In 2009, Web analytics managers have a multitude of different tools to select to deploy at their corporation.  Sets of tools from industry leaders, such as Omniture, WebTrends, Unica, CoreMetrics , Google, and Yahoo, are among the most popular, while options from smaller players like ClickTracks and Woopra exist as well.  In theory, you deploy a tool, customize it to fit your needs, and start analyzing the reports — and it all goes swimmingly, right?

Then why have many corporations already chewed through two, maybe even three tools over the last several years, or deployed multiple tools in an attempt to arrive at where they need to be — delivering comprehensive and systematic analysis to their business community, helping to drive action from insight and taking the mantra of “competing on analytics” and “data driven culture” to the next level?  Several factors cause disconnects between the promise of a tool and the successful use of a tool, which cause a tool to fail:

• Inability to customize to business needs.  As sites adopt more and more AJAX, Flash, and Web 2.0 technologies like video, social media, and RSS, many Web analytics tools do not have features necessary to track these new media.  The catalyst for change comes when the business desires to track events on the site and can’t using the current tool, so the company begins to search for a new tool that can.   
• Training.  A corporation must hire or train people who understand how to use a tool.  It doesn’t always follow that because someone knows how to use Tool X, they can easily move over to using Tool Y.  If a corporation doesn’t budget both the time and money to extend its team’s ability to use a tool, the tool will not be effectively wielded and will wither on the vine.  It’s important to allocate resources to ensure your staff has the most current training available; otherwise, the tools you have could be considered useless because they can’t be employed effectively, which leads to the exploration of alternatives and the subsequent purchase of other tools.
• Lack of analytical resources.  Not a tool problem per se, but this issue reflects itself in an inability to quickly and agilely respond to business requests to extend the tool, provide data, or, worse yet, analyze the data.  If a company can’t dedicate sufficient resources to using and extending a tool and analyzing the data collected, a business can quickly conclude the tool, rightly or wrongly, has little to no value and seek alternatives.
• Too much aggregated data.  Most Web analytics tools provide cumulative sums of data at the visit level.  They will tell you “you have had X instances of Y.”  What most tools won’t tell you is how a particular visitor or groups of segmented visitors behave on the site.  For those companies that want to do targeted email campaigning based on understanding visitor level data or evaluating the performance of ad campaigns on a per campaign ad creative basis based on visitor behavior, many web analytics tools just can’t meet that business requirement.  Or, the analytics tool may require multiple applications that weren’t purchased to fulfill the vision.
• Inordinate complexity.  The idea of analytics tool deployment and extension being “easy” is somewhat of a joke in analytic’s circles. The difficultly and complexity in taking full advantage of a web analytics tool is in how you extend it to meet your business needs.  And many tools make it less than intuitive or in the worse case way too hard to extend a tool across an enterprise — from challenges with page tagging, to orchestrating changes to page tagging, to QAing tags and reports, to building out a custom schema to requiring the configuration and integration of additional applications to deliver against requirements .  When these things go wrong, companies get frustrated and seek alternative solutions often abandoning a tool in the process.

Many other reasons exist, of course, for why tools fail (cost, infrastructures, data availability, and so on), but I think the five issues I’ve mentioned above are some of the primary drivers for tool change in the corporate world.  But now I ask you, why has your Web analytics tool failed you and caused you to switch to another solution?

Last year around this time, I published my 2008 Web Analytics Prognostications. Each worth 11.1111111111 points (100/9…) Here they are:

1. Google Analytics releases a real API for getting (and perhaps setting) data.
2. HBX Analytics goes away.
3. Long live Visual Sciences.
4. WebTrends Rebrands.
5. New and updated standards are released.
6. Standards enforcement is attempted in order to propel adoption.
7. BI tools provide better support for and integration with Web Analytics tools.
8. Web Analytics as performance management.
9. Features for measuring the Mobile Web.

So how did I do?

1. I was right about Google. 11.1 points. In October, the fine minds at Google released a powerful API for getting data out of the tool. Setting data is still done by page tags (for now).
2. I was mostly right about HBX. I give myself 8.88 points. As I understand it HBX Analytics still exists, but OMTR would really like you to do the switcheroo to SiteCatalyst. We saw companies like CoreMetrics offer incentives to migrate HBX customers, and various consultancies offer services to help customers migrate off of HBX.
3. Yup. Right. Discover On Premise (DOP) Anyone? Another 11.11 points. DOP It’s a good tool, if you have people (or budget to hire people) who have the skills to architect and configure the solution. Out of the box? LOL! It’s mostly a blank slate. Gotta create your schema…
4. Right about WebTrends Rebranding. 11.11 points. As far as I am concerned, the whole Marketing Lab, Ad Director, Visitor Intelligence, Score, and Tag Builder products, along with the visual redesign, “open and business intelligence” messaging is all part of WebTrends rebranding, differentiating, and repositioning itself against competitors. And how cool is it that Alex Yoder is CEO??
5. Correct, I was. 11.1 points here. The WAA released new, updated standards. And they are working on what will be some cool social media standards.
6. Enforcement, sort of… 8.88 points. Vendors, starting with IndexTools, er Yahoo, were the first out of the gate with proclamations that they were compliant. Other companies like Unica and WebTrends followed suite. I would anticipate that we’ll see the rest of the vendors move forward with their own matrices, and the WAA publish a comprehensive list of who supports what standards.
7. BI Tools provide support for web analytics data? Ahh, 8.88 points. Yes, they do when you can get the data out of the analytics tool in the right format (xml, csv). Or, if the analytics tool has an open relational backend, you can do cool database stuff.
8. Performance management? 8.88 points methinks. I’ve only heard of a few companies that do this… Companies that measure their self-set goals against actual KPI’s, then make changes to the site to move their KPI’s toward their goals are managing for performance. I’m not talking about “we did some site optimization and then got a 25% lift in conversion.” I’m talking “we want to boost conversion from 1.5% to our goal of 3.0%” and then use web analytics or site optimization technologies to get there. That scenario is different than optimization where you push for lift, it’s relentless goal-based optimization.
9. Features for measuring mobile. 11.11 points, yup. From Nedstat to CoreMetrics to Omniture - they all announced features for measuring the mobile web last year. And several mobile specific vendors such as Amethon, Mobilytics, Bango, TigTags, Xiti, and AdMob are messaging and positioning their various solutions in the market.

All said, I was rather right on with my predictions for a total of 91.07 points. An A- for looking into my analytics crystal ball and pulling out what I saw…

For my next post, predictions for 2009!

Two weeks ago I had the good fortune of attending the eMetrics Marketing Optimization Summit in Washington, DC. It was my 6th eMetrics! I can’t believe I’ve been to six eMetrics. For those of you who aren’t familar with eMetrics (and who wouldn’t be if you’re reading this blog) it’s the big show for analytics and Internet marketing professionals.

One of the cool things about eMetrics is that it covers the gamut of measurement technologies and their application to real business activities. You can get informed by the best and brightest practitoners, consultants, and visionaries. In attendence were folks like Eric Peterson, Jim Novo, Bryan Eisenberg, Justin Cutroni, Bob Page, Matt Cutler, Gary Angel, Jim Sterne (of course!), Alex Langshur, Jon Lovett, Anil Batra, Phil Kemelor, June Dershewitz and many other smart, fun, and friendly folks I should mention.

The event has an awesomely grand, wide focus across several areas in more than 70 different sessions. Tracks include: acquisition, conversion, retention, social media metrics, data driven organization, industry insights, predictive analytics, introductory analytics training, advertising, and landing page testing. All in all, it’s the place to be to learn all about the big picture and the little details on analytics and optimization.

When I think back on what I learned - a lot - a few key themes kept resonating with me:

• Testing, testing, testing, and more testing. Easier said than done methinks on large complex websites with a lot of stakeholders, but the emphasis continues to be on using AB and/or a multivariate testing approach to optimize images, calls to action, points of resolution, site functionality, offers, forms, headings, and more in order to figure out what recipe gives you the best lift toward your goals.
• Different strokes for different folks when it comes to measuring social media, word of mouth, and user generated content and determining ROI. Companies can measure all sorts of stuff: traffic from social sites, tweets on twitter, trackbacks, mentions in blogs, comments, Facebook, but what really matters to measure is, for the most part, completely dependent on goals and the company. There doesn’t seem to be one single set of social media KPI’s applicable in all situations, and that kind of makes sense to me.
• The measurement industry is fragmenting. The analytics landscape is awash with smaller players who want to do things like alert you when the data changes (cool!), measure your mobile experience (excellent!), track the engagement of your videos (rad!), help you orchestrate tagging (thank you!), configure your tool (yeehaw!), build you a custom solution (please do!), and generally fill in any gaps in functionality or services that the big vendors can’t.
• The requirement to integrate.From integrating internal and third party data with analytics systems to feeding certain analytics data and key events into data warehouses to bridging voice of customer data with clickstream and customer experience management technologies, to bringing together visitor level data about performance across online and offline campaigns. Integration will be a major concern for companies that are serious about competing on analytics.
• The problem of attribution. First click, last click, direct, indirect, multi-attribution models, and more, there are some huge issues that online marketers are dealing with when it comes to attributing on-site success to a business activity. No one really agrees on what method is better or which is correct. Expect to see a lot more discussion in this area once the executives start asking.

And so much more too… If only I had more time to blog. My advice is to get yourself to the nearest eMetrics in 2009. Flag me down when you are there. And if you were there, let me know in the comments some of things that stuck out to you.

I was pretty excited to see my blog included in the Web Analytics category on Guy Kawasaki’s new site: Alltop. The site a very cool concept - essentially it’s an “online magazine rack” that acts as a starting point to explore aggregations of topics. I’ve been using it for a few weeks, and it’s pretty darn cool for getting the latest juice without having to squeeze it out of Google yourself or deal with an RSS aggregator.

Check it out.

Anyone who has spent any time doing web analytics knows that you get a lot of requests for analysis. The bigger the company and the more serious they take analytics, the more requests you will get. And the requests will range all over the map - from simple data pulls to dashboarding/reporting requests to comprehensive analytics projects to statistical modeling to pretty much everything under the sun - whatever people consider “analytics.”

People have even told me that requests for analysis can even be a tactic for people outside your analytics team to pass the buck in order to make an excuse for not delivering on their own work. The idea being that because you do analytics, you instantly have all the data at your fingertips and can deliver it in a flash, so why aren’t you helping me?

False conclusion. Analytics is hard and complex. And the bigger the corporation and more integrated the analytics, the harder and more complex.

In many cases with web data, the data may not already have been collected, the data may have to be sourced from another system, extracted, transformed, or made available in a dashboard or a report - if at all even possible to report and analyze.

The data may only exist as a figment in the imagination of the requester.

The data may be impossible to report because you have inadequate data model or an entrenched BI team that doesn’t get how the schema affects reporting and doesn’t care enough to help.

And real analysis of the data takes uninterrupted time, which you may or may not have time to get to today, next week, or next year because you are working at correcting the data model, ensuring the right data is collected, letting queries run, dealing with the intracacies of overly complex tools, building reports, dealing with other requests, working on already prioritized projects, and trying to answer your email and attend meetings.

I am sure this all sounds familiar to the 1000’s of practitioners who subscribe to my blog…

That’s why when you, data and analysis consumers, ask for data or analysis, you sometimes don’t get it quickly from the web analytics team. You are just one of the many people making many requests to what in many companies is an already overburdened, often understaffed team dealing with non-standardized or clearly-defined data coming out of many different systems and third party applications.

Thus, when you are a web analyst, you need to carefully consider what you say yes to do and how you respond to requests for analytics work. Because as soon as you say “yes, will do” you’ve committed to what could be the analytics equivelent of daisy pulling in a sunny meadow on a summer’s day or a Herculean cleaning of smelly crap-filled stables in freezing weather.What follows is some advice on how to prioritize your requests for reports and analysis:

• Is Revenue at Risk? Anyone who has worked with me knows this is one of my favorites. If revenue is at risk, then the analysis will be done! Profitable revenue is the chi, the lifeforce of any business. And analytics that supports revenue generation is of the highest kind of analysis. But you gotta prove revenue is at risk. Just because you work for a group that produces revenue doesn’t mean your request going unfulfilled puts revenue at risk. Tell the team, exactly why revenue is at risk… Not just that you think it is.
• Who’s asking? Is it your boss asking, her boss, their boss’ boss? Then the work gets done. We’re not talking HIPPO here. We’re just talking MOPPO (most powerful person in the organization!). Keep your boss happy.
• How difficult is the request? Just because something is “too hard” doesn’t mean it won’t get done, but as an analytics professional you need to set delivery expectations when requests are so difficult that they will take time.  Perhaps the schema needs to be modified, changed, or just simply remodeled in order to get that data, maybe you need to rewrite the tags, reconfigure the tool, build a bunch of new reports, figure out the data delivery tool.  Maybe 5 other groups need to work collaboratively in coordination with all their other projects just to get the data to a point where it can be reported. Manage the expectations of the requester.
• Can it be self serviced? Just because requesters don’t know how to use the tool (RTFM), it’s too slow, don’t know where the report is, can’t understand the report, don’t get web analytics, don’t know how to write SQL, or don’t where to look, doesn’t mean the web analytics team is going to do for you what your job requires you to do. The analytics team should teach self-servicing as a best practice because wasting time easy fishing in shallow waters means you may miss the big analytics catch in the deep data pool!
• When is the analysis needed? Of course, it helps to know when the analysis is needed in order to prioritize. Requester wants the weight of the world at microsecond N during the equinox by the end of the day tomorrow? They’re probably out of luck unless 1) revenue is at risk or 2) they are the boss. Need it this week? Well maybe, but the weight of world requires querying the Atlas database and queries don’t run like Mercury. The analyst needs to set expectations based on a number of interplaying factors about when the work can be delivered.
• Why is the analysis needed? Just curious about the number of X that goes to Y from Z? Time spent on page Z of your microsite? Or do you need to make a real business decision to advance the core mission of your company? By communicating to your analytics team the importance of the request’s “why” you can get better service. Analysts that know why they are delivering can more effectively prioritize.

As an analyst, use these questions above to:

• Help you prioritize your work
• Figure out what’s really important
• Frame how to manage expectations
• Deliver what’s really necessary to drive the business as soon as possible
• Not get caught in the tarpit of wasted time constantly servicing low value requests

And if you made it this far into this blogviation, leave me a comment on what you think. Am I right, wrong, on target, misguided, and how do you do it?

Wow! It’s been a long time since I blogged. Life has been a bit busy on a number of fronts, but I’m finally getting back into the swing of things. Let me start by (re)publishing a post I wrote over at IQWorkforce. I’ll be back with more fresh, original content this weekend…

Many people aspire to become full-time web analysts, but it is not an easy field to enter nor is there a straightforward path for gaining real-world experience to help you land a job. You cannot just show up one day at work, and say, “I’m no longer going to do what you hired me to do. I really want to be web analyst, so I’m just going to do analytical work.” You cannot get a job as a web analyst with little to no experience (unless you are lucky enough to find that rare entry-level job). When a business determines they need a web analyst, they do not often look at internal resources to staff the function – they want to hire someone who has experience elsewhere doing web analysis. Thus, it is hard to gain on-the-job analytics experience and it is even harder to break into the field full-time without any experience.

So what is an aspiring web analyst to do? What are some ways to learn more about web analysis, get some practical experiences under your belt, and move your career in the direction you want it to go? Over the years, I have learned that immersing yourself in the web analytics community helps prove to employers that you are a serious candidate. Several methods for breaking into the analytics field and/or gaining more experience, through community immersion, to reference when applying for a job include:

• Earning the University of British Columbia’s Award of Achievement in Web Analytics. The UBC offers several courses that focus on the real-world practice of web analytics – from an introductory information to comprehensive explorations of topics such as optimizing sites, managing campaigns, and building an analytics driven culture. These courses always receive rave reviews and more than a few people have used the knowledge learned in these classes as a stepping-stone to full-time careers in web analytics.>·
• Completing the University of California Irvine’s Certificate in Web Intelligence. For those people already in web analytics or for those who want to learn the intricacies of business process management, project management, or data warehousing in the context of web analytics and business intelligence, these courses offer the opportunity to do so. Knowledge imparted in these courses is well-suited for advancing your career. Completing these courses offer the new or experienced analyst a leg-up when competing for a new job or helping to prove their suitability for an expanded analytical role at their current job.
• Joining the Web Analytics Association. With over 1,500 members all over the world, the WAA is the premier virtual venue for learning more about web analytics and collaborating with people in the industry. Membership allows you to participate in the web analytics community and interact with experienced web analysts and even hiring managers. From joining committees, to working on special projects, to getting discounts for local, regional, and national events, the WAA provides a central, global focal point for all things web analytics. Joining the WAA says to an employer that you are serious about the field and want to work within it.
• Reading the Blogs and Books. The Web Analytics community has many contributing members who author blogs and books. If you are aren’t reading the blogs and the books, you are missing the opportunity to gain hard-learned and hard-earned experience from people who have been doing web analytics for years nor are you able to share the trials and tribulations of analysts new to the field. Some of my favorite books include Web Analytics Demystified, The Big Book of KPI’s, Web Measurement Hacks, Multichannel Marketing, Advanced Web Metrics, and Web Analytics: An Hour a Day.
• Installing a Free Web Analytics Tool. The barriers to using a web analytics tool in 2008 have all but been removed. It is easy to get a tag from Google Analytics, Yahoo Web Analytics, and Microsoft Ad Center Analytics, and begin exploring the reports, measures, dimensions, filters, variables, and tool configurations available in these products. Learning these tools and applying them to improving you or your friend’s websites will teach you a fair share of the challenges involved with doing web analytics in the real world. In fact, it is very common to see each of these tools deployed in the wild in corporations around the globe. Who knows… you may even get a job by just having experience using the free tools.
• Attending a Web Analytics Wednesday (WAW). The Internet’s only global monthly social networking event for web marketing and analytics professionals has brought together over 8,800 people in 107 cities. Your local WAW is the best place to meet the local analytics community and talk with the people in it. At a WAW, you will meet people in local companies that take analytics seriously and have a chance to interact with hiring managers near where you live. For community immersion, there is not a better place to do it than WAW.
• Going to Conferences. For those who already work in web analytics or want to find out more about the contemporary practice of web analytics today, attending conferences is a “must do” –at least one per year to stay in the game. The eMetrics Marketing Optimization Summit, the X Change Conference, the Internet Marketing Conference, and the vendor-specific conferences hosted by Omniture, WebTrends, CoreMetrics, and Unica are all well worth the cost of attendance.
• Participating in Social Networking and Social Media.   As Jason Egan states in the comments, lots of conversations occur about new job opportunties on LinkedIn and Facebook.  In addition, commenting on blogs and/or starting your own is a great way to get your name out there and to start networking with contributing members of the analytics industry.  Jobs are often posted on the WAA’s Yahoo! Forum as well.

While it is hard to land that first job in web analytics or to move forward from your current role into a more analytically-focused position, the resources I’ve cited above can assist you in figuring out the best path for getting that position and proving that you can do the job. What hiring manager wouldn’t want to give someone a chance who has taken the UBC courses, joined the WAA, gone to eMetrics, attended a WAW, read the blogs and books, and immersed themselves in the web analytics community? It is that type of commitment to the field that a quality staffing company, like IQWorkforce, can use to differentiate you from the crowd and help you get a job that takes your career to the next level.

Another X Change has come and gone.  This year’s conference at the Ritz Carlton in San Francisco was even better, I think, than last year at Napa.  The huddles were more focused on sharing practitioner knowledge, ideas, and best practices, and I really liked that. 

There were no vendors leading huddles, and I really liked that too.  One of the huddles I led on ”symptoms you’ve outgrown your web analytics tool” contained voices highly critical of a particular popular vendor, and several of the participants told me “we couldn’t have been as honest if our vendor was in the room.” 

The other huddle I led “building a successful web analytics team” was excellent.   I had really smart people from major companies sharing their successes, hopes, aspirational goals, and more about how managers conceptualize, roll out, direct, and maintain their web analytics programs.  Cool stuff.

I really enjoyed everyone’s intelligent participation, so if you were in one of the huddles I led, thanks for being awesome.  As I left the conference (and headed up to Healdsburg CA in Sonoma County - a really beautiful place in the world - where I sit writing this late night), I was left to ponder my key macro takeaways from the conference, a few of which are as follows:

• The big and the small have the same challenges.  Whether it’s resource allocation, budgets, vendor insufficiencies, professional services issues, data integration, data reconciliation across sources, talent shortages, KPI definition, reporting, data collection, data sharing, attribution, and more, the biggest companies in the world are having the same challenges as the even the smallest companies. 
• An acute need for control over IT resources.   Like it or not Marketeer, as I’ve said before “the business needs web analytics, and web analytics needs IT.”  I kept hearing over and over and over again that the web analytics community unanimously believes they need real control over IT resources or at least a direct allocation of IT hours to do their jobs correctly.  I heard this repeatedly from C-level execs to analysts.
• Web measurement is rapidly changing, deeply integrating, and site optimizing.  From measuring Web 2.0isms, like video, mobile, widgets, social, events, the digital media analytics measurement is shifting dynamically and quickly away from basic, mostly meaningless measures like page views to focus even more deeply on business critical measures like site and scenario, macro and micro conversion and goal completion and the measurement of critical success factors, whatever those may be, that drive business value.  Meanwhile, top companies are bringing together previously siloed data and integrating it.  Ad server data, voice of customer data, customer demographic and purchasing data is being joined with web behavioral and 2.o data to realize powerful customer insights.  And then all that’s being taken to the next level through multivariate testing and the creation of persuasive site experiences and predictive and behavioral targeting.
• Severe lack of qualified web analytics expertise.   Thousands of web analytics jobs, hundreds of qualified web analytics practitioners. ‘Nuff said. 
• The importance of sustainable, repeatable, managed processes.  A lot of people at X Change are taking my good buddy Eric Peterson’s 2006 mantra of “process” to heart.  They don’t only want to measure things, report, and analyze. They want to do so in way sustainable way by creating and documenting analytics-focused business processes that tie into activities external to the analytics team (and then practicing them to perfection).  Some of these processes are simple, like “measuring a site” to the complex like “optimizing a user experience” - how to orchestrate these activities using analytics…
• The need to focus on business value and the drivers for that value when measuring.  A lot of what I heard about measuring Web 2.0 was interesting, but the necessity is tying it all back to the value drivers on the site and the core business model - whether that’s selling ads, products, leads, and so on.  Sure you can’t manage what you can’t measure, but you also shouldn’t worry too much about measuring what isn’t managing to generate value.
• The Web Analytics Industry is full of smart, cool, and passionate people.   I had to throw that in there.  :)  If you don’t believe me, go to a local Web Analytics Wednesday.  I host Boston and my pal June Dershewitz hosts San Francisco.

So at the end of X Change, a lot to think about, and lots of fodder for more blog posts.  Hope to be there next year, and to see you there too!   Special thanks to Gary Angel of Semphonic and Eric Peterson of Web Analytics Demystified for running a copacetic, epicurean, and all around ritzy and delightful conference with the best and brightest.

I’ve been thinking a bit about targeting, and how we in the web analytics industry have just a ton of visitor or segment-level data that can be used for targeting ads or content, but most tools don’t let you use the data or easily feed it to other systems to do any targeting.  It’s rather odd, don’t you think?   Even Omniture Test and Target isn’t using, as far as I’ve learned, a single data model or the data collected from their behavioral tools, like HBX or SiteCatalyst, for targeting.  All their data models and thus, their data, are unique to the products in their platform.   So I decided to resussitate/revise a blogviation and offer it as food for thought on MediaPost.  When I reread this post, it’s more of an informational post for product managers on how I’d begin thinking about targeting with analytics data and what types of targeting are possible, so here it goes.   

Targeting refers to the process of delivering content or ads to segments or visitors based on their known attributes.  The goal of targeting is simple to understand: maximizing the performance of content or an ad by serving it to visitors at a time when they are most open to the receiving the message. 

For example, you may visit a site, and see some type of ad unit calling out at you to “meet singles in <insert_your_city>.”  When browsing a real estate site, you may see ad units for realtors and mortgage companies.  After entering a keyword such as “car insurance” and clicking through the search results, you may land on a site and see an ad for a car insurance company or land on a page that persuades you to begin the process for creating an insurance price quote.  That’s targeting in a nutshell.  It’s simple for a site owner to understand:

1. Visitor X has these attributes.  
2. We have content or an ad that we think will appeal to Visitor X’s attributes. 
3. Let’s show the relevant content or ad. 

In online media, targeting is associated with paid search campaigning, ad serving, and content optimization based on recognizing and responding to the following attributes:

• Category and sub-category.  Conceptual constructs like “categories” of topics on a media web site or products on an ecommerce site can be targeted to include certain types of ads or messages.   The idea is that if visitors are browsing your category for “hardware floors,” you could offer them an ad or content specific to “flooring installation services.” 
• Geography.  Country, region, city, state, DMA are all targetable constructs.  You may run a sports site and choose to target people surfing in from 02116 (Boston) an ad for Red Sox tickets or content about Manny Ramirez’s recent trade to the Dodgers.
• Browsing environment such as the connection speed, type of browser, operating system, user software, domain, and ISP.  An ad network could serve an ad for DSL to a modem-based surfer by detecting the visitor’s browsing environment.
• Time.  The idea of only showing content during specific periods of time is called “parting.”  Common types include day-parting and season-parting.  For example, a B2B site only choosing to show ads for a particular manufacturer’s product during business hours — the site’s busiest time of day — would be an example of day-parting.
• Keyword.  There are many different types of keyword targeting.  Search engines target ads based on keywords in queries.  Content Management Systems target content based on site search keywords or referring keywords.  “Keywords” may be associated as metadata with site sections or pages, similar to zone or category targeting on an ad server.  Once a page is associated with keyword metadata in an ad tag, you can tell your ad server to target ads to that keyword on whatever page or pages the tag was placed. 
• Language.  When a language can be detected or known in advance, you can target ads to visitors in their language.
• Demographics. If the ad server is aware of a segment’s demographics, such as age, gender, income, title, purchasing power, and so on, an ad can be targeted on that basis. 
• Context.  Think of AdSense and how it matches text ads based on the semantics in site content.  Or when, after adding a product to your cart, a site offers you “free shipping” if your total purchase exceeds a certain price.  This is content targeting based on context.
• Profile.  Targeting is possible based on conclusions drawn and rules created from attributes about an individual or segment (such as purchasing propensity or job title).
• Rules.  Serve an interstitial ad only to visitors who don’t have a cookie set for the site.
• Events.  Someone deposits a large sum of money into his bank account, so the online banking site offers him a CD product on his next login.

We’ve all heard, of course, about a very specific type of often-discussed targeting in online advertising: “behavioral targeting.”  Behavioral targeting refers to the technology and process in which an ad or content is shown to a visitor based on their past actions and reactions.

Behavioral targeting involves:

1. Collecting behavioral data about visitors.
2. Identifying when those visitors visit a site.
3. Determining the current context of visitors on the site.  
4. Detecting the visitor’s current behavior.
5. Serving relevant ads (or content) matched to the behavior.

The goal being to use past behavioral data to influence the customer buying cycle or marketing lifecycle, in order to more effectively and more quickly deliver on advertiser and site goals.

So where does Web analytics come in?  You would think Web analytics data from “Web analytics” technology would be used to enabling “targeting.”  After all the best Web analytics systems store detailed visitor level data about past behavior.  Web analytics data certainly can be used, but in most cases, targeting is a function provided by the ad server or network, perhaps the ISP, or another technology called the “behavioral targeting platform,” not from data collected by the Web analytics tool.

In order to make Web analytics data useful for targeting, you will need to use your data to:

1. Define segments to target or identify visitors to target.
2. Feed past behavioral data about segments or visitors to the targeting technology.
3. Analyze segment and visitor performance against site or advertiser goals after targeting.

Targeting has a proven ability and amazing potential to generate tremendous returns, especially when combined with the rich, detailed behavioral data available in Web analytics.  As a method for optimizing site content and advertising, targeting technologies that integrate with Web analytics data will only become more important and a necessary “must have” for innovative companies that want to maximize business opportunities on the Internet. 

From an article I wrote for MediaPost a few weeks ago:

Reach and frequency and the core concepts of traditional media planning and advertising.  For a given site, program, channel, radio station, billboard, newspaper section, a target audience (the reach) is exposed to a certain number of occurrences of the media (the frequency).  On the web, these concepts manifest themselves in metrics collected and reported from a number of recognizable services.  Audience measurement firms, like comScore and Nielsen, web analytics firms, like Omniture and Unica, to companies somewhere in between, like Quantcast and Google, all have reach and frequency data.  Many new media metrics can be used to proxy frequency- from time-based measures, espoused by audience measurement firms, to concepts like visitor retention or the repeat visitor rate cited by web analytics firms.  On the reach side, companies refer to concepts like “unique visitors.”

These data, of course, available in free tools or in for pay tools are certainly helpful for planning campaigns.  But reach measures can be dirty (cookies, unduplicated unique users, estimates from panels, coverage error).  Frequency measures can be just as dirty (problems recording time in single page visits or visits on the last page, do page views really matter with AJAX and rich media, cookies again, and so on).  We all are aware of the challenges.

Thus using basic reach and frequency measures for planning or evaluating a campaign does not suffice.   So advertisers and agencies target demographics, like gender, age, income, education, and job title.  It’s a given that advertising in the Robb Report reaches a different audience segment than advertising in Popular Mechanics. 

These brave new days we have “behavioral” tracking too.  By taking into account visitor activity across sessions, such as past actions taken on a site or a roster of previous purchases, we can attempt to deduce what a person or segment responds to or is interested in based on their behavior.

Even with reach, frequency, demographics, and behavioral data to help guide advertising and media buying, we are missing an important attribute for maximizing the potential success of our campaigns.  We do not have an available tool, whether free or paid, for advertising or buying media on or across sites according to measures of past performance.  Such measures include ad clickthrough rates, conversion rates, goal completion rates, delivered impressions, and perhaps even harder to quantify financial measures such as ROI, ROAS, and ROMI.

Sure, historic, tacit knowledge of campaign performance exists and is used by agencies or publishers.  However, there is no shared industry source that can help us answer “how has a site for display advertisement historically performed toward goals based on the reach, frequency, demographic and behavior of its audience segments?”  Interestingly, a company minting money right now, named Google, can masterfully demonstrate performance in paid search campaigning and help advertisers unify it with segmented reach, frequency, and demographics.

Outcomes based performance measurement unified with reach, frequency, demographics, and behavior is what is missing in audience measurement tools, not frequently reported externally by web analytics tools or ad serving tools, and not available in ad planning tools.  When advertisers can target display ads, or even video ads, to desired audience segments by reach, frequency, demographics, behavior in the context of known performance, media planning will be more effective.  

Alright, I had to have fun with the title. :) We’re about 4 weeks ago from the newest and most unique analytics conference on the scene: X Change, hosted this year by Semphonic and Web Analytics Demystified. 

If you missed the first year in Napa, you gotta head to San Fran this year!  Allow me to explain how X Change differentiates as I see it:

• Conversational. You don’t sit in a room and listen to people drone on in front of their powerpoints.  People sit in Socratic circles and talk about a topic of interest in “huddles.”  The huddle leader will bring up a topic, perhaps riff on some hard-learned experience or data point related to the topic, and ask for commentary from the participants.  The conversation then flows, like Jazz, until there’s a cadence, then the huddle leader phrases a few more notes and progression begins again…  Its atypical format depends on participants for success.  No one is going to sit there and read you slides and provide one-sided opinions.  You won’t just be sitting there listening (unless you want to).  The best huddles are interactive and encourage active participation in the pursuit of shared knowledge, not passive reception of an individual’s knowledge.
• Focused.  The huddle topics are highly specific and deeply relevant to the real world practice of web analytics today - from attribution to mobile measurement to integration to privacy to team structure, the huddle leaders selected topics that interest them to share with the participants. The focused conversational format should lead to symbiotic exchanges of information directly relevant to your job.
• Small.  100 people, 20 huddle leaders.  You get to make meet interesting people and build working relationships with them.  Cool folks like Bob Page, Rachel Scotto, Marshall Sponder, John Lovett, Jared Waxman, “Bob” Dylan Lewis will be leading huddles and hanging out.  The Web Analytics Tuesday event will probably be bigger than the whole X Change conference!
• Exclusive.  The huddle leaders were hand selected.  In attendance will be industry leaders, corporate executives, industry analysts.  All of the attendees work with analytics.  And for gosh sake, it is at the Ritz in one of America’s most beautiful and eccentric cities. 

I think X Change is a unique experience and a worthwhile event where you get to really connect, and well, exchange (!) expertise with your peers and go home with new knowledge.  At least I did last year.  I’ll be leading a couple of huddles, one of the web analytics team and one on knowing when you’ve outgrown you analytics tool, so say hello when you see me. 

Make sure you check out the official web site at Semphonic and sign up today.  The event will sell out soon.  15% discounts are available for Web Analytics Association members.