The name of the tool is Wonder Wheel and is available under the “Show Options” link to the top left of the main search results pane. Check out the screenshot below to see how to access it.

Now you get a sub-menu where there are loads of interesting filters for your previous search query, “fancy dress” in this case. There’s a Videos filter, Forums and Reviews filters and time filters which are useful for your average searcher. For SEO’s and keyword research though, the tool we’re interested in is the “Wonder Wheel” down near the bottom.

Ok now click on that baby and now we have a flash and ajax-ified wheel display which may resurrect memories of Set Theory in Maths class, showing search terms harvested from real search trend data in the Google database, which are related to the search term you had originally typed in. So for our original search term, “fancy dress”, we have the following:

We can click on any branch keyword from this star display to display both further search terms related to the branch keyword, which will also display new search results for the term we click on.

So how do we use this tool to help us with our keyword research and campaign setup? Well, we can start with a root keyphrase, such as “fancy dress” as we have in our example above, and split each of the keywords in the Wonder Wheel legs into their own ad group, using phrase and exact matching options. Of course this will also save us time, helping us to find keywords for our campaigns that we may not have found for a while through using analytics (see Building a Deep Keyword Google AdWords Campaign Without Third-Party Tools), or ever. Another great use of this tool is that it can help you to identify negative keywords for your ad groups.

This tool could also help us in a big way with how to lay out a website, helping to define hierarchy and categories etc, using a layout reflecting how the data is presented in the Wonder Wheel results.

Only certain users have been selected to test the prototype for this feature, so if it isn’t available to you then all you need to do to get it is to go to Google.com and paste the following Javascript snippet into your browser address bar and then hit enter:javascript:void(document.cookie="PREF=ID=4a609673baf685b5:TB=2:LD=en:CR=2:TM=1227543998:LM=1233568652:DV=AA:GM=1:IG=3:S=yFGqYec2D7L0wgxW;path=/; domain=.google.com");

So that’s it in a nutshell. Please do comment, ask questions, bookmark and subscribe to updates. Till next time, take care.

In AdWords, bid price is only one factor in its ad position equation. The other main factor in the ad ranking equation is Quality Score, as calculated by combining a bunch of factors, the 3 main ones being (listed in order of weighting):
1. Click-Thru-Rate (CTR),
2. Ad Relevance (keywords in ad title and copy)
3. Landing page quality (keywords in landing page copy, structure, etc.)

Then Google takes your Quality Score and multiplies it by your bid price (Max CPC), and generates a number, called “Ad Rank”. The higher this number is compared to that of your competitors, the higher your ad ranks on the page for the given keyphrase.

Then, your Cost Per Click for your ad is calculated by dividing your Quality Score into the Ad Rank of your next competitor (thus, higher QS = lower CPC).

Here is an example of how this works, with 4 ads all bidding the same amount for a given keyphrase, but all having different Quality Scores.

Google’s Chief Economist, Hal Varian, posted a video on YouTube back in March explaining how all of this works. The video is embedded below.

So this all points to how important it is to maintain a high Quality Score on all of the ads in your AdWords campaigns, in order to make sure that the ad is relevant for the user (ensuring a possibility of making a sale or gaining a lead) and also to keep costs down.

In my experience, it not good if your QS for a given keyword drops below 8. Incremental improvements to QS can be attained by constantly split testing ads against each other in each ad group in order to improve CTR and also by constantly testing and improving landing pages.

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One of the challenging things about setting up an AdWords campaign which is geo-targeting the Republic of Ireland only is that a lot of the third-party keyword tools out there have little or no keyword volume for searches performed in Ireland so therefore aren’t nearly as useful as they would be for analyzing say UK or USA audiences. You could use data collected from these high-volume countries to build out your keyword lists but, from my experience, often this does not reflect the search trends for Ireland.

So for this reason amongst others I have often set about building out a good deep keyword list for a campaign as the wider related keyphrases and synonyms are getting so little search volume in Ireland that they were virtually inconsequential.

So, in steps, here is what I find myself doing: Load up Google’s Keyword Tool (https://adwords.google.com/select/KeywordToolExternal, or by selecting “Tools -> Keyword Tool” from within AdWords) and from there typing in either the domain name of the client that I am building the campaign for, or, even better still, the domain name of a more comprehensive competitor’s website. Do check the box “Include other pages on my site linked from this URL” and click “Get Keyword Ideas”. From there we will get a good list of keywords.  Save/”Add” all of the appropriate keyword groups (some groups may be totally unrelated to your campaign, ignore these) that are displayed to your list which you can later download as a text file when you are finished adding.  Then underneath the saved keyword list on the right-hand side of the page, select “Download these keywords: text” link to get your complete list of keywords as a text file. Save and open using a text editor.

Now what we want to do is use this large list of keywords as an input into the Google Traffic Estimator tool and see which keywords get some search volume in Ireland.  From within your AdWords account go to “Tools -> Traffic Estimator” or go to https://adwords.google.com/select/TrafficEstimatorSandbox and from here you can copy and paste all of the keywords in your text file that you downloaded, into the “Enter Keywords” field. Choose Euros as the currency and leave the Max CPC field blank, and leave the Daily Budget field blank also, and then select and add Ireland to the Selected Countries box and click “Continue”. Now the Traffic Estimator will display a table detailing the search volume estimates for Ireland for the keywords selected. Sort this descending by search volume by clicking the “Search Volume” column heading link. Here now we can see what keyphrases (from the ones that we have gathered) are going to be worthwhile building out ad groups for.

From this I build out an ad group for each of the top trafficked keyphrases, using the keyphrase in the ad heading, and again in the ad text and/or display URL for the ad. This is called the “peel and stick” method. In each ad group I will write 4 different ad variations, making sure that I edit the campaign settings and change the ad serving option from its default setting of “Optimised” to “Rotate”, so that each ad is displayed with the same frequency. This technique is commonly called “split-testing”. Set the Ad-Group-Level Max CPC to be that of the estimated Average CPC (from the Traffic Estimator) for the particular Ad Group for that particular keyphrase. That way we know what our max CPC is going to be for that ad group.   To begin with, set all of the keyword match types in the campaign to be “Phrase”. This way we will avoid the poorly performing “Broad” match types (generally speaking, always to be avoided), and also enabling our ads to be triggered for the exact keyphrases in our ad groups that are searched for and also all searches for keyphrases containing that exact phrase but also with prefix word(s) and/or postfix word(s), for example, the keyphrase “fancy dress” with “Phrase” match type will trigger when the user searches for “fancy dress costume” and also when the user searches for “halloween fancy dress”.

Now, and this is the smart part: When I am starting out an an AdWords campaign like this, using Phrase match type on a broad search term, like “fancy dress”, I always tweak my Google Analytics profile for the target website to expose the exact keyword phrases that the users are finding my ads using. So for example, in Analytics by default, when someone finds and clicks on your ad after searching using the keyphrase “fancy dress costume” and the keyphrase in the AdWords campaign that triggered the match is “fancy dress” with Phrase match type, Google Analytics will only record this as an entry for the keyword “fancy dress”. Not very useful. To see how to configure Analytics to record the exact keyphrases that are finding your ads refer to Semvironment’s excellent article at http://www.semvironment.com/ppc-management-adwords-keyword-data-exposed-with-google-analytics/

Now, as the exact keywords are recorded over the first few weeks of the campaign, we know exactly which keyphrases people are searching for and finding our ads using. To improve the effectiveness of our campaign, and to keep it well ahead of the competition, we then create new ad groups in our AdWords campaign that are based upon the exact keyphrases that are recorded by Analytics Traffic Sources->Keywords report.

So, to be exact, we view this Keywords report in Analytics and take the top trafficked phrases that we don’t have any ads directly targeting and create a new ad group specifically targeting that phrase. So from the screenshot below of a live campaign that I am running, we can see that in #5 is the exact search term “halloween fancy dress”, which is finding the my ad which is targeting the phrase “fancy dress”. I don’t have an ad group targeting “halloween fancy dress” specifically so I will create one specifically for that and also I will add the exact match negative keyword ( -[halloween fancy dress] ) to my Fancy Dress ad group so that I am sure the correct ad group will be triggered when people search for “halloween fancy dress” in future.

Of course, other AdWords basic rules apply, such as always targeting specific landing pages on the target website which is most relevant to the keyword and therefore ad that the user clicks on, as well as using SplitTester.com to monitor competing ads within each ad group to see which one to drop and replace.

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So anyway, my friend mentioned to me that he wanted to get an AdWords campaign going in order to get some traffic to the site and generate more sales as business from the website have been slow. I remarked “ah, ok, well how many visitors are you getting to the site per day at the moment?” to which he looked at me perplexed and replied “don’t know!”. So we hopped up to the computer and logged into his Google Analytics account.

We were presented with the Google Analytics dashboard:

Ok, now correct me if I am wrong, but 1535 visits and over 7000 pageviews in the past month is not bad going, given that the site has not had any inbound link building done or any real SEO of any kind. So I suggested to my friend that we should first concentrate on getting more sales from the existing traffic before we go throwing money into Google AdWords when the conversion rate is abysmal.  

So what does he do to improve the conversion rate?  Well, we can see that most of the traffic is from the UK, coming in through the parked .co.uk domain. So the first thing my friend is going to do is to take a look at his pricing, taking a good look at his main competitor’s pricing for the products on the most trafficked product pages on his site. He can see which are his most viewed products by going to Content -> Top Content.  So an experiment would be to slash the prices of the top 5 or so viewed products and then see if sales on those products pick up.

But there is more that can be done of course, using Google Analytics more advanced features.  So, from a scientific metrics point of view, it will be useful for us, using his most trafficked product pages as a starting point, to use Google Analytics’ Goals and Funnels to chart the purchase path (the series of pages starting at the product page on through to the order “Thank You” page), and see using the Funnel Visualisation tool which pages along the way are causing the user to abort the purchase process. If most are aborting on the product page itself then either the page needs to be changed and re-tested, or the price needs to be reduced, or perhaps both.  Other pages along the way to the “Thank You” page may be leaky also. We will use the modify and re-test approach for these also until we are happy that the conversion rate is reasonably good. There is no limit to what you can do with the right tools and the commitment to using them.  We can also use a website optimisation tool like Google Website Optimizer to test variations of the pages on the site, analyse,  improve and re-test. 

But one thing is for sure, we can improve the conversion rate using Analytics data and tools. And this is the best approach at this point. Once we have the conversion rate improved to a satisfactory level we can then consider sending more traffic to the site with organic SEO methods and Google AdWords.  I’ll write an update post at a later date to tell you how we are faring on this project.  

Update: Part 2 of series of articles is available here http://webreach.ie/blog/checkout-goal-and-funnel-analytics-where-are-we-losing-sales-on-our-online-store/

Please feel free to leave comments if you have any feedback.