By now, you should have chosen the pages you know are preventing you from achieving conversion rate bliss. Your next tool is decidedly low tech: your eyes. Observe and decide what you think needs to be done; be a dispassionate observer; imagine the testiest of customers and try to be him. And then take advantage of some of the following tools and techniques.

Heat-mapping

I know heat mapping is as old as the hills, so why don’t people shout about it? In communicating a concept to a client who isn’t paid to be an analytics guru, heat mapping is unrivalled. Marketers get heatmaps. More importantly, their bosses do too. If you’re a marketer, you know that getting top-level buy-in for a conversion optimization project can be tough. Heat maps help. Heatmapping lets you understand how users are interacting with individual elements on your pages, and is also quick and cheap to set up.

Most paid-for web analytics solutions provide heatmapping as part of the service. For some unknown reason, Google Analytics has always done a poor (and temperamental) job of their site overlay. For instance, the overlay report can be misleading as it tracks clicks by link destination rather than location on the page. If you don’t have an enterprise solution, fear not, try one of these (cheap) standalone solutions.

We use Click Density in the UK. In the US Crazy Egg is almost identical. From £20/$20 and ten minutes of snippet pasting you can get started and track enough clicks on one or two pages to get some meaningful data pretty quickly.

The three features I find most useful are:

Tracking the relative popularity of individual elements of the page (of course). We were pleasantly surprised to see a high proportion of users going through to our Team Page in the below example.

Understanding which elements are masquerading as “clickable” when in fact they are not. “Dead” clicks seen here in red, lead to frustration and exits.

Understanding the chronological order that the elements are interacted with. Some heat maps will show you clicks by seconds from page load, use this to gauge which elements of your pages are most attention grabbing. We were concerned that our flash heavy homepage was distracting to users. The number of clicks on the animation in the above screenshot suggests we might have a problem; however shot below shows that very few of those clicks occurred before 20 seconds had passed. Perhaps people are reading the text after all!

Google browser size tool

Say you have three calls to action on your page—you’ve given users the choice to proceed as they see fit. Perhaps one of them is a call-back, one is an enquiry form and the third is a price check (this is a recent anonymous client example). The problem is you want your users to book but a tiny percentage are clicking on that button. Well, just maybe your call to action has fallen off the page? Remember, not everyone has a bank of 22″ flat screen monitors at their disposal. The rise of the netbook and mobile internet means that small screens are in abundance. Check your screen resolution report and try emulating some different sizes in your browser. Then paste your URL into the Google browser size tool and see what percentage of users can view your call to action now.

Usability on a budget

Do some testing on the site. The importance of this step can’t be overstated. You don’t need to spend a fortune on testing. Try using some interns with less experience of the site, or ask your friends or family, neighbor, the guy in the newspaper shop to have a go. It doesn’t matter who but try to pick people with as little experience of your site as possible. Ask them to carry out 3-5 tasks on the website that are important to the success of your business, such as:

• Buy an 8gb iPod touch in pink
• Find the store nearest your office
• Find out how much shipping would be on product X
• Check availability for a deluxe room on the 28^th of April

Set these scenarios, then let your victims start at the homepage and observe. We use the excellent Google Docs spreadsheet and form creator to ask our volunteers a series of questions at each step of the process. If you can observe them doing it and even use a desktop recording suite such as Camtasia to follow their movements you will get a good idea of which page elements are the most important and the poorest performing. I’m willing to bet that at least one of the below applies to your site :

• Auto-complete postcode field error
• Broken button in Firefox
• Missing shipping information
• Lack of backwards navigation to recap on basket
• Site search returning zero results on a plural search
• Site search returning out-of-stock results above in-stock products
• A form with infuriating resets
• Bizarre inexplicable signup dead-end

There are numerous budget online usability testing services online which you can use if resources are scarce. Personally, I think you can gain a lot by watching users interact with your site yourself. Another option is to install a feedback/survey solution such as Kampyle to gain feedback from actual users or install Clicktale to record actual user journeys.

To recap: you know which of your pages are receiving the most views, and of these you have identified the thorniest. You even know which bits of these pages are driving your users into the grateful, open arms of your competitors. Your mind is awash with colors to test and shading and beveling for your chief action button. Stop, think long and hard about the elements you want to change. Then focus on bold changes. Contrary to popular belief, you will not double your conversion rate by changing your buy now button from blue to red. Similarly, switching from “enquire” to “get started” is unlikely to set your world alight.

Tie up all of the considerations and findings you have come across and build some mock-ups, then seek opinions of others. Try to prioritize; you can do too much at once and lose the connection between a change and the results it brings. Before launching a test, make sure you’ve got base metrics in place to compare with the test results and that you’re awash with before and after screenshots for your reference. Now, test away!

While this seems to me to be a fairly accurate description of the process, it’s also important to note what conversion optimization isn’t.

• It isn’t making a website “better/look nicer/rank better”
• It isn’t concerned with current customers. In fact some of the processes can stand at odds with best practices for retaining current customers, and we should be mindful of this.
• Last but not least, it isn’t mindless chopping up of website content and structure “for the sake of it.”

The important thing to take into account when beginning a conversion optimization project is that the planning is at least if not more important than the test itself.

Before running a conversion optimization project we need to make sure we’re barking up the right tree. This first part looks how to decide which pages to think about optimizing. Next month we’ll look at some further cheap/free techniques to get more information from the behavior of your users.

Important metrics: bounces & exits

Starting with your excellently deployed analytics package, let’s look at which reports are useful to us in determining the focus of our conversion optimization process.

First, take a look at pages with high bounce rates/exit rates. That’s an easy one—pages with high bounce rates are bad and need to be optimized right? Well, maybe. I would suggest some segmentation here wouldn’t go amiss. Remember, we’re looking predominantly at new visitors here, the ones which haven’t been to your site before and need that extra helping hand to get them further involved. Try using advanced segmentation to convert more new users.

So, how do the high bounce rate pages look now? Select pages with bounce/exit rates which are significantly higher than the site average and then ask yourself:

• Do these pages have a significant proportion of website traffic?
• Are they important in the path to conversion?
• Are the traffic sources to these pages good quality and relevant to the content?

Pick no more than two distinct pages to follow up, look at the navigation report to understand where users are going to from this page. Once you’ve answered these questions you should have a couple of pages (or groups of pages) to analyse in more depth.

Advanced funnel analysis

Regardless of the pages you identified in the previous step, it’s almost certainly worth analyzing the pages which are directly involved in the conversion process, whether they be a lead generation conversion or an ecommerce sale. This involves tracking every page of the funnel, but also creating multiple funnels to analyse users coming from different areas of the website. You can do this by making required first steps of different pages, for example product or category pages.

This requires your analytics package to allow you to create multiple goals. I often find that there are subtle differences in the way users interact with different site categories or content types. Once you’ve got your funnels set up, collect data and analyse which funnel pages are causing users to exit the process. One word of caution here—keep in mind what a true funnel “exit” is here. To understand what I mean here, take a look at my colleague’s analysis of the true Google Analytics funnel post.

Listen to your users

Anecdote time: I recently carried out a project for a website which had at least 2 years worth of data. The site was receiving 12M+ pageviews per month and over 700,000 monthly searches. Yet the company was completely unaware that the site had 150,000 searches per month for “iphones” which resulted in an empty results page (and a huge search exit rate). If your website has a site search function, hopefully you are mining this data already. If not, get it set up!

At the risk of sounding cheesy, I always say it’s like having a conversation with your customers. If you listen carefully you’ll hear them. Sometimes it’s a whisper; sometimes it’s a sign that they’re confident of using a site search function and don’t mind typing. At other times though, they’re screaming at you. They’re angry; they took time to find your site, they invested time in browsing your navigation and come up dry. But, in searching again, they’re giving you a second chance. This time show them what they want to see. First concentrate on finding the screaming, angry users.

Which pages aren’t satisfying users simply with their content and navigation?

These pages are the ones which are most searched from. Once you’ve identified them, try to understand whether or not users are sticking with the searches or leaving the site. If there is a high search exit page from these pages then they should be a focus of your efforts. Some questions to ask:

• Do I need to add content to this page?
• Is the look and feel of this page incongruous to the content?
• Am I providing an obvious “next step” to my users?
• Are certain products/content less prominent than they should be?
• Do I need to make certain information more easily available?

Finally, after studying the stats, forget the stats. Look at each page under suspicion and think critically about what you should expect from this page. What is its purpose? Does it make sense for it to have relatively poor stats? Is there a logical reason or should you be concerned?

Take a decision

By now, you should have two or three pages or types of page which you have singled out for treatment. They should have the following attributes:

• Poor bounce/exit rate
• Low contribution to the conversion process
• Other signs such as high number of searches and search exits
• No reason, intuitively, to have this poor performance

The next step is to look at which aspects of these pages need improvement. That’s the focus of my post; brining the site search back into the arena along with some other interesting technologies in order to create priorities for optimization A/B & multivariate testing.

That is, unless you are a paid-for web analytics provider residing at the SMB end of the market. In fact, even those at the top end are facing challenging times with the possibility of further consolidation following Adobe’s recent purchase of Omniture, which has emerged as a 42% market share holder (estimations were used where it was impossible to get official figures). The outlook for the industry on the whole is very good however.

The market

The positive picture found by Econsultancy seems reflective of the current ascendancy of web analytics in the UK. By Econsultancy’s estimation the UK web analytics market is up 9% in 2009, from £78m in 2008 to £85m in 2009. Although this was down slightly on the 12% growth recorded in 2008, it still represents a good growth rate in a year where recession has hit many related markets. Interestingly, the user experience and usability market, increasingly a close cousin of web analytics, grew only by 5%.

The reason for this growth? Well, the recession may have been both a help and a hindrance:

“While the economic climate has compelled companies to take measurement and understanding of return on investment from digital activities even more seriously, there has been an inevitable slowdown as some companies have either delayed investment or cut budgets for technology and analysts.”

There is a general sense here that marketers and business owners are only starting to realize the potential impact their website data can have on their bottom line. We also believe that marketers have had more time on their hands to finally get to grips with their website data in 2009, a side effect of a return to the tried-and-tested in their marketing efforts.

Good news for web analysts

Even better news for web analysts is the finding that companies increasingly focused their investments on internal web analysts rather than the technology itself. The share of expenditure on staff to analyse website data grew from 36% to 42% while the share of expenditure on web analytics technology fell from 45% to 38%. 1% share in spend also went from the technology platform to professional consulting services.

Econsultancy sees this as a positive shift for the industry and that decision makers are taking note of what thought leaders are saying:

“…web analytics experts stress that the interpretation of the data and use of information to drive business decisions is of key importance, rather than the data itself.”

However, the same report found that 46% of companies still don’t have a dedicated web analyst. Meaning there is still huge potential for growth in this area. As Avinash Kaushik puts it: “you need a person with a planet-sized brain” to avoid becoming “data rich and information poor.” There is a feeling in the market that finding the right staff is difficult:

“It is arguable that companies are not investing in staff, simply because there is a shortage of people with the right skills in the industry. Web analytics requires a unique combination of skills that includes an understanding of statistics, business acumen and deep knowledge of digital and interactive marketing.”

Google vs. everyone else

80% of companies surveyed for the Econsultancy 2009 Online Measurement and Strategy Report use Google Analytics. We’ve yet to see full launch of Yahoo Analytics in the UK and it remains difficult to even obtain an account (my YSM! rep told me recently that they are kept completely in the dark regarding all of this). However when it does launch it can only pile more pressure on the paid-for solutions. To my mind, two questions remain about the future of the free web analytics solutions:

1. Can Google Analytics bridge the gap to the upper echelons of the paid-for solutions? (and do they want to?)
2. Can paid-for solutions targeting SMB markets remain profitable in the face of this free competition?

The first question can be further divided by asking whether this “gap” exists at all or if it is just a perception that there is a gap. From the Econsultancy vendor matrix, it seems that there is near convergence at a grass-roots level purely in terms of the features offered by each company. On paper there are few features not provided by Google Analytics which the others can boast, particularly after a busy 2009 on Google’s part. In practice however there may still be a gap in flexibility in some areas.

Google’s privacy terms and conditions could also be a thorn in their side; 17 out of 19 of the other solutions provide some form of personal information or form data capture.

Google’s inability to provide personal information about users is possibly most relevant at a small business level where every lead counts, and due to the smaller volumes concerned can actually be chased up. It is also relevant for larger organizations wishing to tailor direct marketing for user groups based on personal data.

I was involved in a case recently where Google Analytics was removed from the tender list of a multinational publishing group after it became clear it could not be used for ABCe audits (independent verification of media performance), another symptom of their cagey safety stance. In the end, this may provide an answer for question 2—in exploiting these and similar potential weaknesses Google’s direct competitors could find their own defendable niches; otherwise, the future for lower-tier analytics tool providers looks bleak.

A shift in focus?

Econsultancy asked 20 web analytics providers to plot their current positions (where we are now) and in the future (where we are going) to get a sense of where each product fits in the marketplace and what their plans for evolution are. The chart below shows them all plotted in the “where we are now” position. Already the upper right space is looking cluttered. Tellingly, 12 of the “where we are going” labels moved into or further into the upper right quadrant, a sign that these companies see differentiation and revenue growth through higher levels of customization and the providing of a service to complement their product’s revenues. Even more tellingly Google and Coremetrics continue to hold their cards to their chests and didn’t release this information.

At the top end, paid-for solutions are bolstering their offerings with new features galore to keep increasingly sophisticated users happy. This often means going beyond what is traditionally considered web analytics with features like usability studies, PPC management modules and merchandising bolt-ons. This is also a response to the threat of commoditization. For example, 14 solutions out of the 20 showcased provide email marketing campaign management modules. Likewise 14 support pay-per-click campaign management (in addition to analysis). The lines are blurring, and as Andrew Hood, Managing Director of Lynchpin Analytics points out, soon a study of this kind increasingly becomes more a question of “what exactly is the defined market for web analytics” than “how fast is it growing?”

It’s clear from Econsultancy’s report that industry leaders see this as a dynamic period for web analytics with focus shifting towards the tracking of multiple marketing channels and then using test data to make measurable improvements to websites. As Brewster Barclay, Managing Director of Clickstream Technologies puts it, we are “moving from web analytics to customer analytics.” Meanwhile, Dennis Mortenson of Yahoo suggests automation and predictive modelling are the future:

“We’ll be seeing a lot more predictive analytics. After that, automation will sneak into the tools as well. That’s where we’re headed—there’ll be a whole different level of intelligence applied to it.”

Whichever direction web analytics takes, it seems clear that it is only going to become a greater part of everyday life for digital marketers around the world.

To take the pain out of the process for you I have compiled our top five web analytics resolutions for 2010. These were based partly on some of the recurring analytics sins we see over and over and partly on what I feel can be underused or undervalued features. They all have the common theme of making your website data more useful. I make no apologies for the heavy bias towards Google Analytics here; it’s what we use most often, and for a reason. However, most of the below features are available to some extent in most serious web analytics packages.

Fix site search (and use it)

Tracking user searches on your website is the closest most marketers will get to having a conversation with their customers. If you have a site search function on your site, it will be getting use from your visitors, and probably more than you realize. Omniture, Webtrends and Nedstats to name but a few all support this. In Google Analytics, I’ve always felt access to this data is hidden away unnecessarily and this actually limits the number of users who set this up and are actively using it. In most cases it’s incredibly easy to activate. If your website search function doesn’t transmit a search term to the url in a Google Analytics friendly fashion, fear not. You can fix this by creating a virtual page view with the page URL “q=keyword” where “keyword” is the search term taken from the source code of the page.

For starters, take a look at some of the below once you’ve got the data coming through:

• Which pages are most searched from?
• Which pages do most searches land on?
• Which search terms are the most popular?
• Which of these most often lead to exits from the site?

Clean up content reports

This is another simple trick but is nevertheless crucial to getting accurate and useful content reports. Many (particularly ecommerce) websites have multiple session IDs which automatically append to the page URL as users browse through the site. By default Google Analytics tracks these as page entries. The result is often thousands of pages with single visits, essentially rendering your content reports useless. If you haven’t done this before it’s easy-peasy, just go to the settings page and look for “Exclude URL Query Parameters.” Compile a list of all the session IDs you’ve seen in your content report and put them in there.

Tag marketing initiatives (and then analyse the results)

Web analytics can be clever, really clever. So often though, you have to put the effort in proactively to reap the rewards later. In order to track the performance of your various marketing efforts you have to first tag them. That means giving email campaigns meaningful names and dates and tagging all paid search and ad display channels individually. Web analytics tools have different ways of doing this. Google provides a handy tool meaning there really is no excuse for not doing this. We marketers and web analysts have to constantly prove our worth. Here’s where we can prove and improve the return on investment we provide for our businesses.

Annotate reports and charts

It sometimes seems that at least half of the time we spend on web analytics involves working out what happened to make a certain trend chart shoot up or down. I’m going to admit that occasionally, this has to be carried out a second time several months down the line to refresh the old memory banks. With the new Google Analytics annotation feature you can add notes to any event or interesting trend shift. This can be done pro- or retro-actively and by any member of your team with access to a profile. Now, use your new found spare time to play with the next tip.

Segment, segment, segment!

Borrowing from political speech writing 101, I wrote “segment” three times. The intention was to give it more gravity and that’s because I really think this should be high on the lists of all DIY web analysts for 2010. Segmentation is simply selecting individual portions of your data to look at separately. If it helps, think of your data as a big juicy steak; it would perhaps be more efficient and quicker to just shove it down your throat in one. As we all know however, steaks are more pleasurable (and safer) if we chop them up into little portions which we then eat one-by-one. With that in mind and with Google doing an excellent job of providing easy (free) access to segmentation there really is no excuse not to spend the whole year dicing your data up into little, more palatable portions. It really does digest easier that way. For some excellent segmentation ideas check Avinash Kaushik’s post on segmentation or you could even use advanced segments to convert more new users

All the best for 2010 and happy segmenting!

That’s why it always comes as a surprise when I come across Google Analytics users who aren’t aware that this knowledge is at their fingertips. It may be because in the past, ad position reports were only available at keyword level and one had to check every keyword individually and interpret a qualitative view at the campaign level, a truly time-consuming process and resulting, ultimately in a ropey set of data. Now with a combination of custom reports, advanced segments and pivot tables, Google has blown the door open on this and a meaningful analysis at multiple levels is possible and, (gasp!) easy to do.

Below, the process and the path to ultimate AdWords enlightenment. On a less dramatic note, it should also serve as a good introduction to the Google Analytics custom reports feature for the uninitiated.

Building a custom report

This brief tutorial is for anyone who hasn’t had a chance to play around with custom reports in Google Analytics yet. If you’re already familiar, skip this and pick up my shared custom report link at the top of the next section.

If you haven’t played with custom reports yet, you really should. There’s a good chance you can condense two or three of the standard reports you faithfully schedule and manipulate every week into one custom report and even add some analysis you didn’t know was possible.

Getting started:

1) Click on the custom reporting tab on the left hand menu (this should have an eye-catching red “beta” stamp next to it).

2) Choose and drag your chief dimension from the menu on the left hand-side. This is the area you are wishing to scrutinize—for example, campaign, medium, source or even hour of day.

3) If required, choose which dimensions to zoom in or “drilldown” into following your chief dimension.

4) Pick the metrics you want to look at.

Once you’ve mastered this, you can start to look at adding multiple tabs to your custom reports. Over time you should be able to build one report to show the metrics and dimensions you wish to look at weekly or monthly and deal with one report instead of many.

Applying the report

Thanks to the excellent new feature which allows sharing of customizations such as custom reports and advanced segments, you can Click this link and save the custom report which I have created, to your profile/s. Or you can follow the below format and alter the metrics to suit your KPIs. In fact, while you’re there, why not play around with the order of the dimensions, just to see what happens?

Once you have this report saved in the “my customizations” section, simply apply it and get started, the report you have should look something like this:

You will now have the ad positions (1-12) down the left hand side of your table and the metrics in columns to the right. If you have chosen to set your custom report exactly as I did then you are essentially looking at account level data.

At this point I strongly recommend that you delve down further into your AdWords account, either to campaign or ad group level. The best way of doing this is to pivot! Use the pivot table by clicking on the icon that looks a bit like an old graphic equalizer just to the right of the pie chart symbol. Pivot either by ad group or campaign and add in the metrics you want to see.

Then, voila! You have confirmation that each of your AdWords campaigns is targeting the right position—hopefully.

Interpreting the data: it’s simple really

I enjoyed Evan Lapointe’s recent discussion on whether web analytics is easy or hard to do. The comment drawn by this article is testament to the fact that it clearly is a contentious and unresolved issue. For my part, I would have to say that web analytics doesn’t have to be hard. This report is a great example of how you can get some really useful and actionable data without needing to be a statistics graduate. The metrics are calculated for you and the relationship between ad position and (for example) conversion rate is there to see.

This report shows that imagination, common sense and thoroughness are all you need to get started. I would argue it took some imagination to come up with the idea and common sense requires that I ensure there is enough data there from which to make a well-informed decision. Finally being thorough means not taking the initial findings at face value, pushing further into a sub-dimension or pivot table.

A note on thoroughness

This final point is a key one. We’re using the above analysis to make some fairly important decisions about our AdWords campaigns. With that in mind, we have to be extra careful about the validity of the data we are working with. It’s worth thinking about the structure and characteristics of your different AdWords campaigns and ad groups. What is right for one campaign/ad group/ keyword might not be for others.

In the above illustration, if we were to take the first report we generated at face value it may lead us to the conclusion that position one is the most efficient for us to be in. Acting on this decision may have lead to a serious drop in performance. Using the pivot table to drill down we see that the brand terms for this account are showing predominantly in position one and are effectively skewing the data set at account level. Suddenly position one becomes a less attractive proposition!

All I ever want to end on are some words of encouragement: give this a try, think about the results you are seeing and then, go and make the required changes. Repeat after me; analyze, act, monitor, analyze, act, monitor, repeat.

1. Those heavy on keywords or phrases made up of low number of fairly generic terms (for the purpose of this article, referred to as “shorter keywords”)
2. Those using a high number of terms ranging from generic to improbable (or “longer keywords”)

Our approach has always been to go for broke with the number of terms we use in our campaigns. So, we’re better aligned with option two and I know we are not alone.

As spelled out in Convert More New Users Using Advanced Segments, I’m a huge fan of the new advanced segments feature in Google Analytics. This article offers another example of how advanced segments can help us chop up our data into more digestible morsels. My aim here is to show the workings of this type of segmenting and combine it with a custom report to analyze keyword data more efficiently. I finish with a note on applying similar filters and the many possibilities that follow. The goal? To demonstrate that there is now an easy way to measure the reward for the effort of building those keyword lists.

A common misconception

I know some of you are thinking “why not just use “long tail” and “head” instead of saying “shorter” and “longer.” I can hear a lot of experienced search marketers and analysts replying “because they aren’t the same thing!” But then there are some slightly quieter murmurs coming from an unnamed corner of the SEM population. This sounds more like “erm, well, yeah, they are. Aren’t they?”

To clear things up, the “long tail” refers to the hundreds or thousands of keywords sending users to your site which, individually have few visits but collectively form a substantial chunk of your total traffic. The length or number of words in the keyword in this definition is irrelevant.

To make the second group feel better about this situation, I would like to point out that the long-tail often is full of keywords containing 3 or more words. To compound this, we also don’t really have a collective term for these long, ungainly but highly profitable terms. If we did it might prevent this misconception.

Defining “longer keywords” & “shorter keywords”

In the example below, the obvious starting point for defining a “longer keyword” is four words or more because from this point onward, the sum total of all the bars in the chart is less than the total of the previous (three words) bar alone. By that mark, keywords with three words or less become my “shorter keywords.” As a general rule, your “longer keywords” will be between 3+ and 5+ terms. Either go with that or your own analysis.

Create the segments

I doff my cap to the Google Analytics Twitter team for publishing this screenshot of a regular expression (regex) filter and to my colleague James Carswell who took the Google regex and made it even shinier.

Now that you have your definitions, add the below regular expressions as in the images. Think about whether there are any other terms or traffic types you might want to exclude at this point.

The longer keywords segment

I have used this segment to specify that we want to see keywords which contain at least three spaces and therefore have four words or more (\s|\+).*(\s|\+).*(\s|\+) (we altered the original Google regex to take into account the fact that occasionally words are separated by “+” signs instead of spaces in Google Analytics).

The shorter keywords segment

Use this segment or copy from the below image to create a segment which only shows keywords containing three words or less.

Build a custom report

This example is taken from an ecommerce website so I have included revenue data. You can equally use goal conversion data or bounce rate and average time on site. I have included source and medium here so that the data is available should you want to take it to the next level.

After applying both the custom report and advanced segment to a keyword report you can start to play.

A small tip here: the maximum number of keywords you can view in one report in Google Analytics is 500. However, when viewing a keyword report you can change the limit by appending “&limit=n” to the url in your browser. Where “n” is the number of keywords you would like to see data for—I believe the maximum value is 20,000. You won’t see any change in the interface but when you download the report as a .csv file you should have all the data you wish.

Go out and play with your segments

There are so many ways we can apply variations of this segment to suit different job roles and business needs. Also, the results really will be different for everybody. Here are some different ways of looking at interpreting the data for this segment.

Value of short vs. long keywords. Try comparing the data for the two above segments. What do you see? Is it worth bidding on those big generic terms anymore?

SEO value. Try charting the rise of visits of your “longer keywords.” Is the situation improving? As above compare the segment with the shorter keyword segment. Are they both going in the same direction?

Campaign cross-pollination. You may be able to segment by paid search, and in turn use the data to inform and help guide your SEO work. Similarly, are there some nice long terms you haven’t got in your paid campaign which are working for SEO?

AdWords optimization. What is the relative CPC of the longer vs. shorter terms? Do you need to come up with different bid strategies for each?

Google ad position. Combine these two segments with a custom report showing overall data for the different ad slots. Do longer terms perform better?

Sussing out the true value of the head vs. the long tail. If you really must define things by long tail and head status then try adding a number of visits quantifier to each segment. A great use for this is to see the shorter terms which are effectively in the long tail in terms of volume. They can often give you great ideas for a whole new array of terms.

Compare the engines. Look at the relative performance of Google, Yahoo! and Bing, segmented by longer and shorter keywords. Which one is doing a better job of bringing in the traffic on longer keywords? How does that affect your strategy on these terms?

Above all, experiment! You might not know something is useful until you try it.

Q. How many different types of website visitors do you have?
A. Two.

The answer is easier than you thought, right? We should qualify that statement: For the purpose of this test we can divide your users into two camps:

• Those who know your site (or business)
• The “newbies”

Newbies are users who have no prior knowledge of your site or indeed your business proposition. These people are immensely important to you—they are the ones you’ve spent so much time and money getting to your site. They are future loyal customers, the drivers of growth and prosperity and healthy looking ROI charts. But not if you lose them at first touch.

This is where those often quoted statistics about how many seconds you have to engage a user when they land on your site come into play. Whether the official figure be 2, 3, 5 or 0.5 seconds, we’re all united in the belief that it’s not long. We also all know that we need to do everything we can to keep these people on the site long enough to channel them towards behavior that both satisfies them and helps achieve our goals, turning them into customers.

Whether your site’s purpose is ecommerce, data capture, whitepaper download or something altogether different, it’s almost certain that there is some kind of action you want users to complete. Channelling as many of your prospects as possible to this action should be pretty high on your “list of stuff to do on the site.” First step, make sure you have some way of tracking these actions and that you know what your conversion rate is.

Segment & conquer

There are problems inherent in defining a “new” or “returning” visitor in all web analytics solutions. For that reason we use segmentation to refine the definition somewhat. Here we want to see site usage data for those users arriving at our site completely that have never visited previously. All they know is that they have just clicked on a link (be it paid or free), most likely from a search engine result page, and voila, here they are! With that in mind, the segment we create must exclude all users who we can reasonably assume have some experience of your website or prior knowledge of your business. For our website we can define this as anyone who fits (or doesn’t fit) these criteria:

For your site there may be more criteria. Take some time to get this right and make sure you really have excluded all unwanted traffic. For example: subdomains, or any strongly linked sister sites, email campaigns, etc.

As an exercise, this is a good intro to the immensely powerful “advanced segmentation” feature you’ll find in the top right of your dashboard in Google Analytics these days. If you’re running a different package, fear not: most web analytics solutions will allow you to segment traffic in some way.

Identify the right pages

Start by gaining an understanding of which site content is being accessed most frequently by our prospects. Apply the segment you just created to the “top landing pages” report. You might be surprised by what you see in the top landing pages report. We have seen many times that one page is optimized better for natural traffic while another is preferred for paid search traffic. Remember we’re talking about first impressions here so we’ll base this analysis on landing pages.

Once we know which page(s) they are, we can set about understanding two things:

• How these visitors are reaching the site
• What they are doing (or not doing) once they are on there

Answering these questions will help you to decide if this is good quality traffic that should intuitively be converting at a good rate. For example, are the sources either good quality and/or keywords highly relevant? If the answer is yes then this page is worth optimizing for conversions. If no, you can refine your segment to exclude these elements and re-evaluate.

Next, get an idea of how often users landing on those pages are converting. A good tip here is to add a landing page qualifier to your segment and duplicate the segment, one-per-page analyzed. To get a full picture, use a mixture of conversion rate, bounce rate, time on site and average page views to form this opinion. One of these metrics alone may not give a true picture. You may decide to weight it in favor of one or other metric depending on what is of value to your business. In most cases, goal conversion rate should be the focus here.

Analyze & theorize

To recap, you have segmented your traffic into newbies and old-hands; you’re focusing on the behavior of these newbies. Now you even know which pages they land on. Hopefully, you’ve worked out at what rate they are converting into customers and decided whether you think this is reasonable or not.

Now the hard part: being objective with your own website is truly a difficult thing. Putting yourself into the shoes of one of your new prospects doesn’t make this any easier. So have a stretch, make a cup of tea, crack your knuckles and, when you’re ready, load new prospect landing page number one. So, what is your first impression? Within 5 seconds can you identify and satisfy the answers to the kind of questions your new prospect is asking? For example:

• Do I know what this company does?
• Do I know what their price/quality proposition is?
• Have I identified that they have the right product/service/information for me?
• Most crucially: do I know what to do next? (Channelling towards the goal)

Be honest! You might have spent days crafting that synopsis of your professional life to give your site an air of authority. But does it answer the above questions? Your SEO-optimized homepage text might be great for search engines but does it really provide a succinct synopsis of what your business does? Likewise, another issue we’ve seen frequently is the confusion of different possible actions. Imagine a user is satisfied with the answer to the first three questions above. All they need now is a quick, easy way to proceed to the next step in the process. Does your website provide this for them? If you are dealing with internal sensibilities relating to the website design, this process will help you build a case for change.

Drive change

You should now be left with a few informed assumptions/hypotheses about your website. It’s good to hypothesize so long as you don’t confuse an assumption with an empirically proven truth.

Test these assumptions, prove or disprove your theories. Depending on what these are, you will test different things. Here are some common examples:

• Think your signup process may be too long? Cut the form short and observe conversion rate, ideally test side by side.
• Think your homepage may be too wordy? Create a B version and A/B test.
• Think the call to action isn’t clear enough? Add a clear call to action to the landing page

There are some great tools to help here. We’re big fans of the free Google Website Optimizer. There are plenty others out there. Use a program like this to test different versions of key elements in either an A/B or multivariate test.

Whatever testing you conduct, remember to be objective with the results. It’s also crucial to have the right measurements in place. Once you’ve made an improvement, it doesn’t have to stop there. Move on to another segment or another portion of your site, measure, hypothesize and test again.