Google Introduces Weighted Sort

August 25, 2010 by  
Filed under Website Analytics

If you want to fine tune your website in an effort to increase sales you absolutely must pay attention to what your website analytics is telling you. In short, you must know where your problem spots are if you are going to be able to correct them.

Now, there are a number of website metrics that should be focused on in an effort to uncover problems. One of them is your bounce rate — both website wide, and on individual pages throughout the site.

Now, don’t confuse bounce rates with exit rates. The two represent different items. A bounce rate can be defined as follows:

Bounce rate represents the percentage of initial visitors to a site who “bounce” away to a different site, rather than continue on to other pages within the same site. Or in other another way of saying it is the percentage of visits where the visitor enters and exits at the same page without visiting any other pages on the site in between.

It goes without saying that ideally you want your bounce rate as low as possible.

To determine where you have problems associated with bounce rate it is best to drill down on a per page level and determine which pages produce the highest bounce rate. Until now, doing this worked similar to the following: you locate the content sections of your site within analytics, you click the bounce rate column to sort from highest bounce rate to lowest, you are presented with a list of pages from 100% bounce rate to lowest and then you start to scan / scroll / analyze those returned based on what is really important and indicates a problem area.

The initial results returned using this method are useless. You get pages with 100% bounce rate that really don’t indicate a problem spot and for the most part are only listed at the top because of their ‘100%’ measurement. Now, you might be asking how can a page with a 100% bounce rate not be a problem. I’ll provide you with a brief example of this very concept.

One visitor arrives at a page deep within the site (we’ll say the contact page) and then bounces leaving analytics with a 100% bounce rate. One thousand visitors arrive at the home page and 500 bounce leaving a 50% bounce rate for the home page. Although the visitor who arrived at the contact us page resulted in a 100% bounce rate, the more important bounce rate to pay attention to is the bounce rate of the home page. Why? More visitors arrived at the home page than the contact page and thus, the ‘weight’ of the bounce for the home page is more of an indicator of a problem point on the site. Think of it as working with a “larger data sample.”

Having provided this example it now becomes clear that the past sorting method offered by Google Analytics did little to quickly provide the information needed to help make decisions fast. Yes, you could get to it, but even after sorting you had to wade through pages of information to really find those pages that should get attention.

All that has changed with the addition of weighted sort in Google Analytics. Weighted sort enables you to now drill down on previously useless sort results based on the ‘weight’ each page (in this example) has over others on the site. This provides more relevant results faster and thus makes it easier to determine where to focus your attention when attempting to improve your website.

You can read more about the new feature and how to use it here:

http://analytics.blogspot.com/2010/08/introducing-weighted-sort.htm
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80 Google Website Optimizer Tests for Ecommerce Sites

April 23, 2010 by  
Filed under Conversion, Website Analytics

For those of you have have not seen these yet I thought I’d post links to two very nice articles on what you can do to utilize the power of Google’s Website Optimizer.

Google’s Website optimizer is a powerful tool that enables you to increase the conversion of your website using testing methods such as Multivariate and traditional A/B split testing. When setup and utilized correctly, Website Optimizer gives you the ability to discover what really works to turn visitors into sales (or just about any other action you are seeking from your audience). No more guessing.

For any of you have have already tried your hand at “manual” split testing, you know it can be tedious, time consuming, and down right tough to keep track of all the possible combination’s that are put together for any given test. That process is made much simpler with the use of Website Optimizer.

The instructions for setting up optimizer are easy to follow and do a great job for even newbies on helping them get up and running (although if your cart requires programming to integrate the code into the pages you may need the help of your technical team).

One article circulated last year and was titled 55 Google Website Optimizer Tips and Tricks.

The other surfaced February 23 of this year as a “sequel” to that original post and it is titled 25 Google Website Optimizer Tips for Better Product Pages.

Together these articles combine to provide a total of 80 different tests you should consider running on your site. I highly recommend reading them and keeping them on your bookmarked list for future reference.

Tracking Google Product Search Traffic in Google Analytics

April 7, 2010 by  
Filed under Website Analytics

I recently had a member ask me this question and after some research found a number of answers. All were interesting.

The question we are looking to find an answer to is “how can we track traffic arriving from Google’s Product Search separately from all other traffic within Google Analytics?” For those of you not familiar with it, Google’s Product Search provides product based results on items that are loaded through Google Base accounts.

Here are two main options I found for accomplishing this.

The first option is a quick way to use filters in GA to segment the traffic. This option might be best for sites that have a lot of product linked urls listed in Google Product Search. The second method uses url tagging (we talked about this in recent posts) and can be arrived at two different ways and although works, might not be the best if you have a lot of urls to change.

Option 1 (Use GA Filters):

Using filters can help alleviate the need to tag many urls. We can setup a filter within GA that segments the data we are already receiving and breaks out the portion of traffic that comes from Product Search. Here’s how:

When you perform a search on Google, you’ll see that your results page has a URL that looks something like the following:

http://www.google.com/search?hl=en&q=product+search&aq=f&aqi=&aql=&oq=&gs_rfai=

The key point to note here is that when performing a search from the Google Search Engine all searches start with the following string:

http://www.google.com/search?

However, when you perform a search from Google Products the url looks something like this:

http://www.google.com/products?hl=en&q=product%20search&um=1&ie=UTF-8&sa=N&tab=wf

The key point to note here is that when performing a search from the Google Product Engine all searches start with the following string:

http://www.google.com/products?

Knowing this we can setup a filter using the Referrer field to differentiate between the two and then use the Source field from within Analytics to view the data based on referrer.

Tracking Google Product Search

Field A in our filter looks for a Referrer like one of the Google URLs shown above. Field B limits the data returned to only organic searches (AdWords ads can appear on the Google Product Search pages also, and we don’t want to mess up that reporting). The Output To section actually changes (or rewrites) the Source to “google base” instead of just “google”.

Here is what it would then look like in reporting:

product search source reporting

Option 2 (Use URL Tagging):

Method 1:
If you don’t have a lot of URLs to tag and want to do option 2 then you might be able to get away with the information presented here:
http://www.google.com/support/merchants/bin/answer.py?hl=en&answer=160634

Method 2:

If however you have lot of product URLs to tag and want a better way of doing it then the following might be a better option.

This method uses the same concept I had introduced in previous post on using Google’s URL Builder to for tagging.

In this method though we tag the URLs with:

?utm_source=google&utm_medium=base&utm_campaign=products

The components that are going to let us get segmented reporting on the traffic from Product Search within Google Analytics are source=google and medium=base.

When you go this route your reports will be broken down as follows:

product search tracking

Here are the steps to follow for tagging your URLs:

We are going to use the native Excel format for the feed to address the tagging.

1. Download your product feed and open it with Excel

2.Insert two columns to the right of the column that contains your product URL (column header should be “link”)

3. Write your tracking code into the cell to the right of the URL (see below):
tracking_productsearch_ga4

4. In the next cell to the right the one you just inserted, write the following formula, substituting cell numbers if appropriate:

=CONCATENATE(B2,C2)

You should see a result similar to the screenshot below showing the two cells merged together:

tracking_productsearch_ga5

5. Copy the cell formula all the way down to cover all of your products so that all of them having the tracking added.

6. Highlight all of your product URLs (in the third column of the spreadsheet) which now have the tracking code added, and then copy and paste them into Notepad

7. Delete the columns you’ve just created so that you only have the ones you started with.

8. Paste in all of your new URLs over the top of the old ones – you have to do this in order to get the spreadsheet back in a format to be uploaded – the extra columns will be rejected.

9. Upload revised feed to Google

10. After a day or two, log into Analytics and go to traffic sources. You should be seeing traffic from Google / Base showing.

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