Targeting refers to the process of identifying characteristics of a segment so that relevant content may be matched to it and delivered at a time when the segment is most open to the message. The idea is the right content to the right visitor at the right time (optimally in real time). 

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 real estate you may see ad units for realtors and mortgage companies.  After entering a keyword such as “car prices” and clickingthrough the SERP, you may see an ad for a local car dealer.   That’s targeting in a nutshell.  It’s simple: 

1. Visitor X has these attributes. 
2. We have content that we think will appeal to Vistor X’s attributes. 
3. Let’s show that content. 

While targeting has helped to increase ad clickthrough rates, it’s far from an ideal science.  Current methods for targeting have inefficiencies.  What if Visitor X just bought a new car after his recent marriage?  Unless the targeting engine is made aware of the visitor’s current state, the targeting may be off and not yield desired results. 

Even with limitations around “current awareness” targeting is perceived in the Internet industry as a crucial activity for maximizing the effectiveness of advertising and content.  Targeting is the next stage after A/B and multivariate testing.  Once you determine the preference of segments based on testing, you identify content to target. 

In new media, targeting is something associated with paid search campaigning, ad serving, and content optimization.  It’s not uncommon for targeting activities to be based on:

• 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 notion of a “zone” fits in here as well.  The idea is that if visitors are browsing in 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 choose target people surfing from 02141 (Cambridge, MA) an ad for pre-sale Red Sox tix or content about Mike Lowell’s recent contract.
• Browsing environment such as the connection speed, type of browser, operating system, user software, domain, and ISP.  An ad network serves an ad for Verizon 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 manufacturers 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.  Google does fantastic things with targeting ads based on the keywords in queries.  Content Management Systems can target content based on on-site search keywords or referring keywords.  “Keywords” may be associated as metadata with site sections or pages, similar to a zone or a category targeting on an ad server.  Once a page is associated with “keyword” metadata, you can tell your server to target that keyword (and all pages where it exists as metadata).  If two categories each with different content share a targetable keyword, I can target ads across both categories to pages tagged with that specific keyword.
• Language.  When a language is set, you can target ads to visitors with that setting. Think Google.  Keep in mind that when you target by language, the creative copy is not translated. 
• 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.  Sometimes this is called “profile targeting.”
• Context.  Think of Google AdSense and how it matches ads based on the semantics in site content.  Now you understand content targeting based on context.
• Profile.  Targeting is possible based on conclusions drawn and rules created from the known attributes (such as purchasing propensity) about and individual or segment.

Enter one of the holy grails of online advertising and new media: “behavioral targeting” – an advanced form of targeting. Behavioral targeting refers to the process in which content is shown to a visitor based on the web sites they visit (or have visited) and the actions they take on those sites.  

Behavioral targeting involves:

1. Knowing where a visitor “comes from” and what they’ve done in the past. 
2. Determining the context of the visitor on the site. 
3. Detecting the visitor’s current behavior.
4. Serving relevant content and/or ads matched to the behavior.

By understanding the visitor’s past history, current state, and most recent behavior the marketer can target content in order to influence some point in the customer buying cycle- often at the stages of awareness and consideration.

So where does web analytics come in?  You would think web analytics data from “web analytics” technology would provide the seed data for enabling “targeting.”  It can be but in most cases, targeting is a function provided by the ad server or network or another technology called the “behavioral targeting platform,” not the analytics tool… the data does not come directly from the web analytics tool.  I’d love to hear how well (or if at all) Omniture TouchClarity is integrated with Omniture Discover or other offerings. 

In order to make web analytics data useful for targeting (if you can at all), you will need to use your web analytics data to:

1. Define segments to target (hard to export from web analytics tools)
2. Feed those segments and associated behavioral data to another tool (achievable if you own your data and run a tool in-house.  Harder and more costly if not).
3. Report on segment performance after targeting (that requires employing the right people and enabling them with the right tools)..
4. Analyze segment performance after targeting (again employ the right people and enable them with the appropriate tools and resources).

While I’ve only covered a very little bit about “targeting” and even less about “behavioral targeting” in the context of web analytics, I hope that my simple description of current methods for targeting and some thinking about “what is BT” will help you understand the emerging ecosystem in which analytics tool are interoperating now and will interoperate in the future.

Web analytics data is the raw material from which companies will realize new online products and deliver differentiated services that generate future value.

Right now as I type I can get web analytics data from so many sources.  Google Analytics and Open Web Analytics provide the data for free (once I spend the overhead to set it up).  So does Compete and Quantcast.  Many other companies are willing to broker this potential commodity to me at various price points - from the low four to seven figures. 

The price from web analytics firms for what is essentially the same data is all over the map!  Why? Perhaps because enterprise vendors know that you the customer will no longer pay for just data (thanks Google!). 

Features and services provided on top of the core data that’s valuable to a practitioner.  To the company employing the practitioner, it’s the insights generated from the data that’s valuable.

We’re seeing a lot of web analytics data operationalization via features for:

• Extension into business intelligence (and in the future leading to business analytics).  The best web analytics firms are providing open relational databases and creating methods for joining data from other systems to “extend the data model” or feed the enterprise data warehouse.  
• Automated testing and optimization.  The notion that “you aren’t doing web analytics, if you aren’t testing” provides evidence that siloed, lonely data won’t do much for your business.  In that light, automated testing is only as useful for prediction as the people setting up the tests.
• Targeting.  Using analytics data during the session or after it to automatically target content based on key visitor attributes will increase conversion.  While targeting technologies use analytics data, the value derived isn’t from the data, but from the potential conversion lift of the activity we call “targeting.”
• Proxy scoring.  Assigning a value to an event, interaction, page view, or visit can identify high-value segments and customers.  Scoring abstractions operate on the data to indicate value. 
• Profiling.  Building a picture of your online audience by aggregating data from various sources including web behavior, customer transactions, and demographic data enables one-to-one marketing.  Web data is part of the profile.  The value is in the profile.
• Integration.  Joining analytics data with data from other systems in a unified data model, or enabling machine-to-machine communication of analytics data will yield value.  Again the data is important, but the value is in the outcome.
• Alerting.  Indicating when data exceeds pre-defined upper and lower bounds and where those thresholds have been exceeded is valuable.  Once again, the data is crucial, but the alert is catalyst for value creation.

Data SEPARATED from the application, from the presentation, is extremely valuable.  When “unsiloed” and described or made available using open standards, it can be reused by other applications.  Insights realized from moving/sharing/synching data that drives enterprise value 

Yet functionality on top of the data layer doesn’t make web analytics easy and instantly drive value.  Few corporations have the slightest clue about how to take advantage of all this functionality. 

To creation value the modern web analytics practice requires:

• Dedicated in-house professionals. No duh, here.  You need people who understand the data, use ”features” to help analyze it, and who can then test hypotheses to optimize and measure outcomes.   
• Vendor and third-party professional services.  Consultants must go beyond “repeating back what you say, then claiming they can solve the problem” and deliver quantifiable, measurable value that improves business process.
• Web services. Web analytics tools need to use web services.  The “web analytics” tool of the future will take advantage of technologies that provide platform-independent protocols and standards used for exchanging data between applications. 

We’re going to continue to see highly-specialized web analytic’s “experts” at companies work with services firms (or professional services staff from vendors) to combine the ”off the shelf” web analytics products with web services technologies to create the automated marketing architectures of tomorrow. 

These “marketectures” will:

• Bridge quantative analytics data with qualitative data to identify the 360-degree view of the customer experience.  Ahh buzzwords…
• Use web services to operate on analytics data to solve highly-specific and specialized technology and business problems.  For example, using a WS to pass custom interaction events in Adobe AIR/FLEX to understand behavior in your RIA. 
• Enable HUMANS to realize new *predictive* insights from data.  Yeah, automated testing will help, but think about it… who sets up the tests?