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.  

Back from another excellent eMetrics.  I’m a very big fan of the eMetrics Marketing Optimization Summit…  Props go to Jim Sterne for growing this event from a little seed into an incredible, blogworthy blossom.  How involved is Jim in eMetrics?  I’d say he’s completely immersed in every little piece - he even came up to me at the SF WAW (way to go June D!) to find out about the renegade AV work I did in one of the sessions, and to get my take on how it could have been avoided.  He’s that intimately connected to what’s going on.  Macro and micro, micro and macro.  And when you have one of the best Internet Marketers in the world, keeping a tight rein on the Clydesdale of conferences, you know you’re in for one heck of fun ride. 

And so it was for about 500+ of the top web analytics in the beautiful Palace hotel.  Props to consummate conference organizers Matt Finlay and his crew at Rising Media for keeping the road smooth as we all trotted on it as well.  Fanny, you are one helpful polyglot of a marketing manager!  I never knew German keyboards were so wild… Thanks.

The eMetrics sessions were informative and actionable.  The lobby bar and after-hours parties fun and enlightening.  You really can’t ask for more out of a conference.  As I flew home thinking back on it all, there was a lot to blog about, including:

• It’s all about attitude, dude – as in attitudinal data.  Like my father says “it’s all about your attitude.”  And so it is on the Internet in 2008.  From ForeSeeResults, to iPerceptions, to OpinionLab, to CRMMetrix, the often missing link in customer analytics is attitudinal data.  I’m talking here about Voice of Customer (VOC) technology that allows you to ask a question set to site visitors and then apply some sort of algorithm or model to express the meaningfulness of the data in quantifiable terms.  From the American Customer Satisfaction Index to 4Q.  VOC technology enables you to participate in a continuous, automated dialog with your customers in order to identify problem points on your web site and enable you to measure purpose and success of your most valuable segments.  Expect to see some of the big players gobble up these smaller companies.  Omniture, Unica, WebTrends, and CoreMetrics should be thinking about acquisition in this space to round out their offerings.
• Testing, 123… as in multivariate, MVT.  The rage is site optimization technologies beyond the simple A/B, champion challenger, test.  In this category you find folks like SiteSpect (the only non-intrusive multivariate testing solution!).  I’m a big fan of these guys (and was in 2006 long before they ever sponsored a WAW, thanks to a nice demo from Larry at my old job).  Eric Hansen and his crew have specialized software that you install in your data center.  No futzing with damned tags.  Swap out your variations, create different recipes, determine what’s statistically significant in giving you a lift to your macro or micro conversion goal, and you’re off to the races.  The good folks at Google are doing it and doing it well with Google Site Optimizer (thanks for the t-shirts!).  Interwoven is baking in Optimost to the CMS, and Omniture has their Test and Target integrated with the Business Optimization Suite.  Accenture has Memetrics.  Kefta too. And what ever happened to Verster?

In a nutshell, these technologies enable you to test variations of content themes, colors, creative, calls to action, points of resolution, buttons, navigational elements, –whatever you want to call the stuff on the screen—to determine what combination performs best against your goals.  But of course, this is all just software, so don’t get too excited.  The tests are about as good as the people creating them…  And complex tests that take a long time to execute may not finish.  Imagine 1-800-Flowers starting a test in January and not finishing until March, missing Valentine’s Day.  Or Intuit running a test beyond April 15th for a tax product.  Go humbly and carefully into this space, my friends, or you may end up optimizing for everyone and appealing to none.

• Tying it all back to the dollar for profit-generating sites and to the mission of non-profit generating sites…  It seems like a “no, duh” moment but metrics for the sake of metrics can be a big waste of time.  If you can’t tie metrics or visitor actions back to value on a revenue-producing site or to the betterment of a non-profit site’s core mission, then what’s really the point of the measurement…  That’s why I’m a big fan of the stuff ZaaZ does.  They totally get the fact of how actionable metrics turn the wheel of Internet commerce and ad-based models, and they can model it all to prove it out the ROI.  Folks like newly elected WAA Director Alex Langshur’s company Public InSite do similar stuff for content driven sites.  That is they know how to use metrics to optimize the channel to goals, not to just puke confusing data, like most web analytics tools do.  Again, it’s all about the people you hire, not the tools you use… My good friend Avinash, right again!
• The emergence and rise of deeply psychological and neuro-behavioral methods for automating persuasion and conversion.   Anyone who knows my good friend Joseph Carrabis, over at NextStage Evolution, knows that besides being one heck of giant kite flying, music master, he’s also got the models and the patents to help target and respond to human behavior across programmable devices.  We’re already seeing some companies, like Seven Billion Joe’s, er People, taking what he’s been saying for years and going to market with it.  The idea here being that if you can identify the affective, behavior, and motivational drivers of site visitors, you can maximize cognition in elements on the site (like pictures, text, informational flow) to appeal to target segments and persuade/provoke desired behavior.  It’s like a higher rung on the optimization ladder.  It’s not test what they see, it’s figure out how they think, then make the site better because of it.  Cool stuff.  Blows my mind.
• Integrated, multichannel marketing.  Just ask my good friend Akin Arikan, author of the newly released Multichannel Marketing.  (Disclaimer: I was a technical editor on the book.  It’s easy to do when you edit brilliance).  Make sure to check it out!  Marketing in general will become more Internet-centric, but will continue to clutch the roots of broadcast and print.  You will have the database marketer and statistical modelers working with a union of web channel and offline data.  What’s preventing it now?  A unified marketing database.  You see companies like Salford Systems circulating in this space.  And take a look at Unica’s blend of Enterprise Marketing Management…  I’d stay tuned to see what Unica has up their sleeve for bringing together online and offline.  When you can segment and target across online and offline campaigns, if I were pure web channel player only, like Omniture or CoreMetrics, I’d be a bit concerned that people are waking up to open systems, not closed black boxes.  WebTrends is already moving in this direction…  But they all remain far behind Unica when it comes to multichannel marketing.

And that’s just a few of the things the phenomenal eMetrics got me thinking about…  I hope to see you in Washington DC in October!