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- Web analytics winners and losers? It’s the people that make the difference.
- Simple segmentation for your website and better web analytics understanding
- Web Analytics Wednesday in London – the future of web analytics
- Digital cream: revealing debating at econsultancy’s marketing event
- Google Analytics Tip: Ecommerce tracking set up, screenshots and why it’s useful
- Reliving my customer’s experience and some nice screenshots
- Internal site search part 2
- The best charts ever and food for thought for us web analysts
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My Blogger Friends
Index tools goes free – I had the pleasure of using indextools on a couple of websites last year – and can honestly say that it does favourably compete with other much more expensive tools. So, going free with Yahoo is very good news for business (and for Dennis Mortensen – congrats). Lots of talk of whether indextools (yahoo) analytics will provide serious competition for google, depends on how seriously yahoo take their new web analytics solution.
The founder of venture capitalism in America,Georges Doriot (aged 21, Doriot sailed by steamship to the US from his native France and went on to become a brigadier general in the second world war, one of the most influential professors at Harvard Business School and founder of Insead, Europe’s first business school. But his main achievement was to pioneer the US venture capital industry in 1946 by setting up American Research & Development (ARD), which backed one of the first blockbuster technology start-ups, Digital Equipment Corporation).
George Doriot famously said, his strategy relied on backing talent, not technology.
“An average idea in the hands of an able man is worth much more than an outstanding idea in the possession of a person with only average ability,” he said.Not that google analytics or index tools are average, as there is alot of vested interest, and subtle bad PR in this industry, in fact in the hands of a good web analyst, there are both powerful tools – Brian Clifton’s new book on advanced web metrics with google analytics is an example (I am going to his book launch party tonight so I am sure, I will see lots of googlers.) So we will how this one pans out over time, I imagine (with Dennis at the helm, I am sure Yahoo analytics will be a big success). The point being it is the people, the team behind the product that make it or break it (apologies for the cliche).
In the interest of web analytics and my passion for it, I am always, vendor neutral. My passion is in better understanding and improving online behaviour.
Thanks so much for reading and apologies for my recent (infrequent) blog posts, which I very much intend to change.
A little bit techy today. I know. But really useful stuff.
With Google Analytics, you have the ability to track how many products are bought on your website, with it’s ecommerce tracking. You can also use this for your website, even if you don’t actually sell products but to categorise your lead generation, pdf downloads or whatever the call to actions are on your website.
On your product bought / lead confirmation page, you will need your programmers to add some code to the page, after the main google analytics tracking tag.
For example, I can track what type of property, the country, the postcode, the specific ID, the number of bedrooms in the property, a fictional value of £10/$10 for each lead, the price that each property is on the market for, – all by getting the programmers to use a “get” for each bit of information I am retrieving from the website. But it could any important information about either your product sales or leads.
Category is really useful because that’s how you sort your products / leads into different buckets.
Product name and Product Sku are where two different fields where you can specify what details about your product or lead you want to be able to see in google analytics as your product details type information.
The others, like price etc, are pretty self-explanatory.
In this instance, I don’t use shipping and tax – because I am using the ecommerce tracking for tracking leads generated.
This is my sample code which goes on the product bought/lead confirmation webpage code:
“$property.getCountryCode() $property.getBranch().getID()”, //Order Id
“$property.getCountryCode() $property.getBranch().getID()”, // Order Id
“$property.getPostcode().getArea() $property.getPropertySubTypeName()”, //’SKU’
“$property.getPropertySubTypeName() $property.getBedrooms() beds”, // Product Name
“$selectedTab”, // Category
“$property.getPrice()”, // Price
“1″ // Quantity
And this is what ecommerce categories ends up looking like within the google analytics interface, eg renting:
This is extremely easy to set up, 20 minutes of a programmer's time, and once done, gives a huge benefit and understanding of either ecommerce usage or leads generated in exactly the way that makes sense to you.
Have you tried it out yet?
The charming Brett Crosby from google analytics, with some fanfare, announced several new google analytics features at emetrics last week in Washington – which you will have heard about by now, being ability to report on internal site search, ajax and event tracking, outbound link tracking and new Urchin software fixed price, all of which will be launched in the next few weeks. But here I have outlined a guide to how to use and getting the most out of Site Search.
Being able to track site search is a great step forward. Internal search refers to the keywords that people use while exploring your site (not the keywords they use on the search engines such as Google). For many websites, in particular holidays, recruitment, publishing and large retailers, internal search can be the most important and used feature on the website and can account for 50% of all pageviews on the site. So this is pretty amazing stuff that a “free” analytics solution is letting us do this – I’m really excited.
How do you use Google Analytics internal search:
1. Switch on ”Site search” on your Google Analytics profile(s)
2. You can find Site search in the Content section of Google Analytics reporting interface
3. Site Search reports show you the keywords that people use and the pages from which people begin and end their searches.
4. You can also filter search on your site against site usage, conversion rates, and e-commerce activity.
MY GUIDE TO GETTING THE MOST OF SITE SEARCH
1. What do people search for and do any keywords stand out?
2. What searches results in failed searches and what proportion are failed searches?
3. Let’s do some segmenting and why it is so fundamentally important
1. Here is how to get your long tail of internal search keywords:
First, create a report of all the internal search keywords and unique visits for a 2 month (dependent on site) period and upload to excel. Grab all the keywords and visits into smoothed line chart with data points (so that you can easily see the keywords that stand out). Then try making a list of the keywords that stand out. These are words that need to be looked at carefully as they will be benefit from being presented on the site in an easy to find way so that your visitors do not always need to search for them.
2. Which searches result in failed searches
A failed search is when a visitor doesn’t find what they are looking for. For example, keyword searches on products or services that you do not offer would be a failed search as would a time sensitive product or service that is not available within the results of the keyword search made. For example, visitors that click on the back button after making a search would be classified as failed/frustrated searchers. To reduce failed searches make sure the site reflects at the minimum easy to find information on the more significant failed search keywords (this is just a quick fix and not the solution if only information is presented but is a needed first step until a good solution is found).
Then look at the percentage of all visitors that have a failed search. In addition, you can create a visitor segment where the search results page is also the exit page and compare this against all failed searches to see how many “failed search” visitors, leave the site immediately.
3. Then we segment, to confirm our suspicions and insights
Assuming we can label visitors segments with specific keyword searches, we begin to drill-down further. If on a recruitment agency site, a noticable search is for “web analyst”, we can see which were the most popular pages they visited before searching. From this, we could learn that they visited the “marketing jobs” page and the “Web jobs” page and as a result of not being to find what they were looking for, searched for the term “web analyst” and subsequently left the site. Therefore, it would appear that both of these jobs page would benefit from having information about web analyst jobs on them (until the recruitment site started posting web analyst jobs that is).
We can segment against new versus returning visitors, time spent on site, by navigation path etc. For example, we can see how these visitors came to the site in the first place by looking at search engine keywords. If there is a noticable percentage of visitors who arrived at the site after having searched for “analyst job” or “web analyst job” on search engines, then it is clear that the hopes and desires of visitors coming in from the search engines is not being met by the site – as well as a PPC (pay per click) overspend on keywords that are resulting in a high number of failed searches and exits from the site. The key is to reflect on the site what visitors are looking for, in a holistic and thorough way.
Google Analytics “Site Search” will not be offering the same level of functionality as the big paid for boys in analytics solution. But amazing that, internal site search will now be offered because it is such an important part of so many websites and not being able to track it with GA was a waste. So excited, but will wait and see once it performs when actually does launch.
For example, earlier this year when I began working with a global recruitment agency (no names, I’m British) where they had set up google analytics internally, I discovered that visits weren’t 120,000 a month as they thought – but instead closer to 50,000 visits a month. The reason being that there were 16 domains/subdomain so every time a visitor went from one country site to another (with different domains) google analytics was treating them as new visitors. So quite a big problem that needed to first resolved technically – in order to get the correct numbers – and then with tact (obviously). You also need to make sure you add these extra variables to every webpage/tag where visitors can navigate from one domain or subdomain to another.
For example, if you have a product website, a shopping cart on a subdomain and a company website,
1. www.product. com: product website
2. store.corporate. com: shopping cart
3. www.corporate com: company website
You will need to add three variables to your standard GA tracking tag so that when one visitor for example navigates from corporate.com to product.com to store.corporate.com they will be treated as one visitor and not three which is what would happen otherwise.
The three variables you have to add:
First, utmLinker. This variable should be set to “1″ which means on. The reason is that utmLinker is the actual mechanism that transfers the cookies from one domain to the other. Without utmLinker you’d have two sets of cookies with different data – google analytics would not identify the same visitor crossing domains without it. With utmLinker, two different sets of cookies have the same data so that google analytics knows that this is the same visitor.
Second, uhash. This variable is named _uhash and should be set to “off”. This variable adds a hashed (or encoded) version of the domain name to the tracking cookies. This value is used during processing by the GA system.
Third, udn. This variable is named udn and should be set to “none” for the product website and “company name” for the store.company.com and Company.com
This is what you would add to the google analytics tracking tag for each site:
On Store.company. com:
N.B What is a cookie:
Cookies are used by Web servers to differentiate users and to maintain data related to the user during navigation, across multiple visits to a website and as a way of remembering what visitors do on a website for example buying, removing items online or logging in to a site. For example, they were originally developed as a virtual shopping cart/basket into which visitor could “place” items to purchase, and as they navigate add or remove items from their shopping basket at any time.
If you have any thoughts or questions, or happen to know of any other ways of doing things then do please let me know.
This will only be a shorter post than normal because it doesn’t apply to everyone – well mainly to those who use google analytics with sites where their pages have unintelligible endings looking something like this www.yoursite.com/default.asp?docId=15097 (usually running on cmses) or running on different subdomains or domains eg www.yoursite.com/analytics or blog.yoursite.com/analytics who want to have clean logical webpage names in their reports.
In this post I will describe three things:
1. How to use google analytics advanced filter so you can see the whole uri including host name in your reports
2. How to add urchin tracker to your web page code to give your pages logical web page names
3. How to name your webpages carefully
1. If your site has different domains (I’ll do another post on this later but if your site is running across different domains you’ll need to use the 3rd party shopping cart and utm linker or as visitors go across domains they’ll be treated as a new visitor each time so inflating your numbers) or subdomains, you won’t be able to see the domains in your GA reports. The reason being by default Google analytics only picks up the last bit on your uri so you can’t drilldown and see which pages come from which domain or subdomain. In addition, if you have the same uri ending eg /analytics, GA will be lumping them together (even if the reality is these are directing you to different pages on the internet).
For sites with different sub domains and or domains, I use a Google Analytics advanced filter to allow me to see the full host/domain name. If you try to work out how to do it from GA’s documentation – you might have a bit of a hard time here so I have spelled it out here – see Robbin Steif’s quite fab GA “worst of” documentation competition if you don’t believe me. And on a similar note, I am really looking forward to Justin Cutroni’s new pdf book called Google Analytics Shortcuts which will cover every imaginable detail on link tagging, setting up shopping carts etc.
First in GA click on the profile settings of your site and then edit filter.
Under filter type choose – custom filter from the drop down menu and tick on advanced.
Field A > Extract A – Host name – (.*)
Field B > Extract B – Request URI – (.*)
Output to > constructor – Request type URI – $A1$B1
Field A required – Yes
Field B required – No
Override Output Field – Yes
Case sensitive – No
What this does is extracts all of your host names and outputs them to all uris requested (.*) (all regular expressions) to all of the fields ($A1$B1) so that you end up being able to see the uri including domain in your reports.
3. You need to give your pages logical, meaningful but concise names that reflect their position in the site. The easiest place to start is to look at the wireframes/site tree and define page names according to the section of the site (eg books – quite general), then analytics books (the type of books or products) and finally Google Analytics Shortcut (the actual book name or product). So we end up with, (”//books/analyticsbooks/googleanalyticsshortcuts”). The final name is the current position on the site. Do not use spaces or other special characters such as ‘&’, ‘%’ or ‘+’ in your page names or the tag will not work. Whilst setting up a list of pagenames, map out all of the site pages with the help of the site tree/wireframes to avoid using page names that been used elsewhere on the web site. Also consider prefixing for example your FAQ page with a unique identifier – say, ‘analyticsfaq’.
I welcome your feedback, agreement and disagreement. And if anyone knows a better way of doing of these things then please do let me know.
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