Web Analytics Applied

Web Traffic for Free

In this economy, there is growing interest in understanding, measuring, and increasing, the amount of traffic coming to a website from non-paid sources.  A year ago, much of the reporting and analysis I did was about analyzing the performance of banner ads, paid search, direct mail vanity URL's, and other marketing initiatives.  Now, what people want to know is how much traffic is coming to their website without their having to pay for it.  Not surprising, as marketing budgets are being cut across the board.  But Web Analytic solutions have not kept up to speed in terms of measuring free traffic accurately, or actionably.

 

When one thinks about free traffic, one naturally thinks about Organic Search.  Surely enough, this is usually an out-of-the-box reporting element in most analytical solutions.  But it doesn't tell the whole story.  Basically, a "search engine referral" in most web analytics solutions means that a visitor has typed in a keyword on a search engine, and the tag on your website recognizes that a search has taken place. It thus parses the referring URL to populate keyword and search engine reports.

 

But Yahoo, MSN, and even Google now have many "free traffic" venues that would not be picked up in standard Organic Search Engine reports: blogs, posts, chat, news, and emails coming from search engines might well not have a keyword query string in the incoming referring URL, and may not be counted as Search Engine (free) traffic.  To give an example, one recent analysis I did showed that while the analytics solution reported 25% "Search Engine Referrals", in actuality 38% of traffic was coming from a Search Engine site (like Yahoo Mail, or Yahoo chat).  This is also why referring domain reports, when parsed manually to look for search engines, usually come up higher when compared to a Search Engine report in a solution like Omniture SiteCatalyst.

Email traffic is part of a kind of referral usually called “viral”, and this kind of referral is usually free.  A visitor has seen an offer on a site, or an interesting news story, and emails their friends with the link to the site.  Unfortunately, unless these friends are using a web-based email solution (like Yahoo Mail), the referral is un-measurable and appears as a “direct load”.  To my knowledge, there is no systematic study that might provide a proxy for how much email traffic is coming to a site that can be related to "measurable" email sources.  Such a study would look for measurable referrals, and establish a coefficient which would estimate to the total email referrals based on the sample that is measurable.

The other type of “viral” referrals which has also not received as much attention in web analytics solutions as they should is blogs, posts, chats, personal websites, or other sites with user-generated content.  These are picked up as referrers, but unlike search engines, there is no “Blog” report that would identify certain referring domains as blog sources.  This can be done manually, but is a tedious exercise.  Some sites I’ve looked at receive between 7-12% of their total traffic from these kinds of sources. While it may be difficult to maintain a list of all such sites, certainly the big players in this space like facebook, blogspot, fatwallet, slickdeals, missycoupons, or mommy$avesbig could be flagged by a web analytics tool and categorized.

How can one increase or influence viral traffic?  Some sites put up distinct links for “tell a friend about this offer”, or they will put up direct links from their site to other post or share sites.  Other managers monitor the larger post or blog sites using their web analytic solution, and will physically go into them to promote a particular product.  In any event, as marketing budgets are being cut, one question that naturally comes up is “how much will this cut affect my overall traffic?”  Measuring “free” traffic is a necessary step to answering this question. 

Referrers: When Bad Practices can be Best

Omniture is the kind of tool where redundancy in data allows you to still get reporting and measurement from one kind of report when something breaks in another report.  Ironically, sometimes not following “best practices” can provide measurement and reporting capabilities in places you might not expect.  When websites are being launched with minimal control over tracking, or convoluted technical impediments to clean implementation, this redundancy or “dirtiness” can be a life-saver for reporting.

Take the familiar “best practice” guideline of filtering out internal domains.  When you set up a report suite in Omniture (or an account in HBX or WebTrends), guidelines and support will tell you to make sure you define what your internal domains are, so that these can be treated as “internal” and can be filtered out of referrer reports.  Sounds good – internal campaigns, or campaigns being driven by affiliate channels, would be measured and tracked through campaign reporting, with the appropriate campaign tracking codes appended.

But suppose this campaign tracking breaks?  Tracking codes from vanity URLs might not be implemented correctly, or the tag is on a page-frame that can’t pull the parameter from the URL, or a dozen other technical bugs might be present that could interfere with tracking these campaigns.  Source data is therefore lost, and marketing managers frustrated.  But if you did not define internal URL’s, often this kind of data will show up in referrer reports.  Here are some of the data we’ve been able to pull from referrer reports because (unintentionally) internal URL’s were never defined:

·         Incoming campaign source codes: the parameter is usually still preserved in referrer reports.  Data Warehouse can even de-duplicate this for visits metrics.

·         Engagement or success clicks from external sources: your media initiative goes to an external website, where the visitor is encouraged to drive to your website.  The source code www.external.com/?source=MEDIA shows up in your external referrer reports.  Combined with third-part media response reporting, you can get conversion rates without any tag being present on the external site.

·         Universal header and footer links driving to your site: your tag may be on the main frame of your site, blind to universal headers or footers.  But you might find www.mysite.com/include/header.html in referrer reports.

·         Links and pages per visit: when your site is not treated as an internal domain, you might find in referrer reports data such as “www.mysite.com/page2….1,025 instances.  This data should be interpreted carefully, but can be used to supplement or reconcile pathing or link reports.   Furthermore, unlike these latter reports, you can get data from pages on your site that are not tagged.

·         Internal Search Terms: these are usually URL parameters, and if tracking them in a custom variable either never happens or doesn’t work, these can often be pulled from referrer reports.

Am I saying that defining internal URL’s is a bad practice?  No – defining internal URL’s makes referrer reports more clean and reconcilable to overall traffic metrics.  Without internal URL filtering, these reports should not be digested by anyone other than a web analytics practitioner who can interpret the data correctly.  But in messy or uncontrolled implementations across many sites, one might consider NOT defining internal URLs in such places as global report suites, so that some of this data may be available in case something else breaks or slips through the cracks.

Flat CTR’s and Google Enterprise Analytics

This week, various news outlets reported the results of a ComScore analysis showing that CTR’s on Google ads have essentially flattened out (http://biz.yahoo.com/ap/080328/google_paid_clicks.html).  YOY January data showed no change, while February was up only 3% from last year.  This compares unfavorably with the historical double-digit YOY growth.

As a web analyst, there are so many reasons this doesn’t surprise me: AdSense ads have now been around for years, and web-users may have become immune to their novelty; saturation of Google ads on top keywords probably reduces user interest or confidence.  But above all, it boils down to the inconvenient truth that as a PPC campaign expands, using new keywords, Content Match, Broad Matching, and the like, the quality of leads goes down, with the result that (eventually) advertisers take notice and scale back.  It’s a quality-over-quantity calculation that many times leads to the axing of Pay-Per-Click altogether from a company’s media spend, and there’s very little Google can do about it.  When PPC visits see bounce rates of 80% or more, or when brand-keywords are used primarily as a substitute for the browser’s address bar, or when most content match referrals come from AdSense gamesters’ auto-search websites or trashy foreign blogs, no wonder advertisers scale back ad placement on Google. 

But advertisers don’t know any of this unless they have a web analytics solution in place.  Google’s own spin in response to this week’s articles has been that they are going after quality of clicks, not quantity, in order to make each click more “valuable” to the advertiser.  That’s good rhetoric, but how is anyone going to validate this, and what will Google do to act on this initiative?  Google’s algorithms are still, after all, CTR-based, because, for the most part, they don’t have insight into website engagement or conversion, for which there are few standards anyway.

Enter Google Analytics.  Many have puzzled why Google launched this free application to begin with, much like other free toys available from Labs.Google.com (?Google Mars?).  But Google may use this flattening of CTR’s to re-think how they market – and use – web analytics.  Google may now discover that it might be worth investing in – and supporting – an Enterprise Level Web Analytics Solution that would be able to measure and analyze all these pieces of PPC engagement.  By providing this application (for a price), Google might be able to both control and exploit the data it generates to optimize its ranking and bidding algorithms, while putting some action behind the rhetoric of “more valuable clicks”.  It could even do this without the advertiser or client ever knowing, just as Google exploits its page-content databases to syntactically model the English language for Search Algorithm refinement.  The dozen or so SEM Analyses possible with a robust, enterprise-level web analytics solution could be automated and streamlined by a Google version – much like Omniture’s Search Center but without the third-partly hassles.  And because Google has a vested interest in tying this to its PPC, it can probably afford to price such as solution much lower than those currently on the market.

We’ve often pondered whether Google will enter this space, and one answer has always been, “Why would they?”  Flat CTR’s and rhetoric about click-value might be a good excuse.

Web Analytics Overkill

When people ask me “what should I track?” my usual philosophy is that tracking should be implemented in such a way as to record in detail anything that is analytically valuable, but also in a way as to make reporting as easy and digestible as possible.  In a forthcoming article, I’ll be discussing the last piece of this statement; but I’d like to say a few words about the first piece – the key phrase is “analytically valuable”.

There is such a thing as “overkill” in Web Analytics.  It often occurs when there is poor communication between the business requirements group or product owner, and the team responsible for designing and implementing the tags, who decide on a policy of “cover our bases” and tag everything they can think of.  This means scores of success events on the site, meaningless variable roll-ups, over-complex campaign tracking codes, and custom link-tracking server requests which slow down performance and result in huge amounts of data within the web analytics tool, the analysis of which would not be worth the effort (literally).

What does “analytically valuable” mean, exactly?  It does not refer to data for reporting or KPI’s (that’s what variable structure is for); rather, it refers to a data-set whose comprehensive analysis would be worth the time and resources, producing valuable, actionable recommendations about website design, user behavior, or marketing effectiveness.  A deep-dive analysis on usage of the “close” button from different popup windows is probably not worth the money.  A team implementing web analytics on a site has to not only ask, “can we capture this behavior?”, but more importantly “can I foresee someone analyzing this data to potentially produce actionable results that would be worth the resources expended?”

This last element is why the overall budget allocated to a website – now and in the future -- should be taken into account when designing a WA implementation.  As a web analyst, this may sound like heresy, but in the real world, it’s why mom-and-pop shops choose Google Analytics (if anything) instead of Omniture.  Alexander’s Pizza Shop on Main Street might have a website with a menu, directions, and pictures, but obviously would waste its money by implementing NetInsight.  It would be dishonest to recommend a state-of-the-art, “measure everything” implementation when it is clear that devoting resources to executing it and analyzing it subsequently would be a waste of money because the website is small-scale and not a significant part of business success.  That’s overkill.  If it becomes a bigger piece of the business, then a more robust implementation might be warranted.

Even within robust, enterprise-level implementations, measurement overkill is possible.  Here are some examples:

·         extensive tagging of the footer on a site.  “Terms of Use”, “Privacy Policy”, “Corporate Info” – these pages on a website usually exist for legal or compliance purposes, and exhaustive measurement of their usage would make no difference.

·         an extensive analysis of a Site Map would probably make no difference either, because they often exist more for SEO than for anything else (though if it’s used more than your navigation, you have a problem!).

·         Over-use of campaign classifications: channel, creative, ad type, adgroup, keyword, date-stamp, banner size, link within an email, landing page à all these can be legitimate and useful pieces of a campaign tracking code.   But requirements and resources will dictate what is more important; thousands of permutations are possible with all these being tracked in combination, and before implementation, questions should be asked as to whether all of these are useful.  A walk-then-run approach might be more appropriate.

·         Over-Use of success events: while websites typically can have multiple success events, some implementers take advantage of the availability of dozens of success events to tag almost any action as a separate success event.  This leads to opaque reporting because it becomes unclear which of these success events gets included in an overall total effectiveness calculation. 

·         Over-redundancy in page-naming: some sites record pages as the URL, then pass a user-friendly name into one or two variables, then pass a hierarchically-defined page name into another variable, then pass another variable in an onclick handler recording another version of the page-name of the link clicked on, while also populating site section and hierarchy variables.  When a manager wants to see a pages report, they don’t know which one to use: “www.mysite.com”, “homepage”, “hp_”, “mycompany|mainsite|homepage” – you get the picture.  And chances are, the numbers for these won’t match up.

There are many other examples, and I’m probably guilty of a few. 

I’m not saying that a “measure everything” approach is bad – on the contrary, with analytical resources available it can be a vast asset for value-optimization of the online channel.  Rather, I’m saying that a “measure everything” approach has to take into consideration the analytical value of the data, and the resources that would be required to implement and take advantage of it. 

The Future of Web Analytics Consulting

We recently had a Hajj-like gathering of all Semphonic consultants at the headquarters in California, to plan generally for 2008 and discuss web analytics more broadly.  One topic that came up was the question: Will Web Analytics still exist as a separate discipline in a few years’ time?  It was an offhand remark during lunch, but left me pondering some basic identity issues about Web Analytics as a separate consulting discipline.

The argument goes like this: companies will eventually realize that online business success is not just complimentary to, or an interesting side of, overall business success, but is actually the same thing.  This means that a traditional Web Analytics consulting company will no longer be justified in coming in and optimizing the web channel, but must necessarily focus also on all facets of a given business, both online and offline. 

Take advertising effectiveness, for example.  Traditional web analysts can analyze the quality of leads coming from all online media – banners, PPC, emails, etc.  But increasingly, I’ve seen offline media work its way into the picture: vanity URL’s tied to TV, Direct Mail, or other Print media; call center integration with distinct phone numbers or robust visitor sourcing; online activity correlated with offline spending behavior matched through credit card or telephone numbers; more nebulous but certainly valid concepts like “brand equity” associated with website interaction and compared to offline advertising.  Where once online cost-per-conversion was the baseline KPI for advertising effectiveness, we now have “lifetime visitor value” or “lifetime user margin”, encompassing all online and offline customer value.  “We propose to analyze and optimize your website” may well give way to “We propose to analyze your business,” in this way of thinking.

Such an expansion is exciting, but also dangerous.  There already are numerous consultancies who claim to look at all facets of a business and provide comprehensive consulting around this.  These are the Accentures and McKinseys of the world, with vast resources, histories, relationships, and branding, against which any Web Analytics consultancy would stand little chance.  Web Analytics consultants have thrived in the last few years largely because such companies do not have the expertise to analyze or integrate web data into the “big picture”, and because the online channel has always seemed independent and distinct, with its own “peculiar” issues and challenges, and managed by separate teams within an organization.  But if the online business success starts to be treated (as it should be) as consubstantial with general business success, these consulting directions may start to converge.  Along this train of thought, Web Analytics consultants will either be bought up by the big consulting agencies, or will have to diversify or partner in order to tackle the non-web aspects of business success. 

I don’t think this has to be the case.  Web Analytics can continue to be its own discipline and field of expertise as long as the online channel remains at the core of the consulting engagement.  A Web Analyst can be happy to analyze call-center data, but only in the context of call-center savings because the same visitors use the website instead of the telephone.  Offline conversions are interesting in so far as the same people can be identified as also visiting the website, or were sourced online originally.  By not keeping the online channel as the focus of analysis or consulting, Web Analysts run the risk of losing their identity.  In my view, Web Analytics consulting has a future as long as it remains true to its core: the online presence of a company and how it contributes to business success. 

A Problem with Coupons

Suppose you’ve got a site where, deep in the site, there’s a coupon for some kind of offer.  Visitors are invited to print the offer or use the offer to link to a merchant website.  Some clients I work with have this kind of setup, and we observed an interesting phenomenon on one of these sites.

There was a large traffic spike all of a sudden to the website one week – in the range of a 50% increase in visits.  Something close to this 50% increase was also seen in single-access visits, and time on site plunged.  Online campaign data showed nothing unusual, and search engine referrals showed nothing unexpected.  It was only when we got to the referrer report, that we realized what was happening.

Someone on two websites – Mommy$avesBig.com and FatWallet.com – posted a blog linking directly to a coupon on the site.  Visitors then saw the blog, followed it to the site, printed the coupon, and left.

A successful visit?  I suppose if the goal of the website is only to drive people to coupons.  But clearly the greater intent of a website like this is to drive visitors to many offers, instill brand loyalty, and get visitors to be otherwise “engaged” through browsing and interaction with the site.  This kind of visit described above would be like being led, blindfolded, to one item at Walmart and then being led straight back to the checkout counter.  I guess it’s better than nothing, but certainly not the desired or anticipated behavior on a site.

What can one do about it?  Don’t ignore these coupon pages as the end-in-themselves.  Post attractive links on these coupons to drive people elsewhere.  Show them similar items or prominently display a homepage link.  In other words, don’t count these pages out as an end point, and realize that they might be the first page people see. 

Discover 2.5 and Visual Sciences

What Omniture will do with Visual Sciences has been a hot topic – and one we’ll be focusing on during our first Ask Semphonic this coming Tuesday (see http://www.semphonic.com/analytics/asksem.asp).  It’s impossible to predict the future, but I’ve been using Discover 2.5 recently, and there are things about it which might be a foreshadowing of things to come. 

Firstly, at its heart, it’s still Omniture Discover.  If its parents are SiteCatalyst and Discover, the genes are almost entirely the latter.  If they try to integrate it with Visual Site, which they’re going to have to do, in my opinion, those same strong genes will trump anything coming from VS. 

At the same time, there is a strong SiteCatalyst look and feel to Discover 2.5 – the left navigation and color schemes, for example.  Discover 2.5 does not, in its present form, replace SiteCatalyst 13.5, but one has to ask whether Discover’s new look hints that this may be Omniture’s intention down the road.

In which case, what would fill the vacuum created by the phasing out of SiteCatalyst?  HBX.  Or rather, a product whose parents are HBX and SiteCatalyst.  The half-way point between the richness of SiteCatalyst and straightforwardness of HBX would be a product that could be too simplistic for a current, heavy user of SiteCatalyst, thus pushing them to Discover 3.X and producing more revenue for Omniture. 

In any event, that’s one scenario worth speculating about.  Ask me again on Tuesday.

Omniture Excel Client – How to Deal with Bugs

The Omniture Excel Client Tool is indispensable for reporting purposes.  It is also as sensitive as Middle East politics.  I have very little idea of how it technically works, but over the years I’ve found some practices which help make the tool more reliable. 

First, how do you know the tool is broken?  Here are some things I’ve seen:

1)      Data Blocks return all “0’s” where last week there was real data.

2)      Line-items you’ve defined come up now as “Undefined” or “Unspecified”.

3)      Instead of the data-block, you get an Excel version of the Omniture Login screen.

4)      Your date-range is nonsensical (such as dates in the 1990’s).

5)      You get a “No Data Returned” message when you know data should exist.

6)      On “refresh”, the data-block disappears and nothing happens.

7)      The structure of the Data Block changes  (e.g. dates are now left-right instead of top-down)

8)      You get error messages telling you that an Excel-referenced value is missing.

If any of these happens, there’s a bug.  Here are some tips to try to fix it:

1)      Logout, then login again. 

2)      If you’re trying “Refresh All”, go to each worksheet and use “Refresh Worksheet” instead (Refresh All is notoriously unreliable).

3)      Quit Excel and re-enter, opening only the Omniture-fed document (other Excel Documents might be confusing the Excel Client).

4)      Do a “Edit Data Block”, don’t change anything, and refresh the request manually.

5)      Make sure there are no other people logged on as you in either Excel Client or SiteCatalyst (I don’t think this affects SiteCatalyst, but I know it effects Excel Client).

6)      If you’ve renamed the document recently, go back to the earlier version and refresh that one instead (this is a tip for symptom #8 above).

7)      Wait an hour or two, use a different computer, change your IP address, or use a proxy server (I think bugs are specific to different Omniture servers).

To avoid bugs, here are some tips:

1)      Keep all Omniture Data Blocks in one worksheet, with no formatting, and have forward facing, pretty tables in other tabs.

2)      Never insert, delete, or move columns, rows, or cells in the worksheet with SiteCatalyst Data Blocks.

3)      Excel-referenced values should be on the same worksheet as the Data Blocks, and should be kept to a minimum.

4)      Don’t move around in Excel when you’re refreshing a Worksheet.

5)      When designing the Data Blocks, decide whether to have data going top-down or left-right, and keep that consistent.

6)      Use the same login when creating multiple Data Blocks.  Also use the same Excel version (2003 or 2007) when constructing the document.

7)      The tab with the Data Blocks should be called something very simple – no caps, spaces, or strange characters.

If anyone has other tips or tricks, please let me know.  The last thing I ever want to do is call Omniture and open a ticket. 

What will Omniture do with Visual Sciences?

I suppose I can’t help but to chime in on this news.  Having worked with Omniture for quite a while, here’s what I think they’ll do:

If they acquire VS in Q1 2008, at first they’ll do nothing.  Omniture does not have the support infrastructure to change or integrate the already existing organization at VS, let alone deal with the VS client-management volume that such a change will undoubtedly create.  VS will likely continue to have its own website and GUI through Q3 2008.  The Visual Site tool will also be unaffected.

Then, in a big push, sometime in Q3 or Q4, they will create a kind of VS platform within SiteCatalyst, along the same lines as Genesis, Discover, TouchClarity, and SearchCenter.  It will be a separate tab in the user-interface, thus both promoting the Omniture brand and encouraging the transition to SiteCatalyst. 

I think they will create two levels of enterprise web analytics.  The old HBX will become a “Web Analytics Light” with much the same capabilities it has today, but perhaps without the customization, flexibility, or Visual Site components. 

SiteCatalyst, on the other hand, will become the more upscale product, fusing with both Omniture Discover and Visual Site, dropping Data Warehouse and ASI’s (which are already, according to rumor, going to be phased out), and (hopefully) leveraging VS’s Report Builder capabilities to merge with MS Excel.  Current clients of both Omniture and Visual Sciences will have to make the choice to upgrade to this new version, providing the opportunity for Omniture to increase its contractual revenue.  Those who do not upgrade will likely struggle in negotiations to maintain their current level of implementation and complexity.  Highly customized contracts – current today in both Omniture and Visual Sciences – will increase.

Such a two-level system would allow Omniture to compete with Google, increase its revenue, and still promote a product that will keep it competitive with WebTrends and other enterprise-level solutions.  Merging the two – HBX and SiteCatalyst – might take place eventually, but I don’t see that happening before 2009, and the two-tier system might prove profitable enough to postpone such a fusion.  Certainly, Omniture has been keen to move people away from the out-of-the-box SiteCatalyst implementation and into Discover and its other high-end products, and by having a cheaper, but far less analytical, alternative, they might get current clients to upgrade.  And a “lighter” version of HBX might be attractive enough for small businesses to replace the revenue stream that will be lost, as both current VS clients abandon VS, and prospects shy away from it, because of this merger.

What Visitors are Thinking and the Dangers of the Standard Design

In recent weeks, I’ve been in meetings and in conference huddles where “hard numbers,” quantitative, web analytics behavioral analysis was pitched against qualitative studies from surveys and usability teams.  The mantra goes, “web analytics tells you what a visitor did, and usability/survey data tells you why they did it.”  I’m not going to argue for or against this mantra right now, but I’d like to raise a slightly theoretical point at this “why they did it” question – and a concern for web analytics.

Qualitative analyses. where subjects are asked to test the usability or satisfaction of a website, have the usual challenges of bias, sampling, people lying, but there’s a deeper bias which could be more insidious: people could behave on (and evaluate) a website based on how they are culturally conditioned to behave on a website, by the universe and culture of website architecture and navigation with which they have been familiar for years.   There are whole theories around this kind of social behavior (Bourdieu called it habitus, Giddens called it structuralization).  Basically, surveying a heavy web-user about an unconventional website would be like plopping an ancient Greek in front of the Forbidden Palace in Beijing and asking him what he thinks of the architecture.

Let me give an example: usability studies on subjects in the early 2000’s revealed that people’s eyes are drawn to the top-left corner of a website page.  So many major portals – Yahoo, AOL, MSN, WebMD, Wikipedia, Martha Stewart, You Tube – put important navigation or important information on the upper-left.  Five years later, people surfing the web are used to important information being in the upper-left, reinforcing this paradigm.  If asked to comment on a site where important information was on the upper-right, I’m sure they’d complain.

Another example is conversion funnels.   Almost every purchase-conversion funnel I can think of asks for payment information at the very end of the process.  7 years ago, this was probably a statistically significant increase in satisfaction; now, I’m sure every survey study would say unanimously that’s where payment information should go. 

Other examples: Search (why always top-to-bottom?), Videos (why is it always a box with a >?), Products (always a thumbnail to be enlarged), header/footer (why not lefter-righter?), tabs (why always at the top?), drill-downs (why not drill-rights?).

The danger, I think, is that website designers looking at usability and satisfaction surveys are going to drive the web to a point where every website basically looks the same, much like it’s hard to tell the difference now between a BMW, Corvette, or KIA from afar.  In the last week I’ve been asked three times if there’s an “industry-standard” or “universal best practices” for website architecture or design.  But if such a “Standard Design” does emerge, my fear is that web analytics for the purposes of website optimization will be obsolete down the road. 

The classic studies of module- or link-placement, real estate analysis, funnels, forms, navigation, home page optimization, perhaps even information architecture, will simply become redundant, because there will develop a standard way people expect websites to be organized.  All web analytics will be about reporting, content analysis, and marketing, and not site-architecture or page optimization. 

Maybe it’s too late to do anything about these ur-templates – this may all have been decided in the 90’s.  But what about global websites in Asia, South America, or the Middle East designed by American firms, whose audience has not been similarly preconditioned to the Standard Design and who may have other cultural dispositions against the American or Western paradigm (recall that much of the world reads right-to-left)?

Why not build a site adventurously and creatively, and then use – you knew it was coming – testing on behavioral data to see if the real population respond significantly to these changes?  I’d love to see a website with a bottom-nav, tabs on the side, internal search where you scroll right-to-left (think of the advertising potential), new ideas for navigation (I’ve seen some good Web2.0 examples).  Let such a site launch in Qatar, and rather than sending surveys to focus groups there, use cheaper behavioral data to see if it rivals the Standard Design?

I’m not a web designer, nor do I have that background.   But as a web analyst, I have seen a certain standardization in many site templates which often are driven by focus-groups and qualitative analysis, after which traditional web analytics applied to site or page optimization is reduced to miniscule changes in the ordering of top-nav elements or placement of banner ads.  And thus loses its value-proposition for us.