Earlier this year, a previous post addressed using web analytics
to optimize a paid search campaign.  This drew some attention, as web
analytics (WA) are one of those things that everyone knows they need, but not
always sure exactly what actions are called for in a given set of data. I
suppose some of this can be blamed on the usability of the dashes as it’s pretty
easy for them to get unwieldy…which can lead to a full blown case of
“analysis paralysis.”

It’s time to share some direction on using WA to optimize
natural search campaigns. The following reports can be found in almost every
web analytics package and are fairly consistent, but depending on campaign
goals, the insights and actions taken may vary:

1. Top Referring keywords. Look at the long tail here; 25% of the queries
   performed are ones Google has never seen. The referring keywords to the site will show themes and patterns that
   are otherwise difficult to discern.  More than once we’ve changed
   directions on a campaign because we’re seeing volume around a type of query
   we’ve never seen.  Many times the impetus for a decision like that is born
   here.
2. Referring Domains. This metric is essentially telling you
   who your biggest traffic drivers are.  Search engines and sister sites
   tend to occupy the top of the list.  However, we see some interesting
   things happening in the middle to end of this referral list.  This is a
   great way to: A. Measure the effectiveness of a link building campaign (if
   we’re seeing visitors from sites we built linking relationships with that we
   haven’t before that’s a good thing right?) and B. Find site themes and
   verticals that you may be able to generate buzz with (e.g. if we’ve got a
   particular blogger reviewing a product or service, this can inform the type of
   content on the page which could lead to greater link bait).
3. Click paths. This metric is more or less telling you how
   people navigate through the site.  There are a lot of things to be
   determined here but a big one is the effectiveness of your site layout.
   If we’re seeing a lot of people having to go through a few clicks and all
   ending up on the same page, it would indicate that there’s an opportunity to
   engage people more effectively by improving that click path.
4. Paid vs. Natural. This is an excellent metric for identifying
   gaps.  With paid search, we can quickly target the high volume terms and
   use ROI/conversion rate data to inform our decisions on where we want to
   compete organically.  Likewise, the places where we’re seeing a lot of
   activity in organic terms where we don’t have paid coverage can help us expand
   a paid campaign.
5. Geographic referrals. The more targeted and niche the web becomes,
   the more important geography is (and no, the irony of this isn’t lost on
   me).  Nevertheless, we’ve had instances where a flurry of offline
   promotions leads to a surge in a particular geo-specific market.  Certainly
   the offline team will want to know that the radio blast in Philly led to an $X
   lift in revenue. We’ll use geo data to
   develop new content, launch targeted landing pages, and in some cases, even
   modify service offerings to better target geo revenue sources.
6. Visiting trends. Almost every analytics package puts this
   metric on the forefront of their dashboard so you can see how many visitors you
   have this month verses last month and so on.  Resolution Media uses this to evaluate
   seasonality and optimize accordingly. Correlations between seasonality and
   referring keywords are also helpful in determine where linking opportunities
   could be.
7. Top Landing Pages. Lots of useful action items come from
   here.  This metric essentially tells you where your buzz is.  Keep
   these pages fresh and make sure your users can access them easily.  Seeing
   where human visitors land is a good indicator for what spiders are crawling in
   on as well.  From there, this data can be used to optimize the structure
   and internal linking scheme of the site.   For example, if we note
   that Page A is a much more popular landing page than Page B, C or D, we should
   make sure B, C and D are linking to A with optimal anchor text (based on what
   themes and keywords are on page A).
8. Conversion Rates. This is the million dollar metric right
   here!  When visitors come to the site, are they doing what we want them to
   ultimately do?  How often?  More than they were? Conversions are what
   answer these questions.  From there, we may have a number of action items
   we need to take based on what the data is telling us.   Don’t take
   brash actions if conversions suddenly drop (or spike).  However, KNOWING
   when those spikes or drops occur, and looking at what other things happened
   around it (see almost any other metric listed here) as soon as possible is
   absolutely essential to taking the right actions.  It could lead to
   campaign spending changes, landing page optimization, re-targeting keywords,
   building new link bait and a host of other scenarios.
9. Bounce Rate. This is one of those metrics that I think
   varies quite a bit from project to project.  One bounce rate may be great
   for one kind of site and a total failure for another.  It’s essentially
   telling you how many people happened upon a single page on your site and didn’t
   bother to go elsewhere.  We generally chalk that up to them not finding
   the information they were looking for.  If we’ve got pages targeted
   specifically around one or two keywords, we may be looking for a lower bounce
   rate than a page that casts a wider net.  Measuring bounce rate according
   to page type is essential to evaluating the effectiveness of our content and
   the messaging.
10. Browser type. It’s weird how this was a metric that fell
    out of favor for awhile and is starting to make a comeback.  I’m talking
    about mobile here people!  If we’ve got a project that is a multi-media
    extravaganza with elements that aren’t visible to a spider or a mobile browser,
    and our browser type metrics are telling us a significant amount of traffic
    comes to us from this type of user, then this should absolutely impact how we
    present that information.

There are a host of additional web analytics reports that
lead to the optimization of a natural search campaign.  Reading back over
the list, much emphasis was put on site usability, which supports that driving
traffic to the website is half the battle, and having a website that drives the
traffic to take a desired action can be just as important.

22-May-08 2:00 PM
Continued here:
Using Web Analytics for SEO

Popularity: 31% [?]

1. People wants to know the shipping cost: people sometimes add items to their shopping basket just to know the shipping cost, however this action will increase the shopping cart abandonment rate, and make shopping inconvenience for your customers.
2. People will use the shopping basket as a wish list or shopping list, that will come back to once they decided to buy, after maybe comparing your products with your competitors, reading online reviews or consulting their friends or family.
3. People might exit the shopping basket because they just realized that the VAT wasn’t included in the final price.
4. People might exit the basket because they just realized that it will take longer time for the product to be delivered.
5. People might exit the shopping basket unintentionally, they click on a link, it takes them to another page they will read, brows many other pages and forget that they have something in their basket to pay for.

How to overcome this problem,

• Reduce the number of links on the checkout pages to the minimum.

• Make the links open in new browser.

Popularity: 27% [?]

Bounce rate: is a metric that shows the percentage of entrances on any individual page that resulted in the visitor’s immediate exit from the site
One of the reason for having high bounce rate and high exit rate is when no call to action is existed on the site, web masters need to make a clear call to action by adding Buy Now buttons or limited-time offer for example to encourage visitors to take action and add the products to their baskets.
High bounce rate could indicate that something is wrong with the website design or content, also it might suggest that the source of your traffic is referring untargeted traffic.

However high bounce rate is a good metric that can help you identify whether any particular campaign is reaching the right audience and identify suspect high levels of click fraud on a pay per click (PPC) campaign. Another way to reduce the bounce rate is to reduce the number of external links on the webpage.

Popularity: 44% [?]

There are no globally agreed definitions within web analytics as the industry bodies have been trying to agree definitions that are useful and definitive for some time.
The main bodies who have had input in this area have been Jicwebs (Industry Committee for Web Standards), The WAA (Web Analytics Association) and to a lesser extent the IAB (Interactive Advertising Bureau). This does not prevent the following list from being a useful guide, suffering only slightly from ambiguity. Both the WAA and the ABCe provide more definitive lists for those who are declaring their statistics using the metrics defined by either.

• Hit - A request for a file from the web server. Available only in log analysis. The number of hits received by a website is frequently cited to assert its popularity, but this number is extremely misleading and dramatically over-estimates popularity. A single web-page typically consists of multiple (often dozens) of discrete files, each of which is counted as a hit as the page is downloaded, so the number of hits is really an arbitrary number more reflective of the complexity of individual pages on the website than the website’s actual popularity. The total number of visitors or page views provides a more realistic and accurate assessment of popularity.
• Page View - A request for a file whose type is defined as a page in log analysis. An occurrence of the script being run in page tagging. In log analysis, a single page view may generate multiple hits as all the resources required to view the page (images, .js and .css files) are also requested from the web server.
• Visit / Session - A series of requests from the same uniquely identified client with a set timeout. A visit is expected to contain multiple hits (in log analysis) and page views.
• First Visit / First Session - A visit from a visitor who has not made any previous visits.
• Visitor / Unique Visitor/UniqueUser - The uniquely identified client generating requests on the web server (log analysis) or viewing pages (page tagging) within a defined time period (i.e. day, week or month). A Unique Visitor counts once within the timescale. A visitor can make multiple visits. N.B. The Unique User is now the only mandatory metric for an ABCe audit.
• Repeat Visitor - A visitor that has made at least one previous visit. The period between the last and current visit is called visitor recency and is measured in days.
• New Visitor - A visitor that has not made any previous visits. This definition creates a certain amount of confusion (see common confusions below), and is sometimes substituted with analysis of first visits.
• Impression - An impression is each time an advertisement loads on a users screen. Anytime you see a banner, that is an impression.
• Singletons - The number of visits where only a single page is viewed. While not a useful metric in and of itself the number of singletons is indicative of various forms of “Click Fraud” as well as being used to calculate bounce rate and in some cases to identify automatons (”bots”).
• Bounce Rate - The percentage of visits where the visitor enters and exits at the same page without visiting any other pages on the site in between.
• Funnel analysis Regardless of your visitors’ initial wandering path on your website, they must often pass through a well-defined series of pages in order to convert. It is possible to see the efficiency of each step in this linear process. The funnel narrows as people drop off during each step. High drop-off percentages may signal that a particular step is especially problematic. If problems are uncovered, they may suggest breaking the process up into smaller and more manageable steps, or simplifying it. E-commerce shopping cart abandonment is a common example of this kind of funnel analysis.
• Top exit pages Exit pages are the pages where visitors leave your site. Each exit page can be viewed as a leaky bucket. If visitors exit your site, they probably did not find what they were looking for. In some cases, there is nothing that you can do about this. But for many of the visitors who left, you could have probably improved the page to provide more relevant information or better navigation. The total number of exits and the exit percentage of a page can be used to prioritize among problem pages.
• landing page The first page that a visitor lands on as a result of a traffic acquisition activity. The landing page can be a stand-alone page, a part of a special-purpose microsite, or a page on the company’s main website.
• Click-through rate The percentage of Web page viewers who click on a particular link (also abbreviated CTR). CTR is often applied to the percentage of Internet users who click on a PPC advertisement and land on the advertiser’s landing page.
• Multivariate testing A type of landing page testing methodology where data is collected while simultaneously changing a number of different experimental variables (contrast with A-B split testing
• Deep linking In PPC campaigns,the practice of landing traffic on the most relevant landing page possible within a website.
• Cloaking The practice of showing different content to search engine spiders and human visitors to a Web page for the purposes of manipulating the ranking of the page in search engine results
• A-B split testing The simplest form of landing page testing. A new visitor to the page is randomly shown either the original version (“A”) or an alternative version (“B”).

Popularity: 46% [?]