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.