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. 

AVG has released an updated version that corrects the LinkScanner bot issue (build 138, July 4), which we’ve all noticed slamming our servers and analytics data over the last several weeks:

We have modified the Search-Shield component of the product to
only notify users of malicious sites.Search-Shield no longer
scans each search result online for new exploits, which was
causing the spikes that web masters addressed with us. However,
it is important to note that AVG still offers full protection
against potential exploits through the Active Surf-Shield
component of our product, which checks every page for malicious
content as it is visited, but before it is opened.

As you’ve just read in the quote above, AVG has stopped scanning each page that returns in a SERP for users of their free tool.  Instead pages will be scanned by proxy after a user clicks on the link. 

For paid users, it’s a little different.  SERP’s will still be scanned but via a pure database approach (not the DDOS approach :), which means the sites listed in SERP’s will be compared to a black list of known “bad” sites.  The blacklist is based on internal AVG research and from the real-time results reported by users who have opted-into AVG’s “prevalence reporting system” (a feature of AVG 8).  This means AVG is still scanning sites, but on a very limited basis, thus the detrimental effects on analytics should be very minimal and only caused by users who participate in prevelance reporting.  Still some data pollution will occur…  

AVG hasn’t confirmed that they’ve released a fix to the “noscript” issue I mentioned.  I do know they are working on it and have fixed the problem in internal builds.  Regardless, if the LinkScanner is working in the way they say it is, the problem will be negligible (but some data pollution will still occur ;).

Kudos to AVG Corporate, Roger Thompson, Pat Bitton, Greg Mosher, and all the other engineers who listened to the community on the web and worked quickly to fix the problem.  Now let’s hope the the build 138 update works as described. Time will tell.

AVG has obfuscated their user agent.  One of the current agents for customers of their free and paid tool now cloaks itself as IE6:

Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1)

In addition to the easily detectable user agents:

Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1;1813)

User Agent:Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1;1813)  

User Agent:Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1)

This news is not good.  If you filter SV1 agent, you risk filtering legitimate traffic from the IE6 browser.  A few folks have commented to me that one should filter the user agent anyway, because 1) IE6 is in decline and 2) most IE6 users have .NET installed, which will show in the user agent.  Still filtering it makes me a little uneasy.

Is this the death toll for log file analysis and services provided by ABCe (since they can’t filter this user agent either)?  Maybe it is.  AVG is touting that agent lacks HTTP Accept-Encoding, which is just dandy, but that information isn’t normally captured in logs.

So the current situation is this:

1. AVG has two user agents.  Both are filterable, but the SV1 agent is problematic to filter because you risk filtering legitimate traffic.
2. Both agents in the current version request gifs in noscript tags, inflating counts in page tag implementations with noscript configurations.  AVG claims they will fix this issue.
3. The bot uses”mad” bandwidth.  I’ve heard stories of bandwidth increasing 100x normal levels.  Some webmasters are serving dummy files to the recognizable user agents, some aren’t serving content to IE 6 browsers (crazy), and some are redirecting the bot back to AVG (thus inflating AVG’s bandwidth, LOL!).
4. Evidence points to this bot NOT inflating clicks from paid search (i.e. PPC) and thus NOT committing click fraud.   But it doesn’t remain out of the realm of possibility that the scanner may be accessing an ad vendor click redirector and causing a click.  Not trying to spread FUD here, just making a point. 
5. AVG is looking at option of checking either an external db (hosted by AVG) or a local cache to verify sites in SERP’s have been “scanned by AVG,” instead of repeatedly scanning sites every time they are listed in SERP, to reduce the bandwidth issue and minimize fraudulent entries in log files.
6. AVG is thinking about enabling white listing of sites, so they are skipped by the scanner.
7. AVG is thinking about exposing a meta-tag that instructs the scanner to ignore the site.

Good luck with this nasty bot!  Interestingly, here’s how you smurf a site with the AVG LinkScanner. 

Here’s the deal.  AVG LinkScanner doesn’t execute javascript nor take cookies.  I had that confirmed by the Chief Research Officer at AVG, Roger Thompson. 

So why is the AVG user agent showing up in that data collected from certain page tag configurations?  The AVG LinkScanner currently requests gifs in noscript tags!

A best practice in web analytic’s page tag configuration is to use the noscript tag to serve the gif to non-javascript executing browsers.  Here’s some commonly seen (obscured) code for doing that:

<noscript>
<div><img alt=”foo” id=”bar” width=”1″ height=”1″ src=”http://
foo.bar.com/xyzab57yw10000s1s8g0boozt_9t1x/foo.gif?baruri=/
nojavascript&xy.js=No&xy.tv=1.2.3″ mce_src=”http://
foo.bar.com/xyzab57yw10000s1s8g0boozt_9t1x/foo.gif?baruri=/
nojavascript&xy.js=No&xy.tv=1.2.3″div>
</noscript>

<NOSCRIPT>
<IMG
src=”//foo.bar.com/xyz.gif?Log=1&URL=/javascript_disabled” mce_src=”//foo.bar.com/xyz.gif?Log=1&URL=/javascript_disabled”
BORDER=”0″ WIDTH=”1″ HEIGHT=”1″ />
</NOSCRIPT>

<noscript>
<img src=http://pt.foobar.com/images/xyz.gif?js=0” height=”1″
width=”1″
border=”0″ hspace=”0″ vspace=”0″ alt=”"> 

Thus, if you are using noscript tags in your page tag *and* someone with the AVG Linkscanner views a SERP (search engine results page)  from Google/Yahoo/MSN that lists your site, the traffic from the LinkScanner will be counted. 

Of course the simple solution to fix this problem is to exclude the user agent: 

Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1;1813)

If don’t have full control over your page tag based web analytics implementation (i.e. hosted), you need to verify that your vendor has excluded this agent.   And you should have them audit your data going back to April, and refund/credit you any money.  Good luck with that though!

How big is the problem?  Well, it depends!

The amount of AVG traffic will vary dramatically by site.  Your site must show up in the SERP’s on computers of visitors that have AVG LinkScanner installed, and you must be using noscript tags to serve the gif.

I’ve made AVG aware of this issue.  And frankly, they’ve been a fantastic company to work with, so I’m sticking with them (for now ;).  First they allowed me to join a private Google group to discuss my findings, both the Head of Global Communications and Chief Research Officer quickly responded to all my emails (good social media response), and their engineers are looking into this issue so that they can fix it…  That’s pretty impressive and quick response.  So cheers to them!

It’s worth mentioning that the LinkScanner isn’t _supposed_ to request images, so I do think this issue will get fixed.

Only time will tell whether or not AVG obfuscates the user agent so it looks just like a “normal” browser.  Let’s hope not! 

What I do find interesting is that I’m already hearing that an agent exists with the string (Mozillia/4.0 (compatible; MSIE 6.0; Windows NT 5.1;1813). Note the “ia” mispelling of Mozilla as incorrectly documented here.  And it accepts cookies.  So AVG’s agent is already being spoofed.  Not good, not good.

A question any practitioner of Internet-based analytics will be asked by many different stakeholders is “why don’t the numbers match?”  Counts of the identically named metrics from ad servers don’t match the web analytics tool, which don’t match the for-pay third party audience measurement tools, which don’t match the free audience measurement tools, which never match any of the homegrown internal measurement tools.  And none of them ever match each other.

So it’s a good question certainly valid to ask.  The answers are even fairly easy to understand, but the root causes are often difficult to pinpoint and even harder, if possible at all, to remedy.  The fact of the matter is that data discrepancies in analytics result for a multitude of reasons, such as:

• Different data collection methods.  We have a bunch of tools and services that collect web data using various, non-standardized, proprietary data collection methods.  Ad servers use javascript page tags.  Many web analytics tools use page tags too, but it’s not uncommon in web analytics to use additional methods, such as log files or packet sniffers.  Or perhaps a combination of these methods, called hybrid data collection.  And all the tools have different algorithms for processing the data collected.

On the audience measurement side, data is collected from self-selecting panels who install proprietary software (i.e. toolbars and so on) on their computers, perhaps at work or at their university, but most likely at home.  Then, the collected data from different panels is rolled-up and combined, and the limited subset of the Internet population that chooses to be monitored, in exchange for some incentive, is inflated and projected to the entire Internet audience using proprietary statistical methods.  We also have data collected from a limited set of geographically specific ISP’s.  And regardless of whether we’re talking about audience measurement or web analytics, the different data collection methods often, but not always, involve cookies and all their inherent issues of cookie deletion.  

• Unique data models.  Ad servers aren’t focused on counting page views and the other dimension of web analytics (visits, time, and so on).  Rather ad servers focus on serving and counting impressions served (and loads of related derivative calculations, like CTR, CPC, and view–thru).  Metrics are based on an ad request and an ad code.  Ads may or may not be targeted to a page, and instead to various constructs, like a “zone” or “keyword.”  What that means is that the “page” dimension may not even exist in your ad server’s data model.  In other words, you aren’t looking at impressions measured on a page, but rather at the number of impressions served in a different conceptual construct.  That’s one of the reasons why people say metrics and ad-serving systems “don’t measure the same thing.”
• Untagged pages.  Specific to technologies that collect data or serve ads using javascript page tags, there are challenges to ensuring and verifying complete coverage of page tags across every page on a site.  When the pages aren’t all tagged with the different tags for the assorted technologies, guess what?  The numbers won’t come close to falling within tolerable variances.  And questions and skepticism will ensue.
• Non-JS executing clients and ad blocking software.  Let’s imagine for the moment, your site is perfectly tagged for all technologies, so the numbers between your ad server will be close to your web analytics system, right?  Nope, regardless of data model issues, not all browsers execute javascript and many Firefox users have installed Ad Block Plus. 
• Cookie issues.  When you’re counting based on cookies, third-party cookies get blocked (often by privacy software).  Many ad servers and web analytics tools still serve third party cookies, and many corporations have not tricked out their DNS to accommodate this issue.  And we all know how cookie deletion affects unique visitor counts, even if you use first-party cookies.
• Many other issues.  Latency from visitors moving off the page prior to the tag executing to latency in the call to pick up an ad from a third party while your ad server counts the traffic (so your ad count differs from the agency’s count), to refresh rates making it hard to correlate page views and impressions, to no rich media installed and no fallback, to robotic traffic not being filtered from logs or tags, to certain types of user agents (such as mobile devices) not executing javascript… there’s a whole host of other factors that cause data discrepancies.

And of course, there’s always the nebulous issue around the complete lack of consensus-based, enforceable standards for online measurement.  No industry organization can say what vendors or companies “must” do, only what they “should” do… And no industry body is going to get successful companies to change their secret sauce just because they said so…

So what’s a practitioner to do?  Understand the potential sources of discrepancies.  Work with your team (from IT to vendors) to prevent and minimize the root causes when possible.  Educate your team when discrepancies are not remediable.  Ensure you use the different sources of metrics judiciously in the context of your business goals.  Finally, realize that none of the tools are more “correct” than any other.  All of our analytics tools serve different, and sometimes overlapping, business purposes - from counting ads, to influencing media buying, to sizing audiences, to measuring business performance, and to optimizing the site.

One of the many things on my mind in the online world these days is “deep packet inspection.” 

First, let me digress, packet sniffing isn’t new to web analytics.  From Accrue to Omniture (Visual Discover Sensor?) to AuriQ to Metronome Labs.  Packet sniffers are used to “do web analytics.”  It’s an uncommon method when compared to javascript page tags.

Web analytics packet sniffers are used to write logs for sessionization (and thus measure) the traffic on behalf of site owners (who don’t want to use tags or logs).  Once you’ve logged and sessionized you know what content people have looked at or downloaded on your site. 

“Deep packet inspection,” like WA sniffers looks at the entire payloadof packets in real-time across a huge number of simultaneous sessions.  Deep packet inspection, like regular packet sniffing, examines the files downloaded and the content of the pages viewed - the whole ball of wax. 

Deep packet inspection is being offered as a hardware/software technology by companies like FrontPorch and Sandvine (in the US) and Phorm(in the UK).  These companies are selling the technology to ISP’s (like Charter, Comcast, and Virgin Media) so that they can monitor the sites visited and the keywords used by customers, and then use the data collected for behavioral targeting.  The ISP’s want a slice of the juicy, lucrative online ad business.

What’s the difference?  Site owners collect data about what you do on ONE site (or a portfolio of their sites).  ISP’s collect data about what you do on EVERY site you visit.  As I understand it, some of these companies create an anonymous profile of your surfing activity by assigning a unique key to your browser.  Then they monitor the site’s visited by your browser, and use that data so that the ISP, or the companies to which they sell your data, can serve you what they conclude to be relevant, behaviorally targeted ads. 

Get it?  Packet sniffing by site owners = knowing about one site you visit.  Deep packet inspection by ISP’s = knowing about every site you visit.

Now to digress… In web analytics, we know that web analytics data is collected anonymously.  Unless there’s a login, you don’t know exactly who is coming from that IP address.  And in many cases, most companies data warehouses only contain purchase information, not the entire clickstream.  Once the data is collected, if you have the right architectures you can decode cookie values to people, and make that data non-anonymous (i.e PII).  Not difficult to do with some smart BI folks on your side.  

An ISP already knows who you are and can already identify the sites you visit.  Probably not that easily though on individual level.  They can dig through the logs, etc… 

So what’s the big deal and all the hoo-hah about  the “deep packet inspection” Phorm and FrontPorch are doing?   It’s the data they are collecting and the repository they are building containing data about every site you visit and all the content you view and download… Of course, these companies say that it’s all done anonymously and that your “privacy” is preserved “to the greatest extent possible.” 

Now let me quote Sir Tim Berners-Lee about the data collected from Phorm’s ISP tracking: “It’s mine - you can’t have it. If you want to use it for something, then you have to negotiate with me. I have to agree, I have to understand what I’m getting in return.”

And that’s the point of the blogviation, Tim is correct.  In web analytics, we do this - we try to operate within Tim’s constraints.  We enable opt-in with P3P statements and disclosures when you register/login.  Privacy policies disclose what we are doing with the data.  It’s just ethical and smart business practice to do so.

Thus, I think FrontPorch and Phorm and all the ISP’s who want a piece of online advertising should adhere to the following five rules for their services.

1. Move to an obvious “opt-in” model with full disclosure.  Tracking via “deep packet inspection” should be an all opt-in model.  If you want anonymous data from your browser collected so that you can be behaviorally targeted, then you should opt-in to be.  Right now, it’s seems to be all opt-out.  You probably don’t know if it’s being done to you.  It’s buried in fine print you’ve probably never read.  Is that your fault you didn’t read the fine print? Yeah, but the point is it shouldn’t be buried in the fine print…
2. Provide me with access to the data collected.  If I opt-in, I should be able to see the data collected from my browser.  It’s very simple.  I demand to see what you are collecting about my browser.  If you are building a profile, then I demand to see the data collected in the profile.  If it’s all anonymous, then explain how it is in detail, and then follow rule #1.
3. Enable me to edit or prevent the data from being collected.  If I opt-in, I want to be able to edit or prevent certain types of data from being collected.  If you’re tracking my browser, alert me before the data is transmitted, so I can decide if I want to share it.  If a profile is built, I want to be able to edit it!
4. Let me opt-out at any time EASILY. If I’ve opted in, and I’m unhappy with the service, allow me to opt-out simply.  Having to set an opt-out cookie on my browser is absolutely and completely absurd.  I want to be able to fully opt-out at the ISP level, just once forever, not at the browser level every time cookies are deleted.  Make it easy and permanent, not easily deletable.
5. Disclose who you sell my data too.  Like online list rentals, the next step in all this ISP profiling is selling the data to third-parties.  Let me know what you’re doing with my data-before you do it- so I can opt out or prevent it from being sold to parties to which I don’t want it being sold.

Consumers must be given a choice for preserving their privacy.  Anonymity to the “greatest extent possible” is not enough and neither are short-sighted opt-out cookies.  Companies like Phorm and Front Porch would be wise to apply these rules to regulate themselves.  Otherwise freedom-loving governments will almost certainly regulate them. 

And I haven’t even mentioned the issues with net neutrality and deep packet inspection (i.e. traffic shaping and access restrictions (called “throttling” as Clint points out in the comment), have I?

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! 

Web Analytics Key Performance Indicators (KPI’s) are critical for breaking through the dataglut spewing forth from your web analytics tool.   I mean there’s a just a ton of data in web analytics, and the majority of it tends not to be very useful or applicable for improving your business performance.  While it’s wonderful to have a tool that lets you cut, cross, and slice loads of data every which way but loose, its can be a real challenge to frame the data or put it in context in a way that helps your business optimize the web site.   That’s why I like KPI’s - they identify meaningful, business-focused relationships in your analytics data.  By understanding KPI drivers, setting expectations for KPI performance, and analyzing your KPI’s toward defined goals for those KPI’s, you increase understanding of data, alleviate data confusion, and provide focus for the usage of your web analytics tool.

For those of you who don’t have a KPI strategy or who are just getting into analytics, an easy way to understand a KPI is to consider the example of when you are driving somewhere and trying to get there within a certain period of time.  If your goals is drive 60 miles (kilometers, my European friends) in exactly 60 minutes, you know that you need to drive 60 miles per hour (or KPH).  If you go faster, you will arrive early, if you go slower you won’t meet your goal and will arrive past your deadline.   So as you travel along the road, you measure the KPI of your speed. That’s what is important to measure on your trip.  Of course you may measure other KPI’s like the amount of fuel left or the miles you’ve traveled… those certainly may be KPI’s you measure.  But you definitely don’t need to measure you compression ratio or oil pressure even though it’s available data from your car.  In the same way, when you are looking at web analytics data, you don’t want to track everything, only those things that are important to your business performance toward goals. 

Several activities can assist the creation of KPI’s.  Here are a few of them:

• Determine the Business Strategy.  Why is the company funding and developing an online mission?  What is the strategy?  KPI’s can help you figure out if it’s working.  To find the KPI’s that will help, the web analyst should be asking the question how can web analytics be used to formulate, implement and evaluate cross-functional decisions that will enable an organization to achieve objectives? How will web analytics be used in the process of specifying the organization’s objectives, developing policies and plans to achieve these objectives, and allocating resources to implement the policies and plans to achieve the organization’s objectives?
• Define the Site’s Goals and why the Site Exists.  I covered this in a post a few months ago.  A understanding of why your site exists enables you to effectively use online metrics.  You need to define the purpose of your site in order to create effective KPI’s.  Once you’ve defined your site’s purpose, you are positioned to examine Web data in way that helps you determine whether your site delivers on its purpose — does it exist effectively?   Create your KPI’s, identify goals for your KPI’s, and track your performance against those goals.
• Recognize Value Drivers.  How does the business make money on the site? Monetization, in cases where profitability is important, influences what you should be measuring.  If you run a media site, you probably make money from content consumption (the recency and frequency of content consumption), conversation (social media, such as contributions or comments), and conversion (the rate at which people complete certain value driving actions, like signing up for newsletters, rss feed, webcasts, print subscriptions, or downloading certain content types, like white papers).  So you create goals for and measure KPI performance around those value drivers.
• Map Organizational Roles.  Classify your organization into audiences for your KPI’s based what they do on your web site.  You may create KPI’s around function or action of the actors who receive your KPI reports.  Function defines the group that KPI’s are focused for, such as product development or editorial.  Action defines what those people do on the site to make it successful.  By understanding function and action of key actors on your sites, you gain insight into the type of data needed in KPI’s and the number of different KPI reports you may need to roll out.
• Understand the Customer.  KPI’s purely focused on internal function and actions are important, they need to be customer focused.   If you think measuring conversion is important, while your customers tend to come to your site for informational or non-transactional purposes and then go elsewhere to convert, you may be disconnected from the reality of why your site exists.   Learn customer goals from VOC (voice of customer) data and by examining historic behavioral data of key segments.  Make sure you don’t create KPI’s that are vain or inane.  Instead create KPI’s that help you guide action internally so that your business meets the needs of your customers.

Framing your KPI development around the five bullet points I listed above will help you create KPI’s that assist your team in guiding business performance toward goals - while not forgetting to consider some of the core elements of online business: business strategy, site performance goals, value drivers, the human organization, and the customer. 

Now segment, segment, segment your KPI’s!