A little over a year ago, I wrote a blog post about why you shouldn’t freak out about people opt-ing out of being tracked in Google Analytics.  Yesterday, Google announced that they are using SSL to encrypt search queries and responses for people that are using Google.com and are logged into their Google accounts.  The result of this change is that referrals from Google organic search in Google Analytics and other clickstream measurement tools will not be able to determine the keywords used in the search that brought a user to the studied site.  The reporting of the fact that the visit was referred by Google organic search will be maintained.

Although this has widespread implications for both Search Engine Optimization and site optimization activities, I’d encourage you to not freak out about this change for a lot of same reasons that I outlined last year.

Why I’m Not Freaking Out – And You Shouldn’t Either

I have no idea what to expect with this change – there is no way for me to predict how many of my sites’ visitors are going to be coming from logged-in Google account holders.  I do know the current impact of Google organic search to my portfolio of sites – it is the single biggest driver of organic (as opposed to paid) search traffic, providing, on average, 75% of visits. According to both Comscore and Hitwise, Google had a 66% market share of U.S. searches in September 2011.

This traffic is important to the overall goals of all of our sites.  We spend a lot of time and effort on building and maintaining our sites’ search traffic and this change has serious implications for both the quality and quantity of data that we use to drive these efforts.  Despite this, I’m still not freaking out.

Using the same thought exercise as my previous post, imagine if 50% of Google’s organic traffic had its keywords obfuscated because those visitors were logged into their Google accounts when they performed the search that ultimately brought them to your site:

It (Still) Isn’t about the Individual Visit (or in This Case, Search) – Search data, like all other clickstream data, is useful in aggregate.  By looking at the keywords searched and grouping them by theme, we can calculate the value of certain types of keywords vs. others (for example, brand terms vs. product terms).  Losing 50% of this data will affect the number of Long Tail keywords that get reported and potentially affect the reporting of narrow segments of traffic that have few reported keywords, but not radically affect our conclusions on the aggregate “search intent” of our visitors.

Aggregate Data is More about Precision than Accuracy - With this thought exercise, we are again losing a bit of Accuracy without losing Precision.  There is no reason to suspect that individuals search and post-search behavior is going to change because they happen to be logged into their Google account.  The assumption that these individuals as a group do not significantly behave differently than all Google organic visitors is one that is easily tested within Google Analytics by comparing the group with reported keywords with the one whose search terms are obfuscated.  Because of this (testable) assumption, we can draw conclusions that the “search intent” of the missing 50% in aggregate is going to be similar to the fully-reported 50%.

Perhaps most importantly, from a privacy standpoint, this is the right thing to do.  I spend a lot of time connected to Wi-Fi in other offices, coffee shops, hotels, at conferences, and other places where a nefarious system administrator could easily snoop on my search queries and other non-encrypted web usage data.  Google’s new two-factor authentication makes me secure accessing Google products (including Google Analytics!) while connected to potentially sketchy Wi-Fi.  Now I have the same level of comfort while using Google search in potentially unfriendly places.

The data will show the impact of the change in the next few days as it is rolled out to everyone.  Regardless of the scope of the data that has been affected, I hope this post had made a strong argument for not freaking out about it.

This post is to thank and highlight a video from a person that has had a tremendous amount of influence to my career: Avinash Kaushik.  This video was recorded back in 2007, and talks about a topic that is near and dear to my heart:

Culture.

In the wrong context or to the wrong people, talking about “culture” causes people’s eyes to glaze over.  Based on my experience, I am no longer one of those people. My tale of developing a data-driven culture in a large corporation follows:

In 2006, I accepted a position as a Senior Manager of Web Analytics for a large business services firm.  As the product manager of the organization’s enterprise-wide web analytics software and data collection framework, I had my hands full developing a data capture and reporting framework as part of a complete web reboot by the company.  Although implementing an enterprise click stream tool as well as a framework for web data integration into the company’s data warehouse was a technically complex task, it was fairly straightforward once the requirements were determined.

What was not straightforward, however, was how to develop a data-driven culture in regards to how the company used its web data.

The organization had nine different, quasi-independent business units with about thirty-five different web sites.  Nobody from the business units was focused on web analytics, however, each business unit had a web team that was focused on managing the content on the sites.  My goal was to transform those positions from content managers into data driven product managers of their web sites.

So how did I attempt to accomplish this?  I empowered the web managers with their own data. I trained the web teams on both our clickstream and data warehouse tools and gave them the ability to independently develop actionable insight about their clients’ web usage.

Less than a year later, these managers could look at both individual and aggregate customer data and determine how specific web-based activity affected their business units’ bottom line. They had total visibility into all of the company’s marketing data, allowing them to explore the data and develop objective arguments for action.

Looking at my social graph on LinkedIn, I see that, three years later, some have moved into different roles either within or outside of the firm, but at least four of those former web managers have moved on to be web analysts, two with a top-tier web analytics consulting firm.

My approach here was directly influenced by Avinash’s first book, blog, and talks that he was giving at the time.  His guidance was, and continues to be, useful and inspirational for the entire online marketing community.

Creating a Data Driven Culture by Avinash Kaushik

Although the “QR” code is one type of two dimensional code (other common ones: Aztec Code,  MaxiCode)  the term “QR Code” has been extended to encompass any two dimensional code that is readable by scanner software on mobile devices.  The QR Code standard is a set standard and license-free, so the platforms for both consumption and generation are interchangeable.

So what’s to like about QR codes?

QR codes are easy to consume. All the major mobile platforms either support the QR Code standard natively or have free QR scanning applications readily available.  To consume a QR code, a smart phone user needs to simply “take a picture” of it with their phone.

QR codes are easy to create. Since the QR Code is based on a set standard, there are a number of web services that will produce them based on URL input. I’ve been using Kaywa’s generator, but URL shorteners, such as goo.gl and bit.ly now also generate them along with their shortened URL’s.

QR codes are easy to track. Much like a shortened URL or a vanity URL, there is opportunity to tag incoming URL’s to allow tracking of traffic generated by QR code scans. This is a key practice when attempting to determine use of and return from QR code usage.

Where are they useful? With the explosion in advanced smartphone usage, there is increasing opportunity to embed these codes in a wide variety of applications.  I have personally seen QR codes used in billboards, magazine advertisements, bus shelters, bus wraps, business cards, conference badges, and, oddly enough, men’s rooms.

So why aren’t they everywhere? The sad truth is that they aren’t everywhere.  They are still so rare to see “in the wild” that I am still surprised to see them, even in situations with obvious utility.

What are some other uses? I’d like to see QR codes everywhere where a web resource could be useful.  I’d like one on my appliances or in my car that can point me to product information.  I’d like one at Starbucks and Chipotle that would allow me to order and pay while standing in line.  I’d like to see them on TV that would allow me to connect with shows and their advertisers in addition to vanity URL’s.  The potential applications are legion.

Is there a privacy concern here? No, because the scope of data that is collected is clearly defined – the data collected by testing tools is far less detailed than that collected by click stream tools or transactional data.

Is there a user experience/user sentiment concern here? Possibly.  Although user experience varies widely by platform and site customization features, by the very nature of a test, certain visitors are getting a sub-optimal experience.  Should informing site visitors of the use of these tools, or even the fact that they are participating as a panelist in an experiment, be part of a policy of transparency and potentially cut off any negative sentiments created by the testing?

This all leads to my question:

Do you explicitly inform your sites’ visitors of any A/B or multivariate tests/tools that are deployed on your sites?
	No.
	Yes, we cover all testing generically in our TOS/Privacy Policy.
	Yes, we mention specific tools and/or tests in our TOS/Privacy Policy.
	Yes, pages that are part of test treatments inform visitors to the existence and nature of the test.
Web Polls

I’d love to hear about any specific experiences related to testing and disclosure in the comments!

Although I have years’ of experience implementing and using other web analytics tools, today I use Google Analytics on nearly every site that I manage.  It has become the de-facto standard web analytics tool for content and small e-commerce sites for a reason: it is easy to implement, has enterprise-grade features and a large user base, and it is FREE.  Here are the reasons why I’m not freaking out about a potential loss of visitor data from this tool:

It Isn’t about the Individual Visit

The power of clickstream analytics tools, like Google Analytics, comes from deriving actionable insights by exploring aggregate site traffic across discrete time periods and specific traffic segments.  You simply aren’t going to get very much actionable insight delivered by looking at one person’s visit to your site or even by tracking one person’s visits over a longer time period. In fact, Google Analytics’ terms of service explicitly forbids implementing it in a way that can uniquely identify individual visitors.

Aggregate Data is More about Precision than Accuracy

Here is a thought exercise: what if Google Analytics or some other clickstream analytics tool is delivering actionable insights that boost your site’s conversion rate but is only collecting data from about 95-99% of your site’s visitors?  That extra 1-5% isn’t a big deal as you can safely assume that the missing 1-5% is acting like the other 95-99% of your visitors.

Due to Javascript not loading, the mechanics of the Javascript not triggering the call back to Google fast enough, or visitors’ current use of ad-blocking and privacy tools, I generally assume that I am CURRENTLY missing about 1-5% of my sites’ pageviews.  Moreover, if you have a site with a large amount of traffic (millions of pageviews per month), Google Analytics suggests that you estimate traffic data based on sampling your site’s traffic to speed up the processing of your reports.

Back to the thought exercise: I expect adoption of this plug-in to be somewhere in the neighborhood of 1-5% of all users.  Is your traffic data fatally flawed if you are missing 2-10% of your pageviews? What about 20%?

Unless the people who install the plug-in are going to behave differently (as a group) than those that do not, Google Analytics will become somewhat less accurate with no loss in precision.  In the context of most sites’ objectives, there is not going to be a reason to question the validity of the conclusions that are drawn from Google Analytics unless there is widespread adoption of the plug-in.  This is because actionable site optimization metrics are based on rates (conversion rate, funnel exit rate), rather than on absolute numbers.

Clickstream is Only Part of the Puzzle

There is an ever-increasing amount of information that is being generated by people interacting with your brand online.  On your site, there is the potential to collect transactional data, direct voice-of-customer data, site testing data, contact us form data, etc., that is typically integrated with, but discrete from, Google Analytics.  Off site, there are interactions with your brand on social media, email marketing activities, and any offline interactions that may also be generating data.  It isn’t that your clickstream data isn’t important – it is just that there are other sources of data that may prompt action on the part of the analyst.

Allowing the Opt-out Is the Right Thing to Do

As site owners, we should never lose sight of our objectives.  There is a reason why our sites exist (sell something, provide information, display advertising) that is fundamentally more important than how we measure and improve our sites’ ability to achieve those goals.  Perhaps unfairly, some peoples’ concerns over privacy will cause them to block a tool that is likely being used to understand and improve their experiences, but we should respect their wishes and accept this as a new browsing paradigm in an environment with many other evolving browsing paradigms.

There Are Alternatives to Google Analytics

Of course, there are other web analytics packages out there if Google Analytics is no longer getting the job done.  It is pretty standard for “enterprise-level” web analytics solutions to include a clickstream tool, a CRM tool, a data warehousing tool, a testing and optimization tool, a social media monitoring and engagement tool, etc., along with their “enterprise level” cost and implementation difficulty.  There are other free tools out there with the features that you would expect with a free tool.  Google also sells Urchin, which doesn’t rely on Javascript to collect data, but instead uses server data logs as its primary data source.

In summary, I don’t think that there will be widespread adoption of the Google Analytics opt-out.  Even if there is, it won’t totally strip away the value of the tool and there are other clickstream analytics tools out there (as well as other sources of web analytics data).

EXCEL:

The base function for this is RIGHT.  RIGHT gets passed two arguments, text, which is the text being parsed, and num_chars, which is the number of characters returned by the function.  RIGHT takes the form in Excel of RIGHT(text,[num_chars]).

The text argument is obvious; it is the text of the email address to be parsed.

The num_chars argument is determined using a combination of two other functions, LEN and FIND.  We use LEN to determine the length of the overall email address and subtract the position of the @ operator, determined using FIND.  The resulting differencewill return the length of the domain portion of the email address.

LEN(text) returns the number of characters in the string.

FIND(find_text,within_text,start_num) returns the postion of the find_text within the within_text.  start_num, which we won’t use here, is a way to start selecting text after a certain number of characters.

To put this all together, let’s put my email address in cell A1, place our derived function into the B1, and derive the result.

=RIGHT(A1,((LEN(A1)-FIND(“@”,A1)))) calculates to:
=RIGHT(A1,(21-4)) to:
=RIGHT(A1,17) to:
=tomsanalytics.com

SQL:

SQL is a little bit trickier, as there isn’t a right-to-left text selection function, instead we are going to use a left-to-right function, SUBSTRING.

SUBSTRING returns a string of text based on definition passed to the function.  SUBSTRING takes three arguments, value_expression, which is the text being parsed, start_expression, which is the starting character of the returned string, and length_expression, which is the number of characters returned, starting with the start_expression.  This function in SQL looks like this: SUBSTRING(value_expression,start_expression,length_expression).

In this case our email address is the value_expression.  The character following the “@” symbol is the start_expression, with the length of the remaining string being the length_expression.

To determine start_expression, we deploy another SQL function, CHARINDEX, which works exactly like FIND in Excel.  Using SQL’s version of LEN and the same math, we can determine length_expression.

Putting it all together, let’s assume a table named email_addresses with a column named email:

SELECT SUBSTRING(email, CHARINDEX(‘@’, email) + 1, LEN(email) – CHARINDEX(‘@’, email) + 1) AS domain_name
FROM email_addresses
WHERE email like ‘%@%’

The WHERE clause is in there to prevent malformed email addresses from crashing the CHARINDEX function.

The math works the same way as the Excel, except that you have to remember that we are working from the left, so the need arises to add one to the character counter in the CHARINDEX function.

Simple and powerful.  I hope this is useful to someone – the inspiration for this post came from this post at Chandoo.org.  Pointy Haird Dilbert is easily my favorite as well as one of the most useful and entertaining Excel blogs out there.

Export from Tom's Planner

Tom’s Planner is a web-based collaborative project management tool. Despite the name, I have nothing to do with Tom’s Planner. It must be run by “that other Tom”.

Perhaps due to its moniker, but more likely due to its power, flexibility, and ease-of-use, Tom’s Planner has earned a spot in my toolkit.  With a simple drag-and-drop functionality, you can create Gantt charts and easily export them to an image, MS Project file, or even its own (public or private) web space.  That is pretty much all the application does, but it does it well, making it ideal for Project Managers that don’t want to deal with hassle of learning the intricacies of modern Project Management software.

Check it out!  They offer a fully-functional, registration-fee (!) demo at:
http://www.tomsplanner.com/tomsplanner.aspx?template=example

Rupert Murdoch has been grumbling Google’s usage of the Wall Street Journal’s content for its Google News product and threatening to dis-allow Google spiders on WSJ properties.  In the first few minutes of the interview above, he gives his opinion on Google and the state of his internet businesses.  It is worth a watch.

Much has been made of the traffic, and revenue losses that the Journal would incur if it voluntarily de-listed itself from Google and/or Google News.  I think that all of the analysis that I have been reading in the past week is missing the subtext of his posturing.  Murdoch’s frustration with Google goes way beyond their re-purposing of WSJ content.

First, some context: News Corporation, the parent company of the Wall Street Journal, is one of the largest media conglomerates in the world. The company’s market cap is currently about $35 billion USD. In addition to numerous newspapers, magazines, book publishers, film and TV studios and distributors, and other companies under the New Corp. umbrella, the company also runs some of the largest web sites in the world, that combine to generate tens of millions of site visits every day.

News Corp Sites in U.S. Top 100 (Alexa):
MySpace (5)
PhotoBucket (24)
Hulu (29)
Fox News (39)
Wall Street Journal (88)
IGN (93)

Of these, only Hulu (which is 45% owned by News Corp.) and Fox News do not currently participate in Google’s AdSense program to help them monetize their unsold ad inventory on their sites.  The enormous amount of ad inventory available on these sites through Google AdWords (the buy side of AdSense) implies that they lean heavily on AdSense as a revenue-generating device.  Looking at the cost of MySpace, Photobucket, and the WSJ through AdWords and subsequent revenue split with Google, News Corp. cannot be happy with with the revenue produced by Google’s platform compared with the amount of traffic that they get.

I am by no means implying that the pricing for this inventory is unfair; Google runs a continuous auction-based system to determine advertiser placement.  My point is that Murdoch is feuding with Google because AdSense isn’t able to generate the incremental revenue that News Corp. expects from these properties based on the amount of traffic that they receive.

Here is a solution for News Corp:  MySpace has a self-service advertising program called MySpace Ads.  Spend a few million making the system easier for advertisers to target their desired audiences and roll out the platform across all News Corp. properties.  That way, News Corp. has the ability to keep 100% of these ad revenues as well as cross-sell their inventory.  After a couple of years of development, they could even be in a position to offer their platform to other publishers and directly compete with AdSense.  That would make News Corp. a serious player in the internet advertising business.

If you are the type to eat quietly at your desk, why not spend your lunch listening to two Web Analytics gurus discuss testing?

The other day, I found a great podcast that Bryan Eisenberg produced with Avinash Kaushik that was created in support of Bryan’s book: Always Be Testing: The Complete Guide to Google Website Optimizer.  Their discussion focuses on the importance of testing and the usage of Google’s testing platform, Google Website Optimizer.  There is really some great stuff in there from two people that are passionate about testing.

This podcast, recorded on September 11, 2008, is available on Avinash’s web site from the Videos, Podcasts, and Media Mentions page.  The podcast is 24 minutes long.

Download the podcast here (5 MB).

The main point of the speech was to highlight the city’s efforts to bring accessibility to city services using internet-based technologies, including making the city’s famously successful 311 service available through Skype and Twitter.  According to the Mayor, “the more accessible we make it [city government], the more accountable we make it.”

Of note to the Web Analytics community, the Mayor also mentioned that the city is going to be taking to look at the traffic to the site for the purpose of changing the “site architecture” to improve the online experience for city residents.

The Mayor also announced that the city is going to partner with Google to study “anonymous usage data” to “allow us to optimize the content on the web site based on what people are most often searching for.”  Could it be that NYC.gov is implementing Google Analytics?

It is great to hear that New York is committed to both using cutting-edge technology to connect to its residents (Twitter, Skype, whatever Google technology they are using) as well as optimizing user experiences so that New Yorkers have a more positive and effective experience dealing with their government online.

Given the amount of social media participation with the 2008 election, and the subsequent appointment of Vivek Kundra as our country’s first CIO, I expect that there will be increasing pressure on government agencies to not only move services online (for example, see DC’s API’s), but do so in a way that is most useful to those agencies’ constituencies.

An excellent, and timely, resource that examines the state of Web Analytics in the public sector is the recently-released Web Analytics Asoociation’s Public Sector Survey Analysis.  The study describes and analyzes survey data from web managers within government, academic, foundation, non-profit, and other public-sector focused organizations.  The report describes the web analyst roles, metrics, and optimization strategies of these organizations.

The Web Analytics Association is a great resource to organizations and people in the public sector seeking to make their sites more useful to their constituents and (perhaps more importantly) developing and fostering a culture of analytics within their organizations’ web management.

As an aside, I am currently attending the PDF conference.  If you read this post and are at the conference, I would love to have an opportunity to meet you.  Email me tom*at*tomsanalytics*dot*com or DM me on Twitter @tomsanalytics.

Did I mention that I am afraid of heights?

MarketingSherpa just published a free mini-case study that looks at how Mountain Hardware was able to successfully launch a social network as a supplement to their website.  The article can be found at When a Blog Isn’t Enough: Expanding Customer Interaction with a Branded Social Network.

Although the article is light on details and doesn’t contain any numbers regarding ROI, it does highlight some key practices that are in line with my personal experiences in building social networks:

Find a good partner: Mountain Hardware teamed up with White Horse, a digital marketing agency out of Portland.  Judging by their methodology, creative samples (located at the tail of the article and worth reviewing), and reported outcomes, this firm clearly understands how social media works and was able to translate the culture of the Mountain Hardware brand to an extended online audience.

Don’t reinvent the wheel: Mountain Hardware used Ning as their main social networking platform (See the actual site here).  They also leveraged an existing blog, Facebook page, YouTube Channel, Twitter account, and Flickr stream to promote and reinforce the Ning-based social network.  There was no reason for Mountain Hardware, to develop, host, and maintain a codebase that drives a social networking platform.  With Ning, Mountain Hardware has an out-of-the-box solution that is easy to customize, popluate with content, promote, and leverage all of their exisiting online resources.

Spend a few bucks promoting the network: “If you build it, they will come” is not a viable social media strategy.  However, building a highly-targeted PPC keyword campaign to topics and sites that you know your core visitors follow is a cheap and easy way to seed your network.  I like how the article implies that Mountain Hardware used keyword marketing to promote properties outside of their own web site – more organizations should do this!

Content development is more about process than anything else: I’m sure Mountain Hardware generates a lot of content from their product managers, internal bloggers, marketing/PR people, and all of their other external communicators.  Combine this with content that they generated as part of the social network launch and the question becomes one of organization, timing, and flow.  Making sure that content gets placed on appropriate venues and cross-promoted is something that takes a bit of project management.

Email: Mountain Hardware recognized that their email was key component of establishing its online community.

So read the article, check out their properties and creative samples, and get a feel for the mechanics of launching a social network.  Now if only they would publish some statistics that illustrate the impact of this intiative…

Twitter gets bought or enters into a major partnership with another firm this Summer (2009), probably by the end of July.

Why? Not monetization, but infrastructure.  They can’t keep the lights on because their platform doesn’t scale.  You can’t hang ads on the Fail Whale.

Who? Obvious money goes on Google.  Nobody does infrastructure as well as they do.  I think a partnership with LinkedIn would also make sense, but then again, my perspective is off because I use Twitter professionally much more than I use it personally.

I know that I am about the 10,000th blogger to speculate on this, but I want to be able to point to this post in the coming weeks when Twitter sells out.

The title of my session is “Social Media Analytics: Monitor, Measure and Manage Your Reputation on the Wild Wild Web of Social Media.”  I will be sitting on a panel with the following people:

Kate Kaye, Clickz (Moderator)
Peter Corbett, iStrategyLabs
Stan Magniant, Linkfluence
Joe Mansour, David All Group

I am extremely excited about our potential discussion since we are all hands-on practitioners with different perspectives and experiences related to Social Media and politics.

My session runs from 2:00pm – 3:00pm on Tuesday, April 21st.  The conference runs from Monday, April 20 through the 21st in the Reagan Building – hopefully I will see you there!

Last week, Devaney was before Congress touting the demand for this information.  His testimony was picked up by several news outlets, including these stories at CNN, Federal Computer Week, and the L.A. Times.

In his testimony, he described traffic levels at Recovery.gov to be about “4,000 hits per second” (emphasis mine).  This means that in a day, the site would get about 350 million hits per day (4,000 * 60 seconds/minute * 60 minutes/hour * 24 hours per day).  To most people, this would imply a traffic level equivalent to every internet user in the United States visiting the site more than one time, every single day.  However, this statistic is misleading.

A “hit”, commony confused with a “page view” or a “visit”, is a single HTTP request from a web server to a browser for a specific resource.  HTML pages, images, flash objects, CSS sheets, external scripts, and other other files requested by a browser to render a page each generate a single hit upon their request.  The net effect is that single pages can generate several hits for the display of a single page to an individual user.

In the case of Recovery.gov, the home page loads 30 files to fully render.  If the 4,000 hits statistic is accurate, then there are really about 11.5 million page views per day.  Assuming that the average visitor views 5 pages on the site (which is a big assumption), the true number of visits to the site is more like 2.3 million per day, or about 27 new visits per second.

27 is a lot less impressive than 4,000, but it may be a much better picture of the true nature of traffic to the site.

Twitter’s back-end system allowed an unlimited number of attempts and also allowed such a weak password, so Twitter’s development team is partially to blame.

A share of the blame should also go to Twitter’s web analysts. Much like you should set up some type of custom reporting based on server error messages (404′s and 500′s), setting up an alert system and analytics on administrative systems, particularly when it comes to access to those systems, should be a priority for the launch of any site.

However, the majority of the blame should be paced at the feet of the administrator.  Whoever did this is not alone.  A number of years ago I was involved in a site security audit where we essentially ran the following SQL statement (it wasn’t this simple, but you get the idea):

select password, count(password)
from users
group by password
order by count(password) DESC

The results of this on the site (and, I imagine every site) were shocking.  The top positions were held down by passwords such as “password”, “12345″, and the names of cartoon characters.

Factoring in proper nouns, capitalization differences, and prefixes and suffixes, there are only a couple of hundred million passwords that are based on the English language. If the hackers were able to brute-force attack the form at the rate of 10 tries per second, they could exhaust every iteration of every word in English in a month.

If you look at using random letters, numbers, and symbols in a password, the possibilities balloon. A six-character password of this type has over 200 BILLION combinations. At the same rate, it would take a brute-force attack over TEN YEARS to exhaust the possibilities. An eight-character password would take over fifty thousand years! So you can see the power of strong passwords.

To avoid a screw-up of such magnitude with your personal and professional applications, try the following two sites to generate strong passwords (and avoid ones that are actual words):

Automated Password Generator Online: This allows you to set criteria for your potential passwords such as length and character set.

GRC’s Ultra High Security Password Generator: This site generates three types of truly random strings with each page refresh. It also goes into the math of 512-bit encryption.

Good luck and stay safe!

Why am I lucky enough to have an eMetrics in my city: Washington, DC!

I love DC.  I am lucky enough to live in one of the greatest neighborhoods in one of the greatest cities in the world.  However, the point of this article isn’t to discuss life in DC, it’s to make the argument that DC is the best possible location for the eMetrics Marketing Optimization Summit.  The reasons why:

It is easy to get here. And not just for me.  There is great transportation infrastructure in this city.  You can take a train from New York or Boston, a direct flight into National Airport from nearly every regional airport in the States, and direct flights from the Americas and every major European city into one of our three airports – National, Dulles, or BWI.

It is easy to get around once you are here. The Metro trains that service the city and close suburbs are clean and inexpensive.  We have approximately two taxis per resident in the city (I made that up, but we do have more taxis per capita than any other major American city).

We have tons of hotel options. Since the summit is in late October this year, you‘ll have a wide variety of options.  If you have the means, stay at the Willard Intercontinental – it is worth every penny.

We have tons of conference hosting options (that aren’t the Hilton Alexandria Mark Center). DC hosts lots of events and hosts them well.  From the smallest trade show to a multi-million attendee Inauguration party, we’ve got events covered.

We have fantastic opportunities for extra-Summit activities. Most of my best conference experiences have come outside of conference venues.  We have great bars and world-class restaurants.  The Smithsonian museums and the National Zoo are free.  If you come to DC and don’t take advantage of these “touristy” activities, you are truly missing out.

But most importantly, there are some great people that attend eMetrics in DC. DC is a city of networkers.  Attending eMetrics in DC, you will have the opportunity to meet people that work for trade associations, lobbying firms, political organizations, interactive agencies, e-commerce companies, NGO’s, non-profits, social networks, government agencies (just don’t ask them about third-party cookies!) and web analytics vendors and consultants.  Meeting this wide variety of people at eMetrics who are enthusiastic practitioners of their particular craft and are willing to share their experiences in a collaborative manner makes the summit a great experience, year after year.

It’s true: a big reason why the conference is so good is because it is here.

I’m curious, so I give the number on the site a call. To my surprise, a man named Carlos answered. I spent the next several minutes trying to figure out what this is all about. Highlights from our conversation:

When I asked to talk to the “internet“, he patiently explained that the
internet was really a global network of computers. Silly me.

He works in a call center with “thousands” of others.

He offered to describe a site for me. When I gave him a URL of another blog,
he described it as a “bunch of pictures of elderly ladies eating hamburgers,
some in restaurants, some at home, some in their cars”.

This could be some type of local art project, like PostSecret, or it could be the nascent stages of some viral marketing campaign. If this is performance towards some marketing aim, it could get very expensive as it scales up. If the campaign gains traction, the number might generate thousands of calls per hour during peak times. Would it be worth the call center expense? Could this ever generate a positive ROI for any marketer?

Along those lines, was the Subservient Chicken worthwhile? That campaign is considered one of the most successful viral internet campaigns ever, generating north of 20 million visits. Doing a back-of-the-envelope calculation, even if Burger King spent $500k on the campaign (they probably spent more like $100k), their cost-per-visit is still only two and a half cents. It doesn’t take too many extra hamburgers sold to make up the costs of the campaign; any analyst should be able to pair web traffic data with product survey data to quantify this impact.

I agree with Avinash in that Bounce Rate is one of the most important metrics for any type of site. A high bounce rate negatively affects Time Spent on Site in ways that may not be immediately apparent; have a look at my article on Life Tables and Time Spent on Site to see how. However, we need to take a closer look at our data collection methodology before we assign too much importance to this metric.

Bounce Rate Defined

The classic definition of a Bounce is a visitor that views a single page and then immediately leaves without viewing any other pages. A site’s Bounce Rate is the ratio of single page visits by the total number of visits. Anil Batra defines it (in his post “Bounce Rate Demystified“) this way as well, but with an added definition adding people that visit a single page, but exceed some predefined time limit on that page.

Problems with Bounce Rate

There are potential problems with Bounce Rate in that it is not consistently defined/recorded by analytics packages, it is not applicable to certain type of sites, and there is potential for the metric to be over-emphasized, particularly by those who are not aware of the shortcomings of their own analytics methods.

Bounce Rate in an E-commerce Site

As an example, if I were optimizing an e-commerce site, bounce rate would be very important to me on a number of levels. Presumably, I ensure that both my site’s homepage and sub-pages (perhaps individual products) would be indexed by search engines and other product search aggregators. I would also market my site through keywords purchases and banner advertising. I would want to closely examine individual page bounce rates to see which sites are most inviting to people to remain on the site and presumably stay on a path to a purchase. I would also closely look at the bounce rate of visits generated by each of my keywords; a high bounce rate indicates that I may want to look at spending my money on keywords that are more in-line with my site’s offerings.

Bounce Rate in a Content Site

If I were optimizing a site with a “walled garden”, a subscription-based content site, I would be much less concerned about a high bounce rate as it is defined now. Bounces to login pages would be expected, these could be attributed to visitors without access. Bounces from individual article pages could be attributed to subscription users that have individual articles bookmarked or users that email articles to another subscriber.

If I were optimizing an open content site, such as this blog, I would not have the tools at my disposal to make any conclusions based on Bounce Rate. Visitors to individual article pages come from search engines, RSS readers, social networking sites, and linkage from other blogs. A bounce from any of these sources may not be a bad thing – I want my visitors to read a single article if that is what they are seeking. The problem with our classic definition of a bounce is that there is no difference between a visitor that spends a minute to read an article and a visitor that clicks into the blog and leaves within five seconds.

A Solution for Google Analytics Users

One (messy) workaround for Google Analytics users is to simply call the urchinTracker function after a certain delay (perhaps 15 seconds). This will count another pageview for the visit, but no longer count that visit as a bounce.

The Future of Bounce

Unless we change our methodology for measuring bounces, bounce rate will cease to be a “sexy” (or even relevant) metric for most sites due to increasing popularity of social networking, RSS, and alternate browsers such as mobile browsers, televisions, and other types of syndicated content. For example, I regularly read “newsy” blogs such as TechCrunch and Engadget. However, my path to TechCrunch is my Google Homepage >> Techcrunch and my path to Engadget is Google Homepage >> Digg >> Engadget. My personal overall bounce rate to all three of these sites is probably 95%. Deep-linking and social news will continue to drive Bounce Rates up (and the overall relevance of Bounce Rate) unless we change the way we collect and interpret bounce data.

Blogs Cited in this Post

I highly recommend Avinash’s blog, Occam’s Razor and Anil’s blog, Web Analysis, Behavioral Targeting and Advertising. Check them out.

Some Background on Life Tables

The Life Table is a tool typically used to analyze and predict patterns of mortality among populations. The most familiar measure that gets generated by a Life Table is Life Expectancy. Life Expectancy is always great fodder for news headlines; at the time of this article’s publication, the Centers for Disease Control and Prevention just announced that Average Life Expectancy for people in the U.S. is nearly 78 years. This statistic was calculated using a Life Table.

A Life Table works by taking an imaginary population cohort (typically 100,000) and “aging” them through their lives by using currently estimated Age-Specific Death Rates. Why do this to calculate life expectancy rather than looking at vital statistics (for example, averaging the age of death of everyone that dies in a given year)? Because our population is influenced by generational patterns of growth and is not uniform. There are a lot more baby boomers and children of baby boomers than people born during the depression or during the “baby bust” (like me) that followed the baby boom. These age-specific population differences would skew Life Expectancy if calculated using the average age of death low now (since the boomers are entering their retirement years) and would skew it high in twenty years, when the likelihood of their mortality most increases.

Life Tables and Time Spent on Site

Using the Life Table technique to examine Time Spent on Site is fairly straightforward. Take a look at this table and/or follow the link to the Google Spreadsheet below it.

View/Download this Spreadsheet

Working left to right, fill in the first two columns with your period bounds. In my (completely made up) example, I use minute-by-minute data to “age” the site visitors unitl there are none. Pay special attention to the first row, which are reserved for bounces. Next, fill in the period-specific site exit rate in the fourth column for each period. Again, the first row is reserved for your bounce rate.

From here, simple calculations take you home. First, calculate the visitors left on site (starting with 100,000 in the first row and subtracting visitors that left during the previous period) and visitors exiting site (calculated by multiplying visitors remaining by the current period-specific exit rate) for each period, working your way down the table until there are zero visitors left. Next, calculate the total time on site for that period by multiplying the length of the given period by the visitors remaining on site, then subtracting half of the product of the length of the given period and the number of visitors exiting the site. Accounting for people exiting the site in this manner assumes that people in each period leave the site at a uniform rate and leads to a calculation error. The scale of this error is in proportion to the size of the periods being measured.

The final two columns are where the great value in this method come to light. The first of the two, total time on site, is calculated by starting at the bottom and creating a cumulative total for all minutes spent on site in the current period and all subsequent periods. The final column, average time spent on site for everyone in this period and beyond, is calculated by dividing the total time on site by the number of visitors remaining for the current period.

Interpreting the Results

Looking at the final column, representing the total average time spent on site for this period and beyond, will give great insight into your time spent on site metrics. As you can see from my example (and has a similar pattern in real life tables with infant mortality rates), bounces have a big effect on time spent on site. In my example, the average time spent on site is about .50 minutes (which will exactly match the metric reported from my analytics software). However, factoring out bounces, you can see that the average time spent on site for a non-bounce user is 1.24 minutes. Visitors that come to my site and stay for 3 minutes will, on average, stay an additional .60 minutes.

With the growth of web video, AJAX-based site structures, and other new technologies, Time Spent on Site is growing in importance. Perhaps this method of interpreting visitor behavior can help some people understand this metric a bit better.