By “Stickiness” we mean:

• Someone visits your site for the first time
• Stays there to explore the content
• Returns later to look for more.

For most web sites, attracting quality traffic is the major goal. Stickiness provides information about visitors’ interest in your site and quality of your site’s content.

Measuring stickiness is important to a website with online advertising, as visitors are exposed to ads if they navigate through the site. For an ecommerce site, stickiness indicates that visitors navigate through the pages of the site and are more likely to purchase. The stickiness of a blog is a measure of the quality of its articles. But for a lead generation site, where the aim is to get the visitors to fill in a contact form, download a white paper or register, the measurement of stickiness is not as important as conversion data.

So, in order to know the quality of your site, the measurement of its stickiness is important. Google Analytics helps in measuring the stickiness of your site with the help of some Key Performance Indicators (KPIs).

KPIs play a significant role in web analytics. Analytics throw up a bunch of data, some of which may not be relevant for your site. To get the best out of any analytics tool, the first step is to define a set of KPIs for your site. Without proper KPI definition it is easy to get lost in a lot of irrelevant data. Once you define what you want to measure, finding the data relevant to your work becomes easy.

On the basis of the defined KPIs, it is possible to conduct a historical analysis of existing data to establish baseline measures. These baseline measures can be used to evaluate the future success of the website, business goals and marketing campaigns.

Listed here are some basic but important KPIs to measure the stickiness of your web site:

1. Average Pageviews per Visit
2. Percentage of New vs. Returning Visitors
3. Visitor Loyalty
4. Bounce Rate

1. Average pageviews per visit (Pageviews/Total visits):

This is a good indicator of site navigation as a visitor usually leaves your site early on if it’s difficult to navigate. It also indicates the quality of your content as a visitor reads more and visits more pages if it is well written. For a site with a CPM business model, high average pageviews per visit means an increase in the average value of each visit. Conversely some sites like support sites that want users to find information quickly would like this value to be lower.

For sites based on new technologies like AJAX and Ruby a low value of Average Pageviews per visits does not indicate poor quality of content because interaction on them can occur without any change in the URL. For a product site a high value of this KPI may indicate difficulty in navigating it.

This metric can be extracted from Visitors Overview report under Average Pageviews.

2. Percentage of new vs. returning visitors (New visitors/Total visitors):

This is a good indicator of your web site’s stickiness, as a high percentage of returning visitors means that your content is relevant and interesting to them. A high percentage of new visitors shows that you are successful in driving traffic to your site (through PPC, SEO, and referrals). For an e- commerce site returning visitors are important – they may have come back after some research and are now ready to buy, or they liked what they bought previously. This metric can be used to further explore the source of your visitors, as well as the keywords with most visits.

This metric is available in the Visitors report under New vs. Returning.

3. Visitor Loyalty:

For a pure content site like a blog or a site that features whitepapers, visitor loyalty gives an indication of stickiness of your site. This is measured by numbers for the following::

• Loyalty
• Recency
• Length of visit
• Depth of visit

Loyalty is how often visitors visit your web site within a given period of time. If this number is high, it means a large number of visitors are visiting your site repeatedly, and proves strong site engagement.

Recency is the time gap between two subsequent visits of a visitor to your site. For a content rich site, repeat visitors on the same day indicate quality, and relevance of your site’s content to their needs.

Length of visit is the time spent by a visitor in one session in your site, during a given period of time. It indicates the quality of a visit to your site in terms of time spent; how long visitors spend exploring your content. If the length of a visit is long, it means your content is engaging them.

Depth of visit is the number of pages navigated by a visitor on your web site, in one session in a given period of time.

Visitor’s loyalty data is provided in absolute numbers instead of averages. This helps you set a benchmark for your web site’s ‘stickiness’ goals.

This metric is available in the Visitors Overview under Visitors Loyalty.

4. Bounce Rate:

The bounce rate is the percentage of instances where visitors left your site right at the landing page – people that exited the web site soon as they entered it. It is the percentage of single page visits, or of visitors that stay on a site for a very short duration. A high bounce rate means your site’s content is not of relevance to visitors, or that it does not engage them.

This metric can be used to measure traffic quality , and can be analyzed across different data segments and across landing pages to identify poor performers. The definition of a high or low bounce rate depends on the purpose and type of web site. In an e-commerce site, a bounce rate of over 60% may indicate low interest in items for sale. However, a high bounce rate for a blog likely means visitors read the latest post and leave; they still find the content engaging.

This metric can be found in the Visitors Trending report of Visitors under Bounce Rate.

Contributed by Sasthi Sarma, Analytics Team, Position²

• Google Analytics Asynchronous Code Helps Website Tracking & Improves Page Load Time (3)
• Track goal conversions without actually setting goals in Google Analytics (2)
• Tracking keyword performance in content network campaigns (0)

Google Analytics introduced asynchronous tracking in Dec 2009 as an “alternate way to track your website”. According to Google, this code is processed separately and can execute without blocking other code or content. This allows the Analytics code to be placed higher in the page, unlike the traditional code.

The asynchronous tracking snippet is said to offer the following benefits:

• Faster overall page load time
• Improved data collection for short visits to rich media or script-heavy pages
• Collecting (and retaining) user clicks that occur before the tracking code loads

We decided to test Google’s claim that the new code snippet improves page load time. Here’s what we found.

Problem: To study the effect of using the asynchronous code as against the traditional code on page load time.

Hypothesis: Page load time with the traditional code will be higher (i.e. the page will take longer to load) than with the asynchronous code in all possible placements of the codes.

Limitation: The experiment does not attempt to study the effect of the codes on script-heavy or rich media pages. It also does not attempt to study tracking errors when the script has not fully loaded.

Methodology: Six simple HTML pages were created and tested on a local Apache server. Each page consisted of a single line of text and a Google Analytics code. The only difference between the pages was the version of Google Analytics Code being used and its placement.

The six combinations were:

1. Traditional/ Synchronous code placed just before the </head> tag.
2. Traditional/ Synchronous code placed just after the <body> tag.
3. Traditional/ Synchronous code placed just before the </body> tag.
4. Asynchronous code placed just before the </head> tag.
5. Asynchronous code placed just after the <body> tag.
6. Asynchronous code placed just before the </body> tag.

Ten readings of “page load time” were taken for each page using the Firebug add-on. Browser cache was cleared prior to each reading to ensure that page was not loaded from the cache.

Experimental Results

Observations:

1. Overall, the asynchronous version of the Google Analytics code is faster than Traditional/ Synchronous code by 17.03%.
2. Placing the asynchronous version in any part of the file, does not significantly change the load time unlike the synchronous version.
3. Putting the code on top of the page no longer slows the page. This is very important if tracking the click is as important as loading contents of the page. Incomplete page load is a major reason for disparity between GA visits and Google AdWords clicks.

Conclusion:

The overall results support Google’s claim that asynchronous Google Analytics code improves page load time. We recommend that the traditional synchronous code be slowly phased out and the asynchronous code be adopted

This is especially important in paid campaigns where cost per click is being analyzed and where the Google Analytics code should be placed in the header. In today’s internet scenario where users expect to see instant page loads, this is a welcome move by Google

We’ll be testing the other claims that Google makes about its asynchronous page tracking code. Watch out for the next one!

Contributed by Ravi Shukla, Analytics Team

• Measuring Stickiness with Basic Google #Analytics’ Key Performance Indicators (0)
• Track goal conversions without actually setting goals in Google Analytics (2)
• Tracking keyword performance in content network campaigns (0)

More than just a fancy graphic, the Analytics Funnel can provide you with clear, actionable insights. The big challenge for website owners when it comes to improving conversions are high bounce-rates, conversion path drop-offs and exits. A Funnel report can provide key insights into visitor behavior which helps to improve website metrics including conversion rates. There are many funnel reports in various analytics packages, but few have the capabilities of Lyris HQ (formerly ClickTracks). It is one of the favorites of our Analytics group at Position² because it has resulted in increased conversion rates of 15-40% for our clients.

In this report, we will not only discuss Lyris HQ Funnel Report key features, but best practices on how we have used it with our clients.

Multi Dimensional Funnel Definition

Most analytics tools assume that a visitor’s progression to a goal or conversion page is linear. The visitor starts at the Landing Page, clicks on ‘Buy Now’, fills the form, completes the purchase and reaches the ‘Thank you’ page. Studies have shown that visitor behavior is not linear, but far more complex. Visitors navigate back and forth between different sections such as ‘About Us’, ‘Privacy Policy’ or move between different product pages before choosing one. This behavior in the extreme is called Pogo-sticking which has been shown to reduce conversion rates. A multi dimensional funnel analytics tool like Lyris HQ can be configured to take this behavior into account. This leads to more relevant and results oriented insights. Analytics Pros such as Avinash Kaushik of Google have consistently rated this funnel report tool as one of the best.

How It Can Help You

A key function of the funnel report is to a visitor’s path from entrance to conversion. It sheds light on the following:

1. Paths that people take towards the goal. This can help eliminate redundant steps. For example, if the report reveals that most visitors tend to read the ‘About Us’ section, it might be useful to have the some summary information about the company on relevant pages.
2. Exit points in your site. These provide insights into reasons why visitors leave and which sites they go to. Exit points in the funnel are highlighted indicating where visitors drop out in the conversion process.
3. Click Patterns. For example, if you find that visitors click on the ‘Contact Us’ button across pages, then it may indicate that the relevant information is not easily accessible to the visitor. You can then modify your website accordingly.
4. Usability experiments: Another use for funnel reports is in usability experiments. You could create different versions of key pages like the ‘Landing Page’ or ‘Registration Form’ and see which design has the best visitor progression to deeper stages of the funnel.

Key Features

Let’s use a consumer financial tools site to highlight key features of the Lyris Funnel Tool like visitor progression and visitor segmentation.

Visitor Progression

To understand the flow from start to end, you need to map visitor progression across stages. Each stage is defined as a group of inter-connected pages, not just as an individual page. It displays which page groups enable visitors to progress to a deeper level by measuring the progression rate. For example, if a page group is requested in 100 visitor session and 50 of those sessions result in a visitor going deeper, the progression rate for that page group is 50%.

This is indicated in shades of blue as represented below. The darker it gets, the more influence the page has in driving visitors towards the goal. In the example below, the ‘Home Page’ and the ‘Sweepstakes Landing Page’ are the two most influential pages.

Visitor Segmentation

Segmentation is created by using labels. Using this mechanism, it is possible to study how different groups of visitors react on various page groups. Progression data for each label can then be viewed by clicking on the label name at the bottom of the report.

For example: Stage 1 can be defined as a group of landing pages. You can analyze which landing page had the highest visitor progression for all visitors as well as which page had the best progression for ‘PPC visitors’ vs. ‘Email campaign visitors.’

In the example shown below, ‘Social media” visitors (segment shown in Green) had the maximum progression from the ‘Home Page.’



Testing The Funnel

We conducted an experiment for a client who wanted to test the existing form completion (which was all on one page) against a registration process broken out over a couple of pages. The results were striking enough to re-evaluate the registration process. The current Registration Page was on a single landing page with 8 fields. The modified Registration Form (New Register) was split across 2 pages with 4 fields in each.

We found that at an overall level, the progression rate improved from 61% to 91% when we used the two step form.



However, this was not the same for all visitor segments. The Progression Rate for visitors landing on the home page via organic and social media sources was higher on the New Register form (two page form). In contrast, visitors who came in from paid advertising sources continued to show higher progression on the current Registration form.

Based on this critical insight, we recommended and helped implement different registration pages based on the visitor’s entry page. The result – overall conversion to registration increased by 23%.

Best Practices In Defining LyrisHQ Funnel

Defining the funnel correctly makes all the difference. Here are some best practices:

1. The first stage of the funnel must be your entry page. This includes the home page.
2. The pages should not be distinguished on the basis of their tracking URLs or parameters since that distinguishes a visitor source and not the page itself.
3. Use labels to create different groups of visitors and analyze visitor progression for each group.
4. Defined page groups should share common attribute. For example: A group of ‘Product pages’ or a group of ‘Registration pages.’ You should also analyze if page modifications have improved the progression rate.
5. Note that if a visitor visits two pages in the same page group, say Landing Page1 and Landing Page2, then the progression is assigned to the first page group (the one on the extreme left in our example). Thus interchanging the order of page groups can change the funnel data.

As you can see, analyzing visitor behavior on your site provides powerful insights on how visitors interact with your website. It can be a lot of fun as well.

If you have any questions or need help with your marketing analytics challenges, please contact your friends at Position².

Contributed by Divya Krishnan, Analytics, Position²

• Social Media Marketing: Execution vs. Hype (0)
• Increase brand visibility through Press Releases (0)
• Brand Monitor™ v2.0.1 Released! (0)

Mobile ads are a format of Adwords ads that appear on mobile websites and Google search pages when a user searches from a mobile device. Mobile ads are available as either text or image ads.

Mobile ads work on a pay per click as well as a pay per call basis. Every ad contains a headline and a description with a maximum of 18 characters each. The third line contains the site URL or a telephone number or both. Advertisers can therefore choose to connect to customers via whichever medium – phone or website – that they prefer, or they can let customers choose.

Mobile ads can be targeted only to following countries:  Australia, Austria, Belgium, Canada, China, Denmark, Finland, France, Germany, Greece, India, Ireland, Italy, Japan, Korea, the Netherlands, Norway, Poland, Russia, Singapore, South Africa, Spain, Sweden, Switzerland, Taiwan, the United Kingdom, and the United States.

Always create a separate campaign for mobile ads. This campaign won’t require a separate landing page, because clicks on the ad will be directed to a Google-supplied mobile-friendly page describing your business.

Mobile ads pricing works same as PC based text or image ad. Advertiser only pays when user clicks on the ad and advertiser can even bid to connect users to their business via telephone (click to call).

It’s recommended to start off with just a small list of strong keywords and a few different targeted ads. Based on performance, the list can be expanded later on.

Opt for contextual targeting so that your ad reaches readers of specific mobile web pages written about specific concepts.

Target campaigns to iPhones, T-Mobile G1s and other high-end mobile devices with full Internet browsers through demographic settings of the campaigns.

Mobile ad performance can be measured pulling campaign level and placement performance report.

Contributed by Manisha Singh

gtafxsumz4

• Improving Performance of PPC Campaigns (0)
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The promotion of a product/service using Videos   is an important part of Social Media Marketing. In this article we discuss some main points related to tracking of such campaigns. This article is not focused on any particular tool instead it tries to enumerate the basic idea behind tracking of such campaigns.

Background

The Video can be embedded on a web page or can be uploaded to a video sharing site such as YouTube.  YouTube provides a free tool called “YouTube Insight” which provides lot of useful statistics for uploaded videos. You can answer questions like

• In which geographical region is the video being viewed the most?
• What day of the week does the video get maximum visits?
• Which are the hotspots in the video? (Hot Spots can be broadly defined as critical points in your video where the interest level of people peaks or drops)

For videos embedded on a web page we can use Event Tracking feature of Google Analytics to keep track of events like play, stop, pause and lot more. It also helps in answering questions like:

• How many people saw the video (Unique Vs All Views)
• How long did the people watch it?

In case a YouTube video is embedded on a web page, then chrome less YouTube player can be used which is a modified version of the normal YouTube player.  Basically one can put the tracking code inside the Player controls which is not possible in case of normal YouTube player.

The following are some of the insights one can get from tracking these campaigns:

Where are the viewers coming from?

Location information of the visitor can be provided in GA or most other Web Analytics tools. Based on this and other demographic information one can know if the video is reaching the target audience or not.

How are the viewers finding the video?

This can be measured using various parameters like duration of video seen by users, percentage of

people on that page seeing the video etc. Bounce rate of the page might not be a useful metric in this case as people might see a very good video and then leave the site without visiting other pages.

How are they distributing it?

One way to keep track of this is from the source of visitors coming to the video page after the video has been there for some time. For these referral visitors we can find the sites where they have come from. An increase in direct visits of new visitors may indicate offline promotion of the video through various means (It might also be because of the link of the video being non clickable on other sites).

What did they say about the campaign (positive/negative?)

A way to do this would be an automated agent which goes through the comments made on all the sites where the video is being talked about.  This agent judges the nature of the comment by parsing thorough it. This would be helpful in terms of tracking effectiveness of a Social Media Campaign. It could do almost real time monitoring and raise an alarm if say for example lots of negative comments are being generated.

Has the video helped in increasing the Brand Recall?

Organic visits based on brand related keywords can give an indication about this. If possible the

Key phrase being highlighted in the video should be somewhat different from phrases being promoted in other campaigns (In case you are running offline campaigns as well).  People who come to the site by typing this term (or similar terms) in a Search Engine can be clearly attributed to the Video campaign.

An increase in direct traffic can also be a result of the video in case there are no other campaigns promoting the product.

Has the brand engagement increased?

This can be measured by content engagement metrics like amount of time people spend on the site, the no of pages they visit etc.  One can segment the visitors coming to the site into

1) Coming from video campaigns

2) Coming from other campaigns

For these two segments a comparative study of the above mentioned metrics can be indicative of the effectiveness of video campaigns.

Are the viewers moving more towards the ‘Action’ page and have customer drop outs increased?

This can be measured by looking at the funnel before and after the launch of the video (assuming the funnel was present before the video was launched).  A funnel is a series of steps the visitor takes on your site to complete a goal e.g.  To register to your site can be a goal, buying a product can be a goal.

Here also we can segment the visitors into

1) Coming from video campaigns

2) Coming from other campaigns

And then analyze the funnel for both the segments to see the effectiveness of video campaigns.

Have leads/sales increased?

The increase in online leads can be attributed to a video campaign using any web analytics tool. In case the campaign also gives a phone no then leads can come on phone/sms as well.  If video campaign is the only one running then it can be easily attributed for the phone/sms based leads as well. In case of multiple campaigns there should be basic CRM related software which stores the source of the lead along with other data.

The above points try to give an overview of Online Video tracking using Web analytics tools and other techniques.  We can certainly hope to see new ingenious tracking techniques as this field continues to grow.

Contributed by Ravi Shukla

• Landing Page Optimization – Tips For Effective PPC Campaigns (0)
• Automated Bid Management – Get More From Your #PPC Campaigns (0)
• Improving Performance of PPC Campaigns (0)

Most of us have a profile on a social networking website such as LinkedIn or Facebook and are familiar with the concept of “degrees away from you”.  So, if X is 2 degrees away from you on LinkedIn then you and X (although not connected to each other) share a common connection say S.

The figure below illustrates this:

This can be viewed in terms of a graph where you and X are nodes on a Graph connected to each other through the node S.  In case you and X have more common connections besides S then there are multiple paths between the node “you” and node “X” as shown below:

In case there is no connection between you and a person z, then you and z are points on two separate graphs (out of your network in terms of Linked In).

Now the question that follows is this:

Just like in Graph Theory where one can find best path(s) among a given set of paths based on some criteria (example, shortest distance between two cities connected by multiple routes). Can we choose the best path in case of Social Networking Graphs as well?

In other words, which is the best path connecting you and X in the example above?

To answer this, we need to come up with some criterion based on which we can run a Standard Algorithm to find the best path. In case of LinkedIn, this criterion can be the number of connections that you have.

For starters, we can say that the more connections a person has the higher his/her quality is. This may be modified further to give weightage related to quality of each individual connection as well.  Based on this, each node may be assigned a weight and then the best path can be determined.

Some of the obvious real life problems this criterion will face are that of Quantity Vs Quality.  Some people might have only 30 connections but all of them might be big shots, compared to someone else who has 100 connections but each of them have just 10-15 connections each.

The above example shows how the concepts of Graph theory can be related to Professional Networking media like LinkedIn, etc.  The same concepts can be used in some areas of SEM as well.

There are many other interesting examples about application of Graph Theory concepts. In fact, among the scientific community there is something known as an Erdos number which measures the “collaborative distance” between a person and prolific mathematician Paul Erd?s, which is measured by authorship of mathematical papers. You can read about it here.

Contributed by Ravi Shukla

• Ghost Blogging: Does it pass blogging best practices and blogging ethics? (0)
• Engagement – The cornerstone for social media marketing success (2)
• How to effectively connect with your target audience through Social Media (1)

We can use four keyword match types for PPC campaigns:

1. Broad: Display your ad if your Keyword is anywhere in the Search term

2. Phrase: Displays your ad only if Search Term and Keyword are in the same order

3. Exact:  Displays your ad only if the Search Term exactly matched with the Keyword

4. Negative: Your ad will not be displayed if a negative Keyword is included in a search term. These keywords help us control broad and phrase match keywords. We can use Negative keywords in all Broad, Phrase and Exact match types.

Glossary

Search Term: word used by the user

Keyword: word bid on by the advertiser

The grid below will help you understand the various terms used by the top three search engines:

Identifying Search Terms and Optimization:

There are different ways to optimize keyword match types, here I will explain how you can track Search terms (exact keywords) triggered by broad and phrase match keywords for different search engines:

Google Search Query report:

1. The search query report gives us the Search Terms that are triggered by phrase and broad match keywords

2. Identify the Search Terms that are used by the users and that are not there in your campaigns

3. Separate them in relevant and irrelevant categories.

4. Again, cluster the relevant keywords into different clusters and add them to relevant adgroups

5. You can add these relevant keywords in all three match types

6. Irrelevant keywords can be added as negative or excluded keywords

7. You can add negative keywords in all three match types, depending on the keyword

Yahoo:

1. Yahoo doesn’t provide a report for Search Terms triggered for broad and phrase keywords

2. You can track Search Terms used in Yahoo by using a URL parameter

3. Keyword={OVRAW} – use this as a URL parameter to capture Search Terms

In my next post, I will explain how you can track keywords from MSN and how to track Search Terms using Google Analytics.

Contributed by Sudheer Dayanand

• Google Wonder Wheel (0)
• Improving Performance of PPC Campaigns (0)
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Generally, Pay per click (PPC) account managers find it difficult to monitor performance of their content campaigns. In order to optimize their campaigns, it is important for them to understand whether the chosen keywords are the right ones, especially to measure the performance of the keywords they bid for.

The content network displays ads to users as they search for topics of interest and browse sites that relate to their keywords. The ad group’s entire keyword list helps determine where ads show on the content network. Here the rule is, the more closely related the keyword, the more likely the ads will find the right audience.

It is important to know which keywords actually worked on which site. However, Google does not provide this information in any Google AdWords report or directly through Google Analytics (GA).

When you use “Auto Tagging” to track AdWords’ data in GA, all applicable information such as campaign name, ad group name, keywords, ad content, etc. are tracked by the query parameter gclid – Google click id, which is a unique combination of characters and numbers.

Here is a sample GA analysis for content campaigns in a typical keyword report:

Getting data for high-performing keywords for content campaigns

By manually tagging and extracting data or logs to your server, you can accumulate data for high performing keywords of content campaigns. Here is how we do this:

First, tag the landing page (for example: http://www.mysite.com/users/home.aspx) with the proper utm, Network and SiteTarget parameters as follows:

http://www.mysite.com/users/home.aspx?utm_source=google&utm_campaign=contasap&utm_medium=cpc&utm_term=

quick&Network={ifContent:C}{ifSearch:S}&SiteTarget={placement}

Note:

• For more on utm parameters go to:

http://www.google.com/support/googleanalytics/bin/answer.py?hl=en&answer=55578

• Network – is used to differentiate search and content ads

• SiteTarget – is used to obtain the site name where your ad is displayed

When a visitor clicks on your content ad, which is displayed on www.nytimes.com, the URL captures the information as follows:

Example:

http://www.mysite.com/users/home.aspx?utm_source=google&utm_campaign=sitedefeb&utm_medium=cpc&utm_term=

iphone&Network=C&SiteTarget= www.nytimes.com

Next, gather the query parameter values on your back-end data.  The parameter value of the utm_term will provide you the keyword that helped you display your ad on the website.

Conclusion:

By using this method, one can understand which keyword was converted from which campaign and from which site. This will give you the insight into which keywords to retain and which ones to remove from your list of keywords for PPC, thereby maximizing ROI through content campaigns.

Contributed by Bhagawat Jadhav

• Measuring Stickiness with Basic Google #Analytics’ Key Performance Indicators (0)
• Long Tail Keywords and SEO – What is the SEO Value of Long Tail Keywords? (0)
• Google Analytics Asynchronous Code Helps Website Tracking & Improves Page Load Time (3)

Since 1995 when ‘e-commerce’ started becoming mainstream, entire industries that are wholly web-based have emerged, and traditional businesses have turned to the Web for an ever increasing contribution to their revenues.

It is estimated that more than 94% of America’s active users have a broadband connection to the Web. Consumers now reflexively go online to window-shop, educate themselves, and generally spend more of their waking hours surfing the web.

It’s imperative for every business to follow and understand their target customers’ online behavior. These businesses have to then structure their online presence and their off-line, traditional processes to meet the needs of their customers.

To achieve these objectives, decision makers are increasingly adopting Web Analytics. On March 20, 2007, a Google search for “web analytics” + definition displayed 642,000 results and 21 months later, December 31st, 2008, it returned 138,000,000 results: 21,395% growth! This is but one base level indicator of the rapid rise and widespread recognition of Web Analytics as vital for better business decisions and strategy.

Simply put, Web Analytics, through the rigorous mining and analysis of data, better understands and improves the experiences of visitors to a site. This deeper understanding helps achieve desired outcomes such as greater engagement with a brand or company, or higher revenues by perhaps identifying what flavor of “online merchandising” leads customers to transact more business on a certain Web page.

The early, rudimentary practice of tracking online visitors using server log files or by embedding JavaScript code on sites constituted the data gathering and the beginnings of a new revolution called Web Analytics.

Web Analytics often involves the effortless collection of online data. Lord Kelvin said “if you can’t measure it, you can’t improve it!” Once you set some basic tags for your site, all visitors’ information can get tracked on any of a number of web analytics tools.

Compared to other channels and media, data about online business, visitors, and transactions is generally readily available at minimal cost and within a short duration—often on a real-time basis. Businesses can react rapidly to positive and negative trends, seize opportunities and quickly ward off threats.

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