September 2010

20 Sep 2010 02:14 am

Incomplete Make love? Direct Traffic? Really?

I am not kidding. Direct traffic contains visitors that proactively seek you out, everyone else you have to "beg" to show up on your site!

Yet this question seems to bedevil a lot of people:

What the heck is Direct Traffic?

As if that was not sad enough, even people who do know what the definition of Direct traffic is rarely focus on it or work hard to tease out the opportunity that exists in Direct traffic.

I love analyzing Direct traffic because it contains a valuable set of visitors who deserve more love than we currently give them.

I want you to be just as excited.

So let's look at the definition, to make sure we understand, at least on paper, what this traffic is supposed to be. We'll also look at the challenges that exist in ensuring we are looking at the real unpolluted Direct traffic.


Here is the simplest and cleanest definition:

Direct traffic contains all Visits to your website where in people arrived at your site directly (by typing the url) or via a bookmark.

Direct traffic is hence not traffic that results from people clicking on links on other sites to your site (that's referring urls traffic), it is not traffic that comes to your site by clicking on ads (that's Other in Google Analytics or Campaigns in other tools), it is not people who come from search engines (that is Search or Organic or PPC traffic).

 direct traffic visitor metrics performance

The reason Direct traffic is a beloved of mine is that it represents (checkout the sweet contextual – red and green – numbers above):

    1. People who are your existing customers / past purchasers, they'll type url and come to the site or via bookmarks.

    2. People familiar with your brand. They need a solution and your name pops up into their head and they type.

    3. People driven by word of mouth. Someone recommends your business / solution to someone else and boom they show up at the site. Uninvited, but we love them!

    4. People driven by your offline campaigns. Saw an ad on TV, heard one on radio, saw a billboard and were motivated enough to typed the url and show up.

    [If you were really smart you would use campaign tagged vanity url so you can segment them!]

    5. [Remember the part below, but.] Free, non-campaign, traffic.

In a nutshell these are people show up without invitation (email, display, social campaigns) or they are people who already know you. There is an extra motivation connected to their visit which causes them to type your url of find the bookmark they made.

That little bit of extra intent, when compared to other visitor segments, is the reason that conversion numbers  (on ecommerce or non-ecommerce sites) for clean direct traffic usually look like these. . . .

direct traffic goal conversionsThe only goal that is red is supposed to be red (fewer registrations from people who already know you is not unusual right?).

Now you'll agree when I say your job is to be extra sweet to them?

Segment them in your data, the delightful numbers you see in your KPI's will show you why.

So if Direct traffic is so important and often the metrics show very positive results then why don't we all obsess about it a lot more?

broken chain

The Problem: Unfortunately. . . You!

Most website tag and campaign tracking implementations are poor (to put it charitably). This is always disappointing but it is particularly harmful to Direct traffic.

You see if you don't implement your links properly the person shows up to your site without any tracking parameters and thus fail to help your web analytics tool to put that visitor in the right source bucket.

Typically Direct traffic also contains all the Visits that originated from improperly tagged campaigns, untagged campaigns and problems with your JavaScript tag. I am sitting in a puddle of tears as I write this, that is how often Direct traffic is polluted and that is how painful (and profoundly sad) this is.

Here is a simple example:

You are the Acquisition manager for a company called Omniture.

You have purchased banner ads in various Android applications using AdMob to target high value analytics decision makers. You goal is to get people to buy your Discover data warehouse product.

You are using Google Analytics to track all you display campaigns.

The proper way to link your banner to your Discover2 website is:

You actually use this url:

Guess where this category will be categorized?



You see mobile applications don't send a referrer and it will look like all of a sudden you got very high converting Direct traffic.

With a simple stone you've killed two beautiful birds:

    > The direct traffic is polluted and you'll never be able to focus on finding real insights for actual valuable lovely people who are seeking you out directly.

    > Google Analytics will show that your mobile campaigns with AdMob stink. Of course that's not true, but you'll have no way of knowing that.

Not a great situation right?

Oh and what do you think is happening to the trackability of all your shortened urls in Social Media that you are not tagging with campaign parameters? 78% of people consume Facebook and Twitter content via applications and unless you use campaign parameters all that traffic is sitting in Direct. So sad.


Direct traffic is a fantastic segment to analyze because it contains desirable Visitors and yet because it is often polluted (due to our own inability to implement web analytics tools correctly).

Let's aim to fix this because it is too important not to.

no problems only solutions

. Why Does Direct Traffic Get Polluted / Mistakes You Should Avoid:

The good and the bad are all mixed in, and it is your job to ensure that that is not happening inside your web analytics data.

Here are the main reasons traffic that should not be Direct ends up there, try, please please pretty please, to ensure this is not happening to you:

1. Missing web analytics tag from landing pages

    Perhaps the most common source of traffic being miscategorized.

    Your urls are all tagged correctly with campaign parameters, or maybe people are just coming to from sites that link to you.

    They land on a page that is missing the web analytics tag.

    They click on a link on the landing page to go deeper into the site.

    Guess what's the traffic source for this traffic?


    So sad.

    You worked so hard to get that referring link / execute the campaign. Now not only do you not get rewarded for that work. you actually messed up your direct traffic.

    Don't be that person.

    Go purchase WASP from iPerceptions or an account with ObservePoint and address the cheapest problem to fix in Web Analytics. If you are a little bit tech savvy then go get REL Software's Web Link Validator, it's pretty good.

2. Untagged campaigns (search, email, display, social media etc)

    This is perhaps the second biggest reason data in web analytics ends up in wrong places.

    In case of untagged emails (to people who are using Outlook, Thunderbird etc) and mobile ads and mobile application links (think of all those Twitter / Facebook apps) and Adobe AIR applications (like my beloved NY Times Reader) and in rare cases where people are clicking on links in PDF documents etc, the data ends up in Direct (no referrer).

    In case of untagged display campaigns usually there is a referrer so it will end up there rather than in Campaigns were you want it.

    In case of untagged paid search campaigns it usually ends up in organic search data.

    On behalf of your company you are spending precious budget on acquisition, not ensuring your campaigns are tagged properly is near criminal behavior. Don't be that person. Tag.

    Oh one more thing.. if you are practicing bigamy and have two tools, say Google Analytics and Adobe's Site Catalyst you better remember to have campaign parameters for both GA and SC because they use different parameters for campaigns. Whichever one you forget to tag for will show your campaign traffic as Direct!

    If you want to track the campaign in the first part of this post with both Google Analytics AND Omniture the url would look like this, as an example:

    See both set's of campaign parameters? You don't do that one of them is wrong. Not so shiny to practice bigamy is it?

3. Improperly tagged campaign parameters / site tags

    This one is probably not that hard to understand.

    Instead of utm_source and s_scid you use utm-source or s-scid and you are. how to say this politely. screwed.

    In both cases your two (or one) web analytics tool will most likely ignore the improper parameters and throw the traffic where it does not belong and mess up your ROI analysis.

    Auditing your campaign tracking before they go live is a great idea. Do this at the very minimum for the 20% of the campaign that are responsible for 80% of your traffic / revenue.

    If you use Google Analytics grab the Google Analytics Tracking Code Debugger. See this blog post for troubleshooting guide & detailed instructions: Debug Your Tracking Code.

    Omniture, WebTrends, CoreMetrics, Unica all come with such debuggers. I can't link to them as location are not public (or you need to pay first!). Please reach out to your Account Managers to get access, just in case you don't already have them. Debug!


    Ben Gaines from Adobe/Omniture was kind enough to share that a free debugger is available to Omniture clients. Log into the Knowledge Base and look for KB ID 534 and you are set! But here's something cooler. The debugger is actually a bookmarklet and here it is:

    Create a bookmark in your browser. Copy the code in the above text file. Click edit on your bookmark. Paste the code where the Link is. Go to any page on your site with Site Catalyst. Click on the bookmarklet and bathe in bugs! :)


    4. Improperly coded redirects / vanity urls etc

      Another silly issue that causes problems with direct traffic.

      When you get a email or a mobile campaign, and keep a close eye on the url window, you'll notice the click goes to your campaign solution provider and is then redirected to your site.

      That's one example of a redirect. We use redirects / vanity urls in our multi-channel campaigns, in our display or search campaigns or even just for the heck of it.

      That is not an issue.

      Make sure they are permanent, 301, redirects. The delicious type of redirects that dutifully pass the referrer string to the landing page telling your web analytics provider where the person originally came from.

      You use temporary, 302, redirects and the referrer never gets passed on. Depending on how the redirect server is configured either the click looks like it came from the redirect server or with a blank referrer (direct!).

    5. Really heavy tag at the bottom of the page (switch to Async!)

      A smaller problem for normal sites with just text and some images, but a huge problem for fat ugly flash heavy websites (especially the, still annoying, ones with flash intros).

      It takes such a long time to load the flash file itself that person might have clicked skip intro or some other link on the page well before the fat flash file loads or before the web analytics JavaScript tag loads.

      The data tracking behavior is exactly as if issue #1 above existed, no tracking code on the landing page.

      I would recommend putting the tag in the header, except that is the selfish lover strategy and no one likes a selfish lover.

      Make your pages as lean as you can, especially campaign landing pages. Keep the tag in the footer, you don't want the page to hang because of issues at your analytics provider.

      If you use Google Analytics you are in a little bit of luck. Switch to the magical GA Async Code. It goes in the header, captures data without ever hampering your page loading and as if that were not enough is leaner and meaner.

      One of these days all web analytics vendors will migrate to the Asynchronous making the Internet a faster place to live in.

    6. Corner cases causing traffic to end up in Direct.

    Here are some reasons that don't happen a lot but you should be aware of:

      ~ Links encoded in JavaScript clicked in some browsers will send a null referrer (i.e put traffic into Direct). Often times you can't help his because you don't have control over people linking to you can do whatever they want. But do check that your campaigns in Facebook or Yahoo or other places are not using this method.

      ~ [Update, via Alec Cochrane:] https to http and vice a versa also won't have referrers passed due to (good) security reasons. So if possible make sure you put campaign tracking codes in links from https pages to ensure those visits don't end up in direct. For this you would have to know this is happening and then be able to find the person who will oblige you by changing the link. Tough to do but when you can do it!

      ~ Some smart folks will make changes to their browser configurations that cause referrers not to be passed. Happens in a tiny minority of cases.

      ~ This might impact only some tools but check with your vendor how this scenario is credited. . .

      First visit: From a campaign (search, referring url, social, display, whatever).

      Second visit: Direct to the site.

      If you are using Google Analytics then that second visit will still be "credited" to the campaign (non-direct) because the _utmz cookie will be present in the browser.

      In your web analytics tool that might not be the cause. Please check with your vendor.

      ~ Multi-domain / sub-domain "unique" web analytics implementations across many websites. With any tool these are really hard to do right, and really easy to do wrong. If you have one of these polka dotted puppies then get your expensive Consultant to triple check the code and cookie customizations with a special eye on Direct traffic.

      ~ [Update, via Pritesh Patel:] You could also have polluted Direct traffic if your entire company (hopefully of a good size!) has their home pages in browsers set as your company's website. This will clearly skew your direct traffic (and your bounce rates, after all they don't actually care about your site :)). You can easily use your tools admin settings to filter out all your internal IP's which would solve this issue.

      ~ [Update, via Deric Loh:] #1. iFrame: Whenever someone links to your site via an iFrame it is possible for them to code it in such a way that it does not pass referral data and the visit will look like Direct. We can do much about this but in case there are sources where you can avoid this issue or get it done properly then it is worth the effort.

      #2. Company Gateways: Some companies might have a security gateway which has been set up to strip the referrers from request calls. This of course is not great for your clean Direct traffic. It won't happen a lot of times and then limited to just one source. But it is something you certainly should be aware of as a cause.

    That's it. Six simple problems for you to take care of. : )

    All kidding aside know that you'll accomplish a major clean-up if you address the first three issues and then YMMV.

    Also know that it is totally worth it to get this data clean, the orange line below is Direct traffic conversion rate and the blue is overall conversion rate. . . .

    direct traffic goal conversion rate

    Not bad eh?

    You want to know who these people are.

    You want to know what you can learn from analyzing their geographic locations.

    You want to know their Visitor Loyalty and Visitor Recency profiles.

    You want to know what content they are consuming.

    You want to know what products they are purchasing.

    You want to know what the differences between their behavior on your site is from your other campaign traffic.

    You want to know if any of the spikes are correlated to you offline campaigns or catalogs you have sent out (and then establish causality between offline campaign calls to action and behavior by these people).

    You want to establish the value of these visitors and then pay special attention to them if they are of value to you.

    For the New York Times website I'll always be Direct traffic. I use a bookmark, I go to the site at least once a day, I click on Ads (I have on my adblock white-list!), I subscribe to the Times Reader, I am a big evangelist of their brand.

    But only if they care to ensure their Direct traffic is clean, and then analyze that traffic will they ever know that.

    If they are like every other company that obsesses with PPC and Yahoo! Banners and Facebook Display ads and Email campaigns etc etc then they'll never know that some of their best customers they should make happy are right under their nose.

    I know that the NY Times web analysis team is super sharp. Are you?

    In the small chance that you were not before I hope I have convinced you to truly bring the "make love" type of passion to this valuable, and usually large, segment of traffic to your site.

    Good luck!

    UPDATE: A clarification specific to Google Analytics:

    Every tool uses delightful sets of attribution rules when it comes to assigning visits or conversion to campaigns. To share with you how Google Analytics will attribute these things here are a couple of scenarios….

    Scenario 1:

      Visit 1: Came from SEO click on keyword "ASOS Fashion"
      A few days later…
      Visit 2: Came direct to the website

      In Google Analytics you will see this in your reports:

      Keyword "ASOS Fashion": Visits: 2
      Direct: Visits: 0

      In effect Google Analytics will "understate" direct visits. It is difficult to have a perfect scenario here, some people will vehemently make the case that GA is doing it right and that the Visit did come via the organic click first so second visit should be attributed to it. I am personally in the camp that that is sub-optimal and that because we can't read too much into anything (we just don't know what is influencing what) we should report keyword visits = 1 and direct visits =1. But at least you know what GA is reporting.

    Scenario 2:

      Visit 1: Direct to the site.
      Visit 2: Came from Affiliate Campaign click.
      Visit 3: Came direct to the site.

      In GA it will show:

      Direct: Visits: 1
      Affiliate Campaign: Visits: 2

      See how that works? Regardless of how you think it should be you now know how it is. : ) Make sure you keep this in mind as you analyze the GA reports.

      [My heartfelt thanks to David Williams for his help with a test for above cases.]

    This stuff is complicated right? Remember none of this takes anything away from the importance of direct traffic or how hard you have to work to make sure your reporting of it is clean (tips above) or that it is worth focusing on. Whatever tool you have, do all of the above!

    Ok your turn now.

    Do you obsess about Direct traffic just as much as I do? What insights have you found from you analysis? What methods have you deployed to ensure that your Direct traffic segment is as clean as possible? Do you also look at any "Direct" traffic to really long complicated url's on your site and instantly doubt that could be direct?

    Please share your experience / feedback / tips / critique via comments.


    Couple other related posts you might find interesting:

07 Sep 2010 02:07 am

Magical Arthur C. Clarke said:

"Any sufficiently advanced technology is indistinguishable from magic."

That quote comes to mind when I think of a new feature in Google Analytics that carries the unassuming name of Weighted Sort. It is an advanced implementation of technology (mathematical algorithms in this case) and when used it very much feels like magic!

In this blog post I want to share with you why I am so incredibly excited about this feature, how it works and how going forward you will reject every tool that does not come built in with this feature (ok so maybe that's a stretch, but I promise you this is so cool that at least for a few minutes you'll think other tools are lame by comparison!).

Let's take a couple of steps back, get some context before we dive in.

The Problem.

We have a very long tail of data in web analytics. Tens of thousands of rows of keywords in the Search Report (even for this small blog!). Hundreds and hundreds of referring urls and campaigns and page names and so on and so forth.

Yet because we are humans we tend to look at just the top ten or twenty rows to try and find insights. The problem? The top ten of anything rarely changes (except in rare circumstances like a sale or on a pure content – think news – site).

Hence I have persistently evangelized the need for true Analysis Ninjas to move beyond the top ten rows of data to find insights.

How? Advanced table filters, tag clouds and keyword trees are a good start.

But we need more.

One more problem though.

As if massive data we have is not enough of a problem, we also rely on Averages, Percentages, Ratios and Compound/Calculated Metrics in a profoundly sub optimal way, as a drunken man uses lamp-posts – for support rather than for illumination.

Take a percentage, for example Bounce Rates. The top ten won't change.

bounce rate normal table view

Hmmm. what to do. what to do?

You know what I'll try to  find the keywords with the highest bounce rates and fix them! After all I don't want to have all those visitors say: "I came. I puked. I left!"

Ok analytics tool: Sort descending!

bounce rates descending

Arrrrrh! Useless!

See all those single visits? Would improving these bounce rates have a huge impact?

Ok maybe I should learn from keywords with low bounce rates so I can perhaps take the lessons from my awesomness and apply it to others. Tool: Sort ascending!

bounce rates ascending

Arrrrrh! Again! Useless.

What could I possibly improve by focusing on these keywords with so few visits? Nothing.

So to recap:

  1. We tend to only understand the top ten rows of data, because that's what is easily visible.
  2. Gold exists beyond the top tend rows.
  3. Using percentages, averages sub optimally makes it impossible to find the Gold!

Yet gold I must find if I want to improve the outcomes for my web business (for profit or, as in the above example, non-profit).

The Solution!

The Google Analytics team has built a innovative and mathematically intelligent new feature called Weighted Sort to precisely solve this problem.

Now when you sort the data off a percentage or a ratio, like in the above case, you'll see this on top of the table.

weighted sort option google analytics

When you press this unassuming checkbox something magical happens. Google Analytics brings back for me the rows of data I should analyze further to have the highest possible impact on my business.

It looks like this. . .

search keywords weighted sort google analytics


Notice that the Visits for these keywords are sorted in an "odd" manner, as are the bounce rates.

That is the magic.

Now you don't have to go through wild gyrations (or worse guesses) to figure out the best places to focus your attention on. You can skip combing through the, in this case, 5,777 rows of data. The algorithm will do that for you!

The "magic button" will sort your data from:

"focus here because something very important is going on here and if you focus here chances your improvements leading to reducing bounce rates will have a very high ROI for your business"


"rows/keywords where your efforts might not quite yield big ROI improvements"

Translation: Sort by "interestingness".  What are the most interesting keywords with high bounce rates? [Where things are going "wrong".]

You can reverse sort the table, keeping the Weighted Sort checkbox on, and you'll find the most interesting keywords with the lowest bounce rates [where things are going right].

No more using silly ascending and descending sorting. No more worrying about if you are focused on the right places. Less worrying if you are prioritizing things right.

Save time. Do less data puking. Be happier!

Awesome right? Go try it on your own Google Analytics data!

internals of a machine

How Does Weighted Sort, aka The Magic, Actually Work?

Good question. It is also the reason for the Arthur C. Clarke quote.

Of course there is no magic, it is all the beauty of some wonderful math and ingenuity.

But it is complicated.

Let me try to explain it as best I can using some visualizations and formulas.

What powers weighted sort?

This simple hypothesis:

The true value of a metric (bounce rate, conversion rate, time on site etc) for dimensions with small participants will be imprecise.

English: If the dimension you are looking at is referring urls and if only five visits this month originated from Bing then a conversion rate of 80% (or a conversion rate of 20%) is not reflective of the "true" conversion rate.

There are too many unknown variables, or irreplicable events, that could have contributed to that number (80 or 20) making it incredibly difficult to make any decisions based on just 5 visits.

You saw this problem when I sorted descending or ascending for the metric bounce rate above.

So how do you address this problem?

The fearless developers were given this amazing goal:

Compute the "expected true value" for each row on the table.

It is a difficult problem to solve. But since the actual values are not very useful, applying some logic and mathematical intelligence to figure out what the true value is can brilliantly help identify "interesting" data (aka where to focus).

Google Analytics computes the expected true value (in our case above "expected true bounce rate") and then sorts the data using the expected true value (ETV) giving you the most interesting data to look at.

The expected true value (ETV) is not shown in the UI (as it would simply be distracting).

How exactly do you compute the "expected true value"?

That is a good question.

Think of a scale. On one end there are is a dimensional value (keywords, countries, referring urls, product names etc) with zero visits and "a lot" of visits at the other end.


Let's assume we are analyzing the dimension countries and the metric bounce rate.

Remember out hypothesis above? True value of a metric is not reflective when it comes to small samples (visits in our case).

So if there was one visit from South Africa its actual bounce rate reported in the tool is not a precise reflection of what the true value might be. But if there were A Lot of visits from South Africa then the actual value is reflective of the true value.

Put another way. . . I request you to pay attention. . . .

For values to the very far left of the scale we equate the expected true value (ETV) to be equal to site average. A very safe bet.

For value at the very far right of the scale (i.e. "a lot" of visits) it is quite likely that the ETV will be equal to the actual value. Makes sense right?

All other points between the left and the right will have ETV's that will be a blend of the site average and actual values.

Hence when computing ETV. . .

Those closer to the left (fewer visits) will have a higher blend of site average compared to actual values.

Those closer to the right (many many visits) will have a higher blend of actual value compared to site average.

Here's a image that explains this very critical concept clearly. . .

computing estimated true value

Crystal clear on how ETV's are computed?

The quest is to figure out the estimated true value (ETV) for any metric for a given dimensional value (keyword, referrer, campaign, display ad, social media strategy).

NOTE: Numbered values (0.01, 0.99, 0.5 etc) are for illustrative purposes, just to explain how weighted sort works. Actual values used in your report are intelligently and automatically computed in context of your data.

Can you give me a specific example of ETV computation?


Let's say you are a multi billion dollar multi country multi people corporation with multiple products and services.

The next step in your world domination plan is to figure out how best to move beyond your current list of country domination (United States, Brazil, UK, India, Spain).

What do you do?

You'll look at where your traffic comes from and look at bounce rates, to figure out how you can retain even more people who land on your website. You are confident that if you just retain them beyond one page, engage them beyond your 200 mb flash intro, then you know you'll suck them into your business. Then world domination is but 15 minutes away!

So you log into your web analytics tool and you'll probably see a report like this in Google Analytics, or Omniture / Adobe or CoreMetrics / Unica / IBM or WebTrends or. . .

conversions bounces by country

And you let out a little sarcastic: Just great.

The report has confirmed what you already knew from starting at the same top ten row. You very quickly went nowhere.

But you are in luck, you are using Google Analytics! (At least in my imagination. :)

You click on the Bounce Rate column to sort and then check on the Weighted Sort column and. . . bam!

Something useful. . . .

bounceratesweightedforcountryvisits1 Sorted by "interestingness"!

You are now looking at a intelligently sorted list of countries where if you focus on improving your bounce rates (i.e. lower them) you'll have the best bang for you recession hit buck!

Segment the traffic from Argentina, Peru, Spain, Colombia, Chile and Denmark and you are on your way to the aforementioned world domination.

But how did Argentina rank #1 (4k visits), Peru #2 (1.5k visit), Spain #3 (8.8k visits)?

Analytics used the, again, aforementioned formula to compute the estimated true value (ETV), by leveraging Average Bounce Rate (64%) and Actual Bounce Rate for each country (last column above) and assigning contextual weights based on Visits from each country.

Let us see how the ranking worked by reverse engineering it. Here is what happened:

    Argentina Bounce ETV = (0.01*avg BR) + (0.99*actual BR)

    Argentina Bounce ETV = (0.01 * 63.49) + (0.99 * 79.53) = ETV = 79.37

    Peru Bounce Rate ETV = (0.1 * 63.49) + (0.9 * 80.24) = ETV = 78.57

    Spain Bounce Rate ETV = (0.001 * 63.49) + (0.999 * 77.76) = ETV = 77.75

Now you can see how each country, even though visits are very different, were sorted #1, #2, #3. By interestingness, by computing ETV for each.

Where did the number in red come from? You were not paying attention!!


Remember the scale? (If not see picture with scale above.)

The numbers in red are:

    1. just for illustrative purposes in this blog post

    2. a function of where Visits by a country would fit, closer to the Zero (Peru) or closer to A LOT (Spain), hence the name weighted sort

    3. always computed uniquely for your website data based on a intelligent mathematical formulation (which is patent pending and I can't reveal to you!)

You now understand how weighted sort works! Yea!

What if you wanted to discover which are the most interesting countries to focus on, where bounce rates are already low, and deepen your world domination?

Reverse sort the table. . .

reverse sort best countries bounce rates

Happy birthday.


Examples Of Weighted Sort Analysis You Should Try.

I wanted to close this post by highlighting other places you can use weighted sort and some other types of analysis you could do.

Focus your efforts for attracting New Visitors to you site.

Weighted Sort also works with % of New Visits. So let's say you are a newspaper and up against the "newspapers" of Fox. To survive you must find new countries (or Cities or Regions) from which to attract lots more new visitors from.

Well just sort by % of New Visits and you'll have the answer. . . .

percentage new visits

Now you know where to focus.

[Remember that for a newspaper Repeat Visits are also great! :)]

How about looking at the most interesting countries from where the % of New Visits is already high? Just reverse sort the above column.

You might then want to segment that data to go see if over time Visitor Loyalty for those countries is also increasing, or these are just fly-by-night visitors.

Valuable analysis right?

Understand audience preferences, improve $$, for a non-ecommerce site!

I don't have ads or promotions on this website. But like any good Analysis Ninja I have identified my goals (I have six) and then identified values for each goal. The values define revenue that does not come to me directly, on this site, but rather comes to me in other ways as a result of the work I do on the blog (multi channel impact baby!).

The benefit of Goals and Goal Values is that it helps me do "financial analysis" for all the traffic I get (you all!). That means I can focus on what works for you and what works for me.

The metric I use is $ Index. It is the average value a given page or a set of pages add to the overall pie.

The analysis I want to do is to understand what pages / content I should focus on to create the highest possible impact.

I am not going to look at the normal table found in Google Analytics or Site Catalyst or Yahoo! Web Analytics.

I am going to look at the table with Weighted Sort turned on to identify the rows with "interestingness". . .

customerinterestcontent indexvalue

Who would have thunk that my public speaking engagements page was of so much interest and creating so much value for me, with just 469 page views! Certainly not me.

Some of the other rows of data were also unexpected (I need to do more videos, podcasts!) and others were just plain gratifying (I love killing useless metrics, and so do you!).

But there was also heartbreak.

When I reverse sort the data, to find which blog posts / topics are not generating enough $ Index (value), I was sad to see this was the #1 post. . .


Seven Skills to Look for in a Web Analytics Manager

I was really sad because I was a manager and a senior manager and a director of a web research and analytics teams. The above post distills my little wisdom.

More people should read this post (and similar by others) because day in and day out I see wrong people leading analytics teams causing problems for the company and sucking the life out of the Analysis Ninjas. And I hate that.

See why my heart is broken with that low $0.11 value?

But at least I know!

Money, Money, Honey Bunny!

Can't close a blog post without an example of conversion rates right?

Traffic comes to your website from many sources. We typically tend to look at silos and rare compare across acquisition channels.

Hence I recommend that you look at one of my favorite reports: All Traffic Sources.

Let's suppose you are an Analysis Ninja called Nico Weber. Now at a glance you can compare direct traffic with referral traffic with paid search with organic search with campaigns with. . . everything! Make it your new favorite.

When you report to your Sr. Leader now you can look across ALL traffic channels and tell her/him which ones are most interesting for the company. . .


Did anything in web analytics look more delightful? [Maybe the Intelligence Reports. :)]

The above table helps you prioritize where your most interesting sources of traffic are, not by conversion rate only but rather by using a intelligent mathematical algorithm that weights against Visits while computing estimate true value of the conversion rate.

Oh and don't forget to reverse sort and find the "loser" traffic source prioritized by interestingness!

That's weighted sort.

It's a simple feature, a great addition to the portfolio of techniques that Analysis Ninja's will use to find insights faster and focus on what's important.

It is my fondest hope that web analytics vendors like Adobe, I B M, Yahoo! will take a step back from this constant quest to collect every more data and just puke it out. I hope they'll take mercy on the Reporting Squirrels and Analysis Ninjas of the world and spend 10% of their vendor resources on making tools smarter, a bit more intelligent. We deserve at least that much.

I hope the Google Analytics team also continues to do so.

Ok your turn now.

What do you think of this small feature in Analytics? Do you understand how it works? Do you use it in your job already? What do you think the team at Google did right with this feature? What could they have done better? Are there other techniques you use to move from Data to Insights faster?

Please share your feedback, tips, critique, words of praise, and all else via comments.