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The Effect of Activating Google AdWords Sitelinks Posted: 08 Dec 2010 05:00 AM PST Posted by Petter El Fakiri This post was originally in YOUmoz, and was promoted to the main blog because it provides great value and interest to our community. The author's views are entirely his or her own and may not reflect the views of SEOmoz, Inc. Around one year ago Google announced that sitelinks could be added to Google AdWords ads. What's the effect of this feature on users' click patterns? A company I have been working with activated sitelinks in their AdWords ads on search for CompanyName. They also ranked #1 organically on this search phrase. From experience it is smart to advertise with the company name as long as the paid clicks are cheap and give the AdWords campaign high quality. But was it right to activate sitelinks as well? Having activated sitelinks, the company soon experienced a substantial change to their click profile for users coming from Google. Sitelinks were disabled for the company name after 11 days, it was time to dig in and investigate the effects. The two following graphs show the traffic from Google where the search keyword is CompanyName. The first graph shows traffic from Google AdWords and the second traffic from organic Google. The effect on traffic from Google is obvious. During the period with sitelinks enabled, traffic from Google AdWords dramatically increases, while the organic traffic decreases. Below is a comparison of the traffic before AdWords sitelinks where enabled and the traffic during activation. Both graphs show traffic from Google on CompanyName search. As you can see; between the periods the total traffic from Google on search phrase "CompanyName" is up less than 1%. The periods have comparable traffic quantities.     In the period when AdWords Sitelinks was active there was a 91% increase in traffic from Google AdWords, shown by the next graph. This translates to about 2700 hits per day for this single search term.     Finally the following graph illustrates what happened to organic traffic during the same two periods. The traffic is down almost 25% on searches for CompanyName, where the last period is the one where sitelinks was enabled in AdWords.   Pretty pictures are one thing, but often the numbers can be more persuasive:     The statistics show that the number of visits from organic search is down by roughly the same amount that the traffic from paid search is up. Assuming a CPC of 0,30NOK (about 5 cents) on CompanyName in AdWords, means 300.000 NOK ($ 50 000) per year in extra Google AdWords cost if our client activates Google AdWords Sitelinks. With no traffic increase to show for it! Considering Google allowed purchasing of competitors names in our corner of the world about two months ago, this will probably drive the CPC for company names up significantly. Increased cost for the company aside, the changed click pattern is also noteworthy from a SEO perspective. During the period without AdWords Sitelinks the ratio between organic and paid results was 78/22, which is close to what SEO's consider a normal distribution. When AdWords Sitelinks where active the ratio changed to 59/42. Google has drastically changed the click pattern by allowing Sitelinks in AdWords ads, at least in this case. Are Sitelinks something to avoid? Of course not! There are many solid and valid arguments for ads using the company name with Sitelinks enabled. The clicks are often cheap, they always have high CTR and ranking. This is the foundation for a well managed AdWords account of high quality, good ranking and nice click prices once the whole account is considered. Please let us know if anyone else has similar experiences with Google AdWords Sitelinks.   Do you like this post? Yes No

Google Places SEO: Lessons Learned from Rank Correlation Data Posted: 07 Dec 2010 01:14 PM PST Posted by randfish In early June of this year, SEOmoz released some ranking correlation data about Google's web results and how they mapped against specific metrics. This exciting work gave us valuable insight into Google's rankings system and both confirmed many assumptions as well as opened up new lines of questions. When Google announced their new Places Results at the end of October, we couldn't help but want to learn more. In November, we gathered data for 220 search queries - 20 US cities and 11 business "types" (different kinds of queries). This dataset is smaller than our web results, and was intended to be an initial data gathering project before we dove deeper, but our findings proved surprising significant (from a statistical standpoint) and thus, we're making the results and report publicly available. As with our previous collection and analysis of this type of data, it's important to keep a few things in mind: Correlation ≠ Causation - the findings here are merely indicative of what high ranking results are doing that lower ranking results aren't (or, at least, are doing less of). It's not necessarily the case that any of these factors are the cause of the higher rankings, they could merely be a side effect of pages that perform better. Nevertheless, it's always interesting to know what higher ranking sites/pages are doing that they're lower ranking peers aren't. Statistical Signifigance - the report specifically highlights results that are more than two standard errors away from statistical significance (98%+ chance of non-zero correlation). Many of the factors we measured fall into this category, which is why we're sharing despite the smaller dataset. In terms of the correlation numbers, remember that 0.00 is no correlation and 1.0 is perfect correlation. It's in our opinion that in algorithms like Google's, where hundreds of factors are supposedly at play together, data in the 0.05-0.1 range is interesting and data in the 0.1-0.3 range potentialy worth more significant attention. Ranked Correlations - the correlations are comparing pages that ranked higher vs. those that ranked lower, and the datasets in the report and below are reporting on average correlations across the entire dataset (except where specified), with standard error as a metric for accuracy. Common Sense is Essential - you'll see some datapoints, just like in our web results set, that would suggest that sites not following the  commonly held "best practices" (like using the name of the queried city in your URL) results in better rankings. We strongly urge readers to use this data as a guideline, but not a rule (for example, it could be that many results using the city name in the URL are national chains with multiple "city" pages, and thus aren't as "local" in Google's eyes as their peers). With those out of the way, let's dive into the dataset, which you can download a full version of here: The 20 cities included: Indianapolis Austin Seattle Portland Baltimore Boston Memphis Denver Nashville Milwaukee Las Vegas Louisville Albuquerque Tucson Atlanta Fresno Sacramento Omaha Miami Cleveland The 11 Business Types / Queries included: Restaurants Car Wash Attorneys Yoga Studio Book Stores Parks Ice Cream Gyms Dry Cleaners Hospitals Interestingly, the results we gathered seem to indicate that across multiple cities, the Google Places ranking algorithm doesn't differ much, but when business/query types are considered, there's indications that Google may indeed be changing up how the rankings are calculated (an alternative explanation is that different business segments simply have dramatically different weights on the factors depending on their type). For this round of correlation analysis, we contracted Dr. Matthew Peters (who holds a PhD in Applied Math from Univ. of WA) to create a report of his findings based on the data. In discussing the role that cities/query types played, he noted: City is not a significant source of variation for any of the variables, suggesting that Google’s algorithm is the same for all cities. However, for 9 of the 24 variables we can reject the null hypothesis that business type is a not significant source of variation in the correlation coefficients at a=0.05. This is highly unlikely to have occurred by chance. Unfortunately there is a caveat to this result. The results from ANOVA assume the residuals to be normally distributed, but in most cases the residuals are not normal as tested with a Shapiro-Wilk test. You can download his full report here. Next, let's look at some of the more interesting statistical findings Matt discovered. These are split into 4 unique sections, and we're looking only at the correlations with Places results (though the data and report also include web results). Correlation with Page-Specific Link Popularity Factors With the exception of PageRank, all data comes via SEOmoz's Linkscape data API. NOTE: In this data, mozRank and PageRank are not significantly different than zero. Domain-Wide Link Popularity Factors All data comes via SEOmoz's Linkscape data API. NOTE: In this data, all of the metrics are significant. Keyword Usage Factors   All data comes directly from the results page URL or the Places page/listing. Business keyword refers to the type, such as "ice cream" or "hospital" while city keyword refers to the location, such as "Austin" or "Portland." The relatively large, negative correlation with the city keyword in URLs is an outlier (as no other element we measured for local listings had a significant negative correlation). My personal guess is nationwide sites trying to rank individually on city-targeted pages don't perform as well as local-only results in general and this could cause that biasing, but we don't have evidence to prove that theory and other explanations are certainly possible. NOTE: In this data, correlations for business keyword in the URL and city keyword in the title element were not significantly different than zero. Places Listings, Ratings + Reviews Factors   All data comes directly from Google Places' page about the result. NOTE: In this data, all of the metrics are significant.  Interest Takeaways and Notes from this Research: In Places results, domain-wide link popularity factors seem more important than page-specific ones. We've heard that links aren't as important in local/places and the data certainly suggest that's accurate (see the full report to compare correlations), but they may not be completely useless, particularly on the domain level. Using the city and business type keyword in the page title and the listing name (when claiming/editing your business's name in the results) may give a positive boost. Results using these keywords seem to frequently outrank their peers. For example:   More is almost always better when it comes to everything associated with your Places listing - more related maps, more reviews, more "about this place" results, etc. However, this metric doesn't appear as powerful as we'd initially thought. It could be that the missing "consistency" metric is a big part of why the correlations here weren't higher. Several things we didn't measure in this report are particularly interesting and it's sad we missed them. These include: Proximity to centroid (just tough to gather for every result at scale) Consistency of listings (supposedly a central piece of the Local rankings puzzle) in address, phone number, business name, type Presence of specific listing sources (like those shown on GetListed.org for example) This data isn't far out of whack with the perception/opinions of Local SEOs, which we take to be a good sign, both for the data, and the SEOs surveyed :-) Our hope is to do this experiment again with more data and possibly more metrics in the future. Your suggestions are, of course, very welcome. As always, we invite you to download the report and raw data and give us any feedback or feel free to do your own analyses and come to your own conclusions. It could even be valuable to use this same process for results you (or your clients) care about and find the missing ingredients between you and the competition. p.s. Special thanks to Paris Childress and Evgeni Yordanov for help in the data collection process. Do you like this post? Yes No

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