Digging deeper

Since my last blog entry, I took a few actions to further understand and implement Web analytics on my blog:

1. I created a filter on a separate profile to remove instances that my home computer viewed the blog from any data from this point onward.
2. I created two goals in the visit duration and pages/visit sections that consisted of visits of more than one minute and visits to more than one page. These goals seemed like a challenge and achievable since the visit average had previously been about 50 seconds, and I wanted to establish a baseline for pages per visit to gauge future success. Looking at my most recent feedback, I probably ought to raise this pages per visit goal because my average pages per visit is 4.13. I'm still surprised, partly because this is a student blog for class that it is showing a pages per visit figure beyond one.
3. I spruced up the site to make it more attractive but also add more opportunities for engagement such as buttons to allow viewers to share my blog posts, subscribe to the blog via e-mail, or subscribe via RSS feed.
4. I made the site more targeted to the marketing viewer by adding the blogs of my classmates scrolling down the side and adding Mashable news in a feed at the bottom of the blog. When you look at my chart on referral traffic below, you will see that one of my classmates' blogs directed traffic my way. Networking can be a wonderful thing.

I drove traffic to my site again with an invitation on Facebook that looked like this:

 

It followed up from my previous request that asked my family and friends to look at the site and told them I would try to identify their visits.

Filters

I am beginning to feel anxiety from using analytics software that does not look into historical data after the establishment of certain parameters such as filters.

When I established a filter to remove my home computer's IP address, I wanted to make sure my visits used to check the site for formatting problems, comments and my own curiosity were not being counted. I didn't want to overestimate my success, especially when the blog has a relatively small group of visitors. But since I established the filter on June 28, I only have myself filtered out of the data starting at that point and can't go back. It means that my main profile and the filtered one do not match up because the main profile reported its first activity on June 12 and the filtered profile begins on June 28.

On June 28, my full profile showed one visit more than on the filtered one. All of the other days match. This is because I did not visit my blog during the weekend because of a power and Internet outage after a storm hit the mid-Atlantic states. I had the impression that I visit my own blog more, but the filtered profile is so new and the weekend was more eventful than I had intended.

I can see how in a mid-sized company with a smaller audience, it could be valuable to filter out the domain name or IP addresses for employees, especially for those who maintain the website and are constantly checking functionality (Filters in Google Analytics, 2011). In my current job at a university, you wouldn't want to filter out the domain names for the main profile you check often, I am positing. The approximately 8,000 employees at the university have a domain of mail.wvu.edu and the 30,000 students have a domain of mix.wvu.edu. Remove those two and you'd cut out a chunk of the audience our news site is trying to reach. We are looking for outside customers of course, yet our internal customers are also an important audience. They are practically our only audience on some news items.

While you would not want to make all of your decisions based on a profile that doesn't account for all of your customers, it could be valuable to compare behavior by types of customers. One filtered profile could mark only internal customers. Another filtered version could include everyone but internal customers (Filters in Google Analytics, 2011).

For a news site, it is possible that internal customers would appreciate more the student profiles and faculty awards. Yet, outside viewers might be more interested in sports stories, stories about the mascot or stories about research that is applicable to their daily lives. Profiles filtered this way could tell you.

Goals

Here are the results for the two goals I set in Google Analytics for this blog. Goal accomplishments, like filters, also start counting from the day you input your goals into the software and they do not count retroactive data even if it meets the goals set.

 

Since June 28, I've had 13 goal completions, which includes nine visits that went to two or more pages and four visits that lasted more than one minute. This more valuable traffic came from Facebook referrals, blogger.com referrals and direct traffic.

I can drill down and look more closely at where this traffic is coming from to try to attract similar viewers (Kaushik, 2010). In the goals report, you can find out on which pages the goals were completed. With my blog at such an early stage (and considering it is a blog with pages being of roughly equal importance), I haven't gleaned anything too helpful. However, on a news site like the one at my workplace, I could look at goal conversions and discover on which pages the conversions occurred. This will tell me which stories, videos and pages achieved my company's goals.

Google Analytics also has the ability to track specific URLs as goals (Google Analytics, 2011). For example, you could set a goal for each time a customer reaches the “thank you” page at the end of a sales process. If you create events in Google Analytics, you can also register the event completion as a goal.

Google evangelist Avinash Kaushik offered a vignette about how he put a value on his blog based on the value he earns for his time on other pursuits (Kaushik, 2010). I think his time is worth a lot more than mine, and because this blog is more of a test right now, I personally don't put much value on it, seeing as how it draws its audience mostly from my family and friends who want to help me out, not users who are searching for analytics information.

If I were assigning value, I would wait until I blogged more extensively and did so outside of homework before I set a value. Though I have not done so, putting a value on goals is an incredibly helpful tool when it is done as accurately as possible (Kaushik, 2010).

Funnels

According to the Google Analytics IQ Lessons, the company's analytics software can set goal funnels. Funnels record the series of actions a user must take to fulfill a goal (Google Analytics, 2011). As I set my goals, I thought about which goals would tell me the most important information. I'm most interested in visitors who spend a lot of time on the site and visit more than one page.

Step-by-step processes are extremely useful in e-commerce sites where there's a shopping cart step followed by checkout, registration if necessary, payment and confirmation pages. Of course funnels are useful to more than just e-commerce sites. As I examined the usefulness of implementing goal funnels for my blog, I did not find a process that I wanted visitors to follow. I'm pleased if they visit pages in any order. I'd like them to subscribe to the blog, but I don't prefer a particular order for that either.

However, for my company's news site, we could set a funnel for projects that have stories and videos embedded. We want viewers to enter the story and then move to the video. We can also look at funnels for common links, such as links to the university's main page or to the admissions or application page. This can give us a good picture of how our strategy and goals connect.

Once you have defined your funnels, you can see where your site may have technical difficulties, where it may lose audience/customers and the most effective path to goal conversion (Google Analytics, 2011).

Since I have collected more data, I've noticed a few extraneous changes I wanted to note in this post:

Source traffic

I have a keyword to examine now! My one visitor from outside the United States is in the United Kingdom, and this person used a keyword to get to the site and didn't bounce! The visit originated from Little Chesterford. I can safely say I know no one from there. I feel like I have a legitimate blog now.

 

This one visitor came to the site via the keyword shown in the chart below, “facebook vs google ads diagram.” The visit included two pages, though it was a very short visit. I know my blog post on Facebook vs. Google did not have a diagram though this visitor was searching for one, so maybe this contributed to the relatively quick exit.

Analysis of this keyword underscores the large amount of useful information that analytics can provide even though it is anonymous. I have no idea who this person is. But I know what he or she was looking for, the pages that were viewed and the duration of the visit.

Now let's look at traffic as a whole. Most visits are still coming from Morgantown. I've got one more visit from Moscow, Idaho, and from Broward County, Fla. There are no additional visits from West Palm Beach and Lake Worth in Florida.

But one of my old friends from Kansas City has visited presumably since I only know two people who live there.

I have a visit from Manassas and a visit from Oakland, both of which bounced. The two visits from Philadelphia also bounced. The visits from Dallas and Plano did not bounce.

Overall, the blog's reach is increasing, but as you can see below, the site has a majority of new visitors, which is not a good sign, according to Kaushik (2010).

Bounce Rate

I finally have a bounce rate of 27.78 percent! You should not normally be excited about a bounce rate because that means you have visits that consist of only one page. But I'm glad to see the bounce rate because it means that I am getting more visitors, even those who happen across the blog by mistake, and I am also sure that the analytics software is working. It also helps that my bounce rate is relatively low.

References

Google Analytics. (2011). Filters in Google Analytics. Retrieved from: http://www.google.com/analytics/iq.html

Google Analytics. (2011). Goals in Google Analytics. Retrieved from: http://www.google.com/analytics/iq.html

Kaushik, A. (2010). Web Analytics 2.0: The art of online accountability & science of customer centricity. Indianapolis, Indiana: Wiley Publishing, Inc.