searchengineland.com » SEM Tools: Web Analytics

Behavioral Targeting: Creating A Unique Experience For Each Visitor

Daniel Waisberg — Wed, 18 Nov 2009 05:07:57 +0000

Behavioral targeting (BT) has been the buzzword of the year for the last two years in the web analytics field. But is BT really all that important and valuable to the companies making use of it? The answer is usually yes. And does it take a team of PhDs to implement BT for a website? The answer is usually no. In this and a following post I will explain the value that behavioral targeting offers, and show how a marketer can make use of BT to make the website experience richer for users, and increase conversion rates.

According to Wikipedia, there are two principal types of behavioral targeting:

Onsite behavioral targeting is a technique that uses visitor behavior to target certain content that is proved to be more relevant to a segment of visitors. It should be preceded by an encompassing analysis of users and their characteristics (using web analytics tools). We can either define hard rules (for example, offer a special deal to anybody that adds any two or more products to a shopping cart) or use an engine to dynamically learn about and then target high-converting groups.

Offsite behavioral targeting is technique used by advertising networks to increase advertisement targeting. For example, Google is using this type of targeting to profile visitors to their website network according to subjects they like (their “interests) and then uses this info to provide users with targeted ads across the entire content network.

In this article I’ll focus on the first type of BT, onsite behavioral targeting, the type of technique used by website owners to improve user experience by delivering the right content to each person. Companies that current provide such a solution to website owners are:

From tracking to behavioral targeting

Web analytics has been constantly developing since the 1990s. In the beginning there was data, and initially the struggle was to collect it accurately and provide reports on the state of websites, usually from an IT perspective. Then, with the turn of the millennium, analysts felt the necessity to turn numbers into insights, and the field evolved from simple data reporting to analysis. Today, marketers increasingly understand that testing is the way to go when it comes to design and implementation of websites (in other words, intuition-based decisions don’t really work well). Said another way, the customer should decide what works and what doesn’t (this phase is still rapidly evolving).

In the last two years, marketers, analysts and executives have started to understand that customers should get what they want without having to ask for it. That’s what behavioral targeting is all about: delivering the right content to each visitor to a website. It moves the current focus on overall results to segment results. It enables the website owner to understand which visitors are struggling and which are succeeding with their objectives. It helps marketers build more relevant campaigns to target the right market, be it through search, content, media, or emails. These insights and actions should not come at the expense of website testing, but in addition to it. Testing is very important to recognize the low hanging fruit that is ready to be plucked. It is also a great way to persuade management of the importance of investing in both testing and targeting.

Analytics guru Jim Sterne defined the benefits of behavioral targeting as follows:

“We can attract prospects with customized campaigns according to their interests, engage site visitors with dynamic content in response to their conduct and desires, and put the right message in front of the right person at the right time. We can create a more pleasant and more individual buying experience. We can quickly identify the offers that will more likely convert those prospects to buyers.”

However, the market is still not completely ready for this revolution. Recent research from eMarketer suggests that American internet users are not very fond of behavioral targeting techniques. As seen in the chart below, one of the conclusions of the research is that “respondents showed somewhat more interest in receiving personalized discounts and news, but still, less than one-half of Americans wanted any tailored Web content at all.”

Concluding, we can see a strong trend towards using behavioral targeting to provide web users with richer web experiences. But this will require a market education effort so that users don’t perceive companies using these techniques as not respecting user privacy.

In my next article I will go over a few examples showing how to implement behavioral targeting and analyze its results to increase website conversion rates.

Is Web Analytics Easy Or Difficult?

Evan LaPointe — Fri, 30 Oct 2009 10:00:01 +0000

Two rockstars have emerged in the web analytics field to date. We all know who they are: Eric Peterson has demystified analytics for us, while Avinash Kaushik has helped us take it one day, or dare I say, “an hour a day,” at a time. And one of them says web analytics is easy while the other one says web analytics is very hard. So who is right?

Why web analytics is easy

Yes, it’s true. Web analytics is easy, according to Avinash Kaushik. It’s actually incredibly easy. Assuming that the desired outcomes of web analytics are changes that will positively impact your site and your bottom line (what other purpose can you think of?), you’ll find that even complete beginners can drive surprising value.

Web analytics is this easy because your web site has more issues than you’ll see in a month of Dr. Phil episodes. Starting with some of Kaushik’s favorite metrics like bounce rate by landing page, it’s a virtual walk in the park to identify a host of obvious issues with your site, your marketing, your calls to action and more.

Bounce rate / percent of single page view sessions (times where users enter your site and leave almost immediately) is the online version of your significant other saying “hell no” when you walk up with that new striped shirt you thought was so snazzy. Don’t take it personally when people reject your content, especially when you thought it was great. It isn’t. Just get over it and change it for the better. Test it using website optimizer. Your site is there to make you money, not generate pride.

Web analytics is also easy because you can learn as you go. Almost all valuable insights you will come across in your web analytics quest are a result of you saying, “Huh, that’s interesting.” Then you dig a little deeper, find some answers, and come up with a new way of doing things. It’s rare to be unsurprised by your findings when you’re digging through Google Analytics or Omniture SiteCatalyst or any other tool, because what you’re really looking for are things that you don’t expect.

Whether you’re a seasoned veteran or a complete newbie, you’re going to have to dig in and figure out what’s causing that drop in page views per session or conversion rate or video completion rate or whatever else you’re measuring, and it’s almost always going to be a learning experience. The conclusions and efficiencies that come from the seasoned veteran may be superior, but the newbie can definitely hold the fort, much more than we often give them credit for.

Here are some typical problem areas where you can take advantage of “easy” analytics to gain insights and improve your site:

Landing pages with the highest bounce rates
Landing pages with the lowest conversion rates
Referrers with high bounce rates
Referrers with low conversion rates
Paid search: keywords with high CTR and low conversion (money fires)
Natural search: top keywords for major landing pages (are we satisfying those searchers?)

So keep in mind when I say web analytics is easy, my standard is how sophisticated you need to be to drive performance-altering change on your web site. And you don’t need to be that sophisticated to pinpoint a basketful of site issues that need addressing.

But web analytics is hard!

Very hard, according to Eric Peterson. So, web analytics does have a dark side. It can be hard, but also not for the reasons you might expect.

Web analytics is not hard because you need a statistics degree. It’s not hard because you need to make some sort of model that looks like you’re trying to land something on the moon (or blow it up). It’s not hard because you have to be some Excel whiz or know how to run a multivariate test. It’s hard because people believe that analysts are automatically good when they embody these characteristics, which is completely untrue.

The best analysts are good communicators, not NASA engineers. In fact, engineers are usually the most frustrated and unsuccessful analysts out there (in the long run). While they’re trying to explain that the hypotenuse of a thermodynamic econometric assimilator is equal to the perennial habits of in-market visitor segment recency, the clear communicator is explaining that a shift in budget from paid search to an email campaign is expected to generate $2.6 million in incremental revenue. While the NASA analyst is trying to explain this:

… the articulate analyst is in the CFO’s office talking about the tech support and IT prioritizing savings driven by their investment in Tealeaf, in real dollar terms.

This isn’t to say that formulas like this (it looks like Pac Man, math edition) aren’t downright brilliant. They are. They’re just not universally useful in a business environment. They’re very potent research tools that should be used to make complicated data more malleable for the analyst, but they should never see the light of day in the rest of the organization. While the NASA analyst may think this formula will make him look smart and impressive, the executives just see another propellerhead that will waste time with technobabble. No matter how valuable this technobrilliance is, it’s always going to sound like Urkel to the CEO.

Web analytics is very hard, in essence, because we deal with very complex data sets, statistical analysis, trying to tie online and offline data together, seasonality, and more. It takes a very smart person to do this well, without making mistakes. But the most important—and hardest—thing to do is tie it all back to the two very simple metrics that drive all business value: revenue and profitability. This is the language of business, which is different from the language of the analyst. And while the NASA analyst may think these two basic elements of business oversimplified and unsophisticated, the entire history of business demonstrates that these are the only two things that matter.

Something else we can relate to

The same can be said of SEO, for example. Is SEO hard? No, of course not. You can spend an hour on this site and learn enough to make a huge difference. Is SEO easy? No, of course not. Even if you spend 100 hours on this site, you’ll still be making huge mistakes and missing out on nuances that can cost you valuable real estate in the search results. That’s why there are specialists. But don’t let specialists tell you that it’s so hard that you shouldn’t learn the basics and employ simple recommendations that can make a huge difference.

Our industry is rife with smart people who are on border patrol. We tend to get so caught up in how smart and sophisticated our ideas are, we push lay people out and purposefully alienate them with our big words and complicated explanations, telling ourselves that if a CEO doesn’t understand a simple chi-square test, he’s a dumbass. The truth is, we’re the dumbasses when we can’t reduce the outcomes of that test to simple, explainable tactics that will produce either a revenue increase or a cost reduction.

So if you’re the expert, make it simple for people. Make them comfortable. Don’t make it hard. If you’re the beginner, keep it simple, find clear opportunities, ask smart people for advice, and enjoy the ease of web analytics!

A Primer On Website Testing

Daniel Waisberg — Sat, 19 Sep 2009 21:08:30 +0000

Search marketers can learn a lot from scientists. Scientists spend their life testing things, one after the other, incessantly trying to discover new interactions between atoms, molecules, viruses, bacteria, etc. One of the greatest scientists of all time, Albert Einstein, said, “A theory is something nobody believes, except the person who made it. An experiment is something everybody believes, except the person who made it.” Want everyone to believe in your website? “Experiment” with it—in other words, test it and tune it for optimal performance.

Websites are laboratories, not sculptures

This is the first principle when it comes to website optimization. People tend to think of their websites as a finished product, which was built taking into consideration customer needs and stakeholders requests. But isn’t the world changing? Isn’t it important to tap into new markets, new customers, new opportunities? Isn’t it always good to improve conversion rates?

The website should not exist solely to serve the needs and desires of the designer or the CEO of a company. It should serve the purpose of the customer; otherwise the CEO will be the only one visiting the website in the long run. In a recent two-part paper I wrote with Avinash Kaushik we propose a framework for Web Analytics 2.0: Empowering Customer Centricity (full pdf of Part II, to be published on Vol. 2 Issue 2 of SEMJ). We write about the benefits and best practices when it comes to testing. We believe that “the web analyst must try endlessly and learn to be wrong quickly; learn to test everything and understand that the customer should choose, not the designer or the website manager.”

How to choose a page to test?

With hundreds, thousands, or even millions of pages, how should you decide where to start? Which page, if tested, will bring the biggest increase in revenues? A good way to start is to perform a motion charts analysis on the Top Content report on Google Analytics. The following three metrics (which can be found in all web analytics tools) are highly useful when choosing pages to test:

Contribution to revenue. This metric can take different forms in different web analytics tools, but it basically tells you the contribution of each page to the overall revenue of the website. If the website has an ecommerce platform, this value should be some kind of purchase value divided by the pages seen before a purchase. If the website does not have an ecommerce platform, each goal should have a value linked to it so that we can calculate the contribution of each page to conversions.

Exit rate. This is the percentage of visitors that abandon the website from a specific page. A high exit rate shows that a page is not engaging enough and can be driving your customers away—unless the page is the “thank you for ordering” page, where it’s a good sign. If the objective is to test landing pages, the appropriate metric is the bounce rate, the percentage of visitors that leave the website without interacting with it. A high bounce rate shows that a landing page is performing poorly and should be optimized.

Average time on page. This metric can be an indication that visitors are having difficulty completing a task. For example, if you notice a very high time on page for one of the steps of a registration process, it might be a hint that users are having difficulties understanding what they are supposed to do—though this might not be true for content websites, where you do want your visitors to spend more time reading content.

These are the most common measures of success and failure of a page for most websites. It is important to consider all three metrics (and others that might be specific to your website) and prioritize pages to test. Once you know which pages are the most important, check which ones can be tested quickly. Go for the easiest! Once the first test is running and you tune the pages to improve results, management will implore for you to keep testing.

How long does testing take?

When choosing a page to test, it is also important to take into consideration what elements affect the time it will take in order to receive significant results. Basically, the variables that will affect the duration of your test are:

Number of combinations tested: As the number of combinations increases, the duration of the test increases.
Volume of traffic on the page: As the traffic of the tested page increases, the duration of the test decreases.
Conversion rate of page: As the conversion rates of the page increases (as defined by the tester when planning the test), the duration of the test decreases.
Expected improvement (the percentage by which you expect to improve the website): As the difference between the pages tested increases, the duration of the test decreases.

To estimate the duration of your test, you can use the Website Optimizer Duration Calculator, a very handy calculator provided by Google.

What should be tested?

As a web analyst, I feel the urge to say the magic words it depends! It depends on the type of website, it depends on the targeted public, it depends on your budget and it depends on that totally unscientific factor, the mood of your boss. Here are three things to test that can bring high benefits with low costs.

Calls to action. Too often calls to action are hidden, by a loaded page with too many graphic elements, by appearing below the fold or by a bad design choice (too small, faint color, or a button that does not look like a button). By improving the call to action and making it prominent on the page, you can sometimes boost your conversion rates drastically

Look and feel. Pages must be visually comfy, i.e., people should feel at home in your website. This way they won’t feel threatened and maybe they will become your friends, register for your newsletter and even buy something in the website. One way to do this is to test different images. Sometimes using a baby picture works; sometimes using a couple at the beach; sometimes even using something creepy, like an insect—depends what people are looking for on your website.

Copywriting. For visitors to seriously consider buying your product, signing up for your newsletter, whatever—you must have a good unique selling proposition. And people should see it as soon as view a page or they will leave. So make your unique selling proposition simple and prominent. Depending on the page you are testing, you should adapt the length of your text: for landing pages, short text usually works better; for product pages, go longer, telling visitors all the reasons why your product is the best in the market.

For a comprehensive list of testing elements and methods, you might take a look at the excellent book written by Brian Eisenberg and John Quarto-vonTivadar: Always Be Testing.

Testing tools

The online testing market is growing at a very fast pace, and there are quite a few players offering comprehensive solutions. Here are the biggest players—each has its own advantages and disadvantages. Check their websites to find the most suitable one for your needs.

Testing can bring huge benefits to any website. So start testing today, and don’t stop!

How To Use Google Analytics Motion Charts To Maximize Results

Daniel Waisberg — Thu, 03 Sep 2009 21:24:45 +0000

Last year Google Analytics released a few new features, among them the Motion Charts. The feature is based on software developed by Gapminder, acquired by Google in 2007.

According to the Google Analytics blog, “Motion Charts provide a multi-dimensional, over-time analysis of the data in your report.”

This feature provides a powerful way to visualize data in five dimensions: x-axis, y-axis, size of bubble, color of bubble, and time. In fact, if you choose metrics that are combinations of more than one metric, such as conversion rates (number of conversions divided by number of visits) bounce rate (number of bounces divided by number of visits), and Pages/Visit (number of pages divided by number of visits), you can even increase the number of dimensions to eight.

Here’s an example of a Motion Chart:

When it comes to search marketing, Motion Charts can bring significant value in the following ways:

Improve SEO keyword targeting. As we will see in the examples below, one of the advantages of Motion Charts over other forms of visualization is that it enables the analysis of large numbers of keywords across several parameters. This helps finding emerging keywords both in terms of traffic and performance. In addition, by analyzing bounce rate trends for keywords, we get a feel for landing pages that are underperfoming.

Expand PPC keyword targeting. By analyzing SEO targeting and results (as above), we can understand which organic keywords can present an opportunity to tap into new markets. Sometimes visitors get to the website using keywords that were not previously thought of as a match for the website. Motion Charts can uncover these “hidden” gems, allowing us to incorporate them into paid search campaigns.

Analyze PPC keyword performance. Paid search optimizers often fall into the trap of analyzing the average performance of campaigns or keywords. However, as Avinash Kaushik says about disappointing measurement techniques, “averages have an astonishing capacity to give you “average” data, they have a great capacity to lie, and they hinder decision making”, therefore the need to analyze not only averages, but segmented traffic and trends over time.

How to choose data displayed on a Motion Chart

As we saw above, Motion Charts can be very useful when analyzing search traffic whether we are looking into organic or paid traffic. In the screenshot below we see the keywords report (which can be reached from the Traffic Sources section). We will use it to exemplify how to populate the Motion Chart in the best possible way, according to the website owner needs.

The screenshot uses Google Analytics data from this Global Warming website.

It is important to understand that the Motion Chart data is defined by the report from where it was triggered. Here are the 5 parameters that should be defined carefully:

Keyword medium. Just above the chart, in the upper-left side, you will find three links: all, paid and non-paid. This can be used to analyze all keywords, just paid keywords, or just non-paid (organic) keywords.

Time range. As with all Google Analytics reports, time range can be adjusted from one single day to a few years worth of data. In order to neutralize time series effects I recommended analyzing at least a few weeks worth of data. Searcher behavior varies between the days of the week, the weeks of the month and the months of the year. Therefore, when analyzing daily behavior I recommended analyzing at least eight days (1 week + 1 day). When analyzing weekly behavior, at least 5 weeks (1 month + 1 week) and when analyzing monthly behavior analyze at least 13 months (1 year + 1 month).

Data grouping. This refers to the grouping of data into daily, weekly or yearly buckets. If you choose a six month range and group data in months, you will see six data points on the Motion Chart time-axis. If you choose the same date range but group it into days, you will see around 180 data points in the time axis.

Show rows. With Motion Charts it is possible to analyze up to 500 keywords in one shot. By using the drop down in the bottom-right corner of the table, you can include 10, 25, 50, 100, 250 or 500 keywords in the Motion Chart (although more than 25 keywords is a bit cluttered).

Filter keyword. This can be used to include or exclude keywords on the report. Keyword filters a very useful in when analyzing branded vs. non-branded keywords separately, or to analyze groups of words that hint at specific segments of visitors. It is possible to use regular expressions to build complex patterns if simple filters aren’t adequate for your needs.

After choosing your parameters, click on the visualize button above the chart (see top-left gray buttons on the figure above). This builds the Motion Chart for you.

How to choose Metrics to be analyzed on the Motion Chart

As with all web analytics analyses, the most important step when using the Motion Chart is to understand the website objectives and what metrics should be used to measure and improve these objectives. Often, your objective will be determined by whether yours is an ecommerce site or not.

When ecommerce is enabled in Google web analytics, interpreting results gets very intuitive. You know, for example, the value of a specific keyword (be it organic or paid) because you know how much revenue it brought. In the example below, the following metrics were chosen to be analyzed:

Visits (y-axis) shows the amount of visits for each keyword.

Bounce rate (x-axis) shows the % of bounces for each keyword. This is very helpful since it hints at whether a keyword matches the landing page or not—in other words, are you delivering the promise you give on your PPC ads or search result snippet? As Bryan Eisenberg writes: “keywords don’t fail to convert… we fail to convert visitors for that keyword.” Look for keywords far from the y-axis, especially red and/or big ones. Tip: optimize the landing page for these keywords.

Ecommerce conversion rate (color of bubble) shows the conversion rate for a keyword. This is important since you might have high converting keywords that are not receiving enough traffic. Look for red-small bubbles located close to the x-axis—thee keywords should get priority optimization treatment. Tip: increase exposure of these keywords on the website (and other SEO efforts) and focus on these and related keywords on your PPC campaigns.

Revenue (size of bubble) shows the amount of money this keyword is driving to your website. Look for big-blue bubbles—this is a signal that a keyword brings lots of money but could bring even more if it converted better. Tip: optimize the pages related to these keywords to improve conversion.

For websites that do not sell online (or for those that do not use Google Analytics ecommerce feature), we would analyze results using slightly different metrics.

Visits (y-axis) is the same as above.

Bounce Rate (x-axis) also the same as above.

Goal conversion (color of bubble). For websites that do not sell online, we can measure site success using several parameters: registrations, whitepaper downloads, comments from users, etc. It is highly important to have these goals configured on Google Analytics. We use goal conversion in a similar way we used ecommerce conversion rate above: to see keywords that convert well but do not get enough traffic.

Per visit goal value (size of bubble). Another important setting on Google Analytics is the $ index variable. It is used to understand the contribution of each source/page (in this specific case, keywords) of the website to conversions. In the Motion Charts this metric can be used to show how much revenue each keyword brought to the website.

The Motion Charts you create can be shared by sending a link to your colleagues directly from Google Analytics; this link will lead people to the exact same chart you created (note that the viewer must have access to the Google Analytics account). However, if you want to present the results to management and are not willing to trust internet for meetings, or if you want to share the results in a website widget, I recommend three alternative ways to share Motion Charts:

CamStudio is an open source screen recording tool that lets you record your screen very easily. This has the advantage that you can use Google Analytics as usual and just record the resulting chart. This might be handy for presentations, and you can also share it in a website using a YouTube widget. However, it has the disadvantage that you cannot interact with the chart and change its settings.

Google Docs Gadget is a great way to share, because it keeps all the functionality of the Motion Chart and you can change the settings as you analyze the numbers. Viewers do not need access to the Google Analytics account in order to view the chart. However, you will need to export the data and organize it in Google Docs, which might take a considerable amount of time, especially for large quantities of data (explanations on how to format the data). Unfortunately, as of today, the gadget works with Firefox but not with Internet Explorer or Chrome.

Successive screenshots: although this is far from optimal, since you lose the motion effect and you cannot interact with the chart, it might be needed to include in Powerpoint presentations or if you want to hide your company’s data (pictures are easier to edit than videos).

I believe that Motion Charts are more than just cool graphs—they really provide some deep visualization that is difficult to achieve through other tools. Motion Charts can be used together with other Web Analytics techniques to optimize search traffic results.

Bonus – if you ever thought that geeks are not talented when it comes to singing and playing the guitar, here is the proof that analysts know something about music: Motion Charts anthem.

Below is a Motion Chart created using Google Analytics taken from Google’s official Youtube channel; it was recorded and shared through YouTube:

If you have additional ideas on how to use this feature, please share them in the comments.

How To Choose A Web Analytics Solution

Evan LaPointe — Mon, 31 Aug 2009 13:00:10 +0000

Choosing the analytics tool you will use to track what’s happening on your web site can be a time-consuming, expensive, and incredibly frustrating experience. There are a number of good candidates to choose from and each touts an impressive feature set that promises to significantly improve everything about your web site. But while there’s a lot of hype about what these tools will do to improve your life, it’s important to remember that you’re not actually choosing an analytics provider, you’re choosing a reporting and data gathering system—in other words, a very pretty database. I can’t stress enough that there is no such thing as a tool that does analysis for you, so while it’s wonderful to focus on features, don’t forget to hire a few smart people, too.

Where to start

If I were going help a client choose a tool for their site, I actually wouldn’t start with the tools themselves. We would start by thinking about the types of things they need to know about their site to fuel better decision making:

What are the types of things that people disagree about internally?
What types of key decisions are made on a daily, weekly, or monthly basis that impact the look, feel and behavior on the site?
What information do salespeople, customer support teams, and IT departments need to make their lives easier?
How is content designed, and on what basis is it revisited to see if it can be improved (if this has been defined)?
What are the business’s financial incentives for performance—what motivates the employees who will be working with or benefiting from the tool?
How technically skilled are the people in charge of the web site?
At what level in the organization is web analytics going to be managed (a polite way of asking whether the organization plans on taking web analytics seriously)?

Once we start asking these questions, it starts to remind us why we’re looking for a tool in the first place—to add value, not reports. When we get a demo of a tool later, we can ask things like:

“Can you show a way your tool can help me improve conversion rates for my PPC campaign?”
“Can you provide me an example of how this tool helps me decide how my budget should be distributed between display, email, and SEO?”
“My bonus is based on improving the number of average page views per session—can you show me how the tool will help me identify opportunities?”

Now we’re getting clear answers that reveal which tools can help answer these questions quickly and accurately, without a lot of customization or expense from ad-hoc reports. While the tool might not answer the question directly, you’ll be able to tell how helpful it will be by what you’re shown and how uncomfortable you’ve made the salesperson—and who doesn’t like making salespeople uncomfortable?

Every tool essentially shows the same basic reports to a user. The value of the best tool for you is in how it is able to combine different pieces of information, giving you a “mechanical advantage” over the data. Each tool has been developed with a different philosophy about how data should be correlated and how flexible these “cross-section views” are, meaning that each tool provides a subtly different experience. By asking these types of questions specific to your business, the right analytics tool will be revealed very quickly.

Narrowing it down and choosing your analytics tool

Once you’ve seen demos from a few providers, the next step is to make a decision. What you’re probably going to realize at this point is that there is no “perfect” tool (and you’re much better at making salespeople uncomfortable than you originally thought). You’re going to ask some questions that no tool can answer easily. You’re also going to find that some of the free tools will surprise you with how robust they are. I urge you to seriously consider using them at this point, graduating to the more sophisticated tools as you’ve proven the need for them.

One big argument against this two-staged approach may be that you won’t have your historical data all in one tool, but let’s not forget why we’re getting an analytics tool in the first place: we want a cost-effective way to improve the site. Make a decision based on what’s going to improve your future, not what’s going to store your past. You can always hire interns to climb up into the attic and make spreadsheets of your dusty, old data—and you might find that you never needed all of those extra bells & whistles to begin with once you’ve spent the savings on a truly talented web analytics person.

The importance of implementation

Dare I say it, the implementation of your chosen tool could be 10 times more important than the tool itself. A good analyst can get more value and information out of a 5-year-old, free tool than a shiny, quarter-million-dollar tool if the former is implemented properly. It brings me no joy to say that in auditing dozens of implementations of very expensive tools, I am yet to see a single installation that didn’t benefit from major surgery. There is no question that implementation quality is the single largest preventable barrier to deriving greater value from web analytics, costing companies hundreds of hours in reconciling data, arguing in conference rooms, and lost sleep.

Key: Make the tool work for you

Web analytics tools are like universal remote controls and your employees are like retirees. The temptation is to call the grandkids every time they want to change the channel, but the truth is that these tools are actually pretty easy to use after putting a little effort into learning them. Make sure to provide employees at all levels the basic training they’ll need to be self-sufficient. Nothing will halt the pace of value creation faster than a tsunami of report requests that your “web analytics people” have to fulfill. These tools are designed to let people get what they need quickly, and if they need it frequently, automatically email it to them however often they want. Make sure that the tool is saving your organization time, not drowning smart people in rote, valueless work.

Is Twitter Sending You 500% To 1600% More Traffic Than You Might Think?

Danny Sullivan — Sat, 18 Jul 2009 18:48:36 +0000

Earlier I posted that Google Analytics and other JavaScript-based tracking tools might be undercounting visits from Twitter. I’ve done some more digging, which supports the case. In my test, Twitter seems to have sent 500% to 1600% more traffic than log files or hosted stats packages like Google Analytics might show.

How Twitter Might Send Far More Traffic Than You Think is my earlier article that explains how I’d often seen big gaps in how many people apparently clicked on a tweeted link as measured by Bit.ly versus how many page views that Google Analytics was showing.

To test this further, I tweeted a particular page on my personal blog along with tracking code designed to especially help ensure it appeared in Google Analytics. I’m going to toss out a bunch of numbers as part of this analysis. If they get confusing, skip to the end for the conclusion.

The Numbers Bit

For July 7, Bit.ly reported that the page had registered 58 clicks. Were there 58 corresponding page views? No. Google Analytics only reported 17 page views from 11 unique users. That meant a gap of 41 views.

Was the gap due to clicks from non-human robots that don’t process Google Analytics JavaScript tracking code? Visits from people using mobile browsers that didn’t get tracked, because they might not process the code? To explore further, I went to the raw log files, the records that the server itself keeps. These shows any request made for the page, regardless of any JavaScript issues.

I found that there had been 57 total requests — practically the same as Bit.ly reported. However, 14 of these were for the page without the tracking codes I’d used when tweeting the page through Bit.ly.

In other words, this is the URL I put out through Bit.ly, which was reported to receive 58 visits:

http:///mothers-cookies-closes-the-sadness-for-products-i-no-longer-have-389?utm_source=twitter&utm_medium=micro-blog&utm_campaign=twitter

See the part in bold? Those are tracking codes or parameters. From the log files, I found that URL above (with the codes) had 43 visits (not 58) and the same page without tracking codes like this received 14 further visits:

http:///mothers-cookies-closes-the-sadness-for-products-i-no-longer-have-389

Those 14 visits without tracking codes almost all came from robots (Google: 5; Yanga: 4; Microsoft: 3). Two other visits seemed to be from humans. These robotic visits all likely had nothing to do with my tweet. The requests were from spiders doing their regular crawls of the web, it seems. The few human visits to the page without tracking codes were probably people who came to my article for reasons unconnected with the tweet.

What about those other 43 visits to the page that did have the tracking code? Well, 11 visits were from what appeared to be robots (OneRiot: 1; PycURL: 2; Ginxbot: 2; WebShot: 1; Google: 2; Tweetmeme: 1; Python-urllib: 1; LongURL API: 1).

That left 32 visits that appear to be from humans. That’s almost between the 58 views Bit.ly reported and the 17 page views Google Analytics reflected. Why still such a gap with GA?

One leading argument has been that some Twitter applications on mobile devices load pages within the application, rather than using an external browser, and so aren’t getting registered by Google Analytics. Also, some mobile browsers might not process JavaScript. I could see at least four iPhone-based requests like this. But there were plenty of other requests that appear to be from full-fledged desktop-based browsers. Why weren’t they showing up?

One clue is that of the 34 requests, only 5 of them contained “referrer” data, information that some browsers pass on that indicate how they found the page in the first place. For Google Analytics (or ANY analytics program) to properly indicate how much traffic a particular site is driving, it needs as much referrer data as it can get.

Of those referrers, only 2 of them were from the twitter.com domain (1 more was from my own blog’s domain, 1 from iconfactory.com/twitterific, probably indicating a Twitterific users, and one from powertwitter.me, probably indicating a Twitter-visit via a Firefox plug-in).

In short, based on referrer traffic alone, ANY analysis program would have reported that at best, Twitter sent my page only 2 visits. Yet, both Google Analytics and Bit.ly reported that it received far more than that.

Remember, Google Analytics said the page had received 17 views in all, 11 from unique users. How many of those 11 unique users came to the page via Twitter? Google Analytics said 9. One more came directly, it said; another person did a search to find it (mother’s cookie site:daggle.com was the search, which was me locating the article. Oddly, this request does NOT appear in the raw log files).

The Big Conclusion

All those earlier numbers hurt your head? Here are the most meaningful ones. Thanks for hanging in there!

Based only on referrers, at best, Google or any analytics program would have said Twitter sent 2 visits. But because I used tracking codes, I was able to overcome the lack of referring data and see that Twitter (itself or via applications or web sites using Twitter data) sent 9 visits. That means analytics packages might be undercounting Twitter visits by nearly 500%.

Meanwhile, Bit.ly was showing those 58 clicks to the page. Let’s say it wasn’t filtering out some of the robots. I can still see that there are 32 visits that the log files recorded, all with the tracking codes that never existed until I tweeted the link with them. So those are all Twitter-derived visits. That means an undercount by a standard analytics tool depending on referrer data by 1600%.

And The Analytics Companies Say?

I sent my logs to both Bit.ly and Google, along with a draft of this article, for any reaction.

Google said they’re aware that activity on mobile devices can cause issues with tracking and that they’re looking for ways to improve their product.

Bit.ly said they filter out robotic clicks such as Ginxbot, Google, and Python-urllib, through PycURL. When I asked further about the gap, they emailed back:

It looks like three types of events make up the delta.

First, browser plug-ins and automated url-lengthener applications, which make requests to the bit.ly URL, but don’t follow the redirect to the destination site.

One example is the “eventBox” at http://thecosmicmachine.com. Here’s how it appears in the logs:

(eventBox) : – - [07/Jul/2009:20:41:31 -0400] “GET /cHXSP HTTP/1.1″ 301 410 “-” “EventBox567 CFNetwork/438.12 Darwin/9.7.0 (i386) (iMac9%2C1)” 301

Second, small bots that make their way through our screening system:

(slicehost): – - [07/Jul/2009:21:05:43 -0400] “GET /cHXSP HTTP/1.1″ 301 410 “-” “-” 301

Third, browsers which don’t support JavaScript, as well as browsers with JavaScript settings turned off and browsers running JavaScript-blocking extensions like noscript.

And Some Related Reading

Last week, Fred Wilson posted Does This Blog Get More Traffic From Google or Twitter?, finding that for his personal blog, Twitter traffic has risen past Google search traffic. Fred suspected that the Twitter traffic was even more than being shown, due to undercounting. I think he’s right. While I think Google search traffic still remains a major traffic driver for many sites, those who have lots of Twitter followers or have a story go “hot” through retweets certainly may discover Twitter is a new major traffic resource –and one that’s likely undercounted.

Over at the Zebu Blog, Link Tracking – (lies, damn lies &) Statistics? also looks at the issue, questioning whether Bit.ly is overcounting. In a follow up comment, Mayank Sharma did his own small scale experiment and found:

We created a bit.ly url for this post, and posted it on Twitter. The next instant we saw, that bit.ly’s count was already 4. This only means that some twitter crawler/indexer received the tweet and de-referenced the url mentioned in it. After that I hovered my mouse over the link shown in Twitterfox. Sure enough bit.ly’s count increased by one. We did this repeatedly from multiple desktop’s of several friends and the count just kept on increasing. Not one of these folks during this time had actually clicked on the link.

I agree — Bit.ly seems to be overestimating views. But Google Analytics seems to be underestimating them, perhaps severely based on my small scale log analysis program. Using tracking codes occasionally is one way to get a reality check.

Finally, if you want to add tracking parameters for URLs you tweet, consider the Snip-n-Tag add-on for Firefox. I’ve been using it, and it makes adding these to URLs super easy.

How Twitter Might Send Far More Traffic Than You Think

Danny Sullivan — Wed, 24 Jun 2009 20:43:24 +0000

Over the past year, I’ve seen many people report that Twitter can send tons of traffic to a web site. Certainly I’ve seen first-hand how Twitter has become one of the top non-search referrer sources for Search Engine Land and for some other sites I oversee. But as much as you think Twitter is driving traffic, it might be sending even more that you’re unable to measure.

One key culprit may be that a large number people view web pages using mobile Twitter applications such as Tweetie. Click on a link from a tweet in some of these applications, and they load web pages using their own browsers. Those browser may not run JavaScript. And if JavaScript doesn’t run, then some hosted metrics tools like Google Analytics never realize that your page was viewed.

Consider this recent example. I wrote an article on my personal blog about the collapse of the Clear airport security program last night and how it seems likely that alternatives to it will also go away (TSA Stays Silent As Its Registered Traveler Program Melts Down).

I tweeted that article using Bit.ly, a URL shortener that allows me to track how many people clicked on the link I tweeted:

The screenshot shows that Bit.ly had seen 343 total clicks to that URL through its system (when I did the screenshot), 339 of them coming from the shortened version I created. So Google Analytics ought to show me roughly that same number of people coming to the page from Twitter, right?

Nope. Google Analytics reports that Twitter sent 63 visits:

I know some visits from today have yet to be reflected in Google Analytics. That’s why you see the line drop. But I also know that those visits, based on my Bit.ly data, should be about the same as yesterday. In short, at best, Google Analytics is going to report I had about 130 visitors to the article from Twitter. But Bit.ly’s saying I had about 340 visits. Where are the missing 200 or so people? How come Bit.ly’s showing more than three times the visits overall to the page than Google Analytics does. I’ve seen this time-and-time again for various stories, sometimes with gaps of several thousand visits.

This is where the JavaScript stuff I’ve mentioned comes in. It seems to be the best candidate for explaining the discrepancy. Apparently, a lot of people on Twitter use mobile applications, or view from mobile devices, that aren’t processing the Google Analytics tracking code. There are also a few other reasons, as Bit.ly’s general manager Andrew Cohen explained to me in email:

Good question. You’re right about embedded browsers that don’t run JavaScript. Clients that decode bit.ly links but don’t click through to the underlying content are another challenge. If you were to download a browser plug-in to automatically expand short urls, for instance, it would look a lot like a human user. We’d see a GET request from an API, return the underlying long URL, and increment the click count … but we wouldn’t be able to see the actual CTR [clickthrough rate].

Absent JavaScript on the page, it can be tough to distinguish between a decode event and an actual click-through. That’s why we think that bit.ly complements rather than replaces JavaScript-based packages like Google Analytics or Chartbeat. One precaution that we’ve taken, though, is to filter out HEAD requests:

http://blog.bit.ly/post/89178273/talking-heads

We’re also working on some predictive modeling techniques to screen out more bots and scrapers from our click totals.

You might be interested in this article from the MIT Technology Review — http://bit.ly/qrjrf — which shows that these kinds of metrics issues are an industry-wide challenge.

I plan to look at the issue more in the future, but I wanted to get a post out there now with my initial findings and see what others have to say (please comment below!).

Somewhat related, also consider reading this post from Bit.ly: Registered Applications And Better Click Referrer Data.

Just as some referrers might not be seen if JavaScript isn’t loaded, some of the ways people are following tweeted links outside of Twitter aren’t registering, because the applications don’t send any referrer information. So, you’ll see these as “direct” visits as if someone typed in a URL, rather than clicking on a link. But by working with vendors, Bit.ly is trying to improve the picture (other tracking providers may also be doing the same, too).

Google Analytics Now Considers Bing A Search Engine

Danny Sullivan — Fri, 12 Jun 2009 18:18:06 +0000

As promised, Google Analytics is now logging traffic from Microsoft’s new Bing search engine into the “search engine” traffic source category, rather than in the “referring sites” category. As a result, Google Analytics users can now see for themselves whether all of Microsoft’s Bing marketing is producing more traffic for Bing, which in turn is generating traffic for their web sites.

Looking at our Search Engine Land stats, Bing’s a big winner. For the past three days, it has sent more traffic to us than Yahoo. Here’s Bing:

June 12 is only a partial day, so it’s really June 8 through 11 to look at. It shows Bing sending us between 350 to 400 people per day. In contrast, Yahoo beat Bing on June 8 then fell behind:

From June 9-11, Yahoo’s been sending us less traffic than Bing, about 300 to 350 per day.

I’ve never seen Microsoft in the second place spot for search engine traffic referrers. Going back two weeks ago, it was comfortably ahead of Bing, sending us nearly three times the traffic.

This is only one site, of course. Looking at my personal blog Daggle, yesterday’s traffic breakdown was like this:

There, you can see that Bing is well behind Yahoo, sending Daggle only 5 visits to Yahoo’s 35. So by no means is what’s happening on Search Engine Land indicative across the web.

Google, of course, far outdistances them all — sending just over 1,000 visits. The same is true with Search Engine Land:

While Bing and Yahoo were sending less than 500 people each to Search Engine Land, Google was sending well over 5,000 per day.

This gap between search market share reported by companies such as comScore, Compete, Hitwise and NetRatings is well known. While they typically give Google a 70 percent share of the market, this comes largely from measuring searches that happen at each of the search engines and not what happens after a search is done.

Referrer stats show the percentage of share each search engine has based on the traffic they send to web sites. Those stats often have site owners finding that Google might have a 90 percent share (see Google By Far The Leader, If You Look At Site Owner Traffic Stats for more about this). No one’s really offered a good explanation for the gap — it’s not much studied, and it has been more pronounced with Yahoo. My own guess is that Yahoo recycles more searches at its site back into Yahoo’s own properties or to external sites that participate in its paid inclusion program (anecdotally, when you ask who gets traffic from Yahoo, those doing paid inclusion put their hands up).

More early stats have been coming in about how Bing is doing, and I plan to churn through those next week. Unfortunately, it’s not going to be until early to mid-July that we have full month June figures to get a real solid measure on how Bing’s done. But for a particular site, Google Analytics gives you a way to assess the situation now.

Giving Credit Where Credit Is Due

George Michie — Mon, 08 Jun 2009 11:00:54 +0000

Does your PPC program drive more sales than it’s credited for? The answer is certainly “yes,” but the sources and volume of under-reporting might surprise you.

If you drive your program through your web analytics software you may be missing 10 – 30% of the sales! This is not the fault of the software as much as it is the convenience of using javascript tags for tracking. In past years, part of the problem came from browsers not happy about running third party javascripts, but those problems have been fixed.

The problem now is not with javascript flakiness as much as when the script is run. Almost everyone wisely puts the javascript tracking in the footer of their web pages. The reason? You don’t want the customer to have to wait for the javascript to run before loading the rest of the page. If there is any problem, you want the page to load and the footer to hang so that the user can shop unimpeded.

Therein lies the rub. Because it’s in the footer the javascript can only cookie the browser after the whole page loads. For heavy, slow, image-laden pages, customers often move on to the next page before the footer loads. If the user sits on that subsequent page long enough to fire the javascript the problem will be that you’ve lost the url parameters that allow your tracking system to know the source of the traffic. That user is now flagged as “untracked” even though they came through a paid advertisement.

We know this happens and understand the scale of the problem because it shows up whenever we do data audits with clients, but also because we sometimes employ both our standard tracking and a javascript tag when we’re studying marketing channel allocation for our PPC clients. The problem isn’t that orders we see are tracked to other programs; it’s that the sales we know came through a PPC ad aren’t tracked to any marketing program.

Knowing this, you might say: “Well, if I have a sense that this happens 20% of the time, can’t I just adjust my advertising efficiency thresholds by 20% to compensate?” Yes, you can, but the problem is that some destination pages are more susceptible to this problem than others, either because they load more slowly, or because users are more likely to navigate off of them quickly. This will disproportionately penalize some types of keywords over others resulting in lost opportunity as those ads are mistakenly bid down the page.

A better way to track high-dollar marketing programs is through use of a fast redirect. The redirect is fast if, and only if the redirect server doesn’t have to do a database look-up. If the server has to look up the destination url the redirect will be slow and the server will bog down during traffic bursts. We pass the final destination url to our redirector as an encoded parameter so the redirect takes less than 0.1 seconds and the volume of redirects is almost irrelevant.

Using a redirector provides much more robust tracking, but can/should be cause for concern as well. With all of that valuable traffic passing through a third party box, it’s valid, indeed essential to ask: “what happens if the box goes down?” We stressed out about this, too. Our approach was to build in multiple redundancy by having multiple redirectors. To keep these independent, these servers are located across the country, and use different internet backbones. All the servers share the work, and are self-checking and self-correcting. If a data center becomes unavailable—say, due to a server failure (almost never), or due to DC connectivity problems (very rare), or due to routing hiccups somewhere on the web (not rare, but brief)—we use smart DNS to reroute traffic to the healthy machines within a minute. No system is 100% perfect, but this redundancy and automatic checking provides extremely high uptime.

Handling order allocation issues can be done on the fly, with the confirmation page tag sending marketing allocation along with the order details, or through a back-feed of order IDs and marketing channel credited. Any competent search agency can then base its bidding and reporting on only those sales your system has flagged as paid search or unknown.

We see all the costs associated with PPC advertising, but we don’t see all the sales generated. While this post may cause some eyes to glaze over, understand that this is not minutiae by any stretch of the imagination.

Better tracking technology plugs a big hole, but others remain. Users drop cookies, use multiple browsers, and sometimes search on one machine but place the order on another after shopping around. We can track spillover to the call center, but measuring foot traffic driven to the stores remains elusive. However, those who throw up their hands and conclude that direct marketing metrics shouldn’t be applied to search simply aren’t trying hard enough.

Yahoo Analytics Now Available To Yahoo Advertisers

Matt McGee — Thu, 30 Apr 2009 23:51:42 +0000

Yahoo’s slow rollout of Yahoo Web Analytics continues with news today that its now available for free to certain Yahoo search and display advertisers.

Here’s how Yahoo explains some of the benefits of its analytics tool in today’s announcement:

“Yahoo! Web Analytics offers user insight that you’re not likely to find in other free analytics tools, including demographic and behavioral insight on your website visitors, near-real-time reporting, and visibility into as many as 50 different types of actions that take place on your site.”

It’s available to any Yahoo advertiser that works with an account manager, and interested advertisers can contact their account manager to start using Yahoo Web Analytics.

Yahoo got into the web analytics business when it acquired IndexTools software a year ago. Then, in October, Yahoo announced that the new Yahoo Web Analytics service would be rolled out slowly to Yahoo’s hosted e-commerce customers.