Measuring Success

Tracking RSS blog feeds presents a problem for on-site web analytics tools such Omniture, WebTrends and Google Analytics. Why? Because as the name suggests, on-site web analytics tools measure visitor activity whilst on your web site and so cannot track activity that happens off site.

For example, consider the following scenario: A visitor arrives on your site and likes the blog content so much they decide to subscribe. All activity – from the visitor arriving, viewing pages and then subscribing to your feed is tracked. Note, that even though a RSS subscription usually takes place off-site e.g. to feedburner.com, this can still be tracked using the virtual pageview technique (you could also use event tracking to capture this information). This provides you with an indicator as to the interest of your RSS content. That is, the number of subscribers. All well and good.

However, so far this has all been on-site visitor activity, so it does not tell you what happens next…

Now that your visitor has subscribed, there is no need for them to re-visit your web site when you post your next article. Instead, they simply receive your content in their RSS reader and may never visit you again! Because reading an RSS feed happens off your website, you are not able to track this activity. You will not know if subscribers are reading or engaging with your content.

And that is the problem – your RSS subscribers can read your content, enjoy it (have great engagement, or not) and you will be totally unaware of one of your most important visitor types – your influencers. Unless that is, you make the following changes…

Tracking RSS viewers (using Google Analytics)

Although this example uses Google Analytics, the same technique can be applied to any on-site web analytics tool. Essentially, there are two important parts to creating a trackable blog feed:

1. Crafting the article to entice RSS click-throughs – so they can be tracked on your site
2. Crediting these to your feed using filters – to highlight RSS in your reports

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This is reprint of an article I wrote last month for www.journalism.co.uk .
It came about from a discussion of what is the most accurate method for measuring visitor activity.

Content: This is a multi-page post. You are on Page: 1 2 3

1. Page 1: Summary – there is no accuracy debate!
2. Page 1: Introduction – why most of the web is junk and what role web analytics plays
3. Page 2: On-site versus Off-site web analytics tools and how they work
4. Page 3: Discrepancies – what’s accurate and how can accuracy be improved

1. Summary

There is no accuracy debate between on-site and off-site web analytics tools: if you wish to count the activity on your website then only on-site web analytics tools can do this effectively . Assuming a best practice implementation, these can be very accurate for measuring the number of visits, page views, time on site and page depth.

Off-site tools bring additional data to the table – not accuracy. This includes demographic, search engine query data and competitor intelligence information. Combining these pieces of the jigsaw provides a clearer view of the performance of your site and where it fits in the overall web landscape.

2. Introduction

Imagine using the web to find exactly what you are looking for, instantly. By that, I mean actually discovering authoritative, reliable and accurate websites, with the information you need, in a quick and efficient way.

Maybe the information you want is the best price from a respective retailer, resort reviews from real holiday makers or a news article from an expert that helps you understand what the financial crisis is actually about. The problem is that such sites are like gold nuggets – a valuable resource that is rare in a virtual world of poor ergonomics.

Why most of the web is junk
The truth is that the vast majority of the web contains poor quality content. Even for those sites that do have great content, often they are difficult to find (via a search engine) and the user experience, once you visit it, is so poor, you simply wish to leave.

Despite nearly 15 years of web development, most of us still waste dozens of hours a month trawling the web, weeding out sites that either have irrelevant content to our search query or where the user experience is just too frustrating.

Unfortunately – similar to weeding – those sites don’t really go away. A search engine query the next day can bring up the same poor results and the process starts all over again.

How web analytics can help
That’s where web analytics comes to the rescue – the part art and part science of measuring a website’s performance. Simply put, web analytics is for website owners to understand their online visitor behaviour with the purpose of improving it.

Perhaps the marketing campaign is poorly focused; visitor expectations not met when they arrive on the website; the content displayed is out of date; the navigation system sucks; or an on-site search function returns no results (or worse still the same result) no matter what keywords you use.

Web analytics tools can help you identify these problems so you can fix and optimise them.

There are two types of web analytics data: off-site metrics and on-site metrics (see Figure 1*).

As their names suggest, these relate to metrics that can be obtained irrespective of your website presence or those obtained when a visitor lands on your website respectively.

Figure 1 – the two types of web analytics

* From a vendor perspective, the separation of methodologies is not as mutually exclusive as Figure 1 suggests. For example, Hitwise, comScore and Nielsen Netratings also have on-site measurement tools, while Google, Yahoo and Microsoft have the ability to provide off-site search query data to compliment their on-site tools.

This is a multi-page post. You are on Page: 1 2 3

This is a multi-page post
Page: 1 2 3

The term ‘uniques’ is often used in web analytics as an abbreviation for unique web visitors (i.e. how many unique people visited my site). The problem is that counting unique visitors is fraught with problems that are so fundamental, it renders the term ‘uniques’ meaningless.

Firstly, cookies get lost, blocked and deleted. Research has shown that after a period of four weeks, nearly one third of tracking cookies are missing, which means the visitor will be incorrectly considered a new unique visitor should they return to the same website (see Accuracy Whitepaper for further reading).

The longer the time period, the greater the chance of this happening, which makes comparing year-on-year data invalid for example. In addition, browsers make it very easy these days for cookies to be removed – see the new ‘incognito’ features of the latest Firefox, Chrome and Internet Explorer browsers.

However, the biggest issue for counting uniques faced by both on and off-site web analytics tools is how many devices people use to access the web. For example, consider the following scenario:

You and your spouse are considering your next holiday. Your spouse first checks out possible locations on your joint PC at home and saves a list of website links.

The next evening you use the same PC to review these links. Unable to decide that night, you email the list to your office and the next day you continue your holiday checks during your lunch hour at work and also review these again on your mobile while commuting home on the train.

Day three of your search resumes at your friend’s house where you seek a second opinion. Finally you go home and book online using your shared PC.

The above scenario is actually very common – particularly if the value of the purchase is significant, which implies a longer consideration period and the seeking of a second opinion (spouse, friends work colleagues).

Simply put, there is not a web analytics solution in the world that can accurately track this scenario, that is to tie the data together from multiple devices and where multiple people have been involved, nor is there likely to be in the near future.

Combining these limitations leads to large error bars when it comes to tracking uniques. In fact these errors are so large that the metric is actually meaningless and should be avoided in favour of more accurate ‘visit’ data.

Update (Apr-09): coincidentally Eric Peterson also posted a much longer blog article on the same issues in March – here’s the link: http://blog.webanalyticsdemystified.com/weblog/2009/03/unique-visitors-only-come-in-one-size.html

This is part II (from a two part series) of articles providing a How-to guide for integrating your voice of customer data with Google Analytics. In Part I, I discussed the approach to take when integrating with Clicktools – a popular visitor survey tool. In Part II, I discuss integrating with a very different type of voice of customer tool – Kampyle.

As you have hopefully read in Part I of this series, the general rule of thumb when integrating with Google Analytics, is to export your data from into your second tool. Essentially Google just makes exporting easy for you (though potentially the new GA API may one day also facilitate importing data into Google Analytics). However, exporting data from Google Analytics and into Kampyle is not possible, so a different approach is required.

Kampyle background information

Kampyle is a feedback survey tool – with the emphasis on feedback. For example, unlike Clicktools or SurveyMonkey you cannot build a survey on any subject matter you wish. Rather the system is tailored specifically for website feedback. This may sound like a limitation – but it isn’t. Kampyle is a tightly focused and simple to implement tool for getting your web site visitors to tell you about their user experience.

From a visitor’s point of view, Kampyle’s key strength lies is its ease of use . A popular implementation appears to be the display of a small coloured triangle placed at the bottom of a browser window (always in view despite the page length). Clicking on this launches a pop-up window that is very intuitive and quick to use. The visitor simply rates your web site and then they can select categories/subcategories in which to leave comments.

Try the purple one, to the bottom left of this window. In an Apple type of way, the whole experience is kind of fun!

The company was established in 2007 and is based in Tel Aviv, Israel.

Integrating Google Analytics with Kampyle

I have assumed you have setup your Kampyle account and implemented their feedback form on your web site.

As I mentioned earlier, you cannot export Google Analytics data into Kampyle in the same way I demonstrated for Clicktools in Part I of this series. However Kampyle have come up with their own neat integration in the form of a Firefox add-on. If you have Greasemonkey with your Firefox, you can install the Kampyle add-on which shows your feedback data as part of the Google Analytics Dashboard.

The add-on is still currently in beta, hence the integration shown above is a little basic. For example, the Kampyle charts displayed in the dashboard cannot be changed. For instance, I would like to see the last 5 submitted comments as snippets, as it is these that provide the actionable information you need. The average feedback grade and the distribution of feedback by category is interesting, but it is the comments that is going to drive change on your web site.

Hopefully the Google Analytics API will help with this (as well as not requiring multiple Firefox add-ons be installed).

Summary

Integration with Google Analytics is still very nascent. For the near future, it makes sense that Google’s focus is going to be on integrating GA with their own products such as Feedburner, Website Optimiser and Google Trends. That would be a very powerful suite of tools indeed.

With Google’s focus on internal integration, it means there are lots of opportunities for third party integrations. For example, merging the qualitative data of products such as Clicktools and Kampyle with the quantitative richness of Google Analytics, is a powerful combination that is just waiting to be exploited.

As I have written before, Voice of Customer techniques are your direct feedback mechanism from visitors to your web site. It provides invaluable qualitative data to your web design, development, marketing, PR and content creator teams. It compliments the quantitative data of web analytics by providing the “why” to the “what” and “when”. However it is often the case that this data remains in a separate silo within the organisation, never to be compared with the quantitative data of your web analytics platform.

This post, the first part of two, is a How-to guide for integrating your voice of customer data with Google Analytics. I have chosen two popular VoC tools: , Clicktools and Kampyle, though it is not my intention to review the merits of the respective tools themselves. However I do use them both, which makes me a fan of them both.

This is Part I – Integrating Clicktools with Google Analytics. Part II – coming soon.

My standard word of caution for all “GA Hacks” posts – This is a tech tip and requires you to have a knowledge of html and JavaScript to implement and use it…

Integrating third party data with Google Analytics

Why should you do this? Well, knowing where visitors come from that take the trouble to give you their valuable time and feedback, is a key component of understanding why things may, or may not, be working on your web site. For example, perhaps visitors that come via a banner advertisement have a poor user experience compared to those that click through on a email link or find you via a search engine. In order to know these when viewing your survey results, you need to obtain that information from your Google Analytics data – and specifically at the point when the visitor clicks through to start your survey.

Essentially, there are two approaches to any kind of data integration – either importing or exporting the data of interest. By this I mean importing your third party data into Google Analytics, or alternatively exporting from Google Analytics into your third party system – a third option is to export both data sets into another system.

Google’s approach is to facilitate the latter. That is, make the exporting of your Google Analytics data as easy as possible. An often used phrase at Google is “data democratisation”, which means making the data accessible to everyone (with suitable access restrictions of course). So the general rule for integration, is to always think in terms of how can I get data out of Google Analytics to integrate it elsewhere. This is the approach I use here.

Of course, that may change in the future. The recent announcement of the Analytics API in theory allows for both the import and export of data into Google Analytics. For now though I am considering only the export of Google Analytics data and without any API programming skills!

Clicktools background information

Clicktools positions itself as an enterprise version of the very popular SurveyMonkey product. And although the name isn’t as intuitive, it is a powerful visitor survey system. Founded in 2001, Clicktools is a UK company based in the beautiful coastal town of Poole with offices in San Francisco (another beautiful place, but sorry Poole beats it!). The product already integrates with SugarCRM and Oracle CRM, yet surprisingly not yet with any web analytics vendors. Prices start at $3000 for an annual license, though there is a 30 day free demo.

For me, there are two strengths to the Clicktools approach:

• The power of the standard built-in reporting capabilities that help you understand the results. This includes a function called “Cross Tab”, that can show how responses to one question influenced another. For example, how people who answered “yes” to Q1 responded to Q5 – see screenshot
• The ability to generate custom reports using the Analytics add-on i.e. build your own report dashboard with custom filters

Integrating Google Analytics with Clicktools

By integrating with Google Analytics data, you can understand how survey respondents found you, which search engine, keyword, referral, affiliate etc. – without having to laboriously ask for this in the survey. The result is Google Analytics visitor data incorporated into your Clicktools survey reports.

There are 4 parts to this integration:

1. Setup your survey in Clicktools
2. Add hidden questions within Clicktools to collect the referral information – upto 5, though these can also be combined
3. Add JavaScript to your page(s) where you invite survey participants – this captures the referral information
4. Populate the hidden questions with Google Analytics information by modifying the call to your survey

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