Coast Digital Blog

Advanced segmentation is a relatively new feature of Google Analytics. It’s very powerful and can help you unearth some really specific information about your site's visitors.

What is an advanced segment?

An advanced segment is a real-time slice of the data that is in your Google Analytics account. Segments, just like filters, allow your GA profiles to contain specific groups of users, based on a set of chosen criteria.

The advantages of advanced segments over filters are that:

• you can apply (and remove) a segment on the fly
• they don’t make any permanent changes to your profile’s raw data
• they work retrospectively (you can apply them to old data).

How do I setup an advanced segment?

Login to your GA account and visit the dashboard for the profile that you’re going to be using.

In the top right-hand corner, just below the orange bar, it says ‘Advanced Segments’. Clicking on the ‘all visits’ button next to this will open the correct menu.

You can then choose from the list of existing segments, or click ‘create a new advanced segment’ if the segment you want isn’t already available.

Let’s assume you’re going to create a new segment. When you reach the next screen, you can select from the available dimensions and metrics panels, drag them in to the main window and use them to create the logic that will segment your data.

As you can see in the example below, I’ve created a segment that will contain visits where revenue was greater than £50. To make sure your logic is right, you can hit the ‘test segment’ button. If no visits are returned by the test, it’s likely your logic is wrong.

Some example segments

Revenue

The example above gives us a good start. Looking at visits by revenue value can give you a good idea of how your biggest spending customers are using the site.

It’s also useful to look at those users that don’t spend any money on your site. Doing so may throw up obvious areas where the site can be improved – you should be looking for any pages that have high bounce or exit rates.

Goals

We find the segment below really useful. It allows us to look specifically at users who started a goal process, and didn’t complete it. This is particularly useful if you are tracking a shopping cart funnel, or a sign-up process.

Again, looking for high exit rates is vital. Finding where users are hitting problems and fixing any issues can help increase your conversion rates no end.

Traffic sources

Segmenting by traffic source is one of the simplest uses of advanced segments. Google Analytics gives you a selection of these to use as default.

If you’re paying for traffic to your site, it’s important to make sure that every click counts. There is a pre-existing segment for paid search traffic, but if you’re paying for other traffic via banners, emails, or affiliates, it’s worth investigating how these users interact with your site. Here’s an example:

With these paid traffic segments, you should be paying special attention to landing page performance and other areas of content that are heavily visited.

You can take these segments one step further by combining them: let’s look at visits from a source that didn’t generate revenue.

Expanding the above examples, we can say medium is ‘email’, AND revenue is ‘0’:

The more ANDs that you include in your logic, the smaller your dataset is likely to be. Make sure that you don’t impose so many conditions that you end up making big decisions on tiny data sets.

Experimentation is the key

As with a lot of Google Analytics functionality, the best way to learn is to gets your hands dirty. Experimenting with segments will allow you to see what options are available and how you can imaginatively manipulate your core data to get the stats that you want!

In the last post, we introduced the concept of Google Analytics filters.

In this one, we’re going to look some examples of how filters can be used to make Analytics much more powerful. Some are quite straightforward and will be suitable for most Analytics installations, but others are more specialist and will appeal to the proficient and data-hungry analyst.

Either way, you should find plenty of ideas that will be relevant to your own online activities.

To recap, when you use filters you are specifying rules that keep data clean and in order, as well as splitting off data that relates to specific traffic types and areas of the site. By doing this, you can channel data into separate profiles and apply certain rules to it.

It is also possible for filters to amend data at the point that it is processed by Analytics, allowing additional data to be added to various Analytics fields – we will take a look at some examples of this next.

Top tip – When working with filters, it is good practice to keep a raw data profile in your account that isn’t subject to any filters. This way, if you are testing a new filter with additional profiles, you have a backup of unmodified data if something doesn’t behave as planned.

IP Exclude

Exclude a single IP or a range of IP addresses from appearing in your reports.

This is useful to filter out internal visits from your own organisation, which if left in, can have a noticeable effect on overall site average visit times, bounce rates and page views.

Make sure that you use regular expressions to correctly format the address:

Top tip - Use an include filter to add a profile for internal traffic. This way, colleagues can test out landing pages and other new pages on site that you haven’t linked to from your main site navigation, without affecting your whole-site visitor data.

Organic traffic (or another traffic source) only

This example shows you how track only organic traffic by setting up a profile of its own, but it can be applied to any other source.

One folder only

This tracks traffic to just one folder on site - e.g. sitename.com/blog

Full PPC Keyword Referrer

One limitation of the ‘keyword’ field in Analytics, is the inability to see the exact keyword or phrase that triggered a broad or phrase-match PPC visit.

Without any filters applied, Google will show only the bid keyword that triggered the visit – i.e. the term that the advertiser is bidding on in their PPC account, but not the actual search term.

For example, the broad matched keyword ‘search’ would match to a query for ‘search engine optimisation’. However, Analytics would show the referring keyword as simply ‘search’.

The following filter-set allows the exact search term to be added to the ‘user defined’ field in Analytics, alongside the bid term. This is extremely useful when looking to build out lists of negative keywords to use in a broad or phrase-match campaign, as well as identifying opportunities where exact match terms can be added to an account, helping to reduce overall CPCs (the average cost per click). Ensure that the following filters are added in the order shown:

i)

ii)

iii)

iv) - The RegEx string in field B in this step reads: (\?|&)(q|p|query|qs|qt|encquery|k|rdata|searchExpr|szukaj|terms|text|wd)=([^&]*)

v)

vi)

Keyword position filter

Google is testing and phasing-in AJAX results pages, which allow search term position data to be collected within Analytics on a search-by-search basis.

This is very experimental at the moment, but it’s a great way to learn about the Google referring data from their search results page. If you export this data to Excel, you can - with a bit of cunning data manipulation - extract positions and analyse them in more depth.

It is worth noting that the position is relative to the top of the organic results section and as such, news, products and other items are also counted as positions. It is also relevant that Ajax results are not shown to every searcher at the moment, so there will also be a lot of undefined data in your Analytics profile. The following filter works for Google.co.uk but can be modified to work with Google.com results.

i) Add a filter for Organic traffic only

ii) Add filters for Google referred traffic only

iii) Add position data to user defined field

Note: the RegEx string in field B in this step reads: [?#&]cd=([^&]+)&

Again, once the profile has collected and is displaying traffic data, navigate to the keyword report and select the user-defined value as the secondary dimension. You will then see the positions recorded for the associated keyword.

One of the great strengths of Google Analytics is its adaptability, allowing you to press it into service when you need to measure metrics in complex or unusual situations.

To do this, you normally have to modify the tracking code – the javascript you add to the foot of your pages. Some of the more common modifications can be created automatically from within the Analytics interface, and they follow this format:

<script type="text/javascript">
var gaJsHost = (("https:" == document.location.protocol) ? "https://ssl." : "http://www.");
document.write(unescape("%3Cscript src='" + gaJsHost + "google-analytics.com/ga.js' type='text/javascript'%3E%3C/script%3E"));
</script>
<script type="text/javascript">
try {
var pageTracker = _gat._getTracker("UA-1041707-1");
<Additional Code Here>
pageTracker._trackPageview();
} catch(err) {}</script>

In all the examples below, the relevant code needs to be added before the line that reads pagetracker._trackPageview();

Tracking Multiple Subdomains

If your site spans a number of subdomains, you need to make sure that Google Analytics treats them all as a single site. For example, if your main site is at www.mysite.com and the secure sections of your eCommerce site are at secure.mysite.com, then you need to make sure that referral information is passed between the two. To do this, add the following code:

pageTracker._setDomainName(".mysite.co.uk");

Tracking across domains (multiple top level domains):

If, for example, your site uses a third party checkout on a separate domain (i.e. you use something like www.checkout-provider.com as well as www.mysite.co.uk), then you need to modify the Analytics tracking code so that referral details apply to both domains. If you don’t, then www.checkout-provider.com will be treated as a referring site, which will skew your data.

To measure both domains, you need to add the following lines to your Analytics code.

pageTracker._setDomainName("none");
pageTracker._setAllowLinker(true);

Note: you will also need to add the _link function to hyperlinks between the two sites. To do this, you should add the code in bold (you may be able to set up your content management system to do this automatically).

<a href="https://www.siteexample.com/checkout" onclick="pageTracker._link(this.href); return false;">Login Now</a>

Custom Segmentation using SetVar

It’s possible to use the setVar function to set custom segmentation rules, allowing you - for example - to segment visitors to a specific section of your website, such as the Business to Business area. That way, you can analyse those visitors as potential customers, rather than as consumers, researchers or members of other groups.

To do this, add the following to your tracking code, replacing SegmentName with your own label:

pageTracker._setVar(”SegmentName”);

Information picked up by setVar function appears in the ‘User Defined’ section of the Google Analytics interface.

Filters

Filters are a very powerful Analytics feature in Analytics, and they can work hand in hand with the advanced tracking techniques we have already looked at.

If you use filters, you specify rules to keep data clean and in order, as well as splitting data relating to specific traffic types and areas of the site – allowing it to be channelled into separate profiles.

For example, if you used the subdomain tracking discussed earlier, you could use filters to generate separate profiles, such as for:

• All traffic (without filters)
• Main site traffic (no subdomains – just the www variant of your site)
• The eCommerce store subdomain

Note that any filter applied to a profile works from the moment it is added, not retroactively. For this reason it’s usually a good idea to add filters to a brand new profile, ensuring data continuity.

Top tip: Segment subdomains into profiles of their own by using filters – this way, it is easy to gauge the performance of different subdomains from the summary page and content reports.

One of the most useful ways of using Google Analytics to identify potential problems on your website is by implementing 404 tracking. A 404 page indicates that your server could not find the resource requested by your visitor, but only tracking can tell you why.

Analytics deployments often overlook this simple, yet effective method of identifying broken links (even those off site), along with missing pages and lost opportunities. But by adding a very small piece of custom code, you can reveal a wealth of information about the 404 pages that your users are seeing.

1) Add the custom tracking code to the 404 page template

Assuming that the 404 pages on your site are currently missing the Google Analytics Tracking Code (GATC), add the following code to the page – this is the standard ga.js code, with an amended line, indicated in bold.

<script type="text/javascript">
var gaJsHost = (("https:" == document.location.protocol) ? "https://ssl." : "http://www.");
document.write(unescape("%3Cscript src='" + gaJsHost + "google-analytics.com/ga.js' type='text/javascript'%3E%3C/script%3E"));
</script>
<script type="text/javascript">
try {
var pageTracker = _gat._getTracker("UA-xxxxxxx-x");
pageTracker._trackPageview("/404.html?page=" + document.location.pathname + document.location.search + "&from=" + document.referrer);} catch(err) {}</script>

The code sends a virtual page view of "/404.html?page=[pagename.html?queryparameter]&from=[referrer]" to Analytics, where pagename.html?queryparameters shows the missing page name, and referrer represents the page URL the user came from before they landed on your 404 page.

2) Take a look at the incoming data

Log in to your Google Analytics control panel and look for /404.html in the Top Content report. This will display 404 page statistics, along with the locations of the missing pages and the referring information.

Once the code has been collecting 404 visit data for a while, you should see entries that resemble the following appear in the top content report.

In this example, the /404.html serves as an identifier so that we can see that the pages are 404s (and filter them using the ‘filter page’ feature in Analytics). Next, the page= section shows the address that the 404 appeared at – in this case a gif image. Finally, the from= section shows the source of the traffic – this time it was a referring site called ‘mybrowserbar.com’.

This information is valuable in the following situations.

• Finding any broken links on-site from old or redundant pages. A page may have been renamed, removed or inadvertently broken. This is especially common when a number of people have access to the CMS or the server, and make frequent changes.
• Discovering where redirects can capture traffic from a referring site’s dead link. A new site or URL rewrite will mean that existing links point to missing pages. You need to put 301 redirects in place, and 404 tracking will help you identify any that you have missed.
• PPC destination URL problems (i.e. when a PPC landing page doesn’t exist and paid traffic is being directed to a 404 page). Often these expensive errors go unnoticed for some time, as advertisers tend not to click their own PPC adverts.

The majority of 404s (except for those that are the result of mistyped URLs) can prevented by using one of the following methods, depending on the reason for the 404 status.

• Implementing 301 redirects
• Adjusting PPC keyword and advert destination URL’s 
• Fixing broken links on site
• Requesting that broken links on external sites are directed to a new page
• Creating appropriate new pages and content on topics your users will expect to find
• Developing new URL-specific landing pages
• Generating dynamic 404s that include relevant search results

Top tip: Set up filters to segment 404 tracking into a profile of its own – this way, it is clear to see from the Analytics overview page if there has been a significant, recent increase in 404 errors.

After the technical detail of the last few posts, I’m quite relieved to tackle a couple of more straightforward topics – dashboards and automatic reporting.

The tips coming up in today's blog will help speed up basic reporting within Google Analytics, saving you time and – as a result – money.

Dashboards

Let’s take a look at your dashboard. This is the first page you see when you log in to view your Google Analytics reports, and it looks like this:

The dashboard is designed to let you see at a glance what’s been happening. The site usage statistics provide an overview of activity for the selected time period (by default, this is a month). The smaller tables underneath can be customised to suit your needs.

If you click on the dark grey title bar of any table, you can drag it around and put it in the place that suits you best (this is most helpful if you need to prioritise your reports). If you don’t want to see a particular report on your dashboard, you can click on the cross in the grey title bar, and it will disappear. (Don’t worry if you do this by accident, you can always add the report again).

Adding reports to your dashboard.

If there’s a particular report that you want to view regularly, then the best thing to do is add it to your dashboard. That way, you have the report overview in front of you from the moment you log in, and you’ll also have a direct link to the full report – this can save you many clicks.

Adding a report is very simple. Navigate to the report you want to add to your dashboard, and then click the ‘Add to Dashboard’ button, highlighted in the screenshot below. 

Job done!

Automatic reports

If, like me, you need to check certain stats in certain profiles every day, then you can save yourself the hassle of logging into Analytics by setting up a scheduled report. In this way the report will appear in your inbox as often as you need it.

To set up an automatic report, navigate to the relevant screen and hit the ‘Email’ button, as below.

You’ll be taken to an Email Reports set-up page. Hit the schedule tab at the top and fill out the details. If you’re logged in under your own account, then the correct email address will already be set. If not, you can add your email address in the ‘send to others’ box. 

The other options are self explanatory. Choose the date range, and how often you want to receive the report. Then state whether you want the report to include comparison data, and hit ‘Schedule’.

Automatic reports can save lots of time, so it’s well worth thinking about who needs to see reports regularly, and what information they need to see. If you set reports up and let Google Analytics do the work, you’ll be able to increase productivity significantly – both for you and for others.

Google Analytics makes our lives very easy when it comes to dividing our traffic sources into easily manageable chunks. It automatically splits out non-paid search traffic and paid traffic from Adwords. It also tells us about keywords from all the search engines, plus referrals and direct traffic.

But what happens if you send out an email with links to your site, or run a PPC (pay per click) campaign on Yahoo or Bing? I’ll tell you what: it makes a big mess of all your data.

If you send an email, Analytics will track any clicks either a referral (from web-based clients like Hotmail) or as direct traffic (from Outlook or another offline email client). Your data will be totally skewed.

It gets worse. If you decide to run a PPC campaign anywhere other than on Google, Analytics will record the traffic as natural visits, and not paid visits. This is a real pain, and it ruins your data for natural traffic.

So what’s the solution?

As you know by now, the people at Google are very kind to us. They have provided us with a tool that can generate a tag to stick on the end of any URLs or links we might need to track. If you’re going to track lots of additional marketing activity, Google’s URL builder will become a very dear friend.

This is what it looks like.

And you can find it here: https://www.google.com/support/analytics/bin/answer.py?hl=en&answer=55578

All you need to do is follow the on-page instructions, add the details of where you’re sending the traffic, and then fill in (at least) the source, medium and campaign names. Then hit ‘generate’.

Google will now give you a URL. Any traffic that visits your site via this URL will be automatically assigned to the traffic source, medium and campaign name that you’ve specified.

Example 1 – email marketing.

This is the most common reason I use the URL builder tool. Whenever we send out marketing emails for our clients, we tag all of the links in the email back to the client’s site. This allows us to define the email as a separate traffic source in Analytics. Like so:

Using the URL builder, we put a name for the email in the ‘Campaign Source’ box. You should give your email an easily recognisable name, so when you come to report on it later, there should be no confusion. With email campaigns, it’s sensible to add the send date, as above. 

The ‘Campaign Medium’ should be defined as email. The only other required box is ‘Campaign Name’. If the email you’re tagging is part of a campaign, then you’ll want to use that campaign name. If it’s a one off, we tend to use the source name.

Example 2 – tracking a non-Google PPC campaign

Let’s say you want to track a Yahoo Search Marketing (PPC) campaign in Google Analytics. If you run the campaign without tagging it, Google will automatically record the visits as coming from Yahoo, but it will wrongly assume they are organic clicks. 

To solve this problem, you need to tag your campaigns. Using the Google URL builder, you can specify the source as Yahoo and the medium as CPC (cost per click). You can then specify the campaign as “YSM-PPC-campaign” (or something similar). The URL builder will generate a tag that looks like this:

http://www.coastdigital.com/testpage.cfm?utm_source=yahoo&utm_medium=cpc&utm_campaign=YSM-PPC-campaign

All you have to do is pop these tags on every ad URL in the campaign:

?utm_source=yahoo&utm_medium=cpc&utm_campaign=YSM-PPC-campaign

In this way Google won’t automatically define your CPC traffic as organic.

To gather even more detail, though, you should make use of the ‘Term’ box in the URL builder. This lets you to give every keyword its own tag, allowing you use Google Analytics to measure the success of every single one.

Top Tip: Tagging multiple keywords (some Excel knowledge needed)

Tagging every keyword in your Yahoo account might seem like a daunting task. However, using Excel, you can quickly and easily tag an account of any size. Export all of your campaigns into Excel, and sort the whole spreadsheet by component type. It will then group all of your keywords together. 

Next, use the URL Builder to create a tag that looks something like this (using this tag will work for any Google Analytics user, so feel free to copy and paste). You’ll see the utm_term and utm_campaign are blank.

Scroll across the header row of your downloaded Yahoo campaigns until you find “Keyword Custom URL” (normally column H). Insert a few blank columns next to this row (either side is fine).

In one blank column put the destination URL of the keyword. In the next column add the section of the tag that ends with utm__term=

In the column after that, put the last part of your tag:

Drag these tag columns all the way down so that every keyword has these empty tags in the column next to them. Now go back to your 'Keyword Custom URL' column and use Excel’s concatenate function to tie your columns together. You should tie in your destination URL, then the first part of the tag, then the keyword, then the second part of your tag, then the campaign column. (I’ve colour coded each part so you can see how the formula builds the tagged URL).

=concatenate(destination-url, first-tag, keyword-column, second-tag, campaign-column)

This should give you a fully tagged keyword level destination URL. It will look something like this:

Drag the concatenate formula down your list of keywords and it should automatically fill in all of your destination URLs.

Highlight the ‘Keyword Custom URL’ column, copy it, and then with the column still highlighted press ‘Paste special’ and choose ‘values’. This will overwrite your formula with your new, tagged URLs. 

After all that, you just have to delete the columns you inserted, and upload the changes. 

What else should I tag?

Everything that isn’t already sifted automatically by Google. This will help keep your Analytics accounts as tidy as possible, and allow you to access more information on the performance of your online activity, including elements such as banners, advertorial, or social media.

When you’ve got your Analytics account up and running, you can start tracking the more advanced features on your site. 

Event tracking is a more recent (June 2009) addition to the Google Analytics toolset, and it allows you to track almost anything. 

If your site uses lots of Flash, then event tracking is definitely for you. For example, if you have a Flash music player, you can use event tracking to count the number of times users press ‘play’, ‘pause’, ‘fast forward’ and so on, giving you a great insight into how people interact with your content. Event tracking is also useful if you want to track other user interactions – such as out-clicks or ‘mailto’ links – without affecting or skewing page view numbers.

The code

The code for tracking events is very simple indeed. All you need to do is tag each element you want to track with the following line of code (this example assumes that ‘pageTracker’ is the name you’ve given to the tracking object).

pageTracker._trackEvent('category', 'action', 'label', value);

This code needs to be called in the page after the pageTracker is initialised. Sometimes the best (and certainly the easiest) thing to do, is to move the standard GA tracking code to the top of code – i.e. straight after the opening <body> tag. 

The four fields within the brackets allow you to categorise each action performed on the site. Below is a diagram showing how events are structured when you come to report on them. 

Here’s a bit more information on each of the parameters that you can pass into the code. 

Category. This field is mandatory and should be passed with every event that you track. The category is the highest level in the event tracking ‘architecture’, so it should be used for grouping your events at the widest level. For example, you might have ‘videos’ as the category. This will group all video-related events. Alternatively, you could have ‘downloads’ as the category and group together all download events  

Action. Action is the name you give to the specific way in which the user will be interacting with the site. This field is also mandatory. For example, if you’re tracking video actions, you might name them ‘play’ and ‘stop’. Or, if you’re tracking downloads, then you might use the type of file, ‘pdf’, ‘exe’ or even a specific filename. 

Label. Label is an optional field that can be used to give more information on each action. So, to return to the video example, you could use the video name as the label. You would then not only be able to record all video interaction at a category level and how many video plays there have been at action level, but also how many plays there have been on a video-by-video basis at label level.

Value. Value is also optional. The value needs to be an integer (i.e. a whole number, meaning - unfortunately - that you can’t use cost or revenue numbers). 

Top Tip – If you are using values pay close attention to the tag - pageTracker._trackEvent('category', 'action', 'label', value); You won’t need inverted commas around the value as it’s an integer and not a string. Putting inverted commas around your value parameter will prevent the tag from working.

Using our video example once more, Google suggests that download time could be used here. But if you’re really clever, a better use would be to record how many seconds of the video the user watched before they stopped it. If you get low numbers, you’ll know the video isn’t connecting with your audience. 

Plan Ahead 

It’s really important not to rush into an event tracking implementation. You need to plan exactly what you want to track and then work out how you’re going to structure the events. Getting the structure wrong will mean you won’t be able to get the most out of your stats. 

You also need to think about the future. Ask yourself what else you are likely to be tracking as your site develops. You need to be able to allow for new categories/actions/labels to be added without them affecting your current Google Analytics stats. 

What are goals?

Google Analytics ‘goals’ allow you to measure specific actions that users carry out on your site. They can track events like a submitted enquiry form, a brochure download or a completed transaction. That’s why it’s important to set up sensible and comprehensive goals from the outset – only then will conversion rates be calculated and shown in Analytics reports.

Goals can be triggered by one, or a combination of activities that include page views, button presses and form submissions.

You can use up to 20 goals, grouped into four sets of five, within each Analytics profile – if you need to track more then you’ll need to add an additional profile to your account. But don’t worry: 20 is more than enough for most purposes.

Setting up page view goals

Goals are most commonly triggered by a page view. For example, a ‘thank you page’ view will indicate that a user has completed your contact form and been directed to the next stage.

So, how do you measure something like this in Analytics?

First of all, navigate to your list of website profiles and select ‘Edit’ next to the name of the website you are working on.

You’ll be taken to a screen on which all your available goals are listed.

Click on ‘Add goal’ and then give your goal a clear name – the easier it is to recognise in your reports, the better.

Next, select ‘URL Destination’ as the goal type. We will go on to look at the other options at the end of this post.

Match Types

At this point you will be asked to enter a ‘match type’. You have three to choose from.

Head Match will match your goal URL to the leftmost part of the page URL – this means that dynamic addresses can be tracked. For example, where a unique identifier is used in the URL /thankyou.html?id=1234567 a goal would be triggered if the Match Type is set to ‘Head Match’ and the goal URL is set as /thankyou.html

Exact Match matches the goal URL only to the exact page URL that appears in your reports.

Regular Expression Match allows URLs that use the same regular expression to trigger the same goal. For example, you could ensure that dynamic URLs such as www.mysite.co.uk/?editpage=11567830 and www.mysite.co.uk/instock/?editpage=90385382 both triggered the same goal – both use the regular expression ‘editpage’.

Top Tip: if you need to test which goal URL to use, either because you have dynamic URLs or a number of pages that relate to the same goal, you can construct a RegEx expression in the Top Content report filter box (at the bottom of the Top Content Report page). This will show the page views that will trigger a goal using that expression. Simply copy and paste the expression into the Goal URL section and select “Regular Expression Match” as the match type.

Goal Values

It is possible to assign a monetary value to a goal. If a completed goal has a commercial outcome, it can be assigned to the goal value. If you know you have an average online order value of £500, and you know that about 10% of enquiries result in a sale, you might assign £50 to your goal value.

It is worth noting that, whilst goal values give you an indication of how much your traffic is worth, the function is not as versatile as e-commerce tracking, which is covered later in this blog series. E-commerce tracking can attribute values and specific products to transactions, but there’s nothing to stop you using Goal Values as well.

Using additional steps and funnel visualisations

By including additional steps in the goal process, it is possible to monitor the whole process in a visual format. For example, you can gather statistics for every stage of a purchase – the number of people who add items to a basket, the number who go on to the checkout and the number who complete their purchases. You can add up to ten steps in these ‘funnels’.

In this way, you can see the points at which visitor drop-out is occurring and identify possible weaknesses in the funnel. If you can identify the pages that users are navigating to instead of the ones you want them to go to, it will help you optimise the buying process – and ultimately provide information that can help you to improve conversion rates.

The funnel visualisation:

Engagement based goals

In its most recent update, Google added the option to use different goal types instead of the standard ‘URL Destination’ goal.

You can now measure goals that rely on ‘time on site’ and ‘pages/visit’. These allow you to record goals when a certain threshold has been reached (either greater or less than the predetermined value) for a set time on site, or a number of pages per user visit.

Finally, once your goals are set up and they have collected data, you’ll see new statistics in your visitor, content and goal reports – allowing you to analyse goals within different segments of your Analytics data.

Thanks to goals, you are not only able to measure how effective your website is, but you can use the data you collect to refine the way it works. It’s just one part of what we call ‘marketing you can measure’.