3 last-minute Analytics tips for the holiday season

If you're a marketing professional, you've probably spent months preparing your holiday campaigns. But have you focused so much on great creative that you've neglected your measurement plan? Don’t despair: there’s still time to make the most of your holiday marketing and measurement. Here’s some food for thought to help you make sure your Analytics is adding value this season and setting you up for success next year.

1. Stuffing your stockings: all the best treats for your marketing funnel

Online shoppers are increasingly taking a considered, comparative approach to making purchases. Consumers are now consulting an average of 10.7 sources when making a buying decision - double the rate of 2010. That means that all steps of the marketing funnel are more important than ever. So make sure to take all the steps to conversion into account when measuring your campaigns this season.

With Multi-Channel Funnels reports in the new version of Google Analytics, you can see not just the last click prior to conversion, but also how earlier interactions influenced the purchase decision. For instance, your customer may have clicked on an organic search link immediately prior to purchasing, but in the weeks before to the purchase, he clicked on a display ad, followed links from a post on a social network, and later visited your site directly. With Multi-Channel Funnels, you can see these earlier assists and take this influence into account when optimizing campaigns throughout the holiday season. You’ll also have a rich set of data to plan next year’s campaigns, as you can plan around those channels that drive awareness and consideration earlier in the purchase process.




2. Jingle bells, mobile’s ringing

It’s hard to overstate the enormity of the mobile opportunity this holiday season as consumers turn to smartphones and tablets for both product research and purchases. Mobile searches have grown dramatically in the last two years, and it’s predicted that 44% of searches for last-minute gifts and store locator terms will be from mobile devices.

Providing a great mobile experience is now expected, or you will lose customers. With mobile reporting in Google Analytics, you can see how users are able (or not able) to make purchase decisions. You can segment visitors based on criteria like device types and operating systems. For instance, you can compare if there are different conversion rate for iOS and Android, and make adjustments accordingly. Google offers resources to help you make your site mobile-ready, so you can take action if you find roadblocks. Finally, when measuring your marketing channels, make sure to take mobile ads into account. You can get deeper insights by segmenting out mobile advertising using the recently updated AdWords reports in Google Analytics.

3. Follow Santa’s sleigh in real time

You may have time-sensitive marketing events planned this quarter - whether it’s a daily deal marketing program, viral content that suddenly takes off, or even press coverage. Data that arrives days or even hours later is too late to make decisions during the fast-paced holiday season. With Real-Time reporting in Google Analytics, you can see the impact of these events within seconds. This can be particularly useful for social media efforts. If you post a tweet linking to your site, for example, you can see the immediate visits resulting from the post, and engage in the conversation with your customers. You can also use Real-Time to monitor the immediate impact of email offers and other campaigns that offer customers deals to purchase quickly.

So, grab those reindeer reins and have a great holiday season with Google Analytics. Best wishes for very merry marketing measurement!

Posted by Bill Kee, Google Analytics team

Back-to-Basics: Non-Brand Keywords

The majority of search referrals to the Google Store come from brand related searches -- searches that include brand references like “google store”, “android t-shirt”, or “youtube jacket”. But, as I dug into the data, I was surprised to find that googlestore.com gets many non-brand related search referrals as well.

Take a look at the non-brand searches that send traffic to your own site -- I think you’ll find the data interesting. By isolating non-brand keywords, you take brand recognition out of the equation and focus on the products that people look for -- and click over to find on your site.

Here’s a quick way to see your non-brand keyword traffic. Under Traffic Sources, go to the Keywords report. Then, in the Filter Keyword box at the bottom of the table, select Excluding, and type in your brand name.






If you have multiple brands, type them all in separated by the | sign. Here’s how this looks for googlestore.com:

google|android|youtube|content

You’ll notice that I also excluded the word “content”. This is because the report includes “content targeting” and I don’t want to include content targeting referrals.

Click Go to see the filtered keywords. If there’s anything else you’ve missed, just add it to your exclude list and click Go again. Here are the results for the Google Store.















That’s all there is to it. Try it on your own data and leave us a comment letting us know what you find!

Posted by Alden DeSoto, Google Analytics Team

Back To Basics: Part 3 - The Power of Exclusion

In Part 2, we saw that a store owner gained an unexpected lift in search traffic to his website after running a promotion on a referring site. The questions we left until this week to answer are these: How many extra searches resulted from the promotion? And, what keywords did people search on?

To answer these questions, we need to compare what usually happens versus what actually happened after the promotion. A good way to do this is to use the Compare to Past feature on the date range selector. If we compare the previous week with the promotion week, we can see how much of a lift there was after the promotion.










Notice that we use the exact same days, Monday through Sunday, so that the days of the week line up. Here is the resulting graph. The green line is the search traffic leading up to the promotion (what he would ordinarily expect without a promotion); the blue line is the search traffic during and after the promotion.








From the report below, you can see the specific increase for each keyword.

























The promotion resulted in a 209.68% increase in searches on the first keyword and an increase of 1,242.86% in searches on the second keyword. That’s good to know. It looks like one day promotions are the way to go for this business.

This is good example of how to use Compare to Past. You might also want to check out this tip on how to line up your date ranges when using Compare to Past.

Posted by Alden DeSoto, Google Analytics Team

Back To Basics: The Power of Exclusion (Part 2)

Last week, we saw how a website owner removed traffic from his latest promotion to study the effects of that promotion on his data. The blue line represents all visits over a 2-week period. The spike in the middle is due to a 50%-off referral-based promotion.

The yellow line is where it gets interesting. This line represents all traffic except traffic from the promotional site. Since the yellow line excludes the promotional referrals, why does it show a spike in traffic?

To find out, the store owner de-selected the All Visits segment so that only the Exclude Promo Site segment was active.

Then, he looked at each of the reports in the Traffic Sources section -- the Direct Traffic report , Referring Sites report, and Search Engines report-- to find out where the non-promo traffic spike was coming from. Here’s the graph he saw when he looked at the Search Engines report.

It turns out the extra traffic was coming from search. Happily, the referral site promotion had been even more successful than expected. Because not only was there a big spike in traffic due to the referral, there was also a spike from search. As a result of the increased exposure, more people were searching for his store.

With that mystery solved, we’re ready for the next step. How can we find out how many extra searches resulted from the promotion? And, exactly which keywords were people searching on? We’ll take a look in Part 3, next week.

Posted by Alden DeSoto, Google Analytics Team

Back-To-Basics: The Power of Exclusion

I have a friend who owns a store in my neighborhood. He decided to run a 50%-off special on a site that specializes in one-day promotions to its members. His goal was to get wider exposure for his store and gain some new customers. If you look at the graph, you can see that the day that the promotion ran, traffic to his site spiked. Traffic then dropped off to normal the next day, but this was expected given the nature of this particular campaign.

The store owner was happy to see the spike in traffic, but he wanted to learn more. So he did something clever and created an advanced segment. But instead of creating an advanced segment for traffic from just the promotional site, he did the exact opposite. He created a segment that excluded all the promotional traffic.

Why? It’s a great example of what I call the power of exclusion. More about that in a minute, but first let’s look at how you would create a segment that excludes traffic from a specific site.

First, click the Advanced Segments drop down and click “Create a new advanced segment”.

From the menu on the left, drag Source into the working area. Select the condition “Does not contain”. Enter the name of the site from which you want to exclude traffic, for example “example.com”. Then name and save the segment.

To apply the segment, click the Advanced Segments drop down again and select your newly created segment. (You’ll see it under Custom Segments.) In this case, I named this segment “exclude promo site”.

Take a look at the graph below and you’ll see why this was a smart idea. The blue line is all traffic. The orange line is all traffic except traffic from the promotional site. Notice something interesting? That’s right. The orange line also shows a spike, even though it doesn’t include any referrals from the promo site.

This is the power of exclusion: If you want to find out how effective something is -- whether it’s a traffic source, a promo, or a campaign -- try excluding its influence from your data. You might be surprised at what you find.

So, why is there a traffic spike in the “exclude promo site” segment? Tune in next week and find out. In the meantime, watch this short video tutorial to re-acquaint yourself with advanced segments.

Posted by Alden DeSoto, Google Analytics Team

Introducing Weighted Sort

Have you ever sorted a report by bounce rate and seen nothing but entries with a 100% bounce rate? Have you then noticed that these entries only have 1 visit? Not only is this useless and frustrating, but it obscures the real data points that you care about behind pages of garbage.

Well fret no more! We are pleased to announce a new sorting algorithm called weighted sort. Now when you sort on a computed metric, you can weight that sort by the number of data points, bringing you the most interesting and actionable rows first. For instance, in our example weighted sort will weight the computed value bounce rate by the number of visits. Let's take a look at some screen shots that will make this effect more obvious.

Here we are sorting by descending value on bounce rate. Notice how all these rows have 1 visit each for a bounce rate of 100%. Useless. Now lets turn on weighted sort.

Ah ha! Now this is something you can make sense of. Weighted sort took into consideration the number of visits for each row bringing to the forefront the items you care about. Keep in mind that because weighted sort is no longer a strict sort, there will be values that don't strictly follow the order you would assume.

Weighted sort is also available on other popular computed metrics, including goal conversion rate, % new visits etc. Try it out now on your own data! Take a look at the demo below to learn more or read the help article to get started.

Happy sorting!

Posted by Linus Chou, Google Analytics Team

Using The Wrong Tracking Code Can Cost You $500k a Year

This is a guest post from Tom Critchlow who is an excel ninja, data geek, analytics nerd and head of search for Distilled, a London & Seattle based search agency. Tom provides a cautionary tale on the importance of keeping your site up to date.

This blog post title may appear sensational but I assure you that it's not (much...). I recently spotted an issue with a client's e-commerce website which was costing them £100,000 / month in revenue (~$150,000). The fix took 5 minutes. Even more surprisingly, since I've been on the lookout for this issue I've spotted it on quite a significant number of websites. Hence why I got in touch with the Google guys and asked if I could talk about this in front of as many people as possible to try and spread awareness!

1) Graph of Win

Firstly, let's take a quick look at a pretty graph. This is comparing revenue month on month before and after the fix was made:

Note the increased revenue! :-) Everyone loves increased revenue. For those with beady eyes you will have spotted that this isn't total revenue on the site but it's just IE8 users. What's going on here?

2) The Issue

Before I delve into the issue, let me first give you a little background. When you enter private information online, such as your credit card details, you want to be sure that you're transmitting the information over a secure connection. You can usually tell you're on a secure connection to a website if the page URL begins with https:// instead of http://

Making webpages secure, however, is more resource intensive and so most websites only make their most important pages secure (though Gmail now always uses https:// by default). This means that when you browse a website, for example http://www.amazon.com you will browse around product pages which are all located on http:// URLs. When you want to actually purchase something from the site however you transition over to secure URLs such as https://www.amazon.co.uk/gp/cart/view.html

This is standard practice for e-commerce websites and when you move through the buying funnel you should inevitably transition at some point from a non-secure page (http://) to a secure page (https://).

Now, the issue I'm talking about in this post is with the client's site. The site uses Google Analytics however unfortunately they were using the old version of the code and were using the same piece of code across all pages of their site. The code they were using looked a little like this:

Unfortunately, this means that their secure checkout pages such as https://www.example.com/checkout/payment contained non-secure elements - namely the URL call to "http://www.google-analytics.com/urchin.js". Browsers like Chrome and Firefox don't display a warning but Internet Explorer 8 produced the following security warning when users transitioned from the non-secure (http://) pages to the secure (https://) pages. This error looks like this:

Pretty scary huh! Unsurprisingly this was causing almost all users browsing in Internet Explorer 8 to abandon the shopping process. Since Internet Explorer 8 is one of the most popular browsers on the web this was a huge amount of revenue they were missing out on!

3) How To Fix It

This issue arose because of a non-secure HTTP call within a very old version of the Google Analytics tracking code. The fix for this is very simple - just install the new code! The new code is more versatile than the old code and works both on http:// pages and https:// pages so you don't need to worry about using a different code on secure and non-secure pages. The new code looks a little like this:

There's a complete run-through here on using this new code here and specific migration advice.

And that's it! It's a simple fix but one that can have significant impact on your bottom line. I repeat what I said at the top of the post - I've seen plenty of sites that suffer from this issue so it's really not as rare as you might think!

I should point out here that the Google Analytics code has been able to handle HTTPS and HTTP pages properly since well before the asynchronous code was released, but plenty of sites are still using very old legacy code, which is why this is still an issue for some sites. Also, it's not just the Google Analytics code which can cause problems! Any non-secure elements on a page can cause a security warning so double check your code carefully.

4) How to Diagnose This Issue (simple)

If you're wondering if your site suffers from this problem there's a quite easy way of checking by looking at your conversion rate segmented by browser:

Setting up these custom segments is really easy, but to make it even easier I've set them up for you and all you need to do is click these links to add both these segments directly into your Google Analytics account:

Internet Explorer 8 Users

Non-Internet Explorer 8 Users

Then browse to your conversion rate report and select the segments from your custom segments:

5) How to Diagnose This Issue (advanced)

Of course, the issue I'm talking about in this post is only a specific issue and plenty of other issues may well exist like it. The underlying principle is that segmenting your funnel is a useful thing to do so that you can see if a specific visitor type are not converting or there is a specific drop-off point for them. Unfortunately there isn't a way of segmenting your funnel within Google Analytics at the moment but there are a few advanced ways of getting around this. For example:

• ROI Revolution have a method for segmenting on the fly. Unfortunately this method doesn't let you check custom date ranges.
• LunaMetrics have a way of segmenting the funnel using goals. Unfortunately this method won't let you capture historical data.
• Analytics Ninja has a way of segmenting using advanced segments. Unfortunately this method only let's you track up to a maximum of 3 funnel steps at a time.

None of these methods quite does what I want it to so I'm presenting a 4th option here:

Step 1 - Identify your funnel steps

This is fairly straightforward, all you need to do is understand what the URLs look like for your funnel. For example, let's say our funnel looks like this:

1. http://www.example.com/cart/availability
2. http://www.example.com/cart/details
3. http://www.example.com/cart/extras/
4. http://www.example.com/book/check/
5. https://www.example.com/book/payment/
6. https://www.example.com/book/confirm/

Step 2 - Create some regex

This is getting slightly more complex, however, assuming that all your URLs are exact match (rather than head match or something more complicated) the regex to create is this:

^/cart/availability$|^/cart/details$|^/cart/extras/$|^/book/check/$|^/book/payment/$|^/book/confirm/$

Where we generate this string as follows:

• ^/url1/$ - this matches the exact URL contained between ^ and $
• | - separate each URL with a pipe, this OR matches any of the statements in the string

For a more complete (and very pretty) guide to using regex in Google Analytics download this PDF.

Step 3 - Enter this regex in the top content report

In the top content report copy and paste this regex into the filter box:

This will then filter the top content report to only show you visits to one of the above pages in your funnel. Now we can see the drop off between steps like we can in the regular funnel.

Step 4 - Add custom segments & export to Excel

Now, we add whichever custom segments we want (for example IE8 users like above). This gives us each step of the funnel and the visits to each step broken down by segments:

Unfortunately this data is a little bit difficult to analyse as it doesn't give us the drop off percentage. So, to make this data easier to process and analyse export it to Excel. This will allow us to create a nice little table like this (very minimal excel magic required!):

This is an improvement over the simple diagnosis above because it not only shows us that IE8 users are not converting as well as users from other browsers, but it even tells us the exact step where it's a problem (the cell highlighted in red in the image). Looking back at the URLs we've identified we see that this drop off percentage is the same step where a user transitions from HTTP to HTTPS.

Note: The super-observant among you may have noticed that there is a potential discrepancy here. The warning message that IE8 throws up allows the user to select the option to only view secure elements on the HTTPS page. If the user selects this option then theoretically the user could still carry on through the site and complete the purchase. This visit and the revenue from this purchase wouldn't be tracked in Google Analytics since the HTTP Google Analytics call is blocked by the browser. So the extra £100,000 / month could in theory only be a reported increase in revenue. In reality however we found that the true bottom line still increased by over £30,000 per month as a result of this change. This implies that displaying the pop-up still has a drastic effect on conversion rates.

6) Conclusion

So what have we learned? The key lessons here are as follows:

• Keep your GA code up to date
• Use advanced segments to monitor conversion rate between browsers
• Always perform browser testing to ensure your website functions for all browsers

Remember, the HTTP/HTTPS issue applies equally to all URL calls so even if you use the up-to-date Google Analytics tracking code some other non-secure element on the page might be costing you revenue so always make sure that your website is functioning perfectly across all browsers. If you don't, you might end up losing £360,000 ($500,000) a year or more!

Thanks Tom for a thorough look at how to avoid this problem. And to echo Tom's advice, we encourage everyone to move to the newest version of the GA tracking code. You can follow more analytics insights and online marketing tips at the Distilled Blog or follow Tom on Twitter.

Posted by Trevor Claiborne, Google Analytics Team

Appraising Your Investment in Enterprise Web Analytics

Earlier this year, we asked Forrester Research to help us understand the key trends in enterprise web analytics. The commissioned study conducted by Forrester, "Appraising Your Investment in Enterprise Web Analytics" provides rich insights into what large companies want from an enterprise solution and how they are thinking about their web analytics decisions.

First, let's provide some context for what was considered an "enterprise." The study looked at companies with at least $500 million in annual revenue. Of the 198 companies that met this criteria, almost half (45%) have annual revenues in excess of $5 billion. Three quarters of the companies have over 5,000 employees.

One of the study's findings that we find interesting is around the role of people and web analytics technology. As companies re-evaluate their investments in web analytics, the study explains that:

"Enterprise companies must ask themselves if they are paying too much for capabilities that they simply do not need. In some cases, gaining fewer seldom-used capabilities is a worthwhile tradeoff if funds can be reallocated to hire more resources necessary for analysis."

Companies are recognizing that analysts drive insights, not the analytics tool itself. According to the study, "sixty percent of decision-makers agree that investments in web analytics people are more valuable than investments in web analytics technology." This is in-line with Avinash Kaushik's 10/90 rule for web analytics success: invest 10% of your analytics budget on the actual technology and 90% of your budget in the people who deliver actionable insight, whether in-house analysts, agencies, or vendor partners. It's the people that matter.

Below are other key findings from the Forrester report's Executive Summary:

• Free web analytics takes residence within the enterprise. A staggering 53% of enterprises surveyed currently use a free technology solution as their primary web analytics tool, and 71% use free tools in some capacity. This places use of fee-based solutions in the minority, with only 33% of survey respondents paying for web analytics technologies (12% use homegrown solutions, and 2% use some other option). In addition, it dispels the belief that free solutions are only being used in small organizations or somehow diminished in their capacity to provide value to the enterprise.

• The merits of free analytics products are compelling. Among respondents currently paying for their primary web analytics tools, 66% would consider displacing them with a free alternative. While the primary driver for this consideration is cost, 60% of enterprises are more likely to consider a free tool now because of recent improvements in free solutions. Additionally, 52% are enticed by free tools because they allow enterprises to invest more in the people necessary to drive insight rather than the technology used to collect and analyze data.

• Balancing costs and benefits requires introspection. We found that 52% of practitioners employing both free and fee-based solutions fail to effectively use more than half of the capabilities offered by their tools. This realization is cause for a needs assessment to determine if fee-based web analytics technologies are justified or simply excessive. For many, spending on web analytics technologies could be better allocated toward program development and acquisition of expertise.

• Reliability and ease of use are characteristics that enterprises crave. For 71% of enterprises surveyed, web analytics data plays a significant role in driving decisions. So it comes as no surprise that users place a premium on data assurance, with 45% citing reliable data collection as the most important vendor selection criteria. This was followed by 40% who listed an easy-to-use interface and product pricing as the second equally most important vendor selection consideration.

• Organizations are approaching a point of inflection. Nearly two-thirds of enterprises would abandon their current web analytics provider given the right circumstances. While 74% of large enterprises agreed that web analytics is a technology that they cannot do without, many indicated that alternative tools would suffice. These metrics indicate that organizations are receptive to change and justifiably seek solutions best suited to meet their needs.

If you'd like to learn more, download the full report. We'd love to hear your thoughts. Post a comment and tell us what you think!

Posted by Dai Pham, Google Analytics Team

The Value Of Landing Pages

Imagine that we're launching a brand new advertising campaign for our new e-commerce website that sells Empanadas, my favorite food. The structure of the website is simple. We have a homepage, a few category pages that lists empanadas by type (baked, fried, etc), and hundreds of individual pages for each type of empanada (ham and cheese, steak, chicken, veggie, etc.).

Website structure

(click to enlarge)

Given this site design and our goal to sell as many empanadas as possible, let's look at this question:

Which type of landing page (home, category, or product) leads people to purchase more empanadas?

To answer it, we'll use two Google Analytics features, Custom Reports and Advanced Segments, to find out exactly, in dollars, which is the best type of page. And to perform this analysis we need one of two things: 1. e-commerce or 2. goals with a goal value.

Searching for the answer in Landing Pages

First go to the Content > Landing Pages.

(click to enlarge)

This report is naturally a good place to start but it only gives us three metrics: Entrances, Bounces and Bounce Rate. I want to know dollar amount, not bounce rate. To get the value of each landing page we have to create a custom report.

Step 1) Create the Custom Report
Go to Custom Reporting and create the following report:

Dimension: Landing Page
Metrics: Entrances, Abandonment Rate, Goal Completed and Value per visitor

(click to enlarge)

Great. Now I know the average value for any visitor that starts on these pages. On average the value per landing pages is $0.07. This means for all people who arrive at my webpage, on average each person will buy $0.07 worth of empanadas. Not much huh? However, as you can see some pages have a consistently much better conversion rate than others. For example, my home page -- /home.html -- gives me a per visit value of $0.10. I'd like to compare that with my other two page types: product and categories. We could go through this list and pick out one by one which is better, or write a regular expression in the search filter box, but an easier and more flexible way to identify these page is via Advanced Segments.

Step 2) Create the Advanced Segment
Take a minute to think about the layout of your website. Is there a unique identifier that let's you segment your landing page types? If there isn't then ask your Webmaster what you can do to get around this problem. In our example, remember that our website is very simple. Every empanada page contains the word empanada.html, every category page contains category.html, and the home page is home.html. To begin with, let's create a category segment.

Create the "Category" Advanced Segment
1. Go to Advanced Segments>Create New.
2. Dimension: Landing Page
3. Contains "category.html"
4. Name it "Visits that land on Category."
5. Save and Apply to report

Ouch! Visitors that land on my category pages spend an average of $0.04. Much worse than the average of $0.07. Now let's compare with what happens when a user lands on a page of an individual empanada product page. It's the same process as above except we use Landing Page Contains "empanada.html."

Create the "Empanada" Advanced Segment
1. Go to Advanced Segments>Create New.
2. Dimension: Landing Page
3. Contains "empanada.html"
4. Name it "Visits that land on empanada."

5. Save and Apply to report

Here is what we get:

(click to enlarge)

Wow! Visits that see a product page before anything else spend $0.30 on average. That's over 7 times more than the value of the category landing pages. Which pages should we use? Our empanada pages of course! We no longer have to guess which page is best. Even if we have hundreds of different types of empanadas we can calculate to the penny the potential value of focusing our advertisements on products.

Yeah, that's nice but how do I do the same for my website?

The above is a great example of full circle analytics. Set up goals, then create the reports and segments you best need to analyze the success of the goals. We chose to look at Landing Pages, but after you have goals, reports and segments in place, you can do most analyses.

Here are the key takeaways:

1. Most importantly your URLs must have a unique identifier (like our ?type=empanadas) so you can segment by page type AND either e-commerce implementation or a goal value.

2. Instead of thinking home, category, and product think home, broad, or specific. Usually, the more specific and focused the landing pages the better.

3. If you don't use an e-commerce website don't worry, you can do the same analysis. For e-commerce websites its much easier for us to calculate exact dollar return -- but! we can also use goal value to calculate user value. So, if you don't sell a product, your goal might be to have the users fill out a contact form. If for every 100 users that fill the form you can gain 5 leads that over a month spend an average of $100 each then the value of your form is 5x$100=$500/100=$5 per form completed. This goal value can also be used to calculate landing page value.

Now that you know exactly how to use Google Analytics to identify the value of your landing pages it's time to apply the lessons to your website. How much money do your landing pages bring you?

Posted by Gabriel Francis, Google Analytics Team

Public Broadcasting Service (PBS) - Teach People to Fish

What works better, a centralized web analytics team with deep technical knowledge, or non-expert users spread throughout an organization? This was the question faced by Amy Sample when she joined PBS Interactive as web analytics director in the fall of 2007. Amy shared her story with us in response to our call to share your web analytics story.

Implementation

PBS Interactive helps individual PBS producers and local PBS stations create and promote microsites for programming like NOVA, American Masters, and Sid the Science Kid. Amy had the difficult task of helping these managers make educated decisions about how to improve their online show sites.

When Sample came on board, she learned that PBS had standardized on a single analytics tool. This was a good first step, but few at the company were familiar with the tool and the reports it generated were not being used to take action. Producers wanted to know more about how people interacted with microsites for their programs, but they weren't sure what to focus on. At the same time, the analytics group had a hard time keeping up with demands from so many stakeholders. According to Sample "The producers wanted to dig deeper into their site data than a monthly report could provide."

Amy's response was two-fold. First, after consulting with a pilot group of producers and other members of the interactive group, she decided to deploy an installation of Google Analytics. In Sample's words "We chose Google Analytics because we had to deal with a diverse group of needs and very limited resources. We wanted a system where a user with very little training could get insights right away."

Second, Sample worked with LunaMetrics, a Google Analytics Authorized Consultant, to manage the complex issues PBS faced with cross-domain tracking and a complicated account structure. LunaMetrics also created training materials, hosted an on-site training for PBS staff, and conducted a series of training webinars for producers and local stations.

Applying the Data

As it turns out, despite challenges around getting resources assigned to tag pages and working out the right account structure, deploying Analytics was actually the easy part. In Sample's experience, the more challenging problem came in spreading knowledge and awareness of Analytics through the organization in a way that lets people take action on the data. "My approach has been to teach people how to fish," Sample explains, "It's been about doing training classes and one-on-one work with key practitioners, creating specific training decks by job function and getting other groups to use Analytics data in their daily activities."

Google Analytics has been a key facilitator in the transformation of PBS online. Stakeholders are no longer focused on monthly reports. Increasingly, they are using Analytics to inform actual business decisions. Here are some examples highlighted by the PBS team:

• Site Search Tracking - The PBSKIDS.org site has implemented changes as a result of insights gleaned from site search tracking that have increased traffic to the site 30% in the last year.
  
  
  
• Funnel Optimization - The PBSKIDS Island team used funnels to optimize their registration path resulting in a 3x improvement in conversion rate.
• Content Optimization - An analysis of users’ video consumption behavior on PBS.org and PBSKIDS.org led to the development of the PBS Video and PBSKIDS GO! Broadband portals. PBS went even further, basing a full 2008 PBS.org redesign on the data that indicated which content visitors access.
• Advertising Optimization - PBS' marketing group also looks at post-click behavior for their display ad campaigns to zero-in on referring sites that send high-quality traffic. They use this information to optimize successive campaigns.

Long-Term Vision

Sample's long term vision is to extend Analytics to measure engagement with PBS content both on-site and off-site. She also hopes to gauge the impact of online content on TV tune-in and track online donations, while expanding her training efforts to teach producer colleagues how to segment traffic and drill deeper into visitor behavior on their microsites.

The lesson learned is that no matter what analytics tool you're using, a well-planned deployment is only a first step. The hard part is "teaching people to fish," and making analytics data a key component in your organization's everyday business decisions.

Posted by Sebastian Tonkin, Google Analytics Team