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.  

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.

Mobile analytics for Internet-enabled wireless devices is a fairly hot topic for companies seeking to acquire customers, extend their brand, or expose content in “innovative” ways.  Obviously, the iPhone and Blackberry are pushing development in this area forward, but there really aren’t a lot of players in this space. 

Nedstat, CoreMetrics, and Omniture offer capabilities mixed into their current offerings.  Nedstat even carves out some mobile specific reporting.  You can gain some insight into mobile activity from companies that enable log file processing, like Unica and WebTrends, but be prepared to configure a bunch of filters to isolate the data.

Lesser known companies pushing mobile offerings include: Amethon, Mobilytics, Bango, TigTags, Xiti, and AdMob.  Some of these mobile players are even offering capabilities where they cross-sell analytics as an integrated part of their ad networks, content delivery  and transactional processing systems, marketing and barcoding services, and even as infrastructure or network appliances.

On the audience measurement side, we’ve seen comScore acquire M:Metrics, which was no surprise to me.

On the multivariate testing side, we see my friends at SiteSpect offering mobile MVT testing capabilities. 

And I’ll bet we see Google get into this space within the next 6 months…  I’d even wager an announcement at eMetrics DC…

From what I can gather, when we’re talking about “mobile analytics” we’re talking about “mobile browser” activity across a variety of handsets, not everything that happens on the device. 

Measurement issues in this area include:

• Data Collection.  As many of you know, not all mobile browsers will execute javascript.  They cached the imagesThus, vendors offer us choices.  Folks like Mobilytics and Bango use an image-based data collection method, while Amethon offers a packet sniffer (they call it wireline detection), and we even have Omniture and Coremetrics talking about “no tag” implementations - what my good friend Phil Kemelor mentioned on his CMS Watch blog (”To compensate, you need to stuff the image tag with query strings that will collect the data you require for reporting.”)  Then we have Unica and WebTrends with log files.  Interestingly, packet sniffing has some advantages here because some devices pass unique id’s (such as the phone number) in the HTTP header or other unique id’s.
• Unique visitor identification due to lack of cookie support and IP addresses changing.  IP addresses change, I’m told, as they switch from tower to tower.   In addition many mobile devices will take the IP address of the gateway, making all the devices look the same “person.”  I’ve certainly seen evidence of the host changing pretty quickly during a mobile session. Compounding the difficulty in assessing “uniqueness” is that not all mobile devices support cookies.  In web analytics, cookies are used to define uniqueness.  The fallback method when you can’t use a cookie is IP address/user agent.  If you can’t set cookies and the IP address and user agents are the same, how do you identify uniqueness?   However, when you can detect a unique value in the header, you can easily detect uniqueness.
• Handset capability detection.  Does the device support WAP pushing, streaming video, ringtones, downloading video clips, and so on?
• Phone and Manufacturer identification.  Database from WURFL and DeviceAtlas can be used to identify phone and manufacturer device attributes.  Larger vendors are further behind on integrating this data into their current offerings, whereas the smaller niche players are making use of it. 
• Screen resolution detection.  The Mobile Marketing Association’s (MMA) standards for the four “standard” screen sizes may carry enough weight to push this disdained piece of metrics trivia available from javascript based tagging in web analytics into a brighter spotlight.
• Traffic source detection.  Capabilities for traffic sources seem rudimentary.  I don’t just want to know about search and direct entry.  But I want detection of sources from my marketing and advertising campaigns, rss feeds, and email newsletters, if mobile visitors are coming in from those channels.   Interestingly, Bango solves the campaign tracking issue by pushing you to a Bango-specific URL.
• Geographic identification.  Where are the visitors viewing your site coming from?  And what does the mobile audience environment “look like” in each country.  From this information you can extrapolate country-specifics for site optimization.  But not all devices enable geographic detection because the gateway’s IP address is used or the IP address from the network is used, not a GPS signal.
• No standards.  There are few, if any, commonly supported mobile standards and no web data standards, so the problem is no standards for the devices and no standards for the tools.  There are no standards.  Did I mention that there are no standards. 

So I was thinking, what would I want to see in a mobile analytics solution?  Allow me to riff here.

• Dashboards for KPI and specific-metric reporting.  Views, visits, visitors, referrers, popular pages, traffic sources, resolutions, geography, time-based reporting and custom defined KPI’s….
• Support for multiple data collection methods.  Logs, no-js image tags , and packet sniffers.  Let me pick what I need for whatever application fits my goals.
• Support for mobile-specific constructs not present in historic web analytics data.  Manufacturers, operators, handsets, and device capabilities.
• Advertising-based reports.  CTR, CPM, eCPM, that stuff…
• Tracking for mobile downloads, installed applications, SMS, and MMS.  Seems like a no-brainer.
• API’s.  Closed systems are dead ends for integrated marketing, so give me an API or enable pre-built integrations with other systems, like CRM.
• Segmentation.  By country, by device, by network, by manufacturer, and so on.  It’s necessary.
• Repeat or return visitor identification.  Simple measures of recency and frequency, core to media buying and planning and to site optimization, should be a data point available in mobile analytics.
• Conversion and goal metrics.  Do visitors on mobile devices convert better, worse, the same?  Do they reach site goals?  Without tying performance data  and outcomes to mobile visitor activity, I’m left wondering…
• Value scoring for engagement or proxy scoring for revenue and ROI analysis.  I want to be able to score attributes or actions to approximate an engagement score or to identify value or indicate revenue. 
• Non-human traffic and web-browser based detection and reporting.  Mobile pages are full of links.  The ads are links.  Mobile vendors must support detecting, filtering, and reporting, non human and web-based agents from pure mobile agents - otherwise the mobile data gets muddled and skewed.
• Data Export.  Must be able to export reports to Excel or Word, and email them.

So there’s a quick blogviation on Mobile.  Am I right, wrong, what did I miss?  Let me know…

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?

Hello good readers!  Every month I write a column for MediaPost’s Metrics Insider.  Here’s my most recent one:

Companies that derive revenue from multiple channels often have two analyst teams: the “database marketing team” and the “Web analysis team.”  These groups tend not to communicate.  In some companies, however, these teams are merging to form the “multichannel analytics team.”  This specialized team analyzes, reports, and evaluates both Web data and offline data — often in coordination with the “business intelligence team.”  The emergence of this new team structure makes sense for companies that are shifting their offline business models to become more online-centric, and thus want to understand value-generating connections among channels. 

Several macro-level catalysts are necessitating the shift to a multichannel approach to data collection and analysis.  The ongoing mainstreaming of the Internet channel for enabling commerce, conversation, and relationship marketing is certainly pushing this movement.  The burgeoning set of analytics tools that integrate with other technologies to enable event detection and trigger a customer-specific response is also promoting change in the way companies think about connecting offline and online data to improve overall business performance.

If database marketers and Web analysts are evolving into a new type of team, then what roles are necessary on this new multichannel team?  Here are a few:

• Web Analyst.  The overall Web analytics professional has a deep understanding of the Web channel.  This person uses a Web analytics tool to understand the performance of site traffic, online marketing campaigns, and to segment Web data in order to understand how visitors referred from certain channels navigate (or don’t) through the site.  They understand, measure, and report whether the site is fulfilling its purpose for conversion, task completion, and other KPIs when compared to business goals.  
• Site Optimizer.  A niche type of Web analytics professional, the site optimizer is in charge of determining the right approach for configuring and reporting the results for AB (champion/challenger) and multivariate tests.  This person is all about testing components of site and page design to yield the best combination of elements that provides a lift in a particular metric against a goal, such as conversion rate.  Content targeting may also fall under this person.
• Social Metrician.  Another niche type of Web analytics professional, the social media measurer is concerned about the performance of customer touchpoints outside of the main Web site.  He or she collects, monitors, and analyzes data related to things that happen “out there, on the Internet,” such as syndicated video, mobile, widgets, blogs, social networks, and other social media.
• Database Marketer.  The traditional offline analyst and database miner.  This role analyzes data from channels that are not online but may reference and promote online interaction, such as television, radio, print, catalogs, and direct mail.  Of course, these analytics skills can be applied to online data as well!
• Search Analyst.  The analytics professional in charge of keyword identification/selection, keyword management, bidding, and analyzing the outcomes of search.  He or she may be in charge of analyzing site performance against known SEO goals too, not just SEM.
• Market Researcher.  The traditional market researcher gathers, analyzes, and reports data about the overall market, key competitors, and customers. 
• Qualitative Analyst.  Part market researcher and part analyst, this individual is in charge of online customer and visitor surveying, relating customer feedback and visitor opinions to the context of on-site behavior to help deduce “why” people did something on your site.
• Ad Analyst.  Solely dedicated to assessing the performance of advertising campaigns, the ad analyst assesses and educates clients on ad campaign performance both online and offline.
• Audience Measurer.  The wielder of an audience measurement tool informs competitive decisions, influences media plans, and provides benchmarking and competitive data to give context to other data analysis activities, such as keyword bidding or media buying. 

How would these professionals all work together?  The market researcher’s data is used to help craft a customer-focused and competitively differentiated campaign strategy.  The audience measurer provides data that focuses the strategy on the right online demographics and sites, while the database marketer mines historic data to figure out the best-performing offline tactics for the identified demographics. 

Let’s say a mix of search, social media, and online and offline display ads are selected as part of the campaign.  The search analyst concentrates on SEO/SEM, while the ad analyst tracks the performance of display ads.  The social metrician examines the social media ecosystem’s response to the campaign.  The Web analyst analyzes how campaign-referred visitors behave and navigate through the site, taking into account the context of the qualitative analyst’s voice-of-customer data.  Meanwhile, the site optimizer tests landing pages and funnels to ensure they effectively convert visitors and fulfill business goals. 

For many companies, it would be unrealistic and perhaps impossible to find and hire people to fill each of the roles I’ve presented above.  In fact, in most companies these roles and activities are completed by only a few people, if at all.  An option for companies that seek to expand or combine teams is to look at consultants, contract workers, and full-time equivalents allocated across multiple people.

That said, companies that are unable to bridge together online and offline analytics teams will miss important data points.  In the digital future, we’ll see different types of analytics professionals working together across channels to yield profitable insights that support campaign and business goals.

The infoglut in web analytics is enormous.  So much data.  Companies report that 69% of all people who consume the data don’t understand it.  How does a business go about making sense of it all?  Formulating a comprehensive KPI (Key Performance Indicator) strategy is a big part of differentiating signal from noise and directing appropriate tool usage.  We’ve all heard about KPI’s before.  They are ratios or derivatives of metrics that pinpoint critical, business relevant web performance.   My good friend, Eric, even wrote a book (a BIG one) about it. 

The process of moving an organization through KPI Change Management starts with a well formulated plan for doing so.  Here are some tips for formulating your KPI plan: 

• Educate senior management and get managerial buy-in.  Education and buy-in can take shape via a number of methods.  Maybe you publish and circulate an internal-only white paper about the importance of KPI’s measurement.  Maybe you leave Eric’s book on the chair of your C-level executives.  Perhaps you hold a meeting and present the web site optimization process and how measurement via KPI’s provides the foundational informational on which to make site optimization decisions.  Perhaps you take your boss out to lunch and explain that basic reporting and tool access is helpful, but “Web analytics is hard” and that KPI’s give context to the data to staff that’s otherwise somewhat confused about what they pull for the tool.  You explain that KPI’s provide a focal point for centering analysis around business goals.  Whatever the method, the goal is managerial approval that “yes, you can do KPI’s.”
• Determine the audience for the KPI’s and train them.The importance of KPI’s will vary by stakeholder, and your KPI strategy needs to take that into account. Different segments of stakeholders will be interested in specific KPI’s, and you must accommodate that need.  As an analyst, you should identify the functional roles and job responsibilities of the people who are going to receive KPI reports.   Everyone may not be the right choice (though it could be), and it may make sense to concentrate a KPI rollout on the needs of the few or it may make sense to “go broad.”  Follow up with comprehensive training about your KPI project and how KPI’s can most effectively be used.
• Start with simple, well-qualified, highly relevant KPI’s.  While some folks with want to throw a “kitchen sink” strategy at KPI’s.  That’s a mistake.  If you report more than 5 to 10 KPI’s (imho) per stakeholding group you may end up with a set of unworkable, confusing, and neglected reports.  It’s better to report just a few, well qualified, highly relevant KPI’s.  How do you qualify them? By mapping KPI’s to important business objectives.  How do you know they are highly-relevant? Because you’ve compelled management to buy-in and to agree that they are critical indicators of site success. 
• Elicit the business goals for the KPI’s, compare KPI’s to goals, and report associated variances (i.e. deviations). Make sure you have determined business performance goals for KPI’s.  Goals give context for performance. It’s that simple.  Without goals, you have no context for determining what’s good and what’s bad.  If your conversion rate KPI is 5%.  Great!  So what though?  If you know your goal is 3%.  Awesome job.  If you know your goal is 10%.  Stop reading now, and get back to work - you have much work cut out for you. 
• Identify the frequency and format for reporting.  You need to determine a frequency that is timely and sustainable, and the format in which you present KPI reports needs to common enough that people can easily examine the data. Perhaps you deliver the reporting in Excel, make it available directly in your tool, use Xcellius, or create reports using a BI tool. 
• Automate the delivery of the reporting.Without automation, you may put on the Report Monkey suit and enter Excel hell.  Critical to the successful rollout of any KPI reporting is an automation plan.  Do you email reports, put them in a shared directory, create a set of reports in the tool and provide access, or deliver them in weekly presentations?  The best choice is the option that gets people to use them, listen, and understand what you are trying to do with KPI’s.
• Following the reporting up with analysis and guidance.  Depending on the size and scale or your organization and the resources you have to work with, it may not be possible to provide every stakeholder with detailed analysis.  But you need to do your best to follow up KPI reporting with true analysis and guidance.  Why are KPI’s going up or down?  What are the drivers of the changes? 
• Segment, segment, segment. Site level KPI’s are helpful in understanding overall audience and customer behavior, but they hide important details.  When you slice a KPI by a specific segment, you will realize insights that help you conclude what action to take next.  Overall site repeat visit rate is 37%, but the repeat visit rate for customers who use your “product lookup tool” is 96%.  What does that data indicate about how you market the site, or about why people are coming to the site? 
• Test, test, test.  As you measure > report > analyze > guide based on KPI’s you will undoubtedly determine actions to take on the site.  You should be testing the hypothesis behind these actions via controlled experimentation.   

There’s obviously a lot more to talk about here - from what constitutes a good KPI, to what types of KPI’s different stakeholders should examine, to what are the best KPI’s for particular site types and more.  I guess there’s more blog posts for that, but in the meantime I hope you’ve found this blogviation useful.  Let me know if you have any thoughts to share.

At some point in the career of a web analyst, you will be asked to investigate, assess, and possibly judge the current state of how a company “does” web analytics.  What are some of the areas you should ask about?  Here are some thoughts and a few questions to ask to help inform your analysis (and grease your mental gears):

• Business strategy.  Why does the organization do web analytics?  What’s the goal of having a web analytics team?  Who defines the strategy?  What is the strategy?
• Analytics organization and team structure.  Who is the chief owner of web analytics?  What does the analytics team look like?  How has the team structure been formalized in the organization?  Is the web analytics team effectively staffed and have enough control over resources to do the job?
• Process.  What analytics processes have been defined?  How does a site or site feature progress from not being measured to being effectively measured?
• Data collection. What methods for data collection are being used?  How much data is being collected, and for how long is it stored, and at what level (i.e. detail, aggregate)?
• Reporting.  What data is reported?  What do the reports look like?  Who creates them?  How are they distributed, and in what format?  To whom?  When?  How?
• Analysis.  What’s the difference in this company between reporting and analysis?  How is analysis communicated to stakeholders?  When?  How?
• KPI’s.  What Key Performance Indicators are you measuring?  How are they relevant to the business?  What actions have people taken from KPI analysis that improved business performance?
• Segmentation.  What audience and customer segments exist?  What audience and customer dimensions and attributes are segmented?  Why are they meaningful to the business?  What has the business learned and what action has been taken from the current segmentation analysis strategy?
• Technology.  What analytics technologies are being used?  What does the schema for web analytics look like?  What homegrown technologies are used?  What external technologies have you bought or deployed for analytics?
• Integration.  How is web analytics data integrated with other internal and external data?  Is it integrated with other systems, how? 
• Site Optimization.  Does the company test landing pages, and/or use AB or Multivariate testing software?  If so, whose software, and what business gains have been realized?
• Advertising/Advertisers. How is analytics used to inform or enable advertisers and advertising?
• Privacy.  What safeguards does the company take in protecting analytics data? 
• Qualitative Data.  Is qualitative data contextualized with web analytics data? Do you capture voice-of-customer data?  Use Net Promoter Scores?  Have a research department?  Does web analytics collaborate with research? 

Those are just a few questions to ask.  Many others can be asked.  What would you want to know, and what would you ask?  Please leave a comment.  I’d love to hear your thoughts.

Now for some random thoughts:

• News from Orem.  API / Fusion / Video Tracking… cool.  I’m pretty psyched that Omniture announced a web services API.  That’s fantastic, and confirms how truly important integration is now and will be in the future for analytics data (as I’ve been saying for years… Google will be next). 

Omniture has announced a new methodology, Fusion, and improved capabilities for tracking video.  All sounds very exciting.  But, like Eric, I’m wondering what revolutionary new methodology Fusion really is?  Or is just what Eric’s been saying for the last 4 yearsbranded by Omniture and delivered by the Great Belkin? 

Regarding the video capabilities, I haven’t seen a real demo yet, but I wasn’t immediately impressed with what I saw on my friend Marshall’s blog.  Instead of quartile tracking, it seems like you track the playhead (the part of the video playing) across audience aggregates in increments of one-twelfth, and you get some bubbly visualization (what would that look like with 10,000 videos on your site?), and better access to forums.

I’m hoping I haven’t seen the whole ball of wax, and I look forward to Omniture giving me the grand tour. 

But for a playhead visualization, I was much more impressed with what I saw from Visible Measures and their engagement curve.  And what the heck are those folks at Divinity Metrics up to for measuring video? 

• News from Novato.  One of my favorite gangs of web analytics folks reside in Northern California.  My colleagues at Semphonic have just released a rather impressive “Omniture Implementation Toolkit.” 

I was able to procure a copy, and I’m totally impressed.  It’s full of hard-learned and hard-earned real world practitioner knowledge.  If you are trying to implement Omniture, it is well worth the money. 

Now I’m not sure if this document competes with or acts as a companion to Fusion.  All I can say is that I know the folks at Semphonic are smart, savvy, and very experienced, and there are thousands of Omniture customers out there who could benefit from this document.

• X Change Conference.  I am totally excited for X Change brought to us this year by Semphonic and Web Analytics Demystified.  The last X Change in Napa at COPIA was one of the most intimate, educational, stimulating, and enjoyable conferences that I’ve been too (and did I mention the wine?).  It was pure “class” all the way (in both the sense of style and learning, and did I mention the wine? ;-). 

This year attendance is limited to 100 folks (99 if you count me ;).  Last year, I huddled on “Deploying Measurement Systems in Globally Distributed Enterprises.”  

If you aren’t familiar with X Change or Semphonic  check them out, and make sure to read a few of my favorite bloggers - the prolific deep thinker and expert Gary Angel, the always impressive (and fun) June D(ershewitz), and bright author and web analytics veteran, Phil Kemelor.