Data Collection Challenges

The current web analytics data collection is based on JavaScript. The JavaScript initiates a 1px image request to an analytics service provider and the relevant information desired for an understanding of the visitor behaviour is sent along with the image request. Among the information gathered is both a persistent and a session cookie. However, although some smart phones like iPhone and Android do support JavaScript, there still are hundreds of different kinds of devices in the markets and most of them don’t support JavaScript yet.

Another issue is cookie support. If you use different analytics tools to measure your mobile site at the same time, you will be surprised by the results.  Two different tools can return totally different unique visitor numbers. These differences are because many mobile devices don’t support cookies and analytics vendors have different ways to identify mobile unique visitors.

Bypassing the issues

In order to combat the issue that some low-end mobile devices don’t currently support JavaScript execution, we have to send the image request manually instead of calling preset JavaScript methods. Fortunately, Google Analytics provides a smart solution by calling the server side snippet solution instead of sending ugly image request query string parameters. Google has pre-defined the image request server script in the most popular languages such as PHP, JSP and .Net. Users just need to download the preset script file and put a tracking snippet on the mobile site to call the pre-defined file. You can download the pre-defined file from Google Code, and then put the following snippet (PHP Version) at the end of your mobile site:

<?php
  $googleAnalyticsImageUrl = googleAnalyticsGetImageUrl();
  echo '<img src="' . $googleAnalyticsImageUrl . '" />';
?>

Unfortunately, many analytics vendors, including Google Analytics, haven’t provided any solution to bypass the cookie support issue. As a result, Google Analytics always counts a mobile visit as a unique visitor and displays inflated figures. In order to get correct visitor information, we need the persistent visitor ID. Some enterprise analytics vendors, such as Coremetrics, allow the users to send extra query string values with each tag to associate it with both a session and a cookie ID.  The cookie ID source value will be persistent and based on a value defined within the device and accessible via API or OS layer to the code generating the image request. And the session ID could be randomly generated with each device session or generated using a device session value. As a result, Coremetrics could be able to identify new visitors or repeat visitors.

Pitfalls of Current Mobile Analytics Tracking

Again, like standard web analytics, the data collected from analytics vendors is not 100% accurate. The cookie IDs generated by the device API are not always stable, and manually sending image requests do not support some high level analytics functionality such as event tracking in Google Analytics and Marketing tracking in Coremetrics.   With increasing popularity of mobile usage and high demands of accurate data, I think more improvements will be made in the near future.

It’s important not to forget that ecommerce is still a relatively new concept, and it’s taken many months to persuade consumers that shopping online can be as easy (if not easier!) and as engaging as shopping in store. By making visually appealing, flash focused websites some brands are forgetting the usability aspect of online shopping, and making the online customer experience a lot harder and frustrating than shopping in store.

What makes a good ecommerce website?

There needs to be a compromise between attractive, eye catching design and simple, easy to use functionality. It’s important that while retailers are implementing new technologies and merchandising innovations they don’t lose sight of the overall goal of an ecommerce website; to convert customers.

In addition, the advancement of mobile browsing and m-commerce has put an even greater emphasis on usability. Smaller display areas and touch screen technology is forcing retailers to think more about how to enable consumers to purchase in the least amount of clicks possible. Mobile websites will be covered in another post at a later date but for now the focus is on fashion vs. usability.

Eye-Catching Design

Whistles

This very unique new website from Whistles was launched earlier this year, and sparked controversy among critics. Yes it is different, yes it is innovative and eye catching, but have the designers got a bit too carried away? The style of the site means that users are engaged right though the browsing process, with model imagery used from the homepage, through the category listings right to product details. They also integrate well with social media, offering links to share via email, facebook and twitter. The imagery on the site is good, and there are plenty of alternate images and zoom utilised on everything.

However, it’s the small things that Whistles have forgotten, such as a search box, or refine feature, making it very difficult to find something specific, or within a certain budget for example. Also, there are no sub-categories in the top navigation bar; rather they are displayed on the main page, mixed in with the model imagery. This makes it very easy to get lost in the middle of the screen and not be able to find the category that you want.

Fashionable but Usable

River Island

I thought it was fitting to use River Island as my example in this category as this site used to be another culprit of ‘Flash overkill’. However River Island has recently revamped their site to make it more usable. And in my opinion they’ve done a pretty good job! They’ve managed to retain some individuality by allowing refinements according to swatches of colour, pattern descriptions and even ‘occasion’. They’ve also spruced up their product listings by inserting a top product into a larger box at the top left of each page, similar to a profile picture layout. Not only does this act as a merchandising tool but serves to differentiate River Island product listings from those on their competitor’s websites.

They’ve also added a quick look or closer look feature, which allows you to either view the product in a pop up box while still on the category listing page, or to go right into the product details page for a closer look. Although this is a nice feature, there isn’t much difference between the information given in the two options, aside from a few ‘get the look’ items on the product details page. By adding more in depth product information to the details page River Island could have added value to the feature and given consumers a more compelling reason to visit the product details page if they were interested in the product, thus increasing the chance of up and cross-selling more items.

Finding a Compromise

For many companies, both new to ecommerce and old hats that are looking to revamp their offering, it is important that the basics of usability and good customer experience don’t get lost in a web of flash and unnecessary gadgets. There are some new technologies that will genuinely enhance your customers experience online, but there are some that will detract from it. In all elements of ecommerce, especially web design, usability should be a key consideration, because if your customers can’t use it they won’t buy from it.

Now we have established that Hypothesis testing is a way of systematically quantifying how certain you are of the result of a statistical experiment. It starts by forming a null hypothesis such as “Design A performs better”, and then converts it to a mathematical statement. Finally, we need to put in a probability distribution to test it using a specific confidence level. In today’s article, we will apply this to our real world analytics application: A/B Testing.

What is A/B Testing?

A/B testing is one of primary tools in any data-driven environment.  It’s a way of conducting experiments where you compare a baseline control sample to one or more test samples by assigning each sample a specific single variable change.

For example, you might have a landing page that shows the latest products list. You’ll want to test various layouts to try and maximize the sales made by this page. Normally, we use “conversion rate” to measure the page’s performance. By assigning the control sample and each test sampling similar traffic, we can make a decision by observing the conversion rate.

The Fake Data

You might ask: “Why do we need hypothesis testing if we already have conversion rate to measure the performance?”

Assume you are running an email campaign to show the latest offers. You have 3 versions with different layouts: control sample, test sample A, and test sample B.  You run an A/B test before formally running the campaign. Here are the results you might get:

In terms of the results, could we make a judgement that A is best now? When the sample size is large, the results might turn into this:

Don’t be surprised if you get these results, because the sample size matters. The problem is when you run an email campaign you wouldn’t be able to get a large test sample size. If you make a judgement only based on the comparison of the conversion rate, you might just make a wrong decision.

Hypothesis Testing

In order to avoid the wrong decision, you might need to use hypothesis testing to justify the results you get, especially for the small sample size or similar results.

Remember the statistics principle we mentioned in the last post: if a small probability event happens in your test sample, you could reject the null hypothesis. In this case, the null hypothesis could be that the conversion rate of the control treatment is no less than the conversion rate of our experimental treatment. So mathematically

H₀ = P – P_c ≤ 0

Where P_c is the conversion rate of the control and P is the conversion rate of one of our experiments.

Therefore if the probability of H₀ is low enough, we could reject it and go for the alternative hypothesis, that is “the experimental email campaign has a higher conversion rate”. That is what we want to see and quantify.

In order to measure the probability of H₀, let’s say P(H₀), we need to know its probability distribution. The sampled conversion rates are all normally distributed random variables just like the coin flipping. Instead of seeing whether it deviates too far from a fixed probability we want to measure whether it deviates too far from the control treatment. There is another statistic rule: the sum or difference of two normally distributed variables is itself normally distributed. With this rule, we could do Z-Test and calculate the 95% confidence interval like we did in the coin flipping example.

Z-Test

Mathematically, the calculation of Z-Score for the probability of H₀ is:

Where N is the size of the experiment sample and N_c is the size of the control sample.

Do you remember that we used the z-score of 1.96 to correspond to the 95% confidence interval? This time it’s a little different. We will use 1.65 instead of 1.96. Why? In the coin flip example, the null hypothesis is P = 0.5. Therefore we could reject it if the probability is too high or too low, but this time we only care one way.

If the z-score falls in the blue part, we assume it is a small probability and reject the null hypothesis. In the coin flip example, the blue part distributes on both sides of the normal distribution. It is called a two tailed test.

In this A/B Testing example, we’ll only reject the null hypothesis if the experimental conversion rate is significantly higher than the control conversion rate. The blue part is only on the right trail of the normal distribution. This is called a one tailed test.

Using the formula above, we could get the following results:

We could find that none of the z-scores is large than 1.65, which means the results would likely change if the sample size goes larger. In this case, we can’t decide which one is best, and we’d need a larger sample size to get a right decision.

Hypothesis Testing

Hypothesis testing is something that you can use to make decisions based on experimental data. It can systematically quantify how certain you are of the result of a statistical experiment.  For example, you might want to test if coin flipping would give a fair result. So you make an experiment where you flip the coin 100 times and you get results of 52 times for head side and 48 times for tails side. Would that be fair enough from the statistical view? That’s why you want to do a hypothesis testing.

Most hypothesis testing uses null-hypothesis. The null-hypothesis, denoted H₀, typically proposes a default position, such as the coin flipping is fair. And it is typically paired with an alternative hypothesis, such as the coin is biased, denoted H₁. Normally, we put the hypothesis we care or we want to be true as the null-hypothesis. And the main goal of hypothesis testing is to tell us whether we have enough evidence to reject the null-hypothesis.

Turning to statistics

After stating the relevant null and alternative hypotheses, we’ll need to think in a statistical way. In the above coin example, if we say the coin is fair, that means the probability of getting a head side should be 50%. But the results of the experiment we got were 52%.  But is this normal because we just flipped it 100 times as the experiment?

Statistically, if the probability of something is very low, we consider it as impossible. In this case if probability of having the variance between the experiment results and hypothesis is small enough, we could reject the hypothesis. That’s because if something is considered to be impossible and it happens in the small set of observations, there must be something wrong with the hypothesis.

Mathematically

Now we have to do it completely mathematically.  The null hypothesis in the coin example could be expressed as

H₀:p₀ = 0.5

A 95% confidence level means we reject the null hypothesis if p falls outside 95% the area of the normal curve given above. We can see this corresponds to approximately 1.98 standard deviations.

Then we use “Z-Test” to get the z-score which tells us how many standard deviations away from the mean our sample is, and it’s calculated as

The p is the sample mean, and the P₀ is the expected mean, and N is the sample size.  For the coin example, P₀ is 0.5 and N = 100.

In our experiment we flipped the coin 100 times and got heads 52 times, so the sample mean is 0.52%. After calculating the z-score by the formula above, we get the z-score as 0.4 and make the conclusion that the coin is fair.  If we use coin 2 and get heads 60 times, we can reject the hypothesis and say that coin 2 is biased.

What’s next

Of course, this could be much more complex than coin flipping in real world applications. Fortunately, hypothesis testing for the A/B testing is usually quite similar to coin flipping. In my next post, I will show you how to apply our hypothesis testing knowledge to A/B testing to determine whether new features actually affect user behaviour.

About Predictive Analytics

Basically, the main idea of predictive analysis is to use past data to predict future events. The statistics models is used to capture relationships among many factors to allow assessment of risk, determine market patterns, or predict potential opportunities for growth.

Why Is It Difficult to Include in Web Analytics?

One of the most important factors responsible for reliable predictive analysis is data quality. Because all predictive events are based on the data we have, the information can only be as effective as the abundance and accuracy of data available. Due to the mechanism of data collection, unfortunately, web data for the most part is completely anonymous, usually incomplete and unstructured.  For example, cookies and JavaScript could be blocked by some proportion of users or it could be loaded and activated improperly due to slow network speed or other technical reasons.  And it is important to know that these are quite normal for data collection in web analytics today. So it is really hard to do predictive analysis when the core things you are relying to capture data are anonymous cookies and sensitive JavaScript tags.

Available Predictive Web Analytics

Many web analytics solutions providers have started to research to offer predictive analytics solutions along with web analytics. One example is The Intelligent Offer, which is provided by Coremetrics and generates personalized cross-sell and up-sell recommendations based on web data. It uses cookies to build individual visitor profiles based on historical and current session data. The profile is then fed into the personalisation algorithm, which determines if and how recommendations are tailored to the individual.

Traffic prediction also is a feasible possibility for implementing predictive analysis in web analytics. Because visitor traffic is time series data and relatively cleaner than the other metrics we get from the web analytics tool, many statistical models could be used to analyze it and forecast future traffic based on past data.  Here is a mobile traffic prediction example we did for a client. We employed a polynomial regression model to forecast future mobile traffic.

Web analytics Needed for Multichannel CRM

Many companies now have realized that basic click stream analytics don’t tell the whole story. They are looking for a more intelligent way to find hidden customer behaviors. In order to implement predictive data analysis for web data, they need to expand their web analytics strategies and integrate web analytics with CMS (customer relationship management) systems.

With multichannel CRM, aggregated web data could be integrated into offline channels and provide great insight, even from simple analysis. It would enable online analytical processing (OLAP) to produce historical perspectives on customer acquisition cost, cost per conversion, etc. And combining web analytics with predictive analytics, such as data mining, could provide both historical and predictive insights.

• The length of the form – is it too long?
• Is there help available for completing all of the form elements?
• Are validation messages clear and easy to understand?

Tracking a custom form is not a standard feature of most Analytics packages. This is how we would recommend doing it through Google Analytics.

1. Make sure your site has Google Analytics installed for all pages and it is tracking correctly.
2. Setup an over-arching goal within Google Analytics for the conversion page you want to track, the thank you page of a successful sale for example.
3. Specific events on your website can be tracked via ‘Event Tracking’ which is an advanced feature of Google Analytics.  Most forms within websites use JavaScript validation as a 1st port of call. We will integrate ‘Event Tracking’ within the existing JavaScript validation to track form events.

The ‘Event Tracking’ tag looks like this: _trackEvent(category, action, opt_label, opt_value). Let’s say for example that your form validates whether the 1st element of your form (first name for example) is required. If a user leaves this element empty and submits the form it will trigger the relevant piece of validation for this. The site may show an alert box which tells the user that this element has been left blank and is required.

Now let’s add an ‘Event Tracking’ tag within the piece of JavaScript code which has just been triggered, such as _trackEvent(My Website Form, Validation Error, First Name Blank). Now leave the 1st element of your form intentionally blank and submit the form. The same validation message will appear but this time the event will have been recorded within the Google Analytics profile for your website.

It gives us key information on how a website form is performing. What causes the most errors? How could I improve this? It allows us to actively make changes to a form and test whether it has improved the overall conversion rate. If monitored regularly & improvements are made it should lead to an optimally performing form!

The scores and key areas of neglect clearly varied between the vertical sectors. For instance every one of the ten Sports sites featured a blog and community, Travel sites were also not far behind with 60% of sites maintaining a blog in an attempt to engage with their audience. In other sectors the results were not quite as impressive with the Finance and Telecoms sectors failing to provide sufficient sticky content to hold visitor attention and encourage them to return.

Key Findings Include:

Blogging

Blogs are not getting the attention they require yet they tend to be easy to use, implement, update and offer businesses the opportunity to speak directly to visitors in a personable way.  Just less than half of the sites (40%) featured a blog, however too many were not frequently updated, defeating the key objective of a blog as readers quickly lose interest. Only 34% of the blogs were updated on a regular basis, while only 19% of the brands assessed were identified to be advertising the blog across the rest of the website. The results also differed between the eight sectors as every Sports website reviewed featured a blog along with the majority of Travel and Community websites.

The report identifies that the uSwitch blog is an example of how it should be done, although the blog is not promoted prominently enough across the rest of the site. It is also felt that by pulling in the RSS feed on the homepage the blog reference could be made more prominent.

Communities (Forums, Community Blogs & Message Boards)

Communities allow visitors to generate content on the site but they do require ongoing management to really make visitors feel valued and are continually encouraged to contribute constructively. Only a third (34%) of the companies studied featured a community on their site, with Telecoms brands coming out on top, closely followed by charities. In terms of usage only 44% of companies with a community actually attempted to initiate a conversation with visitors, although more do tend to respond to queries or questions from users, with 70% actively engaging with users. However not a single company studied was identified to be regularly uploading content to their community.

The report highlights RSPB community as a best practise example, with plenty of features and content to keep users interested and keep returning on a regular basis.

Multimedia Content & Rich Media (Images, Videos, Podcasts, Presentations)

Multimedia Content is one of the best ways to quickly and instantly grab visitor attention and encourage them to further explore the website. Only 28% of the companies assessed have some sort of multimedia content on their websites. Sports sites came out on top again, yet other brands have a long way to go in this section.  Copy was also assessed to determine how engaging it is to viewers. On average companies assessed were scored around 4.8 out of 7, thus suggesting there is more to be done to capture the attention of web visitors.

Paddy Power is identified as a stand out website in this category, offering a number of videos and live sports streaming is provided for registered members. The use of video is also closely related to the purpose of the Paddy Power site, thus encouraging visitors to remain on the site for longer.

Social Media

The Econsultancy indicate that other studies have found that brands fail to sufficiently advertise their Social Media accounts on their websites and this study is no different. The scores were significantly low with brands failing to encourage online interaction with their visitors and customers or integrate and optimise their online presence. Only 15% of companies were identified as advertising their Twitter account directly on the homepage and only 19% draw attention to their Facebook fan page. In addition 34% of the sites provide links that allow a visitor to share content on a social network, but only 16% allow the visitor to rate and tag content.

The report identifies ASOS as a strong example of a brand with an integrated social media strategy.  It clearly promotes its social media presence on the website and has accounts on the four biggest Social Media channels.

Whilst many companies have taken a number of steps to make their website more engaging for visitors, the study indentifies that there is still more to be done. As the web is continuing to grow in importance, delivering engaging online experiences will become critical for businesses looking to encourage brand loyalty and improve their business online. Online engagement can ultimately bring a number of benefits including increased brand affinity, loyalty along with bottom line results.

EPiServer also suggest that companies can become more efficient by using an online platform to publish information quickly, introduce multimedia content and build and host online communities. Our Redfish ecommerce application has been designed with improving customer engagement in mind, offering the opportunity to refresh the online shopping experience and provide more inspiring imagery.  Don’t hesitate to contact us to find out more.

Why Do Businesses Need to Measure Facebook Analytics?

Since Facebook is a good place for marketing, there must be some demand for analytics.

• Observing visitors’ interactions with your fan page will give you basic insights into the performance of your business’ Facebook fan page and other apps.
• Facebook analytics can provide much more valuable information than normal analytics, such as demographics, gender, and education level. It is important to know who your potential customers are before you run a campaign.

Why Is Measuring So Difficult?

As I mentioned before, Facebook requires that organizations use their own markup language, Facebook Markup Language (FBML), to build a custom fan page instead of standard HTML. Furthermore, Facebook doesn’t allow standard JavaScript to run on a page on load. This means that when a visitor opens a Facebook fan page, JavaScript can’t be activated if the visitor doesn’t do anything. Instead, the developers have to use Facebook’s own solution FBJS (Facebook JavaScript) if they want to include any JavaScript in their custom fan page. Whether Facebook wants to protect users’ privacy or to build their own empire, this requirement makes tracking using traditional web analytics JavaScript tags impossible.

Available Facebook Analytics Tools

The main analytics vendors have already announced their capabilities to measure social networking. The Facebook analytics war began when WebTrends first announced their Facebook analytics capability. A few days later, Omniture and Coremetrics unveiled their own. Meanwhile, Facebook provide their own free analytics service, Facebook Insights.

• WebTrends was the first company that announced their Facebook analytics capability from the three main vendors.  They use an innovative method which uses a data call to pass parameters from the data collection API to capture all of the typical data as well as tracking flash, pop ups, email signups and other custom fields.
• The partnership between Adobe-owned Omniture and Facebook has enabled marketers to buy the capability to measure Facebook media through Omniture’s search campaign management platform. Ominiture has also developed a FBJS measurement library that allows them to track behavior data natively within Facebook.
• Social network analytics as well as mobile analytics are highlighted as one of the key new features by Coremetrics whenever they promote their new product ‘Analytics 2010’. This allows users to see how interaction with specific tabs leads to web site engagement and conversion. However, Coremetrics hasn’t dedicated any focus to reporting user interactions within Facebook.
• Facebook Insights is a free analytics tool provided by Facebook. It offers an aggregate view of fans’ interactions and demographic information across the custom fan pages and apps. Earlier this month, Facebook announced a new enhanced version which will show data for both fully-integrated sites and sites that use Facebook’s social plug-ins. In other words, now you can view the specific story that people ‘liked’ on your site or how many people commented on posts on your Facebook fan page.

Can I Just Use Google Analytics?

Finally, it is time to turn to our favourite analytics program – Google Analytics. Google hasn’t officially announced their capability to measure Facebook analytics. However, many intelligent GA fans are working on it and trying to figure out how to enable Google Analytics on Facebook. There is an open source tool called FBGAT (Facebook Google Analytics Tracker) hosted by Webdigi which allows users to generate a Google Analytics tracking code for Facebook fan pages and paste it into Facebook custom tabs. The online version of the tracking code generator can be found here. After pasting the code on the FBML page, your results will appear in the Google Analytics content report in the next 24 hours. You can find more information about the Google Analytics for Facebook project at Facebook’s Developer Wiki Page.

The travel industry today is facing severe challenges like price wars, safety and security issues (and that’s not even going into the volcano problems of late). Now the internet is providing a medium that  increases customer awareness of these issues. Essentially being a tour operator online is as difficult as ever. Only adding to the difficulty is the issue of measuring a tour operators website performance, which can be hard at the best of times in comparison to a retail site due to the company selling invisible products like ‘experience’. Website traffic and conversions can easily be tracked, measured and compared but how do you define a conversion for example on website where you can’t directly book? This is a problem a lot of luxury tour operators face. A way out is to find out the Key Performance Indicator (KPI) to measure performance.

What is the difference between a metric and a KPI?

Before I start to go through the travel site examples, it probably makes the most sense to talk about the difference between metrics and KPIs. A metric just is a number, it can be viewed as a count (number of visitors) or a ratio (conversion rate). All of the data we get from analytics tools are metrics.

KPIs are metrics, but not normal metrics. A definition of a KPI is a metric that helps you understand how you are doing against your objectives. In other words, KPIs are a bridge between business objectives and web analytics data. Because different companies have different objectives, the KPIs tend to be unique to each company.

For example, on an ecommerce site like www.24studio.co.uk, the objective is to sell as much product as possible. So the KPI could be based on the number of orders, and average size of orders. For the a luxury travel site www.turquoiseholidays.co.uk, the business objective can be sending out so many brochures to encourage a holiday purchase, so one KPI could be the amount of brochures sent out that lead to conversions.

Web analytics measurement framework

Here is a framework proposed by Avinash Kaushik, which is very useful to prevent us from getting lost amongst the data and metrics.

The framework is split into 4 parts:

• Business objectives:  These are the answers to questions like “Why do I need this website?” or “What do I want from this website?”.
• Goals are specific strategies that will help you to accomplish the objectives.  We can define some high level goals to help identify the specific activities we should spend our valuable time on.
• KPIs: as I stated above, KPIs are the metrics attached to our goals.
• Segments: Without segmenting the data, overall totals can often be quite useless, so the best way to find insight is to segment the data. For example, we could choose visitors who spend more than 2 minutes on the site and look at their activity specifically. These are the useful metrics we want to look for.

KPI examples for an online travel website

Now we could follow the framework and start to identify the KPIs for a Web Travel Site.

After we define the Key Performance Indicators, we should start to think about what segmentations we should make in order to measure and improve the KPIs. For example, a KPI above is Revenue/Booking. So the segments we need might be:  what are the most popular destinations or hotels, where are is the traffic coming from or who looks at these hotels?

What else do we need to do with KPIs?

There actually is a one more thing we need to do with KPIs and that is to set KPI Targets.  They are the numerical values which are pre-determined as an indicator for success or failure. It is crucial to create the targets for every KPI. In order to set the number, we need to look over the historic performance. Without having a KPI target, you can collect all the data you want, but you won’t know if you are hitting your objectives or not.

That’s all for this week, check back next week for another look into the world of analytics.

“Over the past year, we have been exploring ways to offer users more choice on how their data is collected by Google Analytics. We concluded that the best approach would be to develop a global browser based plug-in to allow users to opt out of being tracked by Google Analytics. Our engineers are now hard at work finalizing and testing this opt-out functionality. We look forward to make it globally available to our users in the coming weeks.”

Why Google is offering an opt-out

When I first saw this news, I naturally recoiled at the idea of voluntarily allowing measurable data to slip through our hands. Rationalizing web analytics data is already hard enough, and now it will be even harder.

But I was not surprised. There are increasing concerns about privacy issues and Google’s products. This week, a open letter co-signed by officials in privacy commissioner roles in France, Germany, the Netherlands, the UK and 10 other countries was sent to Google’s CEO Eric Schmidt, raising concerns about privacy issues in Google Buzz and Google Street View.

What does this opt-out mean for Google Analytics Users?

In my opinion, it is highly doubtful that this will do any substantial harm to Google Analytics and its users. Here is my point of view:

First of all, not all users will know about the opt-out plug in, and only a few of them will actually download and install it. And most of those people will be privacy fanatics who might have already blocked the cookies or enabled javascript to make themselves invisible on the tracking list.

Second, even if we assume that there are some people who will undoubtedly adopt the opt-out plug in, it’s still unlikely to strike a deadly blow to web analytics. As far as I know, web analytics is more concerned about data trending, not the data numbers themselves. So even if the numbers drop a little, the trending data will remain valuable for web analytics.

And Google are not foolish enough to kill their own business. If the new plug in did have a negative impact on Google Analytics in the future, Google would find a way around it.

3 Ways to Prepare

Even the flutter of a butterfly’s wing can affect a tornado, so the opt-out plug in will definitely have an impact. There are some things we should start thinking about before it goes live.

1. Benchmark our traffic and known visitors before and after the release of the Opt-Out so we can evaluate the impact on our traffic measurement and whether the traffic drop-off is correlated to the visitors we need to meet our site’s goals.
2. Be ready to accommodate the bias that Google Analytics opt out incurs on our metrics.
3. Be ready to have another analytics tool as a backup.