Politics is once again rearing its empty head in the form of “Do Not Track” legislation. There is a bill working its way through the New York legislature that would effectively limit the ability of web advertisers to collect information about consumers as they move around the internet. The corollary most often cited is that this is the on-line version of the “Do Not Call” lists that became so popular a few years back.

If this legislation plays out like others of its ilk, a noisy, self-righteous minority will rock a large, successful industry back on its heels, and the ultimate loser will be all of us. There are some fundamental differences between what drove the Do Not Call (DNC) initiative vs. what is driving Do Not Track (DNT). We’ve all had telemarketers call at dinner, trying to sell us something we don’t want. It’s annoying because 1) we’re having dinner, and 2) we are unlikely to want what they sell. When the telemarketing industry was in its heyday in the late 90s and early 2000s, the internet was in a very nascent stage, particularly compared to what it’s like now. The only way to reach people then was snail mail or phone, and phone calls gave that critical real-time contact. The timing is annoying, but there’s no point in calling during the day if no one is home. The real issue is not timing, but content, and part of the reason the telemarketing industry has such a bad reputation is due to poor targeting capabilities.

That whole paradigm has shifted very rapidly in recent years with the rise of on-line behavioral targeting applications. The internet as an access mechanism is vastly more data-centric than telemarketing could ever hope to be, which means that telemarketing’s two strongest limitations (bad timing and irrelevancy) are not applicable. E-mails do not interrupt the way phone calls do, and if the right targeting applications have been put in place, the information you get is actually on point. I mean, what is wrong with advertising that matches what you’re trying to find?

The worst people to define technology usage are politicians. They’re not technologists, they’re not even business people; they pander to the loudest voice, and it’s always the extremists. I don’t think most people honestly give much thought to being tracked on-line, they just go about their business, blissfully unaware. Agitators with too much time on their hands feel compelled to get the slumbering masses all wound up by screaming “privacy violation” at the top of their lungs, and they learned very quickly that the fastest panic response will come from politicians. Meanwhile, the ad networks are too busy sniping at each other to notice they’re all about to get a NebuAd style whupping (again). Hope you enjoy getting spam, because if this legislation passes, you’re going to start getting a whole lot more.

One of the things that has always puzzled me are position descriptions for a “Sales and Marketing” VP. The fact that sales and marketing are often mentioned in the same breath demonstrates a lack of understanding of the fundamental difference between sales and marketing, particularly in smaller firms. Having worked with both domains extensively for years, I continue to be surprised by the extent of the number of people at a senior level who don’t understand the difference.

There are lots of analogies at play here, the best I’ve come up with (so far) is the race car model. If Sales is the guy driving the Formula 1, Marketing designed the engine, built the car, paved the road, went out and got sponsors, provided detailed performance specifications on the other cars and drivers, and provides pit crew support (including spare parts, personnel, and fuel).

Designed the engine. In most companies Product Management resides within Marketing. This is the function that essentially tells engineering what to build through process-centric deliverables such as Product Requirements Documents (PRDs), and Market Requirements Documents (MRDs). The MRD/PRD is based on market requirements driven by extensive research on customer needs, competitive offsets, channel requirements, etc. They are generally long, very detailed, and updated continuously in parallel with engineering development efforts.

Build the car. What defines the user experience? How easy to use and intuitive is the product or service? What does packaging and pricing look like? Product Marketing owns this function, and again, serves as a strong bridge between end-users and engineering. The work is (like most marketing efforts) very detailed and surprisingly technical.

Pave the road. Arguably the most complex task. Raising awareness of your product/service requires reaching out to potential customers, channel partners, analysts, journalists, bloggers, as well as competitors (whom you’ll reach whether you want to or not). This is where the analytic aspect of marketing kicks in; Search Engine Optimization, Search Engine Marketing, optimizing landing pages, creation and tracking of microsites, multi-level, multi-touch rich media outreach campaigns, portal placements, using blogs as media advisories, the list goes on for quite a while, and each aspect has a detailed metrics component that needs to tie into profitability analysis both for the individual product and the overall product portfolio. This particular aspect of marketing has become incredibly more complex as the Internet grows into a major distribution and information channel for most companies.

Get sponsors. One of the most valuable assets in a marketing portfolio is a happy customer who is willing to serve as a reference. This is one area where sales gets involved (since they own the customer), but Marketing spends a lot of time cultivating and grooming the customer champion for events that include analyst and media interviews, participation in webinars and public panels, guest blogging, etc.

Competitive analysis. Who is sales going to be going up against, and how are they likely to be attacked? What is the best offense to counter their defense? Detailed, continuous due diligence on competitors and their ecosystem is the province of marketing, and is one of the most useful tools supplied to sales reps before they walk in to speak with a prospect.

Pit crew. Marketing provide sales with a steady stream of qualified leads, provides all support materials they need (both on-line and off-line), schedules participation in industry events–as well as pre-show promotion, post-show follow-up, plus managing the show itself and all the leads that are generated.

All of this is very different from a Sales skill set. Sales has always been more about relationship management (herding the rabid cats), which has an entirely different set of requirements. The main difference? Sales is difficult, Marketing is complicated. I would also point out that Sales is by far the most critical role in a company. No sales, no revenue. No revenue, no company. It doesn’t matter how brilliant your engineering is, or how clever your marketing is, if people aren’t buying, none of that matters. However, sales cannot succeed without marketing; finding someone with the skill set to manage both functions is nearly impossible, because the skills required are so very different. On the other hand, finding someone who understands both functions and is smart enough to hire genuine experts at each should (in theory) be more straightforward, and can provide a genuine framework for success.

Last month the “Future of Privacy Forum” had its public unveiling. Privacy advocates who are long on rhetoric and short on grasp lined up to hammer the on-line advertising industry for “violating” consumer privacy “rights” by tracking on-line behavior in order to serve ads that are more consistent with what the consumer is actually trying to find. There has been a cry for regulation at both the state and federal levels to contain advertisers who are trying to do a more effective job of serving ads that consumers might actually be interested in. This triggers several concerns.

People have to get past the notion that they have a “right” to privacy. If you want privacy, get off the grid. The whole point of being on-line is to have access to unlimited information, and the corollary to that is everyone has some level of access to you (that’s why the information is unlimited, everyone participates). If you’re moving around on-line and looking for something, what is the problem with someone trying to help? It’s hard enough to find information as it is (e.g. 52,000,000 search returns in 0.07 seconds, when I’m only looking for one thing).

Do you enjoy getting spam? Of course not. You know who likes it even less? The people who send it out. They’re looking to make money, not waste your time. Any tracking technology is designed to reduce the amount of irrelevant advertising people receive; you get less spam, the advertising companies don’t waste your time or their money, the ads you do get resonate because they’re relevant, the advertisers make more money, the economy grows, people work, etc. Forcing advertisers to guess by deleting cookies or tightening browser privacy settings takes the irrelevancy aspect of advertising and shoots it through the roof, forcing vast inefficiencies throughout the system; advertisers make less money, the economy shrinks, etc.

And a state level regulatory framework? This is arguably the worst thought through idea in the lot. More people are moving their on-line experience to a mobile paradigm, now privacy advocates want advertisers to comply with arbitrary laws driven by random consumer locations as they cross imaginary lines?

Who is to blame for this morass? The advertising industry. That’s right. If they want to make money, they need to find a way to co-opt the privacy advocates. This will imply compromise (any idea that works for everyone is based on compromise, on-line advertising is no different), but the current stance taken by the ad industry is clearly not resonating. Even if no regulatory oversight is put in place, the ambient noise is still a distraction from their core business. Bottom line? The advertising industry needs to get over themselves and get ahead of this curve, the alternative will cripple what is already a rapidly weakening business sector.

For years the content and data worlds co-existed in relative isolation from each other. Content was the province of authors, reviewers, editors, people who were responsible for communications in written form. The data analysts, architects and developers operated in their own little esoteric world, and rarely came in contact with the content folks. The sudden rise of the internet triggered a fundamental shift in the content model, which has accelerated with the expansion of integrated rich media applications driven by meta-data management. Because of the increasing prevalence of application frameworks such as XML, the content world is finally catching up to the data world in terms of creation, distribution, and manipulation of their operational models.

Data-centric models have always had a huge advantage over content-centric models because of the level of granularity and manipulation they afforded end users. Now that content can be reduced into snippets that still maintain context and relevancy, these content elements can be stored in an object database and manipulated by ontology-driven tools. It appears the content world has finally caught up to the data world in terms of developing a fine-tuned grasp of it’s underlying information.

The implications of this are significant; for decades the advertising and marketing industries have been limited to a one-size-fits-all consumer outreach model, even now the best alternative offered by behavioral targeting firms is a cluster than numbers in the thousands and still only manages a response rate of less than 2%. Content needs to be architected, just like data; this has nothing to do with the narrative or creative process, it has to do with how information will be managed so that it can be reused, repurposed, and targeted to a much finer level of execution. When the content folks finally figure out what the data folks have know for years, you’ll start to see response rates on marketing initiatives climb steeply, because the customer experience has become much more relevant, or as I prefer to say, we can now target a cluster of one.

As social media continues to evolve, expand, and refocus, it triggers the obvious questions of where to next, and why? A number of “traditional” social networks such as Facebook and MySpace have started seeping into the corporate domain, and more business oriented social networks such as LinkedIn are going in at full speed. Most social network companies that are expanding their focus to the corporate market are doing so from a perspective that maps to their comfort zone. The result is a service that has a very similar look and feel to the non-corporate social network, just not as entertaining. This is probably a good idea, since most of what I’ve seen posted on MySpace pages is not something I would want associated with me on an enterprise network (friends are friends, colleagues are colleagues). I’m friendly with my colleagues, but it’s not the same dynamic as with my friends.

The real issue, however, is what is the proper operating context for a corporate social network? You can create extended interest groups around new hires, corporate alumni, customers, etc. but to a certain extent this demarcation is arbitrary. The real value develops when a group finds it’s collaborative efforts have accelerated because of a more effective communications framework (which is really the primary deliverable for a social network). I’ve been responsible for driving cross-functional integration efforts in the past, and the closest analogy I can find is herding rabid cats. This is particularly the case when people are forced to operate outside their comfort zone. A tool that allows professionals with disparate views (for example, marketing, engineering, and operations) to truly understand each other’s needs in aggregate, and in real-time, would be a huge step forward in collaborative thinking. Batting e-mails back and forth won’t do it; there are people who should be in the loop that aren’t, and some who are that should not be. The whole point of a social network is to share more of yourself with others, and in so doing, create a stronger connection. Focusing the objective of a stronger group connection towards a complex project (such as a global product launch) is an ideal venue for a corporate social network. Once this type of deployment gets traction, I think the whole collaborative/social space will really take off.

Now that behavioral targeting vendor NebuAd has skidded off a cliff and burst into flames, it appears that it’s counterpart in Europe, a company called Phorm, is about to follow the same trajectory, and for essentially the same reasons. In both cases the companies have massively underestimated the public response to having a third party track their on-line behavior, and even worse, then sell that data to the highest bidder. The argument that it provides higher relevancy on ads served does not appear to be holding water. It also makes you wonder how carefully this technology was vetted prior to Phorm running out in public screaming “look at me!” I mean, at a minimum they must have seen the whupping NebuAd just went through, and then they go and do the exact same thing? And keep in mind, Europeans are even more finicky about their privacy than Americans, so Phorm is about to step into an even more hostile environment.

How do you get around this type of morass? Embrace your enemy, convert your foes. The first people they should have spoken to were the privacy advocates; there is a way to co-opt people like this, it will be complicated, and may require compromise, but it beats the public humiliation they’re about to go through. This same process has to be followed for analysts and the media that track this space, get the pundits to understand and buy into what you’re selling. There is probably a bit more compromise here as well, but this is what a market-driven launch should look like. Both Phorm and NebuAd are clear examples of an engineering-driven launch, and we can all see how that’s working out.

I was at a dinner party recently where one of the guests was trying to explain the concept of virtualization applications, in the context of a start-up he was trying to get off the ground. It started me thinking that the intersection of analytic applications with virtualization have the potential to provide a much higher level of strategic insight across the enterprise, particularly in complex, collaborative environments. Several years ago I worked with a predictive analytic start-up that was focused on IT Operations planning for manufacturers; our intent was to predict subsystem failure before the event itself. The application was remarkably accurate, but we never really took it past predicting failure rates for very specific areas. Integrating a predictive engine with a virtualization engine could have allowed us to run simulations across the entire information and production lifecycle management process. The main reason we didn’t develop this further was that at that time virtualization technology was still in its infancy, but that is no longer the case. Rather than saying “what if this specific thing happens”, we could now say “what if we change the whole model”, or “what is the highest probability effect of changing out our suppliers, or outsourcing a critical process”? This is another potential technology confluence point coming up, and one that is well worth keeping an eye on. More on this later.

While Business Intelligence, Predictive Analytics, and other forms of metrics-driven insights into corporate behavior have burrowed into select areas of Fortune 500 companies, the use of this technology in broader markets is in its infancy at best. There is potentially a huge opportunity for companies to tap into what is essentially a greenfield opportunity; the vast majority of companies in the US (and globally) are small businesses, with the same problems and challenges as the multi-nationals on a much smaller scale. The primary challenge for small businesses is a lack of sophisticated tools to analyze their business processes, and the hidden, or secondary problem with this is that even if a sophisticated solution was available at a reasonable price, most business owners wouldn’t have a clue as to how to get started.

The day to day processes that define how a business operates are generally not technical in nature, but they are very transactional. Most people tend to deal with the same types of situations on a regular basis, and as such tend to become “experts” in specific aspects of their part of the transaction flow. It’s this type of granularity that is begging for a business intelligence overlay; connecting the expert analyst capabilities with expert process capabilities is what will move this forward, with one caveat. The analyst has to adapt to the process expert, and for two reasons; analysis has to fit the business model, and most important, the process expert is the customer, who is well within their rights to expect to have their needs met.

One of the core gating factors in deploying a Business Intelligence application is its overall effect on production workflow for the company in question. Over the years I’ve worked with companies who went through a Six Sigma process; they were all big (Fortune 1000) companies, with lots of infrastructure and process methodologies already in place, as well as a surplus of people who seemed to have the bandwidth to take on an additional large, complex project. Even within the context of these types of companies, implementing a structured, rigorous process for quality improvement was disruptive (“gee Dan, I know you have a sales meeting in Europe next week, but we really need you here for the Six Sigma meeting”). It’s possible to get away with this sort of thing at a large company (primarily due to excess bandwidth), but it becomes a much greater challenge when you’re dealing with a small or medium sized business where every single person is critical to keeping the machine moving forward.

In order for BI to have the desired effect on the quality of an organization’s information process flow, the deployment of the application has to integrate into the existing workflow without being disruptive. I’m not suggesting that business intelligence should be applied to what is potentially a faulty process, what I’m saying it that these companies can’t be turned on a dime. An increase in focus on quality does not just affect internal processes, it also affects customers, channel partners, customer support, etc (implementing any type of change across a company always slows processes down before speeding them up, and customers may not understand and appreciate the slow down). In an ideal world, the application of BI to an aggregate process flow would be nearly invisible; most interactions within and between systems are transactional anyway, so an incremental transactional improvement would be less disruptive, and because the effect on the workflow (and those responsible) is incremental on a transactional level, it is less likely to be disruptive, and more likely to begin to effect the desired change. Which is to say, the development of process rigor should be an integral and evolutionary part of a BI introduction, rather than a precedent.

There has been a fair amount of recent coverage on the shortcomings of business intelligence as the concept starts to move out of the purview of Fortune 1000 companies and into more mainstream usage. One area referenced consistently as an area needing work is the BI community’s steady focus on structured data at the expense of unstructured data. Business intelligence as an application suite is still relatively nascent in its deployment; while it is widely used by large companies (although not consistently or comprehensively), the vast majority of businesses are not Fortune 1000, and wouldn’t recognize business intelligence if it hit them in the head. The opportunity here is that with no prior frame of reference, there is a great opening for BI vendors to step in with a solution that is ideally geared towards the requirements of the SMB market. Two core drivers here are 1) use of unstructured data as a BI feed, and 2) dumbing down the application as much as possible so mere mortals can feel comfortable using the product on a transactional level.

Most data (over 80%) in most companies is unstructured. E-mails, narrative reports, legal documents, any product centric information (data sheets, functional specifications, etc.) is unstructured, and it’s where the majority of mission critical information exists. There is a huge inventory of information just sitting there, beyond the reach of BI or analytics engines because most BI apps are designed to think in a linear fashion, and unstructured data is by definition non-linear. You can add metatags or some form of XML structure to your documentation (which is finally starting to happen), but this also pre-supposes some sort of referential taxonomy to organize the information once it’s been made ready to be pulled into a BI application. The people who are most likely to be transactional users of this type of technology are not trained to think in terms of a taxonomy, this is generally a luxury that only large companies can afford. So that is one area that would need to be addressed before there is broader market acceptance of sophisticated business intelligence applications.

This leads to the second requirement; make this thing easy to use. If you’re like most of us, your day-to-day work keeps you running at full tilt. Stopping what you’re doing to run up a long steep learning curve is probably the last thing you want to do, yet that is what most BI vendors expect of their end-users. The more you can shield your end-users from the innards of the technology and provide them with a simple, graphical, drag and drop interface, the more likely they are to adopt a system that minimizes a trip outside their comfort zone. This is another, potentially fatal sin of BI developers: “we’ve developed a highly sophisticated analysis product, let us show you”, when what they should be saying is “what type of information do you need to do your job better, and how can we make it as simple as possible?”

Looks like the first serious foray into ISP-based behavioral targeting is finally sputtering to a close. Spooked by misinformed and often hostile congressional attention, most of NebuAd’s customers have dumped the company and beat a hasty retreat, and today their CEO surfaced working somewhere else. What is the take-away in all this? To use a popular term, there appears to have been a distinct lack of “vetting”; introducing this kind of disruptive/invasive technology requires a broad base of support, it’s not just about commercial validation, but about buy-in from influencers prior to pushing the product out the door. Careful legal review, not just from the “technically correct” point of view, but from the “how to socialize this with people who can shut you down on a whim” perspective would have probably been a good idea. Privacy advocates notwithstanding, I think most people would agree that targeted ads are a good idea (or do you prefer spam?), but like a lot of early stage start-ups, there was way too much focus on the technology, not nearly enough focus on the benefits, which would have probably been significant. On the other hand, those of us with an interest in this space now know what to avoid, so again, a big thanks to NebuAd for setting off the traps.

Microsoft recently announced the release of two new features for IE8 that could potentially have a huge impact on the functionality of ad networks at the end-user level. The first feature, called InPrivate Browsing, automates a process that can easily be done manually; remove cookies, delete history, removed cached files from any websites you’ve visited. The target audience for this feature is obvious, and it’s impact on the ad networks is nominal.

The second feature, however, should be of concern. This is called InPrivate Blocking, and is essentially single source domain scripting, the best known example being Google Analytics. Rather than focusing on single domain interactions (which is what cookies do), domain scripting embeds a script (e.g. javascript) from a single source that runs across multiple domains that form part of an ad network, and that’s where the problem starts.

InPrivate Blocking specifically blocks these scripts, so network advertisers are unable to track what sites you’ve visited, what products you’ve looked at, and in general your overall interests. This is a huge win for privacy advocates, and a huge loss for the ad networks. Going forward, this means users of IE8 with InPrivate Blocking will no longer be served targeted ads; relevancy and context in advertising will no longer be possible. Users will still get ads, in fact probably more than before, and what they get will be much more spammy, because the ad networks no longer know what you’re interested in, and will be forced to deliver a one size fits all model. From a business side this will also have a significant impact on ad network revenue, click through rates are likely to drop as ads are forced to become less relevant.

The whole Behavioral Targeting space needs to do some serious navel gazing, they are clearly losing the battle for the hearts and minds of consumers; first NebuAd gets a public ass-kicking and sullies an entire industry in the process, now Microsoft tightens the screws even further. More on this topic soon.

One of the constant challenges for anyone developing an analytic application is how to simplify something that is inherently complex from the start. This requirement rides above the underlying complexities inherent in any enterprise grade solution, regardless of architecture, process flow, or data sources. Once you’ve figured out how to build and integrate an application that provides depth and breadth of intelligence for your business application, how do you make it understandable to a non-technical audience? One of the technologies I’ve been looking into involves creating models using hypercubes, which lets you map multi-dimensional data and keep it updated in real time. Rather than looking at two or three dimensions, a really effective model needs to include far more dimensions than most people are comfortable with from a visual perspective. The challenge is then capturing the data, making sure it’s clean, then integrating across an n-dimensional framework and making it compelling enough for a business person to want to use it. I’m trying to apply this framework to a consumer model across multiple dimensions, essentially creating the data equivalent of a homunculus (a term used to describe the distorted human figure drawn to reflect the relative space our body parts occupy on the somatosensory cortex, and the motor cortex). It would represent a scalable, temporally-driven framework for describing why different consumers interact the way they do in an on-line environment, which would allow people targeting the consumer to build out a site/offer/etc. that would map precisely to that consumers needs. I’ll comment more on this later as I get the idea more fleshed out.

So the uproar over NebuAd continues, and appears to be expanding to include not just NebuAd but the whole Behavioral Targeting space. There have been a variety of posts wailing about whether NebuAd has ruined the market for Behavioral Targeting applications by triggering such an intense level of congressional scrutiny that regulatory oversight that could develop as a result. My guess is no, for a couple of reasons. One, implementing any sort of regulatory framework while we’re in the throes of an election year seems incredibly unlikely. Two, there are some very big fish in this pond (Google, Yahoo, others) who have a broad presence backed by deep pockets and can push back with a lot of force. I’m not saying these companies are in the same boat as NebuAd, but politicians, who are renowned for their lack of familiarity with the business of technology, are likely to use as broad a brush as possible in the name of protecting their constituents from unwanted advertising. Three, rather than ruining the Behavioral Targeting space, what NebuAd has done is (at a minimum) show other companies in this space when to duck, and when to jump. Heck, these guys have done a great job of triggering traps the rest of us can now avoid.

The folks at NebuAd had an interesting week. It’s not often that a relatively small start-up is called to the Capitol Hill woodshed for a public spanking, but that’s pretty much how it played out. Although it appears NebuAd clearly underestimated the impact their technology would have on privacy advocates, the problem is that politicians are famous for not understanding technology (even slightly), and in general have a very thin grasp of business—and yet they find themselves in a position to dictate terms to folks in the business of technology. I think Scott McNealy captured it best when he said “You have no privacy, Get over it.” Organizations who are trying to protect our “privacy” such as the Center for Democracy and Technology have good intentions, but that genie is way out of the bottle, and no amount of chest beating is going to put it back in. The bottom line with companies like NebuAd is their technology produces results, and big companies are more than willing to spend big bucks to get that level of targeting accuracy.