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Web Analytics: Measured Optimisation

Web analytics is the objective tracking, collecting, measuring, reporting and analysis of quantitative Internet data to optimise a web site and it's marketing initiatives.

With web analytics, you can understand your visitors, traffic patterns, marketing campaigns, conversion dynamics and more. This tool can make it easy to fine tune your website and campaign performance to maximize your return on investment (ROI).

 

What Is a Log File?

A log file is simply a file that is used keeps track of all that is happening with your site.

For example, it records:

• Each time a Web page is requested.
• What incoming link a visitor followed to arrive at your site.
• What search terms a visitor used at a search engine before arriving at your site.
• Errors that may have occurred.
• And much more.

 

Why Is Log File and Traffic Analysis Important

Analyzing your log file and traffic will give you insight into:

• How well your web site is performing
• How visitors are finding your site
• What pages are the most popular
• The geographic location of your visitors
• What search engines are visiting your site.
• How much of your site the search engines are crawling

Analyzing your log files will let you know how well your various marketing programs are working.

We run into a lot of companies that don't have the time or other resources to study and map trends based on these logs. Analyzing marketing efforts is the only way to know if they have been effective. So before embarking on web marketing, a company needs to learn how to at least glance at the logs and get a general sense of how things are progressing.

There is some basic terminology that, if you understand it, will make reading your logs an easier experience.
Reading the logs / Definitions:
* View or Hit: Depending on the software, may include hits to graphics files (such as JPGs and GIFs), scripts or any other type of file. 
* Visit or Page View: A series of consecutive views of a Web site by the same user. These figures are considered estimates. 
* Unique Visitor or Session: A person viewing a Web site. Without a cookie - a visitor is defined as a unique combination of a user agent and a host name or IP address. With cookies - a visitor would be defined by the cookie transmitted by the visitor's browser. This can also be an authenticated user name or a parsed parameter.
* Referring urls: Websites that send people to your website (search engines, directories, relating sites, etc.)
* Paths through site: Shows how people are going through site. Important in understanding content interaction.

How to analyze the logs:
Know your most wanted response. Do you want them to sign up for a newsletter, register, order a product? Choose one or two most wanted responses so that you have a goal as you look at the logs.

Look at the logs to see how many people are going to that page and performing your most wanted response. To make things easier, your software can be set up to generate a report specifically for that action. 

Trends to look for - 

a. Where is traffic coming from? Look under Referring urls or Referrer section.

b. What engines, what other sites, where do they enter the site? Referrer section.

c. How many people from each traffic source performed the most wanted response? Look in a section called "Traffic Analysis" or "Content/Path Through Site" to see how users traveled from the referring url through the site to the most wanted response page.

d. What's the ratio? Take the whole # of "unique visitors or sessions" and compare it to the # you noticed above that completed the most wanted response. How do you tell? They are typically directed to another page thanking them, for example, upon completing the most wanted response. If this info is not easy to see in the "Path through Site" section look for a "Path Summary" type of section that will summarize the # of people that ended up on your most wanted response page.

e. If the ratio is lower than you want, at what page do people start leaving the site? (Traffic Analysis or Content/Path Through Site section) Maybe they drop out, for example, at the "Contact Us" page if you don't provide an address or phone number.

f. How long do they stay on each page? Look in your "Visitor Analysis Section" under something like "Visitor Duration." If it's 1 minute or less on the front page and then they leave, they were probably not looking for your service and you are targeting non-qualified traffic. 

g. More specifically, how many people that came from your Overture campaign actually registered for your newsletter or bought product? (You can find this info under "Referring Urls" and Overture referrers can have extended tracking code like: "?source=overture" so you can recognize those referring urls) 

h. How many people that came from your banner ad on your association website signed up for the contest? (You can also find this info under "Referring Urls")

Once you start answering these questions, or other questions you need answered, you can tweak your site and your marketing to increase those ratios to what you need to be profitable.

Implementing Improvements

Since conversions are critical to business success, it is wise to initiate a program of A/B split testing before final implementation of site changes. Test two different versions when testing copy changes on a call-to-action landing page, one at a time. The table below shows the hypothetical results of such a test.

A/B/C Split Test
	PAGE A	PAGE B	PAGE C
Percent of traffic	34%	33%	33%
New sales	200	220	150
Change	N/A	10%	-25%

A/B Testing

One of the most commonly used tools for measuring the effectiveness of a proposed website change or campaign change is A/B testing. You can test different versions against the benchmark, and this test tells you which changes have a better effect and to what degree.

The advantage of A/B testing is that you can send a percentage of your traffic to the page/s with the proposed changes while sending a portion of it to the current page. That way you can retain your current conversion rate for at least part of your site traffic in case some of the proposed changes are unfavorable.

Following are some guidelines that will help you get meaningful, measurable results, if you plan to run A/B tests on a website change or an email campaign.

• Change only one variable at a time. This is a cardinal rule for testing any kind of change. That's because if you change more than one variable at a time, you are unable to determine which variable is responsible for the change and to what degree.
• Learn the precise process for diverting traffic. One of the problems in A/B testing is that marketers don't fully understand the mechanism for diverting traffic, thus are not getting accurate traffic numbers. There are several strategies that can be used to divert traffic, but these are not well understood by the layman. The object of the traffic diversion mechanism is to redirect a known percentage of visitors through a modified process. In the ideal situation, the percentage of traffic to be redirected can be easily changed without having to radically modify pages.
• Establish accurate measures of volume: Start by getting a "visitors per page" count from your web analytics tool as step 1 in A/B testing. This ensures that you actually get the percentage of traffic moving through your funnel that is expected based on the number of changes tested. For instance, if you are running 50/50 through A and B, you should see near equal numbers of visitors to the first page in the process and continue running the test until the numbers have converged to about 50 percent through each path. If it's a three-way test, you should see a distribution of 33/33/34 percent of visitors running through each path.
• Look for significant differences: If you see a difference in the conversion rate for the B test, you need to ensure this difference is significant (>.05) so it can be attributed to the step changed in the sales process. Smaller differences can be due to variations in visitor quality (noise). Run your test until all of the change can be attributed to the exact step that was modified, or until you are certain there was no change.
• Take the time to do a null test: Before A/B testing, run a null test by flowing 50/50 traffic through the exact same pages to be A/B tested. This will verify that you get the same conversion and abandonment rates, and that your measurement tools are set up correctly. If you are not getting close to the same rates (within .05) for both tests then something is wrong, and your data from the A/B test will not be valid. If this problem arises, check that (1) you are sending visitors into the tests exactly the same way, i.e., visitors are not pre-selected, sending more qualified prospects down one path or the other; and (2) you are running enough visitors through the test. Depending on your traffic volume, you need to attain a reasonable sample, and this can take time. But it is worth the effort to run a null test because you will have more confidence in your A/B test data.
• Run your test long enough to ensure results are real. The most common mistake in A/B testing is not running the test long enough to determine actual differences. There may be trends in the first few hours that will reverse themselves later. You need a representative sample before you can assume that B is better than A or vice versa. For information on what constitutes a statistically relevant sample consult a statistics textbook.
• Run segmentation tests: Your analytics solution should provide robust segmentation tools. Segmenting your test subjects will allow you to monitor their activities when they return to your site. This lets you pinpoint a possible high-value group of visitors if it turns out that a good percentage of your B-test visitors (A/B test results favored B over A) returned to the website within two months to make another purchase. You can also compare this information to your site-wide loyalty metrics for a better understanding of the long-term effects of your A/B test.

A/B testing can help you take advantage of the continuous improvement process. The upside of A/B testing is that if your proposed changes are harmful, not all of your visitors are subjected to the unfavorable change, only those that ran through the B test. This is better than making the change without testing and hoping for the best. The downside is that A/B testing is a complex process that takes knowledge, precision and time.

 

Measuring Results

In the past, few marketers were tracking conversions, much less return on investment (ROI) for marketing initiatives. In fact, many marketers thought search marketing campaigns were too hard to measure. A 2003 industry study reported that 3 in 10 marketers were not measuring search campaigns, while 4 in 10 were using outdated web metrics such as click-throughs.

A 2005 search metrics study reveals that nearly 1 out of 2 search marketers who outsource SEO and paid search services either do not elect to measure ROI, are unable to calculate ROI, or can't distinguish between SEO and paid search ROI. This result was obtained despite the fact that most vendors provide access to web analytics tools. One would think that marketers would be interested in validating their vendor's performance by quantifying results.

 

Start Again!

Once you finished the process, the only thing left to do is to start it all over again! Continuously monitoring your conversions and adjusting your goals and metrics will allow you to stay ahead of your competition. They say knowing is half the battle and this methodology is designed to get you there.

By studying your data and implementing different strategies, you will be able to identify the areas that need improvement to better maximize your ROI.

Like any other area related to internet marketing, the best way to be successful is with continual refinement. Once you've reached your goals, go back and make new ones. Your reached goals are your new baselines, there is always more to achieve, so continue to set the bar higher.

Does all this sound too complicated or like too much work?
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