s very geeky of me, but I actually remember some of the image processes which would cause the compression errors, because I had to deal with cases at Superpages.com where our artists made ads for clients, and then the client might call us up and scream because the ad looked bad to them. Such calls could be confounding — imagine an irate attorney who pays many tens of thousands of dollars per year calling you up because he sees crap in the ad your team made for him, and when you pull it up on screen, you can’t see any problem! Then imagine trying to explain to him how he’s seeing the error because he’s on AOL, while the people in your company who create the ads are not on AOL… and you and up with an interesting conversation. Don’t EVER do tech support! But, I digress.)

Anyway, Google’s Page Speed service is taking AOL’s idea just a little further, because they’ve married a few of their services together in order to improve the HTML of the pages they’re caching. Some of that automated improvement will be stuff like rewriting the code to specify heights and widths of images, and minifying CSS or JS code. They might even be doing some of that image compression which was so troublesome with AOL’s rehosting of the entire internet.

But, I’m being snarky with the AOL comparisons! I actually think Google’s idea is a very cool one, and it’s one that I had previously predicted. It made sense once Google rolled out the Page Speed diagnostic (which helped webmasters find areas for improving their pages speed) to then roll out something that would cut down on the network travel time between web site servers and the endusers who request webpages.

This service also immitates other CDNs which came previously, such as Akamai and Amazon’s EC2. I’m a big believer in the power of CDNs, since they have long helped sites move content out closer to “the edge” of the network, reducing the time it takes to deliver webpages, images, Flash, videos, etc. I recommended a CDN for Superpages back when I was there, and it helped improve the user-experience for the millions of visitors we had.

Google’s deployment of this service is a great fit for them, since they already have an array of servers located around the globe, which had enabled them to essentially be their own content delivery network. Also, I wonder if Google couldn’t leverage some of their dark fiber to further reinforce their delivery network’s effectiveness.

The open question at the moment is whether integrating with Google’s Page Speed Service will positively influence search rankings. Since Google started using Page Speed measures as a ranking factor (another development we at KeyRelevance had anticipated), and since using their service would speed up a site’s delivery speeds, it’s easy to connect the dots to see that using such a service could provide a website with an immediate advantage for this ranking factor.

So, Google’s retread of a very old AOL idea may not be all that original, but it is cool since it’s a tool that could help optimize your site a little and improve your user experience very quickly.

Now, I see a wide range of numbers of reviews for all sorts of industries — from zero or just a small handful, to around a hundred, all the way up to a few thousand for highly-popular eateries located in big cities. So, a range of these numbers is normal, and for some cases it’s normal for there to be a few thousand.

For instance, the famous Union Oyster House in Boston is showing a couple of thousand reviews. Considering it’s the oldest restaurant (in continuous service) in the entire country, this even makes sense. It makes even more sense when you see that they have quite a few hundred reviews in Yelp, and OpenTable as well. When someone has placed a reservation for the restaurant in OpenTable, they may be receiving an invite to rate the restaurant after they’ve had their visit there. I’d say this is is a great practice and works well for everyone — the business itself, the person rating, OpenTable, and for consumers shopping for an eatery. The natural outcome is that they’ll have hundreds and thousand of reviews in a relatively short time period.

But, there’s something else going on in vacation rentals which is similar, but isn’t ideal.

Here’s an example from Avalon, California, on Santa Catalina Island where the main industry is tourism and there are dozens of vacation rentals which people stay at annually. Notice the high number (over 1300) of reviews Google shows for a rental agency there:

What I see happening is this: some travel information sites are listing vacation rentals, and allowing consumers to rate/review those rental properties. The rental properties are associated with a particular rental agency, and the travel info site is assigning all those reviews to the rental agency that they roll up under.

Now, this works well for cases where the property is handled by one company which handles the rental and property management. However, there are a lot of cases where multiple rental agents from different companies may be renting out the same property, depending upon who lets it out to a renter first. In those cases, having all the reviews rolling up under one company is unfair.

Where vacation rentals are concerned, I believe that consumers are also fairly naive about what it is that they are reviewing. They are likely being led to believe they’re reviewing the property itself — and if they property isn’t owned by the company managing it, details about its amenities maybe shouldn’t be applied to the rental company. For instance, the process of reserving and renting the property may have sucked, but the consumer is mainly being asked about how great the Wi-Fi was, the hottub, and the quality of the window views.

In most cases I’ve looked at across the country, the large bulk of these reviews are coming from FlipKey, operated by TripAdvisor.

FlipKey’s interface for reviewing the rentals encourages the property be reviewed more than the rental agency, as I suspected. The review form is titled “Rate this vacation rental”, rather than “Rate the property management company”, and it says “Review your stay”. Their tips do suggest that one “Describe the service you received”, but it also says “Tell us how you liked the home”, and to “Talk about the location and amenities” — neither of which are necessarily controlled at all by the property agency.

But, as I see it the primary issue is whether reviews of individual rental properties ought to be rolled-up into an aggregate to represent the rental agency in this manner. Google has no quick way of assessing instances where properties may be represented by multiple rental agents, and property owners sometimes switch the agents representing them — in which case do the reviews then get reassigned to the new rental agency, or do they stay associated with the first rental agent as part of their legacy?

This appears to be a form of spam. FlipKey has been clever with design this so that individual vacation rental reviews will roll up collectively under rental agent listings, and having their data associated importantly with so many businesses is useful in terms of some referral traffic as well as a promotion vehicle for potential advertisers.

This is yet another problem which Google has associated with user reviews and business listings. Not long ago, Google Maps rolled out “Descriptive Terms” derived from user reviews, causing undesirable and often unfair effects for local businesses.

KeyRelevance’s Recommendations:

1. Use CPA Bidding instead, if that is an option.
2. Make small adjustments (e.g. 5-10%, $0.05 – $0.10)
3. Make sure to set upper and lower limits to bound the changes that can be made
4. Make the automation rules at the appropriate level. They can be set at the campaign, adgroup, or keyword level.
5. Use overlapping rules to handle exceptions. Note that when multiple rules apply, ALL will trigger, so approach this techinque with caution.
6. Running rules during a campaign’s “off” time makes sense for a cleaner transition. I have my rules set up to run at 1am using the previous day’s data.

Note: Using CPA bidding eliminates the need to make these finer-grained adjustments all together.

Example: I have a campaign using Position Preference, and I am typically using a 3-6 position preference (avoiding the Top 2). To set up an Automated Rule to approximate this, I would add the following rules.

The first rule raises bids if my avg pos is too low:

At the Ad Group Level, add a “Change Max CPC Bids When…” rule to boost a “poor” Avg Pos:

A similar rule should then be set up to Adjust downwards if the Avg Pos gets too “good”.

Limitations of Automated Rules

1. Editing Automation Rules is like editing ads: the old version is deleted and the new one inserted, but the old rule lingers as a “deleted” rule. Changing the Notifications setting counts as changing the rule, even if the triggers and effects don’t change. The good news is that there are no bidding history issues created by this swap-out.
2. The Automated Rules Management Section lumps ALL rules for the Account together in one huge list, so good naming conventions for organizing the rules is a good idea. (see below).
3. Rules at multiple levels might apply within an adgroup/for a specific KW. Example, you might target Position 3-6 for most of the KW in an adgroup, but exceptions for just one highly competitive term. This KW’s rules might have different avg. position triggers, or a higher max CPC with the same triggers.
4. Rules might not run:
   • Google downtime might prevent a rule from running.
   • Timeouts in pulling data for the account might cause the rule to abort.
   • Finally, rules have a 1 year lifespan after which they won’t continue, unless you manually update the ads (see Caveat #1). You will be warned as this anniversary approaches.
     (Seehttps://adwords.google.com/support/aw/bin/answer.py?hl=en&answer=188713"> https://adwords.google.com/support/aw/bin/answer.py?hl=en&answer=188713 for details on Rules Execution).

Naming Rules Well Will Make Make Maintenance Managable

Multiple Adjustments per Day
The above discussion suggests making bidding adjustments at most 1 time per day. In practice, you could do two updates per day: the first update would use a pair of rules as outlined above. Then, you could add a 2nd pair of rules that would trigger perhaps in mid-afternoon, and which used only TODAY’s positioning information to adjust. We recommend that this 2nd set of rules make smaller adjustments since they are operating on a small data set, and therefore more sensitive to noise in the data. Where your primary adjustment rules might make a 5% change, this 2nd set of rules could make a smaller 2% adjustment. This would make your rules more responsive to changes in the marketplace without causing your spend to bounce all over the place.

For instance, quite a number of newspaper websites choose to disable the cached views of their pages. Just search for “newspaper”, and you can immediately see an example such as the New York Times:

As you can see, the listing in the Google search results for The New York Times has no link under it for “Cached”. However, it does have a magnifying glass — the Instant Previews button — which, when clicked, reveals a screengrab of the NYT homepage from Google’s copy of the page when they last spidered it.

The reason the New York Times doesn’t have a “Cached” link is that they purposefully set up a robots meta-tag that specified that they didn’t want Google and other search engines to make a cached copy available for users to see or access:

The NYT “robots” meta has “noarchive” as one of its parameters. This parameter tells Google not to store a cached copy of the page. Or, more accurately, as Google’s Webmaster Guidelines state, “noarchive” instructs Google to not provide the link to the cached copy (Google has to cache some encoded version of the page if they spider it to offer up in the SERPs).

Newspapers with paywalls have done this in many cases where they desire to be spidered and indexed by Google, but they may allow each user “one click free” where the user is allowed to freely access and read one article from their site, but any subsequent articles may require the user to register, or pay. So, those news publishers do not want users to be able to read the article for free via a cached copy.

Various other types of sites use the “noarchive” when they have changing copy on the page that might get out of sync between Google and the page, or when there’s some sensitive content that shouldn’t be visible upon other sites.

So, when Google launched the Instant Preview, it would be common sense for those publishers who’ve chosen to suppress their cached view to think that it would also be suppressed via the Instant Preview. Frankly, the Instant Preview images are a representation of the webpages. They are a snapshot. They are a type of cached view of the page.

Oh, sure, the Instant Preview is often too small to reasonably read print upon the page. However, that’s not the point. And, the print isn’t the only reason why some have opted to disable cached views — for some, the images are also sensitive. And, the print’s not always too small to be read.

Now, Google has provided a way to suppress the Instant Previews. According to the FAQ, using the “nosnippet” parameter in robots meta tag suppresses Instant Previews.

But, the point is that “noarchive” should’ve been expected to have applied to the Instant Previews. Despite the preview images being chopped in some cases or being difficult to read, they are a type of cached copy of the page. And, there are people who don’t mind the text snippets showing up in the search listings, who do mind having the preview page screen-grabs appearing.

Publishers who were using robots noarchive protocol shouldn’t now have to go back and add nosnippet parameters.

Google’s deployment of the Instant Previews, while ignoring the noarchive specification in favor of a lesser-used control parameter is something of a betrayal of trust for webmasters.

For those who’ve been following our articles and conference presentations for the past few years, none of these broad categories of signals come as any sort of a surprise.

Although Google LatLong declares in their post’s title that it’s about “How Local Search Ranking Works”, they’re still understandably obscure for the most part, and avoid providing all that many specifics. There’s a natural tension between informing businesses on how to provide Google with ideal information necessary for ranking, and providing so much info that search engine optimization specialists have “undue advantage” (from Google’s perspective).

So, I thought I’d take this opportunity to list out a number of more specific factors which could feed into Relevance, Prominence and Distance. Read on for a refresher on local search ranking factors which likely could contribute to each of these.

RELEVANCE

• Business Name matches the user’s search keyword phrase;
• Business Category (in Google Places) matches the user’s search keyword phrase;
• Business Description or additional data fields match the user’s search query;
• Keywords found on the Business’s website match the user’s search query;
• Keywords on the Business’s website and Google Places fields indicate a match for the user’s search location;
• Description text for the videos and images related to the Business include keyword phrases which match the user’s search query;
• Business Categories may be mapped to match related Categories. For instance, if the user seeks “Restaurants”, but you’re only listed as a “Cafe”, then Google would likely choose to display your listing due to thesaurus relativity.
• Fuzzy matching algorithms can also come into play for Relevance. For instance, common misspellings may be matched between the user’s queries and contents of your listing, website, and other data sources about your business.
• Keyword relevance also comes into play with all of the various data sources which Google associates with your listing. For instance, keywords found in your internet yellow pages listings, newspaper articles about you, and places where people mention your business — all of these could be matched up with the search keywords users supply when searching for local businesses.
• It should be noted that it’s likely that information on your website and directly in your Google Place page may be deemed to be more important and therefore more heavily-weighted in relevancy considerations than information on less-central pages such as internet yellow pages, articles, etc. However, having some level of keyword consistency among all the resources citing your business listing could additively help your overall relevancy scoring.

PROMINENCE

• Prominence would be composed of signals which could help Google assess your company’s overall popularity compared with other similar businesses. Google’s classic ranking assessment is therefore also one of their prime signals for local rankings: PageRank. PageRank involves the number of webpages linking to a webpage or website, and the relative importance of each of the pages which are linking.
• Citations are the new link where local search results are concerned, so having webpages that mention your company name, address, and phone number could help to build up your signal weight for relative popularity.
• Your business’s location itself may independently have prominence signals associated with it, somewhat independent of the reputation of your business itself, and this could theoretically influence your rankings. For instance, a Chinese Restaurant tucked away on an off-street in a residential neighborhood might be assessed as less-popular than an otherwise similar Chinese Restaurant which happens to be located next door to a major tourist destination. While this may sound like a proximity signal, it’s really more about the relative popularity of places themselves which then may apply to a local business or organization. So, if your company is located next to a place which merits an article in Wikipedia, or in a highly-trafficed neighborhood, or next to a popular amusement park, you may reap the benefit of the popularity coming off of it.
• Reviews and Ratings are an obvious indicator of popularity and quality, although they’re so obvious that Google has long stated that rating values themselves are not used for ranking purposes. However, there still appears to be some association between highly-rated/reviewed companies and rankings. It’s unclear if this is cause or effect, however, since the most-popular businesses (as assessed through other signals) are also often those which have the best ratings. Further, Google’s recent algorithmic tweaks in the wake of the “Decor My Eyes” story may now be incorporating some review sentiment analysis in determining negative ranking criteria — possibly using such info to penalize rankings of bad companies, while not really using them to influence positive rankings.
• Consumer Click-Throughs to a business from Google search results has been a controversial/theoretical factor among search marketing experts, although Google patent filings document this as a potential metric which could be used for assessing relative popularity/importance. Under this concept, if your business listing info were clicked-upon or otherwise interacted-with more than that of similar competitors appearing in the same SERPs, you could get a ranking boost.
• Social Media signals are likely to be an increasing Prominence factor. Social media interactions are already influencing real-time search as well as personalized search, and I’d bet they may be influencing even more than what the search engines have openly disclosed. For instance, the number of Facebook “likes” of a business and its associated pages should be a valuable signal to search engines. Increasingly, assessments of importance of social media mentions will also rely upon the relative importance/prominence of the person/account posting the mention.

DISTANCE

• Distance has been one of Google Maps prime local ranking signals since inception — so much so that I once jokingly suggested businesses might move locations in order to rank better. It used to be that businesses which were located closest to city centroids had a major advantage over businesses located further out from city centers. Over time, Google has adjusted the weighting of the various ranking factors so that proximity doesn’t have quite the overly-heavy influence it once did, but it still factors in. So, the main element of this has been the relative radial distance from city centroids. There really isn’t much one can do to alter your location, so the classic old business advice still remains valid in this respect: “Location, location, location.”
  

  

• Google has altered some from dependence upon the city centroids, however, and now they are factoring-in the outlines of cities, ZIP code region outlines, or using something like heat maps of population densities in order to figure out location associations according to geographic position.
• For businesses desiring to have a good Distance signal, there is overlap between this and Relevancy signals. It’s important to mention your location’s city name, neighborhood name, as well as various other location nicknames that people may be using to search for businesses in your area. For instance, many denizens of Manhattan use the abbreviation “NYC” when searching online.
• Finally, many service-based businesses provide work in major metro areas beyond the borders of the smaller city region where their listing is physically located. For instance, a Plumber located in Orange, California, might offer services to numerous cities in the greater Los Angeles region. For these types of businesses, it’s very important to mention the names of each of the cities where they offer service — mentioning the names on their Google Place page, website, and other citation sources.
• Downtown searches may still be one special case where centroid distance is particularly influential.

For more information, also be sure to read my “Local SEO Primer” on how to rank in Google Place Search Results.

First, it seems likely that Vitaly Borker, the offensive proprietor of Decor My Eyes, is likely not some stealth marketing genius. Rather, he sounds more like he rationalizes bad behavior in a variety of ways, according to the NYT article about him, and one of his prime beliefs is that negative ratings have helped his Google rankings. His supposed reasons for this were likely wrong by some degree, but he may’ve accidentally derived some benefits from the practice without knowing actual causality.

What makes him more important is that he got Google’s attention, and caused them to react — or claim they’ve reacted — by making some changes to their algorithms. It’s possible that Google responded mainly out of concern over negative press. It’s also possible that they may’ve said they’ve made a change but did not, but it seems equally possible that they could have indeed tweaked their algorithm. The incident really seems to call for us to consider that “where there’s smoke, there may be fire.”

First of all, I think it’s worthy to note that a few different free/public analytics services appear to indicate little or no major changes to DecorMyEyes.com traffic in the past year, and they indicate the site has relatively little overall traffic anyway.

Compete.com shows fairly consistent, unremarkable traffic:

Alexa shows a sharp spike around when the New York Times article hit, and a subsequent decline, perhaps as traffic went back to normal. Previous to that, it also appears there was pretty consistent, unremarkable traffic:

So, if increasing/ongoing negative reviews helped the site in some way, it’s not clear that it was sudden, dramatic, or providing ongoing increases.

A number of people have tried to reverse-engineer what may have been happening with DecorMyEyes.com, and to also analyze what Google may have done in response. Google states that the negative ratings didn’t help the site, pointing out that a number of those sites nofollow their links. Other analysts have found that the main sources of the site’s PageRank came from links on many low-quality sites, and were likely paid links of some sort. From the Compete/Alexa evidence, it appears very likely that the main traffic to DecorMyEyes may have been primarily longer-tail traffic where they had pages that better matched phrase combinations that fewer popular sites have.

For the analysts believing Google’s statement that the nofollowed links provided no value, and instead ascribing the site’s ranking ability primarily to paid links, their seems to be a small conflict that has gone unrecognized: Google states that they are adept at recognizing paid links, and that they don’t count them. So, it seems dichotomous to simultaneously believe that the nofollowed links that a few review sites may’ve included were worthless to Decor My Eyes, while believing paid links may’ve helped.

As I pointed out in my article on the Decor My Eyes fiasco and local reviews tactics, it is possible that they could have ranked due to citations provided in the text of reviews. As you may be aware, in local search algorithm patents, Google engineers have written that it could be possible to use mere mentions of a business’s name and address information as a signal for popularity. While this may never have been used by Google in local search or otherwise, it’s possible that they have been using it and may have even extended some of the algorithmic behavior into regular web search rankings where it could affect sites like Decor My Eyes. If such a ranking factor were at play, it could also explain why major brand names seem to have better ranking ability, as many SEOs have observed.

Google’s post about their action towards Decor My Eyes is the most obscure when describing changes they made to demerit the site due to customer complaints. The rhetoric surrounding that aspect is particularly worthy of politicians. They point out that merely basing rankings or penalizations around sentiment analysis would have negative consequences — such as making it difficult to find politicians’ websites, which would be a poor consumer experience. However, they state that they’ve come up with some solution that may negatively impact sites which offer such poor customer experience.

As I noted, Google personnel have explicitly stated that they may choose to still use some nofollowed links from sites with data that they deem to be valuable. So, it’s possible that they were counting links to Decor My Eyes from any consumer review site which had nofollowed links. After all, before the NYT article aired, consumer complaint sites probably were not a widely-exploited target for links, since most people would’ve assumed they would harm websites’ reputations unacceptably, counteracting any possible value obtained from their links. If this was the case, Google’s action may have been as simple as halting link flow from complaint sites.

However, another explanation could be related to both sentiment analysis and citations. If Decor My Eyes was deriving citational benefit from consumer complaint sites, Google could have applied algorithmic rules to the effect that citation rank benefit would not be passed if sentiment analysis of surrounding text/context indicated it was a complaint rather than a compliment/recommendation.

Conclusions?

• Google’s actions and rhetoric would appear to strongly indicate that there was something about what Borker was doing that may’ve benefited him. From external traffic reporting services, and from analyses of what terms the site seemed to be ranking for, it appears he was mostly getting long-tail phrase matches. However, even his ability to get longtail matches indicated he had a small amount of ranking weight from something. So, theories expressed by some people that he was doing nothing that helped him at all appear to me to be off-base. Where there’s smoke, there’s fire.
• The analysts who say that DecorMyEyes.com ranked only because of paid links, at best are painting an incomplete picture. If it was the case, Google could’ve then simply demerited the site’s external links’ voting ability and done nothing else to penalize them. However, if you’re going to believe Google any, then they are strongly suggesting they altered something else in penalizing Decor My Eyes, and you have to also suspect that many of the paid links and nofollowed links were not passing any PR anyway. If that’s so, there was some other, hidden phenomenon going on.
• Citations might be the hidden phenomenon — the “black matter” missing from our unified field theory of the ranking algorithm universe. If so, Google’s actions may’ve involved tweaking some aspect of assessment or transferring of citation ranking weight.