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.

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.

One of the core requirements that would need to be in place in order to nail the connection between the individual and the merchant trying to reach them is communicating in terms that (as I’ve mentioned before) truly resonate. This is not just a matter of creating an end-users behavioral profile (no small feat in itself), but actually creating copy that is specific to the individual. This touches on a number of issues, one of which, component content management, has been commented on in previous posts. The other aspect that defines the content experience is not focused on how the information is managed, but on how it’s created. This is where the idea of a “personal taxonomy” comes into play. Everyone uses their native language a little differently, everyone has pet terms and expressions they like to use—I know this because I have a five year old who is beginning to sound just like me; while it’s usually cute, it has occasionally blown up in my face.

People in general are more comfortable with people like themselves, and a big part of that is how you communicate with each other. Creating a personal taxonomy that is specific to the targeted individual would be a huge step forward towards personalization, the question of course, is how do you do it? And there is an answer to that as well, which I will address during the coming weeks.

How do you deliver against a core motivation? The first step would be to define the specific motivation in the context of a delivery framework. There are a lot of companies out there that provide demographic segmentation services, grouping consumers into cute-sounding clusters for purposes of selling lists to retailers who can then target them as a whole. This is an adequate solution, if your smallest acceptable level of granularity is measured in the thousands, but even with this level of analysis and filtration, most retailers are lucky to get a response rate in excess of 2% (or as I prefer, a 98% failure rate). Why does this continue to happen, and why are retailers willing to accept such a dismal response rate? One option is to limit your initiatives to the 2% that you know are going to buy, which, if you can sort them out of the larger cluster, jacks your response rate up into the 90+ percentile. You actually end up with the same new/existing customers, but you haven’t wasted time and effort trying to get the attention of people who do not and are unlikely to care. It’s actually possible to have a cluster of one (I’ve worked with start-ups who’ve achieved this level of granularity), the problem-at that time-was the lack of technology to create a message for that one person cost effectively. A few years later, while working at another start-up, we developed the ability to create content at a granular enough level that micro-messages could be crafted on the assumption that the end destination was a cluster of one. So how do you link these two concepts? That’s one of the things I am working on now, and so far the results look very interesting. More on this later.

Predictive analytics is pretty much what it appears to be; it addresses the question of what is someone likely to do, given previous behavior. Psychometric profiling, on the other hand, while addressing the same trajectory on a broad basis, is approaching the consumer from a motivation perspective; why are they doing what they do, as opposed to what have they done before and are therefore likely to do again? When both applications are combined in a consumer’s profile, what you end up with is a model that not only tells you what they’re likely to do, but why? This aspect is huge; knowing why lets you drill in far deeper than a traditional branding approach (Just do it? Why should I just do it?) and allows the advertiser to really connect with the consumer. This enables interaction on a much deeper level, and takes the concept of resonance out of the realm of recognition/familiarity into core motivations.

Buyer motivation varies hugely. Often we have no choice (e.g. gasoline, basic food, etc.), often we have some latitude of choice (do I buy a $500 suit or a $2000 suit? And what is the opportunity cost of the more expensive option?). There are multiple, very mature technologies that are being applied to this issue, the most obvious examples being predictive analytics and collaborative filtering. Another area that is starting to develop is behavioral profiling. Behavioral engines (sometimes called psychometric profiling) are most commonly used in education and human resource applications, and will occasionally dip their toes in the retail sector as a targeting mechanism. While the delimited between predictive analytics and behavioral engines can often be gray, there are some clear distinction between them, and they can often be very complementary. How exactly? I’ll talk to that in my next post.

So if small, concise messages work well in the new mobile ad paradigm, and the content management systems have evolved to the point where micro-ads are a viable payload for delivery on a wireless network, the next question becomes how to get the right message to the right person? For any given situation a broad statement like “just do it” is likely to resonate, but resonance is not the true objective, the real purpose is to get the consumer to take action (safe to assume Nike’s subliminal interpretation of “it” is “go out and buy our shoes”. So in order for this model to be truly effective, the ad has to go below the surface, resonance is useful for branding, but advertisers are ultimately interested in follow-through on a purchase. So how do you go from surface stickiness to below the surface drilling?

So what are the practical limits of going ‘small’? If your objective is to communicate a concept clearly and unambiguously, a good frame of reference would be any bumper sticker or billboard advertisement. When Nike says ”Just do it” everyone knows exactly what they mean. An even pithier version would be “Got Milk?” Simple, to the point copy that is catchy and sticky. The point with these examples is that consumers are already acclimated to the micro-message, in fact, it works better for them because it uses less of their time and is more likely to break through the overall ad static that is an ongoing part of their lives. This type of approach can be extended even further by creating a rich media version of an ad snippet; a short, animated gif file can convey paragraphs of meaning in a matter of seconds, and can tie in very tightly to a branding strategy. The fact that it’s rich media actually works well with most component content management systems, particularly with those that are object-oriented. This also translates very cleanly into a mobile advertising environment, where the footprint is small, the attention span is short, and distractions are rampant.