This is right out of the “why didn’t they think about this earlier” file, but makes perfect sense on the heels of Google integrating Google Analytics goals into the Google AdWords interface.  Google did right by me this time, recognizing another customer pain point: marketers and agencies not having the full suite of data available in Google Analytics reporting that is readily available in the Google AdWords interface.

The Good:

• Trended metrics straight out of Google AdWords, and not just one or two, but a whole heap of great metrics to trend.  My favorite right now is CTR versus Bounce Rate (below).

• Day-parting reports: True, this report could be generated the hard way by using Google Analytics Custom Reporting, but getting it for free is always nice.

• Destination URLs: All my landing pages in one report?  That’s super handy.  All the normal metrics available in Google Analytics for only those landing pages I’m using in AdWords.  Again, a brilliant one-click reporting destination for a standard report marketers should have created using “Custom Reporting”.

The Bad:

• Ugh, TV reports?  Jeez… talk about soft-sell marketing.
• Placement reports: Would love to be able to have a pop-out link to visit content network placement domains/URLs from within the interface.

The Ugly:

• Not sure why, but not all metrics seem to be up to date or accurate.  Realizing this is still “in beta,” core metrics such as spend should be relatively accurate.

This year should mark the beginning of a remarkable recovery in the world economy, and I find myself extremely fortunate to be counted amongst the hundreds of attendees at eMetrics Toronto 2010.  Having attended the show in 2008 but not last year, I witnessed a significant evolution and have to admit the folks at Rising Media have done a fantastic job of transforming what was once a much smaller, admittedly lack-luster conference into a major, must-see event.  Perhaps this is due to a natural maturation process, but I’d like to attribute this success to the increasing awareness and respect the online business community has amassed for web analysis.  The resounding theme of the show has evolved as well.  What once was “how do we do X” has quickly become “define goals, provide actionable insight, measure results”.  Whether through quantitative or qualitative means, the focus has become listening to the voice of the consumer.

Define Goals

Many panelists and speakers admitted that their organizations are still mired in poorly defined goals, however they failed to respect that the mission of a web analyst or business analyst is an evolutionary approach.  Avinash Kaushik explained it best when he described a “virtuous data quality cycle” in his first book Web Analytics: An Hour a Day.  The point is, you will have overall business impact goals, and ad hoc goals that spring to life, die off, change into other objectives, and that’s okay.  Some of the speakers and panelists said it best:

• Theresa Locklear, NHL.com:
  • Goals for analysts were clear, they wanted people to visit the video player more often, change channels more often, and share video with their friends.
  • Business goal is a bit veiled, but it is clearly to generate additional sales and ad revenue.
• Avinash Kaushik, Google:
  • “Most companies think social media is about shouting, this is a dumb ass strategy”
• Theresa Kushner, Cisco:
  • Our vision is planned 5-8 years out, strategy: 2-4 years, execution: 18 months.

Provide Actionable Insights

Great, we have all this data, but what does it mean?  At a minimum, what should distinct groups or areas of the website do differently? At a macro perspective, what are visitor behaviors and voice of customer telling us about what the business should do differently?  Here are some great nuggets of information mentioned at eMetrics about actionable insights:

• Avinash Kaushik, Google:
  • “Focus on outcomes, find out the ‘WHY’ in addition to the ‘WHAT’”
  • Put data in context, “context means comparison of both internal metrics and competitive intelligence”
  • Many first time visitors to landing pages don’t convert, so stop screaming at them, soften messages on PPC landing pages and build a better initial experience.
• Theresa Locklear, NHL.com:
  • Highlights are key, people love highlights so show more highlights
  • The Hockey Show isn’t generating much engagement, is costly to produce, and is a longer production piece which means less opportunity to run pre-roll advertising, consider dropping it
• Michael Helbling, Land’s End:
  • We look for smart lazy people, because smart lazy people automate things so they have time for analysis.
• Shari Cleary, MTV:
  • Don’t just send data, ask the question WHY and find the specific business requirements behind any request for data.

Measure Results

How can you determine whether your defined goals were met, or whether you were successful or not?  Results must be measured and compared to historical data or competitive intelligence.  What can you measure? Pretty much everything, so the question becomes what SHOULD you measure:

• Avinash Kaushik, Google:
  • “There is more data in tools than God wants you to have”
  • “I like Klout because it gives follower retweet %”
  • “I like analyzewords.com, every word you write gives you a window into your cognitive world, scary crap!”
  • RE: Paid analytics tools being very similar to free tools: “Flushing money down the toilet makes a cool sound”
  • “Don’t ignore the long tail or search, it can account for more than 7 times the visits versus top 10 keywords”
  • RE: Content strategy, “Consider % content per section versus % of visits, create what people wants”
• Theresa Locklear, NHL.com:
  • Use multiple data sources and categorize by source participation. First step is segmentation, second step is persona development (see pyramid graphic on the right)
  • “We considered internal production costs versus sell-through ROI when evaluating video content”
  • Analytics insights drive almost everything we do now, editorial content, PR functions, website development, video production, etc.
• Theresa Kushner, Cisco:
  • Be sure you know what you want to measure, gain insight through analysis, share those insights, encourage marketers to use that information.
• Shari Cleary, MTV:
  • There is a clear expectation from multiplatform males that they expect stuff like South Park on every screen/platform. Fueling a vertical strategy.

I figured that since today is April Fool’s Day and it’s also technically the last day of the work week, that I’d forgo the typical #ff (follow friday retweeting) and conduct a non-scientific survey on web analytics users on Twitter.  The question?  Not what their favorite tool of choice is, or how they measure social media — oh no — something much harder to describe.

“When you’re at a party, how do you describe what you do?”

The responses came in at a furious pace…

I spy on you when you’re online… (it makes for better conversation) Eric T Peterson, Web Analytics DEMYSTIFIED

I help companies measure the success of their website… Jim Sterne, Targeting.com

(Same thing I tell my mom:) I help people make better decisions… Avinash Kaushik, Occam’s Razor

Have you read 1984? (and people stop asking questions) Rudi Shumpert, Code by Numbers

I measure the efficacy of the campaigns I employ for our clients. Blake Robinson, blakerobinson.net

Digital marketing including web analytics… (then I get funny looks, to which I reply) …web stuff… Pritesh Patel, Digimarketing Convo

It’s a lot like an episode of 24, but less violent, and I’m way less cranky than Chloe O’Brien. (But that’s a lie, so then I say) I empower companies by giving them tools that make multi-channel customer data consumable and actionable. Kiran Ferrandino, measurethisgirl.com

I connect companies with their customers in a meaningful manner to empower stellar business decisions. (How you ask?) By combining behaviors and attitudes to produce profitable outcomes. Eric Feinberg

I aggregate user data for role identification and behavior analysis over a historical spectrum. (then I change the subject) Bryan J Bell, bjbgraphics.com

And me?

I improve websites and help companies make more money. Garry Przyklenk, Analytics-Advice.com

Ongoing debates surrounding the topic of “free news versus digital media pay walls” present an interesting opportunity for web analytics professionals.  If there’s one thing I learned during my stint in new media, it’s that news organizations have historically had a poor grasp on establishing sustainable revenue models to support substantial distribution costs and sizable staff counts.  I say “historically” because savvy media companies are becoming increasingly capable of correlating content consumption to revenue streams and optimizing their advertising models for greater ROI.  The latter have been forced to cut back on content acquisition or workforce, and even now consider the use of pay walls.  But that’s not necessarily a bad thing! For analysts, pay walls introduce a great opportunity to test the efficacy of both models.

Model 1: Ad-supported content

Needless to say, there are significant challenges facing media sites that operate on ad-supported content.  Recent months have given rise to the worst decline in media jobs the industry has seen in it’s history.  News rooms are cut to the bone, local affiliate stations are closing their doors, the public in general is changing the way it consumes the news — but I digress.  The main problem ad-supported sites face is correlating visitor engagement to true ROI:

• Integration of ad-serving platforms with web analytics systems may not be straightforward
• Costs associated with ad serving platforms are minimal and relatively fixed/known, but custom advertising sponsorship deals have many moving parts
• Publisher and advertiser goals may be in conflict, i.e. visitor engagement with a media site sells ads but visitor engagement with an advertiser reduces engagement
• Calculating true ROI involves knowing the operating costs of the content provided, including but not limited to manpower for article creation, multimedia distribution rights, bandwidth, etc.

Clearly, savvy media sites that are able to manage the complexities of calculating and optimizing user experiences and maintain positive net ROI can make this model work, however it takes a brilliant analytics ninja to implement the myriad of data required to make it truly sustainable.  Avinash’s 90/10 rule might have to be magnified a bit… maybe 98/2 would work?

Model 2: Pay walls

The reason pay walls are such an appealing model for large media sites is that they can reintroduce a subscription model akin to selling newspapers, magazines, and specialty channels on TV.  Logically, using a pay wall follows the age-old business model that built large media empires.  In addition, pay walls provide a number of measurement opportunities that seldom exist on media sites:

• Actual direct revenue numbers from paying customers, and optimizing landing pages and user experience based on dollars and cents. I.e. rather than loosely correlating advertising dollars to visitors, getting real-deal dollar numbers on specific users.
• Measuring distinct user behavior and profiling and recommending packages or custom subscriptions based on user preference or actual consumption. I.e. the iTunes approach; selling content per song, per episode, or per movie.
• Engagement essentially becomes less ambiguous, users are authenticated, they pay for access to certain assets, and a relationship is established.  Data comes to the rescue and can assist in decision making to establish and nurture that engagement.  I.e. visitors now have skin in the game and a voice.

Regardless of the approach, it appears as though several key media destinations will carry-out testing some kind of pay wall model this year.  It’s uncertain what they will be measuring and how sophisticated the approach to maximizing ROI will be, but given this approach, the barriers to testing should be significantly lower, if and only if these same companies abandon ulterior motives and truly adopt data-driven decision-making.

The key is that there is no key.  There is no right way, there is no magical bullet.  Each site’s audience will be different, each traffic segment will be different.

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I’m a big fan of Search Engine Strategies because it’s one of those shows that really draws the best and brightest in online marketing and web analytics.  My special guest today is Dennis Mortensen, former COO of IndexTools, currently the Director of Data Insights at Yahoo! and member of the Board of Directors at the Web Analytics Association.  Dennis is a frequent speaker at SES events, and will be co-hosting a panel entitled “Meaningful SEO Metrics: Going Beyond the Numbers” at SES New York.

Garry: Dennis, I’m so glad you could spare some time in your busy schedule to talk to me.  Lately, it seems many organizations are looking at web analytics and starting to appreciate that standard metrics are meaningless if not tied to business goals.  Calculating return on ad spend for pay per click efforts is relatively straightforward, however when focusing on social media and organic search efforts, marketers can lose track of what’s really important.  With all the data at our fingertips these days, whether it’s from Yahoo Web Analytics (YWA) or Google Analytics, it can be hard to avoid this “data paralysis”.  What are some of your favorite quick wins when it comes to insights associated to organic search?

Dennis: It of course depends on where in the organizational optimization cycle you are (alas how sophisticated an organic search marketer you are). But I believe applying context to traffic influx is important.

So when you think about a traditional organic search engine report (as provided by Yahoo Web Analytics or Google Analytics for that matter), you think of the search phrases as a dimension and perhaps visits (or similar) as a metric for success. As illustrated below:

This is not necessarily bad information, but I am not sure I would categorize it as insights. So it is exactly as you say, a potential “analysis paralysis” situation, where seemingly good information does little to help you.

I suggest, as a quick win, that you apply immediate context and value to your search phrases and use that as your standard organic search report.

Should you be an online retailer, I personally favor the below 4 metrics:

• Visits
• Visit to sale Conversion rate
• Average Order Value
• Revenue

Just by glancing over the two reports, both sorted on visits. Even with the unfair fact of you not knowing the site in question, the products or anything really – you are able to instantly create an attitude on the individual search phrases. The first issue (optimization opportunity) that comes to mind, to be specific, is “plumbing supplies”, which looked like my third most important keyword, but it does nothing for me, from a revenue point of view.

Garry: Great examples!  So, when you co-host the panel “Meaningful SEO Metrics: Going Beyond the Numbers” at SES New York, you’re probably going to be showing more examples like the ones above, right?  Can you give me a sneak peek at some of the talking points you’ll be covering?

Dennis: I’ll be doing a talk titled: Search Engine de-Optimization ..and the bogus celebration of yet another Google organic search lottery winner. It seems fair to debate whether or not search engines and other content aggregators extract too much of the webs value, leaving less for the content creators (originators) – if this is true, one should introduce tactics such as Search Engine de-Optimization to destress the dependency!

Garry: Oh-oh… you’re about to crush some worlds and blow some minds, aren’t you…

Dennis: This is a radical thought of not trying to move up the ranks on Google (perhaps even the opposite) – and should you think I am completely crazy, I believe it is worthwhile visiting as a thought experiment.

Take a look at the below two figures and try to come up with a reasoning why one is better than the other.

The attitude of more visits is always better, might not be as black and white as we would want to believe.

Garry: That’s a great point, and gives a little insight into the possible free content argument unearthed by the recent “News vs. Google” debate.  Food for thought.  From a e-commerce perspective, you could compare organic visits to repeat purchases, or average revenue per order, etc. The key is to measure and look beyond the basic metrics.

Thanks once again to Dennis Mortensen for stopping by and sharing a little wisdom.

If you haven’t done so already, it’s not too late to register for SES New York today.  New York plays host to possibly one of the largest gatherings of experts and attendees in online marketing, certainly not to be missed!

About Dennis R. Mortensen

Dennis R. Mortensen is a pioneer and expert in the Analytics industry. He is an accredited Associate Web Analytics Instructor at the University of British Columbia, the Author of data driven insights with Yahoo! Web Analytics, and a frequent speaker on the subject of Analytics, Media and Marketing. Mortensen is an Entrepreneur and was the COO of IndexTools until it was acquired by Yahoo! Inc., in May 2008. Today he is the Director of Data Insights at Yahoo! and sits on the Board of Directors at the Web Analytics Association, and he maintains the highly popular analytics blog,VisualRevenue.com/blog.

About Search Engine Strategies New York

Search Engine Strategies is the definitive digital event for marketers, corporate decision makers, webmasters and search engine marketers (SEMs), including pay per click (PPC) advertisers and search engine optimization (SEO) professionals. Attend SES New York, network with your peers, meet with industry experts, and learn the tips, tactics and strategies that will grow your business online.

An unfortunate recent statistic unearthed from the Factual Blog has shown that less than 30% of businesses worldwide are using Google Analytics on their websites out of a 4 million site sample set (via Manoj Jasra at Web Analytics World Blog).  Unfortunate only because there seems to be a booming interest in search engine marketing, namely search engine optimization.  By no means does one need Google Analytics to measure indicators of success in SEO, but it sure helps.  Any web analytics tool should top your list of priorities when embarking in online marketing, but there are lots of other resources freely available to webmasters.

Web Analytics: Google Analytics or Yahoo Web Analytics

As mentioned already, the focus should be to start collecting web analytics from day one in order to benchmark any optimization effort.  In fact, collecting historical data a few weeks or a few months prior to optimization efforts can highlight key seasonality trends that you may mistake for successes or failures in website changes.  For example, did changes to your site enable it to start ranking for a keyword, or is it because you always ranked for the keyword?  You may not necessarily know for sure without historical data, but this shouldn’t be a stalling point.  A good guy (Avinash Kaushik) wrote a great article on search analytics, which is a super resource to answer common questions about measuring SEO efforts, I recommend giving it a read as well.

Metrics to watch:

• Visits, time spent on page, and bounce rate by top entry page, segmented by Natural/Organic Search)
• Reverse goal funnel, segmented by Natural/Organic Search
• And to find a hint at visitor intent, top internal search queries segmented by Natural/Organic search
• Visits by non-paid keywords, but filtered for branding, trademarked, or copyrighted terms.  It’s relatively easy to rank for keywords in your company’s name, domain name, or product names, but it’s often those long-tail generic terms you are optimizing.  Remove anything else that will contribute to noise.
• Google Analytics “Intelligence” is a new report that does significance testing for you.  Use it to quickly identify and build segments related to significant changes in visitor behavior.

Google Webmaster Tools

Let’s face the facts, according to recent studies from various sources, the majority of search traffic still originates from Google, on the order of 60-70%.  Google Webmaster Tools gives a ton of insight into which keywords your website ranks in results for, as well as which keywords are clicked on.  Keep in mind, Google tests rankings on search engine results pages (SERPs) and weighs long-term ranking through several factors, one of which being click-through rate and bounce rate.  So by keeping an eye on the keywords your website is ranking for, but not generating click-throughs for, might let you shift optimization efforts accordingly.  Again, Google Webmaster Tools is totally free to use, and easy to setup.

Metrics to watch:

• Number of keywords triggering results for your site
• Position of your site in the search results for specific keywords
• Number of clicks to your site from keywords
• Position of your site in the search results for specific keywords clicked
• Diagnostic crawl stats can also provide insight into increasing, decreasing, or unchanged crawler activity to your site, which should be taken with a grain of salt

Competitive Intelligence

There are a multitude of competitive intelligence tools, free and paid, available online.  Due to sample collection and sample size, each tool has it’s pros and cons in terms of accuracy and precision, but they’re helpful nonetheless.  Free competitive intelligence tools include Alexa, Compete.com (free reports), and Google Trends. Paid tools include Compete.com, Hitwise, ComScore, Nielsen, and a host of other premium services.

Metrics to watch:

• When comparing two sites side-by-side in Google Trends, for example, it’s important to avoid getting wrapped up in the actual values attributed to metrics, but focus on the trending of page views, unique visitors, etc.
• Compare visitors referred from external sites to two or more competitors, but filter out large referrers such as search engines (premium tools such as Hitwise and Compete.com offer this functionality) to increase the resolution of those niche sites your links aren’t reaching.

Link Intelligence

Aside from competitive intelligence, which provides insight into which sites are sending traffic to you and competitors, consider link intelligence as well.  Search engines still value links on other websites as a ranking factor in their algorithm, so even if a link does not provide any traffic, it may still provide a boost in page rank (or SEO juice if you subscribe to “page rank” being dead).  Obviously there is no shortage of sites that track links between sites, however one great resource that is currently high on my list is Majestic SEO.

Metrics to watch:

• Back-link discovery trending graphs can provide insight into how your link-building efforts are assisting page rank.
• External backlinks and referring domains are also useful, but if links to a website are on each and every page in an external site’s template, trending these numbers could be meaningless.  Instead, consider focusing on external back-links PER referring domain.  The goal here is to ensure the ratio doesn’t become inflated with links to your homepage, but links embedded in actual content that’s relevant to deep pages on your site.

We truly live in an age of data overload. By no means is the above list exhaustive, as these tools and services are only a minuscule subset of resources available to marketers and web analysts.  Feel free to share your favorites below!

There is a lot to be said about implementing web analytics on a site, and using that data to improve customer experience and a company’s bottom line.  Guys like Avinash Kaushik, Eric Peterson and Jim Sterne all advocate that baseline implementations provide a boat-load of traffic data that will never be looked at in a million years, with segments out the ying-yang.  However, I’m going to argue that although free tools do offer a whack of great reports out of the box, the real power of web analytics solutions comes from custom variables.  Your web analytics tags are the cake, and custom variables are the oh-so-delicious frosting on top.  Regardless of your tool of choice, here are some of the best custom variables to implement on your site for segmentation bliss.

My top custom variables you should implement:

• Registered versus non-Registered
  This answers the question about what content motivates two entirely different sets of visitors.  Most websites these days will have various mechanisms that enable visitors to register in some way.  On a retail website this would be registering to purchase or formulate a gift registry, on service sites this might include a free user account, and on blogs the registration mechanism might be used for commenting or subscribing to posts via email.
• Logged-in versus not-logged in
  Used in conjunction with registered/non-registered, this can be used to filter out much of the mundane repetitive pathing that occurs with repeat customers/local visitors.  Subtle user behavior can often be lost in a flurry of routine page views and visits resulting from people that know your site, brand, products, and services inside and out.
• Custom site sections and sub sections
  Sometimes the layout of your site doesn’t jive with directory structure or page naming conventions, by coming up with your own silos, you’re saying “yeah, our site organization kinda sucks, but what if it were organized this way.”
• Content ID
  Particularly useful on sites with a lot of content that’s organized using a content management system.  Rather than referring to each page by it’s page name, or by it’s permalink or URL, it might be easier to categorize pages by the ID it’s assigned in your CMS.  Different strokes for different folks.  This is why discovery questions with internal stakeholders can be so important, what do your people prefer?
• Content type
  Answers the age old questions of which pages contain specialty content get the most love, for example pages with articles, photos, audio, video, flash, etc.
• User key
  Sometimes you need an identifier that resolves in two separate systems: your web analytics solution and your business system.  If you’re going to key your users, I’d recommend a somewhat  stronger privacy policy, and only tagging visitors after they are logged into secure areas of your site.
• Demographics
  Gender and age group can really help you form valuable insights into visitor patterns, but collecting representative samples can be tough on low traffic sites.  Again, use this with a bit of caution and respect for visitor privacy.  Ensure that the mechanism whereby you collect this information is documented and people know they are opting-in some tidbits of personal information.

Of course, implementing custom variables will vary by web analytics tool and website, so chasing the holy grail of data overload might not be feasible.  But if the sky’s the limit, try to brainstorm as many different custom variables you can that answer your valuable business questions.

Divorcing yourself from opinion, or qualitative analysis, or wishy-washy suggestions can be a tough task.  Some stakeholders are statistically savvy and can look at numbers objectively, opting to do their own analysis – perhaps by using separate business intelligence. However, most people want to read interesting insights, even if it’s just to look smart at the water cooler.  For everyone to get on board, the trick is to stop saying, “I think” and start saying “the data shows.”

Finding valuable insight in segments of your traffic is a great first step.  Focus on one product, one traffic segment, and one conversion pathway through a site.  Slice and dice the traffic different ways until you notice a trend, or even something interesting.  You might find that first time visitors coming through organic search terms convert five times better than paid search visitors.  A perfect starting point for deeper dives across alternate product lines.

Oh oh, what if your data is sketchy?

What if your segments look a lot alike, and you think that 5% difference could mean something but aren’t sure?  That’s where significance testing and maybe even multivariate testing can help:

• If you DON’T have access to web development teams, test theories using a statistical significance test, such as PRC’s statistical significance calculator.
• If you DO have access to a friendly web dev, consider working together to run an A/B or multivariate test using Google Website Optimizer.  Using it is free, significance testing built-in.

All of a sudden, the data starts showing room for improvement.  Creativity shifts to testing to find a winning combination, instead of intuition- or experience-based decision-making.  Perhaps for the first time ever, you can start attributing measurable difference in revenue and start prioritizing projects by potential shifts in “ROI” (using the term liberally here).

In addition, the data analyst can save measurable results to their war chest.  A bit different than intuition or experience, the war chest becomes a repository of educated guesses.  As you proceed to make improvements in ROI, you can start to reliably predict lost revenue for projects that are put on the back-burner.  Data (not the Star Trek android) becomes your friend; an ally in the fight (ok, tone that down to “challenge”) in development resources.

A new ally in data, new friends in web development, new supporters across business units, new weapons in your war chest.  Becoming a data diplomat is win-win-win.

Return on investment is a sexy KPI right now; everything and everyone claims to provide easy calculations of ROI, value added services that show ROI, and other far-fetched promises.  Probably one of the hardest things a web analyst can do is measure ROI — true ROI.  We’ve become accustomed to using the term “return on investment” to include subjective value propositions; soft and cuddly marketing statements.  True ROI is just not available through analytics tools and web analysts do not have the luxury of full financial disclosure.

Unfortunately, as data-driven decision-making has become more popular, so too has the search for more meaningful business-oriented metrics.  You know, stuff that actually means something to say, your CFO — money.  Somewhere along the way, we lost sight of the true definition of ROI; people have been bastardizing the term ever since.  Let’s look to Wikipedia for a refresher:

Rate of return, or return on investment is the ratio of money gained or lost (whether realized or unrealized) on an investment relative to the amount of money invested.

What’s missing in this definition?  Oh right, all those wonderful value-add statements marketers (myself included) use to inspire confidence in products and services.  Statements describing potential positive ROI should never include subjective benefits, because that’s not ROI.  With respect to web analytics, the same is true.

Regardless of how much data you collect using your web analytics weapon of choice, you’re not seeing the whole financial picture.  If I had to wager my salary towards the truth of that statement, I would probably win 99% of the time.  Web analysts simply aren’t plugged into each and every profit and loss source within an organization, and frankly we shouldn’t be.

The typical web analyst is a highly detail-oriented individual, that includes figures in context of what is being observed online.  So we have to ditch the use of ROI as a KPI and look to more specific calculated metrics that make sense, such as return on ad spend (ROAS), average revenue per order/user (ARPO/ARPU), and maybe just aggregate revenue figures.

Does that mean we should live a sheltered life?  Hell no.

There are wonderful departments known as finance, with highly skilled numbers people just like us that have access to a heck of a lot more business information than we do.  I encourage you to reach out to these financial gurus and ask a ton of questions.  Don’t expect each and every profit and loss center in the company, but if you’re lucky, they might just give you an aggregate ratio to aim for to determine a winning versus losing product, campaign, or change initiative.

Based on just the title of this post alone, can you guess what the #1 sticking point is between web analysts and the I.T. department?  You got it, tagging up pages across your organization’s website usually falls to the coders, webmasters, outside consultants, or I.T. folk within your company, and not the research, marketing, or business development department.  Although many software solutions are turning to single smart tags that can be defined later from within the tool, lots of enterprise class vendors still require on-page variable calls or customized javascript functions to assist in segmenting visitors.  So is perfect tagging mission impossible or the holy grail?  A rare discussion plus a Dwight Schrute bobble-head after the break.

I don’t think I’m speaking out of school here when I say, page tagging can be tough!

Step 1 – Identify the critical few, but don’t ditch the laundry list

In a world of changing business requirements, dynamic content, interactivity, and obscure “engagement” metrics, it’s essential to minimize your tagging requirements down to a critical few metrics or key performance indicators (KPI).  It’s usually easier to win over your implementation resources with a short list of “must-haves” and “nice-to-haves” versus a laundry list including the kitchen sink.

Step 2 – Champion a Web Analytics Dev Lead

This part isn’t easy because development personnel are notoriously overburdened with more important things to do (obviously!).  However, as one ally in development once told me,

“Garry, you probably taught me more than I ever cared to know about Omniture, but it helped!”

That statement implies two things.  Development gurus won’t necessarily care to know all there is to know about any given web analytics platform or tagging methodology, but with that knowledge under their belt, they can go far in increasing their own personal net worth in an organization, or expand their skill set for future opportunities.

Step 3 – Evangelize and Train Followers

Turn into a huge proponent of comprehensive data collection while evangelizing user testing, statistical significance, and challenging intuition.  Consider a strategy posed by Avinash Kaushik: talk to anyone that will listen on the business side of an organization that might support the move to data-driven decision-making, and make them profitable.  Great idea, but not that easy.  What I would suggest is this: evangelize and train enough people within an organization, and funny things start to happen.

When you evangelize and train a critical mass of “followers,” the collective philosophy of an organization becomes fragmented and ripe for change.  I’m not talking about full-scale revolution here; what I’m talking about is evolution.  All of a sudden, the demand for additional metrics and specific KPIs starts to drive the development of better tagging.

Step 4 – Impress with the Impossible

There are ways to get what you need out of a vanilla web analytics implementation.  Many tools offer the ability to slice and dice segments after the fact, regardless of how whacky your requirements are.  The limiting factor being time, of course.  Given enough time, a hundred monkey’s could calculate the value of pi to eleven decimals, and so can you.

One of my favorite asks of all time was a geosegmentation question spanning a distinct site section of a popular media site.  Of course, this question was made all the more difficult by combining conversion metrics with unique visitor calculations.  Regardless of how sexy your implementation is, you wouldn’t be able to answer this question – guaranteed.  But I’m not here to show off, I’m here to prove a point.  If you demonstrate the impossible, you will get closer to your holy grail.  It may have taken a week to process that request, but it posed the question, “was the report that I provided insightful enough to introduce more complex tagging to cut reporting time to an hour?”

Step 5 – Make due with what you got

We’re not conceding defeat, but recognizing that even with the most complex implementation and site tagging you can get, you’ll still have funkified data.  A poorly coded banner ad could mess up your variables, browsers might not accept your cookie, visitors might disable javascript, page encoding might garble your image call, etc.  It’s not the cleanliness of your data that will make you successful, it’s what you do with it.