In addition to recommending Google Analytics (GA) to our client base, we utilize GA as our analytics platform to track our internal web stats, as well. We are one of a few select companies to be named a Google Analytics Certified Partner (GACP) and according to Google “Certified Partners are carefully vetted by Google and meet rigorous qualification standards”. We are very proud of this honor and continue to educate clients (both large and small) on the ins and outs of GA.

Once GA is implemented onto a website, marketers suddenly find themselves with a wealth of knowledge and information they never had before. I get asked the same question over and over, which boils down to: What should I be looking for? While GA is extremely user friendly, especially compared to other platforms available today, it‘s also remarkably robust, thus making it (seem like) a daunting task to sift through all of the available data. You can literally get lost in GA for hours, discovering new and valuable information pertaining to your web stats. That being said, what should you be looking for? If brand new to GA, I find it a good exercise to simply start with the dashboard. As you become more comfortable, it will be less overwhelming to dive deeper into all of the reports GA has to offer.

GA defines their dashboard as “your customizable collection of report summaries”. Below is a screenshot from GA, which shows some of the default metrics; they can be arranged according to individual user preferences. Each metric can be clicked on in order to get more granular. Although the dashboard is rather basic, there are still a ton of valuable insights such as: Avg. time on site, bounce rate, pageviews, new visits, pages per visit, etc. The Map Overlay feature is particularly useful in determining where your site traffic originates from, plus which areas are converting into sales, leads, etc. You can drill all the way down to city.

All in all, GA is a comprehensive (not to mention free) tool that will prove to be mission critical in determining which online initiatives are fruitful for your business and which could be replaced. Every dollar counts and you ought to be armed with as much knowledge about your web stats as possible. If you have not already done so, check out our analytics page now and get started!

That said, paying attention to Analytics is not for everybody, but it’s a skill set that someone related to your e-marketing efforts needs to possess if a program is going to be profitably managed in-house.

The options and triggers available today within Google AdWords and Microsoft AdCenter (soon to be powering SEM for both Yahoo and Bing) to incrementally nudge ahead the performance of your paid efforts are too significant to ignore.

Several steps need to occur:

• Properly coding your website and campaigns for Analytics
• Getting familiar with the data that populates
• Customizing the data to suit your program objectives
• Assessing Analytics data and making modifications to the campaigns based on past performance

The SEM industry has evolved to the point where there are typically a handful of companies in any industry / vertical that are adept at SEM and are effectively utilizing Analytics. Those are the firms to keep an eye on and learn from, but that will not realistically happen without an appropriate understanding and commitment to Analytics.

SEO = Search Engine Optimization
A/B testing = Well…A/B testing! (Like you can do with Google Website Optimizer)
Google Analytics = Web Analytics

Now, how in the world do we do A/B testing on our SEO efforts while using Google Analytics to evaluate the results?

On June 8th of this year, Google rolled out a new search index called Caffeine. This new search index collects and processes information across the web at a much faster rate of speed and accuracy than the previous search index. This means that you can update your website with new content and it is almost immediately indexed and available to be searched for on Google. Website owners who perform frequent updates to their site benefit the most, as Google will pretty much always have the latest version of your website in their search index.

A new search index that instantly updates itself also means that you can perform A/B testing on your organic search results and use a web analytics platform like Google Analytics to determine the rate of success or failure of your experiment. For example, let’s say that you are the owner of HardwareStore.com, and when a person searches for “hardware shop“, they see this organic listing:

As you know from reading our Search Engine Optimization blog over the years, the listings that you see in organic search results derive from the relevant web page’s “meta” tags (specifically, the title tag and the description tag). Before the launch of Google Caffeine, it would be several weeks, sometimes months, for your organic listings to be updated to reflect changes performed on your meta tags. Now, the changes are practically immediate. You can swap out the title and the description meta tags to deliver a different search engine result to your future website visitors who are searching for you on Google. Essentially, you’re running an A/B experiment to see which organic listing produces the best results.

Then what you can do in about one to two weeks after you’ve updated your meta tags is log-in to your web analytics tool and perform a date-range comparison to see what affect your change had on your site performance and task completion metrics, like in this example from Google Analytics:

As you analyze the above image, you can see that the change in your organic listings resulted in higher revenue and a higher conversion rate, which means that you should most likely keep the new meta tags for a while (but keep an eye on that average order value and crunch the numbers to determine if you really are more profitable in the long-term with a lower average order value and per-visit value).

Presto! You’ve successfully performed an A/B experiment with your SEO efforts, while using web analytics to measure the results!

I postulate that the same culture-driven approach can be applied to your company – no matter how big or how small – to create an environment where testing, experimentation, and optimization come first. As you know from reading our blog over the years, we here at MoreVisibility love Google Website Optimizer, a free A/B and multivariate experimentation tool. With Google Website Optimizer, you can create experiments rather quickly and efficiently and get excellent insights as to how your visitors are reacting to the new pages or new variations that you’re sending their way.

Creating a Google Website Optimizer experiment involves some important people that either work directly for you or that you’ve hired to do work for you (depending on your situation). It also involves six steps – from creating the account to installing the JavaScript snippets to launching your experiment – that you and your fellow colleagues, companies or vendors must participate in to ensure that your Google Website Optimizer experiment is a success.

Today, I’m outlining the six critical steps that need to be taken to successfully create and initiate your Google Website Optimizer experiment. Let’s begin!

Step 1: Deciding What to Test
People Needed: Site Owner, IT / Webmaster, Marketer, Web Analyst, Web Designer
Objective: To come up with a crystal-clear picture of what will be tested (what page, what section, what page is the conversion page) and what type of experiment will be run (A/B or Multivariate).
What You Need to Know: This first step should be a meeting with everyone that will be involved in this process, because it is a team effort. Everyone from the owner of the website to the IT guru should be heard and should have something to say about the upcoming experiment. At this step, you’ll decide on two major things: what you will be testing and what type of experiment you want to run with. If you want to do a simple experiment involving an original page and a variation (or “B” page), then the A/B experiment is your best bet. If you have lots of ideas that you want to test on a single page, go with the multivariate experiment. If you’re not sure, you can’t miss by choosing an A/B experiment.

Step 2: Creating the Necessary Pages or Variations
People Needed: IT / Webmaster, Web Designer, Site Owner
Objective: To actually create and upload the variation pages or variation sections (image and text) to use in the forthcoming experiment.
What You Need to Know: Once everything has been decided on, your web designer will actually create the variation page(s) or variation sections for use in your experiment. Once the web designer has done their work, the site owner should be involved to give the green light on any images or mock-ups to proceed. Once the site owner OK’s the variations, they need to be uploaded to the web server (Google Website Optimizer needs to verify that the variation page(s) exist).

Step 3: Creating the experiment in Google Website Optimizer
People Needed: Web Analyst or Marketer
Objective: Creating the Google Website Optimizer experiment and verifying that the pages to be used are on the web (uploaded to your web server).
What You Need to Know: The person who will be in charge of creating the experiment in Google Website Optimizer, which is almost always the web analyst or the marketing person, will go in and start creating the experiment within Google Website Optimizer. They will enter in the URLs of the original, variation, and conversion pages for verification, and grab the JavaScript snippets for the IT / Webmaster to install.

Step 4: Installing the JavaScript Snippets
People Needed: Web Analyst or Marketer, IT / Webmaster
Objective: Working with the IT department or Webmaster to install and verify the JavaScript snippets on the experiment pages.
What You Need to Know: The IT / Webmaster will follow the on-screen instructions provided by the web analyst or marketer and install the JavaScript snippets that need to be installed on all pages involved in the experiment. They will work with the web analyst or marketer to confirm that this has been done exactly as outlined by Google Website Optimizer.

Step 5: Reviewing the Experiment
People Needed: Web Analyst or Marketer
Objective: To review the entire experiment set-up and preview experiment pages before launch.
What You Need to Know: This is the time to review that everything looks good and that there are no design flaws or broken HTML on the experiment pages. If everything looks okay, the web analyst or marketer will launch the experiment!

Step 6: Launching the Experiment
People Needed: Web Analytics or Marketer, Site Owner
Objective: To activate the Google Website Optimizer experiment!
What You Need to Know: Once the Google Website Optimizer experiment launches, it’s important that the owner of the website is made aware that some visitors to his or her site will start to experience different variations of the site. It’s also important to let the experiment run its course naturally (without influencing it by making changes to the web pages involved in the experiment). After a few weeks, everyone on the Google Website Optimizer experiment team should meet and review the report data that the web analyst and marketer will love to analyze and derive insight from.

By following these six steps with the members of your organization, you are destined to succeed, while at the same time, creating a new culture of optimization and experimentation in your company!

As you can see in the screen-shot below, a horizontal bar graph represents the number of visits it takes users to purchase an item from a particular website’s shopping cart within a given date range. While most visitors on this particular website purchased something after 1 visit (17.93% of all purchases), there are many other groupings of visits that have contributed revenue and transactions for this website, including the group all the way toward the bottom of the screenshot (201+ Visits, 14.32%).

Days to Purchase in Google Analytics

You can use this data to learn more about your customers’ behavior online, specifically, when they are on your website and purchasing something from your Ecommerce system. Is your website able to turn sales around in fewer or greater visits? Does it take many days (weeks or possibly months) before customers buy something from you? Have you applied an Advanced Segment in Google Analytics and compared segments, such as New vs. Returning?

Using this data can help your marketing and website optimization efforts as you learn about your website’s sales cycle. Most website owners would love it if every visitor converted on their first visit, but that isn’t always going to be the case. The easier, more competitive, and user-friendly your website is, the faster someone will become a customer of yours.

Most college students must take one, if not two courses in mathematics during their college careers, regardless of their degree program. Most of the time, elementary statistics is the course selected, probably because it’s the easiest math elective to take for most people. In short, lots of people have an elementary knowledge of statistics. So, why are average-oriented metrics put on such a pedestal?

In elementary statistics, you most likely learned about the four measures of center and about outliers. If you don’t remember, that’s OK, it’s probably been a long time since, or you probably weren’t a math person and wanted to forget everything you had learned as quickly as possible.

The four measures of center are mean, median, mode, and midrange.

Mean – The mean is what you know as the average. It is calculated by taking all of the values in a set and dividing them by the total number of values in that set. The mean is very sensitive to outliers (more on outliers in a little bit).

Example: The mean of 1, 3, 5, 5, 5, 7, and 29 is about 7.8571.

Median – The median is not the same thing as the mean, even though in popular parlance, the two terms are often used interchangeably. The median is the number that is in the middle of a data set that is organized from lowest to highest or from highest to lowest. The median doesn’t represent a true average, but is not as greatly affected by the presence of outliers as is the mean.

Example: The median of 1, 3, 5, 5, 5, 7, and 29 is 5 (the number in the middle).

Mode - The mode is the number that repeats most often in a data set. It’s seldom used in statistics as a reliable measure of center.

Example: The mode of 1, 3, 5, 5, 5, 7, and 29 is 5 (it repeats 3 times – the other values only appear one time each).

Midrange - The midrange is calculated by adding the highest and lowest values of a data set together, and dividing the sum by 2. The midrange is hardly ever used as a measure of center.

Example: The midrange of 1, 3, 5, 5, 5, 7, and 29 is 15 (29 + 1 = 30; 30 / 2 = 15).

With four different measures of center, I’ve been able to come up with four different correct calculations for an average. Each measure of center has its benefits and present different sensitivities to the presence of outliers. Depending on the set of data, the measure of center may lose strength and implied value because of how it is calculated and how it is used.

Outliers – Outliers are numbers in a data set that are either way bigger or way smaller than the other numbers in a data set.

Example: In the 1, 3, 5, 5, 5, 7, and 29 data set, the number 29 is an outlier because of how much greater it is than all of the other numbers in the set. 29 is the only number that doesn’t “fit” in this set.

What is the meaning of all of this?
The meaning of all of this is to take your averages (average order value, average conversion rate, average time on site, and others) with a tiny grain of salt. Use average-oriented metrics cautiously and with skeptical optimism, as the presence of a mere few outliers in your data can distort the figures and not provide a true representation of what is really happening.

Take this extreme example of the revenue of five separate orders placed on a web site:

$4.94
$4.39
$7.01
$6.33
$553.93

Your “realistic” average order value here should be $5.67 (the four “normal” values added up and divided by four). But if we’re looking at a report from a web analytics tool, it would report the average order value as $115.32. Clearly, there is a massive difference between $5.67 and $115.32.

To obtain real insights that will help your web site and your organization, you’ll have to dive much deeper beyond the averages to really exact meaningful information and data. Know your measures of center and your outliers, so that you can decide if your averages are realistic representations of what’s happening on your web site.

Until next time, I will leave you with one of my favorite all-time quotes, which fits right into this topic. Think about it the next time you’re obsessing over averages:

“A statistician drowned while crossing a river that was on average six inches deep”.

Cookies – for a long, long time – have gotten an unfair, bad rap. It’s so bad that users will actually go out of their way to delete these cookies off of their machines, even though new cookies will be set as soon as they visit virtually any web site on the world wide web. The reasons for deleting cookies are as varied as the ingredients in a New Orleans style jambalaya. Some say cookies take up too much space (they don’t, cookies never exceed four kilobytes, which is the equivalent to a grain of sand on a beach); that they infect your computer with viruses (they don’t, or the internet would be completely inaccessible, which is isn’t); or, that they are used to spy on your computer (most cookies can only be read by the site that sets them, and the domain [the URL of the site] is “hashed”, which means that it is encrypted with a numerical algorithm).

So, when folks delete their cookies and feel that their internet browsing experience is that much safer, are they really deleting ALL of their cookies? The answer is surprising: no, they are not. Flash cookies, which are set by flash applications, are not stored or viewable in the same places are the regular text cookies that folks have been deleting for all of these years. Because Flash is so prominent (installed on almost 99% of all computers), virtually everyone who has been online has at least one flash cookie installed on their computer, without even knowing it.

These flash cookies can store up to 100K of information, which is a bit more than 25 times what the regular browser cookie is allowed to hold.

Deleting your flash cookies can be done on your computer, but it’s a lot easier if you visit the Adobe Flash Player settings page, where you can find the Website Privacy Settings panel. Click on the little folder icon (which should be the last one of the right-hand side on the top row of icons) to view what sites have set flash cookies on your computer.

If you didn’t know that flash cookies existed, let alone know that you probably have some flash cookies set on your machine, then that is the greatest argument that I can make for not deleting your cookies. You wouldn’t have even known about flash cookies until you read this blog post, so how big of a part do cookies play in the grand scheme of things? Does what you don’t know hurt you?

So, do you delete ALL of your cookies?

As great as Google Analytics is, there are some reports and features within the interface that just take up space. They are hardly ever used and they cause more confusion than anything.

As much as it pains me to say this, Google needs to purge some reports from Google Analytics. There are cobwebs forming and a thick layer of dust is collecting on top of these reports, and it’s time to donate them to those in need. This will make Google Analytics even more awesome than it already is (yes, it’s possible to make it more awesome).

Hey Google! I think that you should get rid of these five reports:

1. Top Exit Pages. This report shows the pages where visitors leave your site. I can’t remember the last time I’ve looked at this report other than to tell someone that they shouldn’t use this report. Think about it: your web site’s visitors must leave your site at some point in time – they can’t stay on your site 24/7. Eventually, they will have to exit the site, and since most traffic you get is usually on your home page, a logical deduction is that most traffic will leave from your home page. What actions or insights can you take from this report? Struggling to answer? That’s a good sign that this report isn’t so valuable anymore.

2. Service Providers. In the visitors report section, there are a few reports that could be eliminated today and it wouldn’t affect me one bit. One of those is the service providers report, within the network properties sub-section. Do we really need to know which internet service providers (ISP) visitors are using to access your site? Is there some change that you can make on your site if your AT&T service provider traffic has a slightly higher bounce rate than your Comcast cable service provider traffic? I don’t think so.

3. Goal Abandoned Funnels. The metric is useful, but the report is not so much. This report is a simple histogram which doesn’t add any additional insight beyond the metric itself. This metric could simply be available on the goals overview report, or available as a metric option in the trending graph. Since we’re cleaning house, this report can get swept away.

4. Navigation Summary. The sheer volume of confusion behind how this report works is a big reason for my request to have this report removed from the interface. I’m by no means advocating the removal of anything difficult or not 100% crystal clear, but this report has a few long-standing issues that severely limit its functionality. Therefore, do we really need it? Would your analysis life be any different if it wasn’t around? Probably not. If you use it as an important piece of your reporting, well, let’s talk it over .

5. Site Overlay. By far, this is the one that pains me the most to want to get rid of. I love the site overlay report concept, but I don’t love the site overlay report functionality. Like navigation summary, there are long-standing issues with it and it doesn’t seem to clearly work in a web 2.0 world. Again, I ask myself if this report ceased to exist, how hard it would affect me? The answer is that it would barely scratch my surface, so there you go.

There you have it – five reports that could be removed to spruce up the place, remove clutter, and not affect your phenomenal daily Google Analytics life.

Today, I used the new ATM machine to make a deposit and noticed a unique and refreshing call-to-action: Envelope-free ATM. I had heard about this new technology a few years ago, but never actually saw one in person until earlier this afternoon.

To make a deposit, you simply slide up to 50 bills or 30 checks through the slot on the right-hand side, and watch the ATM tally up your money. If you deposit cash, the screen will provide a break down by denomination. If you deposit checks, you are given the option to print an image of each check with your receipt. The funds are available instantly with cash and on the same business day for checks deposited before 8 PM.

I thought this was amazing and immediately told everyone within my general vicinity about it. I’m also blogging about it here. I’ll probably also use Twitter and update my Facebook status. All it took was one great user-experience for Wells Fargo / Wachovia to earn themselves some excellent (and free) word-of-mouth advertising offline and on social media channels by yours truly.

Clearly, the Wells Fargo / Wachovia team put in a good amount of time, work and testing during the product’s development cycle, and I am a happier customer for it.

You may be wondering what this has to do with analytics or site usability. A lesson you could learn from my recent ATM experience is that you’ll need to put in time – lots of time – and put in lots of testing and experiments when you launch a new site, develop a new app, or release a new product online. When you hit a home run, the customer satisfaction and word-of-mouth takes care of itself (and as you know, word spreads ultra-fast online). However, when the time, work and especially the testing isn’t done before hand, that’s when the negative feedback, customer dis-satisfaction and angry message board posts start popping up everywhere. It’s very difficult to cancel-out negative comments and do online PR. Let pre-product launch testing results guide your new app, web site or product.

There are always free online tools like Google Website Optimizer and 4Q by iPerceptions to use to gauge customer sentiment before flipping on the proverbial light switch on your new release. Give them a try and let your audience feedback guide you in the right direction.

Currently, AdWords assigns credit to a conversion to the first click’s keyword, ad, ad group, and campaign up to 30 days after that first click occurred. For example, suppose I perform a search on Google for the term brown shoes on March 29th. I click on the ad, view a couple of pages, but I don’t convert. Two weeks later, say, April 11th, I search for brown dockers shoes, click on another ad, and this time I convert. AdWords will assign the credit for the conversion to my original search term of brown shoes (provided the advertiser is bidding on that keyword).

Now, with the new search funnels report section, I’ll be able to see which AdWords keywords helped my original keyword convert, as well as a funnel (hence the name) of each keyword that led to a conversion in succession. This new search funnels report section is going to have nine different reports like “assisted conversions”, “last click analysis” and “top paths” to perform deeper conversion analysis than possible before.

Why it’s “the right step” toward proper attribution
Before this product launch, you had two possible options for assigning credit to a keyword for a conversion. You could log-in to AdWords and view the reports in the interface (first-click attribution), or, you could use Google Analytics to view the keywords that matched goals (last-click attribution). If you were an advertiser, you would, over time, start bidding more for the converting keywords that either AdWords or Analytics were displaying, and less for the “non-converting” keywords. As it turns out – and as the more experienced marketers long ago deduced – other keywords lend a big helping hand along the way, but never received the proper credit. What would happen is that advertisers would either shut down those keywords that didn’t appear to convert, or change their bidding philosophy to such an extent that these assisting keywords become irrelevant over time. The result: the number of conversions would struggle to climb, leaving advertisers scratching their heads.

Now, we can assign importance and value to those assisting keywords, and not automatically cast them off as losers or rejects. They are an integral part in the conversion cycle; keywords that assist in the conversion process should remain active and managed intelligently for optimal campaign success.

Why it’s only “a step” at this point
It’s not an end-all, be-all solution, but it’s a great start. Search funnels in AdWords does have a few limitations. First, you must import your Google Analytics goals into AdWords, which is not a big deal for an administrator, but still something that must be done.

As of now, search funnels can only report on AdWords keywords and web site visits. If a user in the conversion process accesses a site directly after previously clicking on an AdWords ad, that user’s direct visit cannot be tracked in search funnels. Not even natural / organic search engine queries are available in search funnels at this time. Also, while the new search funnels reports look like they belong in Google Analytics and not AdWords, they’re actually not available in Analytics yet.

So, true attribution – if there is even such a thing – is not yet within our grasp. But with search funnels, Google has  taken a very large step toward that general direction. I recommend you log-in to your account today, import your goals, and discover which assisting keywords should be optimized for greater campaign success.