Some of you may have noticed that I don’t blog as much as some of my colleagues (not to mention any names, but this one, this one, or this one). The main reason is that I’m a total nerd (just ask my wife), but in a way that is different from most analytics professionals. I don’t spend all day in the data – I spend all data writing code. And it’s often hard to translate code into entertaining blog posts, especially for the folks that tend to spend a lot of time reading what my partners have to say.

But I recently came upon a fairly broad topic that I think will lend itself to a nice series of posts, on something that I continually see confuse analysts and developers alike – cookies! Over the next few weeks, I’m hoping to dive into cookies, how they’re used in web analytics, and specifically how the 2 web analytics tools you probably use the most (Adobe Analytics and Google Analytics) use cookies to identify visitors to your website. There are subtle differences in how these tools identify website visitors, and I’ve found that most people really don’t understand those differences very well.

What is a cookie?

From the most reliable and trusted source on the Internet, a cookie is defined as:

a small piece of data sent from a website and stored in a user’s browser while the user is browsing that website

I disagree with a few key points in that definition:

1. A cookie can be sent from a website (i.e. a web server), but it can also be set or manipulated in the browser once the page has loaded in the browser and communication has more-or-less stopped with the server.
2. Cookies are stored in a user’s browser – but the duration of this storage can (and usually does) last beyond the time the user browses that website.

So let’s start by redefining what a cookie is – or at least my opinion of what a cookie is: A cookie is a small piece of data about a website, as well as the user browsing that website. It is stored in the user’s browser for an amount of time determined by the code used to set the cookie, and can be set and modified either on the server or in the browser. There are several important attributes to a cookie:

• A name: This is a friendly name for the cookie, usually reflecting what data it is storing (like a username, or a preferred content language).
• A value: This is the actual data to be stored, and is always a string – but it could contain a numeric string, or JSON data converted to a string, or any type of data that can easily be converted from a string to some other format.
• An expiration date/time: This is a readable UTC date string (not a timestamp). If this date is omitted, the cookie expires when the browser closes (i.e. a session cookie). If the date is included, the cookie is persistent.
• A domain: This is the website in which the cookie has scope (meaning where it can be read and written). For example, on my blog I can set a cookie that has scope of analyticsdemystified.com or josh.analyticsdemystified.com. I cannot set a cookie that has scope of adam.analyticsdemystified.com, or for any other website.
• A path: Once the domain has been specified, I can also choose to restrict it to a certain path or directory on my website – like maybe only anything in /products. The path is rarely specified.
• Security and other access controls: These include whether the cookie can be read on both secure and non-secure pages, and (in newer browsers) whether a cookie should be readable by JavaScript. These settings are also rarely used.

 A day in the life of a cookie

You may use a tool like Firebug to inspect your cookies, and be familiar with seeing cookies represented like this:

However, you may be less familiar with what a cookie actually “looks” like. But it’s just a string that is saved as a text file somewhere in your browser’s application data (you can find these text files if you search for where your browser’s cache is located on your computer). For the same example above, the text string looks something like this:

So lets take a deep dive into this particular cookie and how it ends up being available for your analytics tools to use. This cookie gives me a unique user ID on my blog. It is set to expire on the last day of 2014, and has scope anywhere on my blog domain. But what led to this cookie showing up in the list of cookies in my browser?

First, a cookie must be set. This can occur on the web server that responds to your request for a given page – or it can occur with JavaScript code setting a new cookie once the page is rendered in your browser. The code setting the cookie must specify all the details above.

Now, every time I request a new page on my blog, this cookie will be sent back to the server with that request. The server may modify it, and then the cookie will be available to the browser with the rest of the page. Any JavaScript loaded onto the page can then read it, overwrite it, and do whatever it wants to it. But if I leave and go to Eric’s blog, my cookie will not be sent anymore – it will stay stored in my browser, waiting until New Years’ Eve, just hoping that I come back.

This issue of cookie scope is a particularly tricky topic, so let’s take a closer look. Because I set my cookie with the scope of josh.analyticsdemystified.com, it will be included any time one of my blog posts is served. But if I set it with the scope of webanalyticsdemytified.com, it will be included for any of my partners’ posts as well – because we all share that top-level domain. I cannot set my cookie with any other scope – I can use my subdomain or the top-level domain – but other subdomains or other domains (like google.com) are not allowed. And my code will never be allowed to read cookies on those other domains, either. The same is true of the “path” component of a cookie as well – so you can see how path and domain combine to limit which cookies a a site can read, and which ones it cannot read. They essentially act as a bouncer, controlling which parts of this big party called the web my site has access to.

First-party vs. Third-party

Another major issue of confusion when dealing with cookies in web analytics is the idea of first-party vs. third-party cookies. A first-party cookie simply means that the domain of the cookie matches the domain of the website in the browser. In the example above, a cookie whose domain is josh.analyticsdemystified.com or analyticsdemystified.com is first-party on my blog. It will be included in all requests to the server, and returned to the browser where JavaScript can use it.

However, the average page normally includes many assets – like images or scripts – that are hosted on a different domain than my site. For example, my blog loads the jQuery library from Google’s CDN, ajax.googleapis.com, and our Google Analytics tracking requests are sent back to http://www.google-analytics.com. Any cookies my browser has for either of those domains (note: if you look, you will not see any – this is just an example) will be sent back to each of those servers, because they have scope there – but when the requests come back to my browser, the page waiting to receive the assets has a different domain – so it can neither read  from nor write to any of those cookies. They are what we call third-party.

Third-party cookies have a bad reputation, because some companies use them maliciously, and also because it can be annoying to have cookies you never asked for cluttering up your browser cache and passing data back to places you never knew about. But in most cases, cookies – even third-party cookies – contribute to making the most positive user-experience possible. However, because maintaining privacy is important to everyone, most modern browsers offer users the ability to choose whether third-party cookies should be allowed or not. Most browsers allow third-party cookies by default, and most users never modify those settings. But if third-party cookies are disabled, it means that the server will try to set a cookie, and the browser will immediately reject or delete it. And even when third-party cookies are allowed, they can never be used by the site in the browser – nor can the third-party site read any first-party cookies, either. For example, when this page loaded, a cookie was set by disqus.com, the tool we use for comments and discussions. My blog cannot read or modify that disqus.com cookie – and disqus.com can’t read any analyticsdemystified.com cookies, either. Developers reading this post are now shaking their heads, as there are obviously exceptions to this – but most readers probably aren’t interested in cross-site scripting and other hacking techniques that are the exceptions to this rule.

Some browsers like Safari even allow you to select an option that says “Only accept cookies from sites I visit,” which blocks some, but not all, third-party cookies. For example, when I worked at salesforce.com, http://www.salesforce.com was the entry point to all our websites, but we had other websites (like http://www.force.com) that we linked to on that website. Because http://www.force.com included images from http://www.salesforce.com, those cookies with http://www.salesforce.com scope would not be deleted, even though the domain didn’t match that of the page in the browser – because I had previously been to that site. The browser used my history as an implicit acceptance of those third-party cookies. However, even though the cookies would not be deleted, they couldn’t be used by http://www.force.com, either.

I often have clients ask me how they can take advantage on one site of a cookie they set on another one of their sites. Unfortunately, even though they “own” both sites, and the same developers manage both sites, there is no way of doing this without taking the risk that those cookies will be deleted by your users blocking third-party cookies. There are some “hacks” to accomplish this, but they rarely outweigh the risks you’re taking.

Conclusion

Hopefully, you now understand the basics of cookies a little better – because I’ll be back soon with a closer look about how analytics tools make use of them. The next topic I plan to address is how Adobe and GA use cookies to identify your visitors. Then, the concluding topic in this series will be about Adobe’s new Marketing Cloud visitor ID, and how it differs from the other ways Adobe has historically managed visitor identification.

(The following is a guest post from Wendy Greco, General Manager of our Team Demystified business unit. You can meet Wendy and learn more about Team Demystified at our ACCELERATE conference in Atlanta, Georgia on September 18th — learn more about ACCELERATE and register today!)

When Eric Peterson asked me to lead Team Demystified a year ago, I couldn’t say no! Having seen how hard all of the Analytics Demystified partners work and that they are still not able to keep up with the demand of clients for their services, it made sense for Analytics Demystified to find another way to scale their services. Since the Demystified team knows all of the best people in our industry and has tons of great clients, it is not surprising that our new Team Demystified venture has taken off as quickly as it has. As a reminder, the purpose of “Team Demystified“ is for Analytics Demystified to help its clients add web analysis and technical resources to current Analytics Demystified projects by employing qualified and proven independent contractors known to the Demystified partners.

So far, our clients have loved working with Team Demystified. They get the opportunity to accelerate projects by adding additional qualified resources who they can trust due to the oversight of Analytics Demystified partners.  Team Demystified resources are currently managing global Adobe Analytics rollouts at leading brands, helping interpret Google Analytics and Adobe Analytics data for top retailers and helping implement web analytics and tag management tools across large enterprises.

At the same time that our clients are loving our Team Demystified resources, our team members are also benefiting from being part of the Analytics Demystified family. First and foremost, our Team Demystified resources get to work with the partners at Analytics Demystified. Whether it is learning SiteCatalyst from Adam Greco, testing tools from Brian Hawkins or web analysis strategies from Michele Kiss and Tim Wilson, Team Demystifiers get to learn from the best in the business on a daily basis. In addition, all Team Demystifiers were given an all expense paid trip to Portland, Oregon where they attended a full two day training from many of the Analytics Demystified partners so they could build up and round out their web analysis skills.

In addition to classroom training, Team Demystified members participate in a weekly Google hangout in which they get to learn from each other and share tips and best practices. On a monthly basis, we provide a special “brown-bag” lunch and learn Google hangout in which Analytics Demystified partners or Team Demystifiers can present a topic that relates to their expertise. We also have an internal collaboration tool that allows Team Demystifiers to post any questions they have and get answers from other team members and/or Analytics Demystified partners. Finally, all of our Team Demystifiers are receiving an all-expense paid trip to Atlanta to attend our upcoming ACCELERATE conference and some are even presenting at the conference!

As you can see, we are investing heavily in Team Demystified and believe that it is both great for our clients and for the contractors who join us as well. In fact, we don’t think there is any other opportunity in the web analytics field that compares to Team Demystified for those who want to learn as much as possible about the web analytics industry, while also having the freedom and flexibility that comes with working as an independent contractor.

Our model has been so successful with our clients that our biggest roadblock is finding more qualified contractors to join our team. Therefore, if you would like to learn more, we are actively looking for US-based talent in the following areas:

• Front-End Developers who are well-familiar with analytics tags, tag management, and popular coding platforms
• Analysts and Senior Analysts, skilled with either Google Analytics, Adobe Analytics, or both
• Analytics Managers, with demonstrated experience growing analytics teams of their own

If you fit this role, even if you are not actively looking now, I would love to talk to you. You can contact me directly via email (wendy@analyticsdemystified.com) and I can explain more about how Team Demystified works.  Thanks!

Having worked as an industry analyst back in the day I still find myself interested in what the analyst community has to say about web analytics, especially when it comes to vendor evaluation. The evaluations are interesting because of the sheer amount of work that goes into them in an attempt to distill entire companies down into simple infographics, tables, and single paragraph summaries. Huge spreadsheets of data, long written answers, and multiple calls and product demos … all munged down into a single visualization designed to tell the large Enterprise which vendors to call and which to avoid.

In the early days of web analytics having access to these evaluations could be a huge time-saver. At the time there were dozens of vendors all embroiled in a battle for market-share, and so the vendor summary provided an “at a glance” view of the landscape that had the potential to save the Enterprise time and money. Plus, during the early growth period in web analytics, no one vendor had hegemony over the market and so any errors or inconsistencies in the results could easily be swept under the rug based on this being “an emerging market …”

Today, however, the web analytics market is functionally mature, and two vendors have emerged as “market leaders” based on their particular strengths and business models. I don’t even have to tell you who these vendors are; if you work in this industry or you are paying any level of attention to the technology landscape, you already know who they are … and who they are not … which brings me to the main topic I wanted to discuss:

The most recently published Forrester Wave on Web Analytics (Q2 2014) authored by James McCormick is wrong.

You can get a free copy of this report from Adobe, and I would encourage you to have a look yourself, but based on hundreds of implementations, vendor evaluations, RFP processes, and thousands of hours of work on our part, the Partners at Analytics Demystified and I can assure you that only one of the vendors dubbed a “leader” in this document is truly leading in the market today. Additionally, another vendor labeled a “strong performer” has consistently demonstrated more leadership and commitment to digital analytics than any of the vendors evaluated.

[At this point you may be asking yourself “why isn’t he naming names?” … which is a fair question. The old me was kind of a dick; the new me is trying to be less of a dick. I suspect that I am doing a poor job at that, but I am trying …]

I would encourage you, if you are interested, to review the scoring for the Wave reported in Figure 3 on page 9 … and ask yourself “do these results and, more importantly, these weightings, make sense?” For example:

• A zero weighting for “Market Presence” … despite the fact that two vendors have an increasing lock on the market in 2014, especially when you look at wins and losses in the last twelve months.
• The “Product” and “Corporate” strategy … which to me seem arbitrary at best, reporting that Google’s product and corporate strategy is “average” while that of a company that is on their third CEO and umpteenth head of Marketing is second only to A) a true market leader who is tied with B) a behemoth who is buying great companies but struggling to retain key employes who truly understand the market.
• “Application usability and administration” … reporting that again Google is behind a vendor who has not updated their core analytics application for an estimated ten years.
• The inclusion in the report of not one but two vendors whose names have not come up in Enterprise web analytics circles for years …

Take a look when you have a chance and see what you think. Maybe I’m the one who is wrong, and perhaps after 100+ collective years in this industry it is my Partners and I who have completely lost our connection to the web analytics vendor landscape …

…

At Analytics Demystified we rather enjoy the mature technology market we are working in today. With our clients increasingly standardizing on one, the other, or both of the true market leaders, our ability to move beyond the technology to how the technology is used effectively and efficiently in the business context is made that much easier. When analytics is put to use properly … good things happen.

I welcome your comments and feedback.

I had the pleasure last week of visiting with one of Analytics Demystified’s longest-standing and, at least from a digital analytical perspective, most successful clients. The team has grown tremendously over the years in terms of size and, more importantly, stature within the broader multi-channel business and has become one of the most productive and mature digital analytics groups that I personally am aware of across the industry. Their leader has the attention of senior-most stakeholders, all the way up to the company’s CEO, and her evangelism has led to the widespread acceptance of the inherent value of digitally collected data to the broader business, both online and off.

A true success story … but not one that came easily.

While much has been said about “maturity” in the digital analytics industry, my Partners and I have long been skeptical about this term and it’s application. That isn’t to say we don’t believe that maturation happens … but rather that it isn’t something that can be forced. Yes, companies can make better or worse decisions about where to invest their time and money when it comes to analytics and optimization, and yes, having a written plan governing this decision making process is a tremendous help, but nearly all of our experience over the past fifteen years — including the past five in partnership with the aforementioned client — leads us to believe that certain milestones simply need to happen over time and cannot be avoided, accelerated, or otherwise forced.

Why do we believe this? Experience.

In the past three years we have seen amazing things. We have watched an organization, largely recognized as being “the best of the best” in analytics crumble under it’s own weight; we have worked with one of the best recognized software companies in the world to make fundamental (even simple) analytical decisions; we have helped billion dollar digital organizations add hundreds of millions of incremental dollars through testing … and watched other, similarly sized companies fail to take advantage of even the most rudimentary analysis solutions.

Through all of this work — and trust me, with nearly 100 clients worldwide, the previous list only touches the tip of the iceberg — three things have stood out:

1. Leadership counts. Perhaps the single most clear differentiator between “the best” and “all the rest” has been the quality, character, and experience of the day-to-day leader of a company’s analytical efforts. This differentiator cuts across dozens of dimensions — hiring and team development, evangelism up and down in the org, critical examination of analytical output, you name it … your organization is going to be massively more successful with digital analytics if you have an experienced resource that leadership trusts to produce insights and recommendations (as opposed to data and information.)
2. You need to have a plan. While cynics will accuse me of being self-serving in this regard given that I have built a multi-million dollar consultancy based almost exclusively on the creation and adherence to a strategic plan for analytics, the proof is clear. Our clients (and other companies) that approach analytics and optimization armed with a clear and concise plan to drive understanding, adoption, and use of digital insights are far more successful than those who still incorrectly believe that “web analytics is easy” and that analysis will simply happen if the tools are provided.
3. Your analysts are your greatest asset. Even if you have an amazing plan and a great leader for analytics, if you aren’t able to hire, train, and retain great analysts you will still be dead in the water. Tons has been written about the advantage that bright, articulate, passionate analysts and optimization specialists confer to the Enterprise, and the importance of finding the right talent has become so paramount that Analytics Demystified has started actively helping our clients hire digital analytics and optimization specialists. We have long said that web analytics is about “people, process, and technology” … and there is a reason we mention “people” first.

The last point brings me back around to the title of my post: the 80/20 Rule for Analytics Teams.

Great analysts, unsurprisingly, love to analyze data. I am honored to know some of the best analysts in the industry, and I can say with absolute certainty that few work-related things please them more than having the time to hunker down and leverage the available data to produce impactful recommendations. Yes, they will produce reports; yes, they will explain the Adobe Analytics UI for the umpteenth time; and yes, they will drop what they are doing to get you that “one number” you need for a presentation due to your boss … in an hour. But that is not what they love to do, and that is not their passion.

Their passion is analysis.

The problem with this fairly obvious: within most companies there simply aren’t enough analysts to meet the ever-expanding data and information needs of the business. Even in companies that are well-staffed, while great analysis and recommendations are frequently produced, more often than not the output is constrained by either time, specific business need, technology limitations, or all of the above. So we are closer … but we are not there yet.

In thinking about this I was reminded of a program that Google has or had: their “20% time.” Basically the opportunity for programmers to spend twenty percent of their time — a day a week — working on whatever they thought might be good for the business. I’m not sure how much value this effort delivered back to Google and their share-holders, the idea that staff could be trusted to take initiative and focus on opportunities that they believed could be valuable is brilliant (and the program certainly gathered press and accolades for Google.)

What if you gave your analysts the same trust and freedom that Google gave their engineers, only with a few more parameters … what do you think would happen? What if you told your Senior Analysts and Analytics Managers that they were free to spend 20% of their time producing analysis that they thought could benefit the business? And what if you gave them a venue to present this information so that, if their analysis was robust and their recommendations solid, the analysis would make its way up the ranks?

Think about that for a minute.

While not easy to pull off both from a logistical and resource-allocation perspective, I personally think that giving analysts “20% time” has potential that is three-fold:

1. It would create very happy, engaged, and loyal analysts. Remember: analysts love to produce analysis. By taking the constraints of time, business need, and technology off the table and simply saying “provide analysis that you believe can practically and reasonably help drive the business forward” you are turning your team loose to do the thing they love (and potentially helping the business at the same time.) Happy analysts, in turn, help with recruiting and retention — both of which are challenging to say the least.
2. It would further reinforce the value of digital data to the broader business. Readers are well aware of the value that digitally-collected data has to their companies, both online and off, but the same cannot be said for the majority of most companies. Especially in multi-channel and traditional offline organizations, web data is new, confusing, and often suspect. By giving your best analysts additional opportunities to use said data to help improve the overall business you logically increase the visibility and awareness of digital analytics across the Enterprise in it’s “most valuable” form (e.g., insights and recommendations.)
3. It would provide a unique, data informed view of the business. Analysts usually have a very unique perspective on how the business is run given that A) they don’t typically “belong” to a single business unit and B) they are trained to be objective whenever possible. Over the years I have seen amazing analyses produced by digital analysts who aren’t constrained by programs that have been planned, monies that have been committed, or “the way we have always done things.” By giving your analysts the opportunity to take a step back and leverage their knowledge of the business informed by the available data … you might be surprised by what you learn.

Now yes, the devil is in the details. Carving out one day per week and having your analysts work on “whatever” has the potential to slow down projects and further strain resources, giving analysts carte blanche to suggest changes to infrastructure and long-term business plans has the potential to backfire, and given that the analysis would not originate with the business, serious thought would need to be given to the way the insights were socialized.  Still:

• By carving out time for analysis … you are further reinforcing the need to create valuable work product (versus the “spreadsheets and data” output that is so common …)
• By removing barriers … you are increasing the odds of finding insights that have the potential to truly move the needle (versus small, incremental wins and losses …)
• By creating new venues to present analysis … you are both further demonstrating the value of digital analytics and giving your analysts additional experience presenting to leaders

Honestly this isn’t that radical of an idea; it is likely your best analysts have been producing independent analysis all along … they just haven’t had any formal way to share what they have learned with the rest of the business.

So what do you think?

As always I welcome your thoughts and comments. Have you tried something like this in the past? Are you an analyst who doesn’t get nearly enough time to produce recommendations and insights? Do you think this idea is great or simply awful?

Last week at our annual ACCELERATE conference Analytics Demystified announced our new “Team Demystified” web analytics and optimization staffing and contractor services offering. In a nutshell, “Team Demystified” is a response to the profound unmet need for experienced analytical professionals within our client base, a need that we have repeatedly been asked to help fulfill. And while this need is nothing new — staffing is one of the oldest problems in the digital measurement and optimization space — Analytics Demystified has patiently waited to help resolve the challenge until we were confident that we had a value-added way to do it.

When we asked our clients why it was so difficult to hire we consistently heard two things:

1. Internal HR departments didn’t have the knowledge, time, and depth of network to find truly qualified individuals
2. External recruiting firms had lists of individuals who look good on paper but often lack real experience in the field

Based on this we realized that we already had the solution in place:

• We have the world’s largest network of analytics and optimization professionals thanks to our longstanding investment in Web Analytics Wednesday, Analysis Exchange, the Digital Analytics Association, ACCELERATE, and the Web Analytics Forum at Yahoo! Groups
• We don’t lack for qualification when it comes to carefully vetting talent, thanks to the experience of the Demystified Partners and Senior Partners

Still, just knowing people and being able to carefully vet them didn’t seem like enough … and so we put our thinking caps back on and worked out figure out an approach that would truly differentiate our staffing efforts.

“Team Demystified”

At the end of the day we determined that we didn’t really have an appetite for creating another “find ’em and forget ’em” FTE placement and contractor model — the norm in the industry today where follow-up contact with placed resources is little more than “call me when you’re ready for a new gig so I can make some more money off of you.” Instead we set out to create a program that continually created value for both our clients and “Team Demystified” associates … the result includes:

• The ability to work side-by-side with Analytics Demystified Partners and Senior Partners at amazing clients
• Ongoing communication with Analytics Demystified Senior Partners to ensure quality work and analyst development
• Weekly check-ins with Demystified staff to continually monitor for focus, adherence to detail, and overall excellence
• Monthly face-to-face’s with Demystified Senior Partners to continue to develop Team resources capabilities and competencies
• Invitations to our annual ACCELERATE conference and a special annual “Team Demystified” education day
• Invitations to many more special opportunities Analytics Demystified has access to in the industry

In short, the “Team Demystified” effort allows us to find the best talent in the industry, place them with our already great clients, and have them work side-by-side with Analytics Demystified on forward-thinking analytics and optimization projects. We believe this approach is truly unique and we love that we can deliver value to both sides of the equation simultaneously.

You can learn more about Team Demystified on the new Analytics Demystified web site.

Recently I have been exploring the world of “self quantification”, using tools like Jawbone UP, Runkeeper, Withings and more to measure, well, myself. Living in a tech-y city like Boston, I’ve also had a chance to attend Quantified Self Meet Ups and discuss these topics with others.

In a recent post, I discussed the implications of a movement like self quantification on marketing and privacy. However, it’s easy for such conversations to to stay fairly simply, without necessarily addressing the fact that privacy is not an all or nothing: there are levels of privacy and individual permissions.

Let’s take self quantification as an example. On an on-going basis, the self quantification tools I use track:

• My every day movement (steps taken, as well as specific exercise activities)
• Additional details about running (distance, pace, elevation and more)
• Calorie intake and calorie burn
• Heart rate, both during exercise (via my Polar heart rate monitor or Tom Tom running watch) and standing resting heart rate (via my Withings scale)
• Weight, BMI and body fat
• Sleep (including duration and quality)

That’s a ton of data to create about myself every day!

Now think about the possible recipients of that data:

• Myself (private data)
• My social network (for example, my Jawbone UP “team” can see the majority of my data and comment or like activities, or I can share my running stats with my Facebook friends)
• Medical professionals like my primary care physician
• Researchers
• Corporations trying to market to me

It’s so easy to treat “privacy” as an all or nothing: I am either willing to share my data or I am not. However, consumers demand greater control over their privacy precisely because there are different things we’re willing to share with different groups, and even within a group, specific people or companies we’re willing to share with.

For example, I may be willing to share my data with my doctor, but not with corporations. Or I may be willing to share my data with Zappos and Nike, but not with other corporations. I may be willing to share my running routes with close friends but not my entire social network. I may be willing to share my data with researchers, but only if anonymised. I may be willing to share my activity and sleep data with my social network, but not my weight. (C’mon, I won’t even share that with the DMV!)

This isn’t a struggle just for self quantification data, but rather, a challenge the entire digital ecosystem is facing. The difficulty in dealing with privacy in our rapidly changing digital world is that we don’t just need to allow for a share/do not share model, but specific controls that address the nuance of privacy permissions. And the real challenge is managing to do so in a user-friendly way!

What should we do? While a comprehensive system to manage all digital privacy may be a ways off (if ever), companies can get ahead by at least allowing for customisation of privacy settings for their own interactions with consumers. For example, allowing users to opt out of certain kinds of emails, not just “subscribe or unsubscribe”, or providing feedback that which targeted display ads are unwelcome, or irrelevant. (And after you’ve built those customisation options, ask your dad or your grandma to use them to gauge complexity!)

Want to hear more? I have submitted to speak about these issues and more at SXSW next year. Voting closes EOD Sun 9/8, so if you’re interested in learning more, please vote for my session! http://bit.ly/mkiss-sxsw

Likely you have noticed that Analytics Demystified has grown significantly in the past twelve months, adding Kevin, Michele, Josh, and Tim to the team … and we’re ready to grow our resources again. We have a handful of clients who need help — both technical and analytical — and so we are actively looking for talented individuals who would like to work side-by-side with our team at some of the most amazing brands on the planet.

Immediate needs include:

• Front-End Developers who are well-familiar with analytics tags, tag management, and popular coding platforms
• Analysts and Senior Analysts, skilled with either Google Analytics, Adobe Analytics, or both
• Analytics Managers, with demonstrated experience growing analytics teams of their own

Plusses include the obvious: Excel skills, presentation chops, experience with testing and optimization platforms, and at least three years of hands-on work in the analytics industry. Willingness to travel is a big plus … but not a hard-and-fast requirement for several of the opportunities we have.

Compensation is commensurate with experience. Benefits include regular contact with the entire Analytics Demystified team, discounts to ACCELERATE and other conferences, and more!

If you are interested — or you know someone who is — please have them email me directly. All conversations will be treated as highly confidential.

At the intersection of fitness, analytics and social media, a new trend of “self-quantification” is emerging. Devices and Applications like Jawbone UP, Fitbit, Nike Fuel Band, Runkeeper and even Foursquare are making it possible for individuals to collect tremendous detail about their lives: every step, every venue visited, every bite, every snooze. What was niche, or reserved for professional athletes or the medically-monitored, has become mainstream, and is creating a wealth of incredibly personal data.

In my previous post, I discussed what this kind of tracking could teach us about the practice of analytics. Now, I’d like to consider what it means for marketing, targeting and the privacy debate.

Implications for marketing, personalisation & privacy

I have argued for some time that for consumers to become comfortable with this new data-centric world, they need to see the benefits of data use.

There are two sides to this:

1. Where a business is using consumers’ data, they need to provide the consumer a benefit in exchange. A great way is to actually share that data back to the consumer.

Some notable examples:

• Recommendations: “People who looked at Product X also looked at Product Y”, as seen on sites like Amazon.com.
• Valuation and forecasts: Websites like True Car, Kelley Blue Book and Zillow crunch data from thousands of transactions and provide back to consumers, to help them understand how the pricing they are looking at compares to the broader market.
• Credit scores: Companies like Credit Karma offer a wealth of data back to consumers to understand their credit and help them make better financial decisions.
• Ratings and Reviews: Companies like CNet inform customers via their editorial reviews, and a wealth of sites like Amazon and Newegg provide user ratings to help inform buying decisions.

2. Outside of business data, consumers’ own collection and use of data helps increase the public understanding of data. The more comfortable individuals get with data in general, the easier it is to explain data use by organisations. The coming generations will be as fluent with data as millennials today are fluent with social media and technology.

This type of data is a huge opportunity for marketers. Consider the potential for brands like Nike or Asics to deliver truly right-time marketing: “Congratulations on running 350 miles in the last quarter! Did you know that running shoes should be replaced every 300-400 miles? Use this coupon code for 10% off a new pair.” Or McDonalds to use food intake data to tell them that 1) The consumer hasn’t yet eaten lunch (and it’s 30mins past their usual lunch time), 2) The consumer has been following a healthy diet and 3) The consumer is on the road driving past a McDonalds, and promote new healthy items from their menu. These are amazing examples of truly personalised marketing to deliver the right offer at the right time to the right person.

However, it is also using incredibly personal data and raises even newer privacy concerns than simple online ad targeting. Even if a marketer could do all of that today, the truth is, it would probably be construed as “creepy” or, worse, a disturbing invasion of privacy. After all, we’re not even comfortable sharing our weight with the DMV. Can you imagine if you triggered a Weight Watchers ad in response to your latest Withings weigh-in?!

So how must marketers tread with respect to this “self-quantification” data and privacy?

1. We need to provide value. This might sound obvious – of course marketers need to provide value. However, I would argue that when consumers are trusting us with what amounts to every detail of their lives, we must deliver something that is of more value to the consumer than it is to us. This all comes down to the idea of marketing as a service: it should be so helpful you’d pay for it.

2. There has to be consent. This technology is too new, and there are too many concerns about abuse, for this to be anything but opt-in. (The idea of health insurance companies rejecting consumers based on lifestyle is a typical argument used here.) If marketers provide for (and respect) opt-in and -out, and truly deliver the right messaging, they’ll earn the right to broaden their reach.

3. It requires crystal-clear transparency. Personalisation and targeting today is already considered “creepy.” Use of this incredibly personal data requires absolutely transparency to the end user. For example, when being shown an offer, consumers should know exactly what they did to trigger it, and be able to give feedback on the targeted message.

This already exists in small forms. For example, the UP interface already gives you daily “Did you know?”s with fun facts about your data vs the UP community. Users can like or flag tips, to give feedback on whether they are helpful. There has to be this kind of functionality, or users only option to targeting will be to revoke access via privacy settings.

4. We need to speak English. No legalese privacy policies and no burying what we’re really doing on page 47 of a document we know no one will ever read. Consumers will be outraged that we didn’t tell them the truth about what you were doing, and we’ll never regain that trust.

5. We have to get it right. And by that, I mean, right by the consumer’s perspective. There will be no second chances with this kind of data. That requires careful planning and really mapping out what data we need, how we’ll get consent, how we’ll explain what we’re doing and ensuring the technology works flawlessly. Part of the planning process has to be dotting every i and crossing every t and truly vetting a plan for this data use. If marketers screw this up, we will never get that permission again.

This includes getting actual consumer feedback. A small beta release with significant qualitative feedback can help us discover whether what we’re doing is helpful or creepy.

6. Don’t get greedy. If marketers properly plan this out, we should be 100% clear on exactly what data we need, and not get greedy and over collect. Collecting information we don’t need will hurt opt-in. This may involve, for example, clearly explaining to consumers what data we collect for their use, and what we use for targeting.

7. Give users complete control. This will include control over what, of their data, is shared with the company, what is shared with other users, what is shared anonymously, what is used for targeting. There has to be an exhaustive (but user friendly) level of control to truly show respect for informed and control opt-in. This includes the ability to give feedback on the actual marketing. Without the ability to continually tell a business what’s creepy and not, we end up in a binary system of either “consenting” or “not”, rather than an on-going conversation between consumer and business about what is acceptable.

Think about the user reaction every time Facebook changes their privacy policy or controls. People feel incredible ownership over Facebook (it’s “their” social world!) even though logically we know Facebook is a business and does what suits their goals. The tools of the future are even more personal: we’re talking about tracking every minute of sleep, or tracking or precise location. This data is the quantification of who we are.

With opportunity comes responsibility

This technology is an amazing opportunity for marketers and consumers, if done well. However, marketers historically have a habit of “do first, ask permission later.” To be successful, we need to embark on this with consumers’ interests and concerns put first, or we’ll blow it before we even truly begin.

At the intersection of fitness, analytics and social media, a new trend of “self-quantification” is emerging. Devices and Applications like Jawbone UP, Fitbit, Nike Fuel Band, Runkeeper and even Foursquare are making it possible for individuals to collect tremendous detail about their lives: every step, every venue visited, every bite, every snooze. What was niche, or reserved for professional athletes or the medically-monitored, has become mainstream, and is creating a wealth of incredibly personal data.

These aren’t the only areas that technology is creeping in to. You can buy smart phone controls for your home alarm system, or your heating/cooling system. “Smart” fridges are no longer a crazy futuristic concept. Technology is creeping in to every aspect of our lives. This can be wonderful for consumers, and a huge opportunity for marketers, but it has to be done right.

In this series of blog posts, I will explore what this proliferation of tools and data looks like, how it relates to analytics, and what it means for marketing, targeting and the privacy debate.

What Self-Quantification Teaches Us About Digital Analytics

Since April, myself and a surprising number of the digital analytics community have been exploring devices like Jawbone UP and Fitbit. Together with apps and tools like Runkeeper, Withings, My Fitness Pal, Foursquare and IFTTT, I have created a data set that tracks all my movements (including, often, the precise location and route), every workout, every bite and sip I’ve consumed, every minute of sleep, my mood and energy levels, and every venue I’ve visited.

Amidst the explosion of “big data”, this is a curious combination of “big data” (due to the sheer volume created from multiple users tracking these granular details) and “small data” (incredibly detailed, personal data tracking every nuance of our lives.)

Why would one go to all this trouble? Well, “self-quantifiers” are looking to do with their own “small data” exactly what we propose should be done with “big data”: be better informed, and use data to make more educated decisions. Over the past few months, I have found that my personal data use reveals surprisingly applicable learnings for analytics.

Learning 1: Like all data and analytics, this effort is only worthwhile and the data is only valuable if you use it to make better decisions.

Example: My original reason for trying Jawbone UP was for insight into my sleep patterns. Despite getting a reasonable amount of sleep, I struggled to wake up in the morning. A few weeks of UP sleep data told me that my current wakeup time was set right in the middle of a typical “deep sleep” phase. Moving my wakeup time one hour earlier, meant waking in a lighter phase of sleep and made getting up significantly easier. This sleep data wasn’t just “fun to know” – I literally used it to make decisions, with positive results.

Learning 2: Numbers without context are useless.

Using UP, I track my daily movements, using a proxy of “steps.” Every UP user sets a daily “step goal” (by default, 10,000 steps.) Without a goal, 8,978 would just be a number. With a goal, it means something (I am under my goal) and gives me an action to take (move more.)

Learning 3: Good decisions don’t always require perfect data

Steps is used as a proxy for all movement. It’s not a perfect system. After all, it struggles to measure activities like cycling, and doesn’t take into account things like heart rate. (Note though that these devices do typically give you a way to manually input activities like cycling, to take into account a broader spectrum of activity.)

However, while imperfect, this data certainly gives you insight: Have I moved more today than yesterday? How am I tracking towards my goal? Am I slowly increasing how active I am? Did I beat last week? Good decisions don’t always involve perfect data. Sometimes, good directional data and trends provide enough insight to allow you to confidently use the data.

Learning 4: Not all tools are created equal, and it’s important to use the right tool for the job

On top of Jawbone UP, I also heavily use Runkeeper, as well as a Polar heart rate monitor. While UP is great for monitoring my daily activity (walking to the store, taking the stairs instead of the escalator), Runkeeper gives me deeper insight into my running progress. (Is my pace increasing? How many miles did I clock this week? What was my strongest mile?) UP and Runkeeper are different but complementary tools, and each has a purpose. Which data set I use depends on the question at hand.

Learning 5: Integration is key

One of things I enjoy the most about UP is the ability to integrate other solutions. For example, Runkeeper pushes information about my runs to UP, including distance, pace, calorie burn and a map of the route. I have Foursquare integrated via IFTTT (If This Then That) to automatically push gym check-ins to UP. Others have their Withings scale or food journals integrated.

Depending on the question at hand, UP or Runkeeper might have the data I need to answer it. However, there’s huge value for me in having everything integrated into the UP interface, so I can view a summary of all my data in one place. One quick glance at my UP dashboard tells me whether I should rest and watch some TV, or go for an evening walk.

Learning 6: Data isn’t useful in a vacuum

The Jawbone UP data feed is not just about spitting numbers at you. They use customisable visualisations to help you discover patterns and trends in your own data.

For example, is there a correlation between how much deep sleep you get and how many steps you take? Does your active time align with time taken to fall asleep?

While your activity data, or sleep data, or food journal alone can give you great insight, taking a step back, and viewing detailed data as a part of a greater whole, is critical to informed analysis.

The bigger picture

In the end, data analysis is data analysis, no matter the subject of the data. However, where this “self-quantification” trend really shakes things up is in the implications for marketing. In my next post, I will examine what the proliferation of personal data means for targeting, personalisation and the privacy debate.

I am delighted to announce three big additions to the Analytics Demystified family today! The first is our newest Partner and lead for tag management, platforms, and technology, Mr. Josh West. Josh is an incredibly experienced developer and has been working in the digital measurement space for years, both at Omniture and more recently Salesforce.com. Josh adds additional depth to our expanding team, complimenting all of our work, and Josh will be working directly with Adam Greco, John Lovett, and I on a variety of custom analytics and integration projects. More importantly, Josh will be be the Demystified lead for tag management system (TMS) vendor selection and integration projects, adding capacity in what is one of the hottest and most active components of Demystified’s business.

We will be adding Josh’s blog and content to the web site in the coming days, and of course you will be able to meet Josh in person at the Analytics Demystified ACCELERATE event in September in Columbus, Ohio.

Secondly, we are excited to announce that we have become certified partners with both Adobe and Google, adding to our existing agreement with Webtrends. Both companies are giving us great access and insight into their analytics and optimization product families, and we are delighted to be formalizing such great, long-standing relationships. Additionally, we are joining Google’s Premium Analytics reseller program, allowing our firm to be even more creative in how we help Enterprise-class clients make the switch to Google’s most powerful analytics solutions.

You can read the press release about Josh West and our expanded partnerships here. If you have any questions about Josh or either partnership, please don’t hesitate to reach out to me directly.

Is your organisation struggling to see the value of digital analytics? Feel like there are a ton of numbers but no tie to business success? Before you throw out your vendors, your existing reports, or your analysts, stop and ask your leaders the following question:

“What makes our digital customer interactions successful?”

For example:

• What outcomes make a visit to our website “successful”? Or in other words: What do we want visitors to do?
• What interactions make a download of our app “successful”? How would we like users to engage with it? Do we want them to use it every day? Or do we want long periods of engagement, even if they are less frequently? Is there particular content we want them to use within the app?
• What objectives are we accomplishing with our Facebook page or Twitter account that make them “successful”? Why are we even engaging with customers on social media, and what do we want to get out of it?

That’s not to say there is only one behaviour that defines a success. In fact, there are many, and businesses that interact with all kinds of customers create the need for different measure of success.

In a B2B environment, a “successful visit” for a new customer might be one in which they submitted a contact request. For an existing customer, a “successful visit” might be one in which they found the answer to an issue in your support section. For a content site, a visit might be successful if they read or share a certain number of articles. A CPG business may want visitors to research and compare their products. A successful visit to a restaurant’s website might be one in which a visitor searches for a location.

So if your business is not yet measuring successful customer interactions, how can you start? First, gather your major stakeholders. In a working session, ask for their input on:

• Why does your website / mobile experience / social media presence / etc even exist?
• If we took down the website / our mobile app / stopped engaging in social tomorrow, what would we be losing? What could customers not do, that they can do today?
• If a visitor came in and performed only one behaviour on the site, what would you want it to be?
• If visitors suddenly stopped doing one thing on the site that spelled disaster, what would that be?

What you’re ultimately looking for is, “Why are we doing this, and what will make our business stakeholders happy?” Approaching it from this standpoint, rather than “What goals should we configure in Google Analytics?” allows for critical business input, without getting buried in the technical details of creating goals or setting success events. Once you have this, you have clear objectives for digital analytics to measure against.

This morning we are very excited to announce that Analytics Demystified has partnered with Webtrends as a consulting and development partner on their recently announced Webtrends Streams platform. You can read all the details in the official Webtrends press release, and I contributed a lengthy post to the Webtrends blog that details why I am so excited about Streams and what we are able to do with it.

In a nutshell:

• At Analytics Demystified we are increasingly seeing clients integrating disparate data as a rule, not an exception, in their reporting and analysis;
• Because of the disparate and rapidly evolving nature of the connected world, this integration at times becomes complex to the point of being absurd;
• Experience has shown that “traditional” analytics platforms do a reasonably poor job handling new data (e.g., mobile app data, social data, etc.);
• I personally do not believe that this pace of change will abate — we will only have “more data” coming from “more devices” from this point forward.

Given all of this, for the past few years I have been on the lookout for a truly robust “generic” data collector — a device that would allow us to tag anything and that would deliver that data to us in a reasonably fast and programatic way. Essentially a log file for, well, everything digital … web sites, mobile apps, social interactions, geographic locations, in-game actions … even turning up the heat in your house or shutting off your lights when you’re not home.

I have seen many solutions that were close … some very close … but I think that Webtrends has solved the problem with Streams.

When you first see Webtrends Streams you’ll think “oh, yeah, real-time data … I have seen that before … it’s useless.” It turns out that the most interesting thing about the platform is not the real-time nature of Streams; that is really more of a “nice to have” than the core value proposition. Also, Webtrends Streams is not for everyone. If you’re not using the analytics you already have with any level of proficiency to create tangible business value … well, you’re probably better off focusing on that first.

But if you’re like an increasing number of Analytics Demystified clients, and if you’re ready to start really pushing the envelope with what you’re able to do with the multiple, disparate data your business is inevitably generating, we’d love to discuss Webtrends Streams.

(I will be in San Francisco later this month and if you’d like a live demonstration of the product email me directly and we can set up a time to meet.)

On behalf of the rapidly growing team at Analytics Demystified I wanted to wish all of my clients, readers, and friends a Happy New Year! I say rapidly growing because 2012 saw unprecedented growth for the Demystified team:

• In February we added Brian Hawkins to the team to build out our Testing, Optimization, and Personalization practice. Testing has long been a fundamental component of our strategic client engagements, and adding Brian to the team has allowed us to take our support for optimization to entirely new levels. With nearly a year with Demystified under his belt, the one thing that strikes me as most impressive about Brian (and client’s clearly agree) is how he never runs out of great ideas for testing! Check out Brian’s blog to see what he is up to …
• In September we added Kevin Willeitner to the team to build out our Implementation and Systems Integration practice. One of the most common requests we had from clients following our strategic work has been “can you help us implement your recommendations?” While we have worked in the past with a variety of third-parties and partners, at the end of the day what clients were saying was “can YOU help us …” — it turned out more than anything clients were looking for the seniority and stability that Analytics Demystified has long stood for in the analytics industry. When I realized this, I immediately went and hired the best systems integrator I knew, Kevin Willeitner, and not to brag, but Kevin recently helped one of our clients deploy Google Analytics Premium via Ensighten across nearly 50 brand, mobile, and social sites … all in less than 45 days. See what Kevin is thinking about these days …
• In December, again responding to key strategic clients telling us they were tired of working with junior, unskilled, and inexperienced consultants who ended up creating as many problems as they solved, we added Michele Kiss to the Demystified team to build out our Analysis and Analyst Support practice. Michele is a certified analytics industry rock-star, often referred to as “the voice of analytics,” and has an amazing breadth of experience as an end-user client and consultant. Check out Michele’s blog and learn more about our newest Partner …

Adding Brian, Kevin, and Michele to the team, augmenting the amazing work that John, Adam, and I have long been doing for clients, really amounts to a shift in what Analytics Demystified is able to do for our clients. Whereas in the past we have had to rely on other firms, partners, and vendor resources … now clients are able to form a strategic partnership with Analytics Demystified and work with the most experienced consultants in the industry, exclusively. In the short span of 12 months we have gone from a largely strategic firm, providing oversight over dozens of moving parts, to a single-source provider of the deepest body of analytics expertise available in the industry today.

Pretty cool, at least for our clients.

I sincerely hope that your 2012 was as amazing, productive, and exciting as mine was, and that you are similarly excited about what 2013 will bring. If you have any questions about Brian, Kevin, or Michele, or how the entire Analytics Demystified team might be able to help you expand your use of analytics, don’t hesitate to reach out and we can set up a time to talk.

Back in 2007, on the subject of the evolution of the web analytics industry, I proffered that “If Web Analytics 1.0 was all about measuring page views to generate reports and define key performance indicators, and if Web Analytics 2.0 is about measuring events and integrating qualitative and quantitative data, then Web Analytics 3.0 is about measuring real people and optimizing the flow of information to individuals as they interact with the world around them.”

At the time I was thinking about the onset of digital ubiquity — an “always on” Internet that followed us everywhere we went and more or less knew where we were. Given the explosion of mobile devices and our near universal dependence on smartphones, location-based services, and digital personal assistants, the following comment seems almost quaint:

“Just think for a minute about how your browsing experience might change if the web sites you visited remembered you and delivered a tailored experience based on your demographic profile (theoretically available via your phone number), your browsing history (accurate because you’re not deleting your phone number) and your specific geographic location when you make the request?”

Essentially I envisioned a future where anonymous log files gave way to massive data stores that, given much of the data would be flowing from mobile devices that we kept on us at all times, would form a far more complete picture of each of us individually than Web Analytics 1.0 or 2.0 could ever hope to support. What’s more, when subject to enough processing power and computational wizardry, this data would support previously unimaginable levels of micro-targeting and content personalization, possibly knowing more about us than our own loved ones.

At the time I recall having conversations with one particularly smart individual who argued that this would never happen — that phone manufacturers and phone and Internet service providers would never allow this type of information to be used, much less in a commercial context. His argument was that this would be such an egregious violation of consumer privacy that, were this to happen, the government would inevitably step in and, fearing ham-fisted meddling by “luddite politicians” (his words, not mine), industry leaders would come together and attempt to offer at least some level of consumer protection, even if it would negatively impact their business models.

Turns out we were both right.

What I referred to as “Web Analytics 3.0” is clearly the collection, analysis, and use of what is more commonly referred to as “Big Data” — an incredibly powerful source of information about consumers that can be used in an almost endless number of ways to power our new data economy. And, thanks to some spectacular mis-steps on the part of organizations, groups, and companies who should know better, “Big Data” is increasingly subject to regulation.

In the past few days, the California Attorney General has announced that she has the agreement of six of the largest mobile platform providers — Google, Apple, Amazon, HP, RIM, and Microsoft — to begin enforcing a law that calls attention to the use of consumer data in mobile applications. And, even more amazingly, the Obama administration has delivered a “Digital Consumer’s Bill of Rights” that has the major browser manufactures agreeing to quickly begin to support “Do Not Track” functionality designed to limit the flow and use of even anonymous web usage data in some instances.

Clearly, both of these announcements are good for consumers, who will hopefully be better protected from bonehead moves like sending entire address books insecurely up to cloud-based servers. And clearly both of these announcements are good for legislators, who during an election year will have something positive to talk about, at least with the majority of their constituents.

But where does this leave you, the digital measurement, analysis, and optimization worker?

More or less in the same place we were back in December 2010 when this all first came up, on the brink of a sea-change in web analytics, but one that I’m confident that most of us can handle. While I still believe that web analytics is hard — perhaps more so than ever — I’m also confident that individuals who are truly invested in making informed decisions based on the available data will be just fine.

Still there are unknowns and subsequently risk coming down the pipe through the President’s “Bill of Rights.”  Some things that I am particularly interested in knowing include:

• Who decides which technologies will be subjected to browser-based “Do Not Track” directives?
• Will “blocked” technologies be universally blocked? Or, like in P3P, is their a continuum of requirements?
• Will “blocked” technologies be blocked across all participating browsers? Or will browser vendors decide individually?
• Will “blocked” sessions be identified as such? And if so, will some minimal data still be available?
• How will the Bill of Rights “guarantee” data security, transparency, respect for context, etc. as outlined by the President?

I suspect the answers to most of these questions are still being discussed.  Still, the ramifications are important and there is an awful lot of conflict of interest inherent in the browser vendor’s participation.  For example, if you’re Google and have made a pretty significant investment into Google Analytics, what is your motivation to block analytics tracking in your Chrome browser? Or perhaps you’re Microsoft and you have multiple initiatives to improve the quality of search and display advertising — all of which depend on some level of data collected via the browser — are you willing to prevent all of that in Internet Explorer?

It will be interesting to watch this play out.

For what it’s worth, at Analytics Demystified we have been thinking about the explosion in digital data collection and consumer privacy for a pretty long time. Going all the way back to that 2007 post on Web 3.0, and rolling forward to our work on the Web Analyst’s Code of Ethics and more recently our GUARDS Audit (with BPA Worldwide), Analytics Demystified strongly believes that consumer data is a valuable asset, one that needs to be treated with the upmost respect.

To that end, if your legal team or senior leadership are asking you about the data you collect and how you might be exposed based on how that data is being secured and used, you might be interested in Analytics Demystified GUARDS. In a nutshell, GUARDS is a comprehensive audit of your digital data collection landscape performed by auditors from BPA Worldwide designed to help leadership understand what data is collected, where, why, and how that data is being secured and ultimately used.

Either way, my partners and I at Analytics Demystified will be keeping a careful eye on this Bill of Rights, changes in the mobile data collection landscape, and the application of Do Not Track across modern browsers. I welcome your comments and feedback.

A recurring theme in our strategy practice at Analytics Demystified is one of data quality and the businesses ability and willingness to trust web analytics data. Adam wrote about this back in 2009, I covered it again in 2010, and all three of us continue to support our client’s efforts to validate and improve on the foundation of their digital measurement efforts.

Not that I am surprised — far from it — given that the rate at which senior leadership and traditional business stakeholders have been calling us to help get their analytical house in order. It turns out management doesn’t want to “get over” gaps in data quality; they want reliable numbers they can trust to the best of the company’s ability to inform the broader, business-wide decision making process.

To this end, and thanks to the generosity of our friends at ObservePoint, I am happy to announce the availability of a free white paper Data Quality and  the Digital World. Following up on our 2010 report on page tagging and tag proliferation, this paper drills into the tactical changes that companies can make to work to ensure the best possible data for use across the Enterprise. In addition to providing ten “tips” to help you create trust in your online data, we provide examples from ObservePoint customers including Turner, TrendMicro, and DaveRamsey.com, each of whom have a great story to tell about data auditing and validation.

One surprise when doing the research for this document was that multiple companies cited examples of something we have coined “data leakage.” Data leakage happens when business users, agencies, and other digital stakeholders start deploying technology without approval and, more importantly, without a clear plan to manage access to that technology. Examples are myriad and almost always seem harmless — that is until something goes wrong and the wrong people have access to your web traffic, keyword, or transactional data.

The idea of data leakage is one of the reasons that we have teamed up with BPA Worldwide to create the Analytics Demystified GUARDS audit service, and unsurprisingly GUARDS audits include an ObservePoint analysis to help identify possible risks when it comes to consumer data privacy. You can learn more about the GUARDS consumer data privacy audit on our web site.

If you’re being asked about the accuracy and integrity of your web-collected data, if you know you cannot trust the data but aren’t sure what to do about it, or if you suspect your company may potentially be leaking data through tag-based technologies, I would strongly encourage you to download Data Quality and  the Digital World from the ObservePoint site. What’s more, if you need help reseting expectations about data and it’s usage across your business, don’t hesitate to give one of us a call.

Download Data Quality and the Digital World now!

You may have noticed I have been pretty quiet in my blog lately aside from sharing news about our ACCELERATE event in San Francisco in November. It’s partially because honestly I’ve been swamped with new clients, existing work, and the never-ending effort to be a good husband, dad, and friend in the midst of Demystifying web analytics …

But being busy is no excuse to stop sharing ideas and encouraging conversation so let’s dive into something that has increasingly become a pet-peeve of mine: the notion leveraging web analytics to create a “data-driven” business.

I’m sure I have used this phrase in the past in an effort to describe the transformation that companies need to go through in the digital world, relying less on “gut feel” and more on cold, hard data to guide business decision making. Hell, a lot of smart of people have, including Omniture’s Brent Dykes and Google Analytics Evangelist Avinash Kaushik who has gone so far as to describe creating a data-driven culture as the “holiest of holy grails.”

Becoming “data driven” is the way to silence the HIPPO and to more firmly establish the value of our collective investments in digital measurement, analysis, and optimization technology. It sounds great, except for one thing:

A “data-driven business” would be doomed to fail.

I think that perhaps what people mean when they talk about being “data-driven” is the need for a heightened awareness of the numerous source of data and information we have available in the digital world, enough so that we are able to take advantage of these sources to create insights and make recommendations. On this point I agree — better use of available data in the decision making process is an awesome thing indeed.

My concern arises from the idea that any business of even moderate size and complexity can be truly “driven” by data. I think the right word is “informed” and what we are collectively trying to create is “increasingly data-informed and data-aware businesses and business people” who integrate the wide array of knowledge we can generate about digital consumers into the traditional decisioning process. The end-goal of this integration is more agile, responsive, and intelligent businesses that are better able to compete in a rapidly changing business environment.

Perhaps this is mere semantics — you say “potato” I say “tuberous rhizome”  — but given the sheer number of consultants, vendors, and practitioners talking about creating, powering, and working in the mythical “data-driven business” I have started to worry that we’re about to shoot ourselves in the collective foot. We (meaning the web analytics industry as a whole) have done this before, first by claiming that web analytics was easy, then by insisting that cookies were harmless … and personally I’d prefer we avoid yet another self-imposed crisis of credibility if possible.

And while this may be semantics, I do disagree with Brent Dykes assertion that in the absence of carrot-and-stick accountability that web analytics breaks down and fails to create any benefit within the business, although I do understand fully where Mr. Dykes is coming from. I simply have not seen nearly enough evidence that eschewing the type of business acumen, experience, and awareness that is the very heart-and-soul of every successful business in favor of a “by the numbers” approach creates the type of result that the “data-driven” school seems to be evangelizing for.

What I do see in our best clients and those rare, transcendent organizations that truly understand the relationship between people, process, and technology — and are able to leverage that knowledge to inform their overarching business strategy — is a very healthy blend of data and business knowledge, each applied judiciously based on the challenge at hand. Smart business leaders leveraging insights and recommendations made by a trusted analytics organization — not automatons pulling levers based on a hit count, p-value, or conversion rate.

Kishore Swaminathan, Accenture’s chief scientist, in his discussion on “What the C-suite should know about analytics” outlines how an over-dependence on data can lead to “analysis-paralysis”, stating:

“Data is a double-edged sword. When properly used, it can lead to sound and well-informed decisions. When improperly used, the same data can lead not only to poor decisions but to poor decisions made with high confidence that, in turn, could lead to actions that could be erroneous and expensive.”

Success with web analytics and optimization requires a balance, and business leaders who will be successful analytical competitors in the future will need to develop a top-down strategy to govern how their businesses will leverage both digitally-generated insights and the collective know-how of their organizations. Conversely, being “driven” implies imbalance and over-correction — going out of your way to devalue experience, ignore process, and eschew established governance in favor of a new, entirely metrics-powered approach towards decision making.

You can do this, but to Swaminathan’s point, what if the numbers you’re using are wrong?

I think that creating a “data informed” business is a huge victory and for most companies a major step in the right direction. What’s more, working to create a “data informed” business shows respect for the hard work, commitment, and passion your employees have for their jobs and your company and products.

Rather than walk in and “embarrass the boss” with your profound and amazing knowledge of customer interactions, you can actively work with your management team by providing insights and recommendations that reflect your knowledge of how the entire business works, not just your amazing talent as web analytics implementer (or analyst, whatever …)

But I digress.

I’m interested in your collective thoughts here people. Am I over-reaching after a blogging hiatus and unnecessarily sniping in hopes of an early Fall dust-up in Google+? Or have you had the same thoughts and/or concerns, that by insisting that everyone needs to do exactly what the data tells them that we risk alienating (again) the very consumers of our efforts? Do you work at a truly “data driven” business and do what the numbers tell you each and every time? Or are you working to create a practice where otherwise smart, hard-working, and passionate marketers, merchandisers, and business leaders can benefit from the type of information and insights you are uniquely able to provide as a digital measurement, analysis, and optimization specialist?

While you consider your response I’ll leave you with a story that has shaped some of my thinking about web analytics over my career. Years ago my good friend Shari Cleary brought me into CBS News in New York to train her editorial team on Hitbox (yeah, Hitbox, I told you it was years ago!) Most of my clients at the time were “new school” but not these guys — they were hardcore news editors from the TV side of the business who had been tasked with making digital news work.

I talked and talked and talked about how powerful Hitbox was and how real-time analytics was going to power the content they put out there in the world. The editors were polite and showed real interest in the training until at one point the oldest and most grizzled of the group stopped me.

“Son, we’re not going to let the data make the decisions for us regarding editorial content,” he said with all sincerity. I was, of course, shocked to hear this — I mean, hell, that is what Hitbox was for! Figuring out which stories generated page views and which needed to be rolled off the page into obscurity.

“Umm, why is that?” I asked, figuring he’d lay into me about the inaccuracy of the system or how painful it was to use Hitbox …

“Because if we let the data drive editorial, all you will read about at CBS News is Paris Hilton’s breasts and Lindsay Lohan’s drinking problem.”

Needless to say, I stopped talking about real-time, data-driven changes to editorial content.

As always I welcome your comments, criticism, and feedback.

Now that summer is upon us I suspect that some of my personal blogging activity will slow down but I wanted to call my reader’s attention to three great jobs that our good friends at Best Buy just posted:

• Senior Analyst, Digital Analytics
• Associate Manager, Digital Analytics
• Manager, Digital Analytics

Those of you who were at Emetrics in San Francisco this Spring heard some of the story about the work we’ve been fortunate to help with at Best Buy. Those of you coming to Internet Retailer in San Diego on June 16th will get to hear a shortened version of the same story. If you can’t/didn’t make either event I am happy to put interested parties directly in touch with the hiring manager at Best Buy, email me directly for details.

If you are coming to Internet Retailer, come and hear Lynn Lanphier (Best Buy) and I tell their amazing story.