Chief Marketing Technologist by Scott Brinker

Data web marketing and the law

In the future of online marketing, the biggest internal tug-of-war might not be between the marketing department and IT — I maintain the belief that marketing will subsume technology under its own management umbrella — so much as it will be between the marketing department and legal.

As we enter the next era of the web — Web 3.0 or the web of data or the semantic web, whatever you prefer to call it — the big debate is going to be about legal, policy, and intellectual property rights around data.

How much of our data should we share? With whom?

Who will we allow to use that data, under what circumstances? How will we enforce those policies?

Do we actually own the data we think we do? Or do we have obligations to the source of that data, such as aggregate analysis of customer behavior, for which we have limitations in how we can use it?

What about using other people's data? Do we need explicit permission? What constraints do we have in reuse or redistribution? What if the provider's policies change?

Since data web marketing is all about sharing your data and leveraging other people's data — open, linked data as Tim Berners-Lee calls it — as a whole new kind of marketing medium, these questions can become significant hurdles.

Ultimately the real question should be: how do we extract the maximum value from our data, by sharing it, or not sharing it? Within a company's huge data universe, there are probably multiple answers along that continuum, depending on which particular data you're talking about — and how its use relates to the business model and marketing strategy.

Copyright and data reuse

There's an interesting article in the latest MIT Sloan Management Review, Finding New Uses For Information (sorry, registration required) by Hongwei Zhu and Stuart E. Madnick, that tackles some of these legal questions from both sides: data owners and data reusers.

How can someone own data and control its use if the data is openly accessible via the Web? What is the best strategy for those who think they own the data? And what is the best strategy for those who want to reuse data that is available via the Web?

Without any doubt, data can be an important asset of a business. The business "owns" the data when it can fully control who can access the information and how it should be used. But when a company makes data accessible to the public on the Web, its "ownership" to that data will be determined by intellectual property law.

The article mentions that of the 4 kinds of commonly recognized intellectual property rights — trade secrets, trademarks, patents, and copyright — only copyright is even conceivable as the basis for legal protection in this context.

Unfortunately, at least in the U.S., the applicability of copyright protection to databases is somewhat murky. One relevant Supreme Court case, Feist v. Rural, emphasized that copyright only protects originality. "Information" is not copyrightable, but "collections" of information can be — if the collection represents some minimal degree of creativity. Individual pieces of factual data are not protected.

The European Union has slightly stronger protections in place with their Database Protection Directive, which grants special rights to the creators of databases, to "protect the qualitatively or quantitatively substantial investment in either the obtaining, verification, or presentation of the contents".

Given the fuzziness of copyright protection in this context, Zhu and Madnick recommend the following strategies — really more business strategies than legal strategies — for database creators:

• Sell "private" data. Have a portion of your data that is publicly available on the web that anyone — including your competitors — can freely use and redistribute. But then also have more advanced or detailed data that's related to that public data, but is controlled privately and only given to customers under specific terms.
• Become a reuser. If you can't beat them, join them. Essentially, this says that the value a business extracts from data has to shift to being the application in which it is used and related to other data — more than from the raw data itself.

For data reusers, they advise:

• Differentiate. Since copyright law in the U.S. tends to reward originality and creativity, finding new ways to leverage the sourced data — particularly a partial subset of data, not a wholesale copy — and add value is a good strategy. Aside from pleasing the lawyers, this is also likely to be a good marketing strategy to give users something better that they can't get quite the same way anywhere else.
• Analyze the data. Sometimes the greatest value can be provided by crunching the data from multiple sources and then only sharing the net results — not the underlying data that led to those results.

Terms of service and conditional access

While I liked Zhu and Madnick's article — always better in my book to think about innovation instead of case law if you can help it — I felt that they overlooked the 800-pound gorilla in how data restrictions are predominantly enforced on the web today: terms of service and conditional access.

One of the examples given in the article was a case some years back when eBay sued Bidder's Edge — an auction aggregator that has since gone out of business. Bidder's Edge was scraping eBay with web crawlers to grab the data about their auctions. eBay didn't like this and successfully won a preliminary injunction against Bidder's Edge preventing them from doing that.

Although copyright infringement was one of the arguments, the injunction was actually granted around a separate argument, around the claim of "trespass". The judge wrote, "eBay's servers are private property, conditional access to which eBay grants the public." The concern was framed in terms of these automated spiders causing undue loading on eBay's servers. A good recount of the case can be found in the Computerworld article When does 'spidering' equal trespass?

But even that debate seems somewhat moot in the modern world of data access over the web where you need to agree to the terms of service for a site before you can even see the data.

Just the other day, there was an article on TechCrunch about Amazon Killing Mobile Apps That Use Its Data. The new Delicious Library iPhone app — which was supposedly very cool — used Amazon's Product Advertising API for some of its content. However, Amazon's terms of service were recently updated to state:

You will not, without our express prior written approval requested via this link [...] use any Product Advertising Content on or in connection with any site or application designed or intended for use with a mobile phone or other handheld device.

So Amazon simply said: shut it down, or we'll shut you down.

Aside from the legal channels Amazon or other such sites could take in enforcing their terms and conditions, the most direct way for them to handle it is to shut down the account or block the IP address of the application pulling the data.

It's not just Amazon. Almost every major web site — Facebook, Twitter, numerous Google applications and APIs — have terms of service that they can use to throttle data reuse. Given that APIs are often provided specifically to enable data reuse, getting into the weeds of what constitutes permissible reuse could get quite messy.

And even if you find a path through the thorny underbrush, what happens if those terms of use are changed overnight?

Well, as I said at the beginning of this article — although with no joy in saying it — I think the legal department may have a bigger role in the future of online marketing than either of those departments would like.

5 marketing technology truisms

As a shorter and more light-hearted post for the holiday weekend, here are five truisms about technology in the marketing world that I've found as good rules of thumb — somewhat tongue-in-cheek, but not entirely:

1. The Great Paradox

Software developers are usually bad at creative marketing. Creative marketers are usually bad at software development. Excellent digital marketing requires both.

2. The Shoemaker's Children?

If you're considering buying online marketing software or services from a company, how good is their own online marketing? If it sort of sucks, pause to reflect on the implications.

3. The Napkin Test

If a marketing technology vendor can explain their solution on the back of a napkin — and it's compelling and credible — then they just might have something great.

4. The Gym Membership Fallacy

Software that gives you the potential to do something great still relies on you to actually do it. Spending money is easier than spending the time and energy to put that money to good use. (Blog software is a good example of this.)

5. The Support Transparency Rule

If a software company makes all their technical support resources publicly available on the web, that's a good sign. If they don't, that's a bad sign.

Any nuggets of wisdom that you'd add?

Ultra-large-scale marketing operations

A couple of major trends in software development — in particular, open source collaboration and the design of social network/user-generated content platforms — may provide useful insight for the future of marketing management.

After all, the increasing number of marketing channels and the increasing granularity of initiatives in them combine to form ultra-large-scale marketing environments that share similar properties to ultra-large-scale (ULS) software systems.

It's not coincidental that many of the leading web sites whose value is derived from crowdsourcing and peer production — MySpace, Facebook, YouTube, Wikipedia, Twitter — are also at the heart of the digital marketing maelstrom. The underlying forces are the same, and they feed each other. Where would Twitter be without the championship of so-called social media marketers? Where would social media marketers be without the latest infusion of attention to their mission that Twitter has brought?

This weekend, I was reading a presentation called The Metropolis Model: A New Logic for System Development by Rick Kazman of Carnegie Mellon University. Although it's written in the context software development for ultra-large-scale systems such as Facebook, many of the characteristics he describes should resonate with digital marketers:

• mashability
• conflicting, unknowable requirements
• continuous evolution
• focus on operations
• open teams
• sufficient correctness
• unstable resources
• emergent behaviors

At a certain scale, the closed-loop, top-down structures of old-school software — and old-school marketing for that matter — simply don't cut it. Instead, one has to design mechanisms for enabling broad participation and co-creation, yet do so without losing the core integrity of the system as a whole, its gravitational center.

Some of the principles that Kazman recommends make sense in software and marketing:

• egalitarian management — encouraging a broad swath of involvement
• bifurcation — tight control of the "core", openness in the periphery
• fragmented implementation — many independent participants contribute
• distributed testing — get many people involved in quality control
• ubiquitous operations — the system is "always on", always evolving

In reading Kazman's work, I followed a trail back to a 2006 report by Kazman and numerous collaborators on Ultra-Large-Scale Systems: The Software Challenge of the Future. Written for the U.S. Department of Defense (DoD), advising them on their goal of "information dominance", it has a funky yet fascinating twist if you substitute "systems" and "software" with "marketing" and swap "DoD" with "your company".

Again, the areas they recommend addressing in their research agenda seem highly relevant to marketing:

• human interaction around socio-technical systems
• computational emergence using game theory and digital evolution
• design that encompasses individuals, organizations, and software
• adaptive system infrastructure for massive decentralization
• adaptable quality control in the face of continuous change

Have computer science and marketing already converged, but they just don't realize it yet?

Marketing more human, more computerized

Two powerful and parallel trends are underway in marketing.

First, marketing is becoming more human. This is the social media revolution. Blogs, Facebook, Twitter, LinkedIn groups, etc., are thriving with real dialogues, between real people in organizations and real constituents in the market.

This pushes marketing as a whole to be more real — customers aren't abstract models on a whiteboard, but splendidly diverse individuals with emotions, opinions, and microphones. What used to be a novel concept, so-called "voice of the customer", is becoming an integral part of everyday marketing.

Second, marketing is becoming more computerized. A veritable tsunami of new marketing software has arrived — with more on the way — as a result of 7 converging forces:

• largely digital channels of communication to the market;
• cloud computing and on-demand software-as-a-service;
• an explosion data and analytics on customers and markets;
• open APIs for everything from ad networks to CRMs;
• the emerging semantic web (a.k.a., a "web of data");
• a new generation of AI-like algorithms at the intersection of computer science + economics + marketing;
• ever cheaper and faster computing power and networking;

This is enabling many of the ideas around marketing automation and computational marketing. These technologies change the scale at which marketing programs can operate — huge numbers of smaller atomic elements — allowing thousands of micro-segments of audiences to be tracked and engaged, in some cases down to one-to-one personalization and offer optimization.

But... how do these two trends affect each other?

Social media communities quickly reject "automated" participation (see David Armano's excellent post, how to be more human).

And marketing software still lacks human intuition and creativity — and will until the singularity.

Marketers must be the bridge between these two engines of innovation, and the wide range of possibilities for how to do that represents a wealth of strategic marketing choices.

A beautiful paradox of how marketing will be both more human and more computerized.

Marketers: the web of data is inevitable

Flying home from the Semantic Technology Conference 2009 (#semtech2009 on Twitter), I have to confess that I'm drunk on the Kool-Aid.

My presentation on marketing in the semantic web attracted a packed room, and feedback — from both technical and business attendees — was incredibly positive. But it was the sum of the rest of the conference that really inspired me to conclude:

The semantic web — or web of data, web 3.0 if you prefer — is inevitable.

Now, I've been an advocate of the semantic web, and particularly how it might impact the marketing department ("semantic marketing"), for a while. But I tempered my enthusiasm with reservations about how and when it would come to be. After 20 years working in disruptive technology businesses, one develops a certain pragmatism about the "breakthroughs" that never quite break through.

But now I'm throwing off my skeptic's cloak.

Enough of the technical standards are in place, with enough consensus across academia and business, to serve as a stable foundation. Sure, there will be tweaks, refinements, enhancements. But the core of this technology has enough critical mass and momentum behind it to provide a solid, standardized base upon which the next round of innovation can be built.

It's analogous to the shift that happened in the original web once browsers and web servers reached agreement on things like secure SSL connections, cookies, etc., where the web sprang from being an interesting technology experiment into a full-throttle, commercialized Zeitgeist that hasn't slowed down since.

But having a technology mature into useable adolescence is merely a necessary but not sufficient requirement to guarantee a worldwide revolution.

The real reason why the semantic web is inevitable at this point is because the business potential of a web of data — inside the enterprise and out in the public web — is becoming obvious and immense.

Businesses are data-driven. They have been ever since the rise of IT. But that has become a curse, as the far majority of raw data wastes away in silos — unanalyzed, unintegrated, and never refined into valuable, actionable intelligence. The semantic web can take these stagnant pools of data and connect them into streams, rivers, and oceans — from across the enterprise or around the world — in ways that people can harness on demand to answer quantifiable or complex data-driven questions and to construct a new generation of automated and "intelligent" software agents that can reason reasonably well.

This will juice operations and IT — who are already some of the early adopters of semantic web technologies to accomplish their existing needs with less overhead. And there's a synergy between data web standards and the movement toward cloud computing and on-demand infrastructure and applications — they each make the other more valuable and practical, a nice virtuous cycle. That alone could justify widespread adoption.

But that's only the tip of the iceberg. The big enchilada is the potential this has for marketing — and I mean marketing in the broadest sense possible. Creating better customer experiences. Bonding more tightly with your market. Becoming a big, branded authority in the domain you seek to own.

How?

Because your customers' businesses are data-driven too, and through the semantic web, you can supply them with data that can be highly beneficial to their operations — connecting their businesses up to your intravenous drip of miracle data goodness. You help them perform better by giving them a piece of the puzzle that you're uniquely suited to provide. They then mash-up their own special blend of their data, your data, and other people's data to achieve a whole new level of automation and intelligence in their world.

The marketers who deliver that to their audience will be golden — and that is the vision of data web marketing.

If you ever wished you had been at the forefront of the web, in the mid-90's when pioneers shaped the landscape of e-commerce and online marketing for everyone else who followed, now is your chance. While the web of data will share many similarities with the regular web — indeed, the two will essentially merge into one continuous whole — it is also virgin territory. No one has ever used data web marketing as a competitive advantage before. Who will take the lead in your space? Who will claim first mover advantage? Who will grab the glory and the market share?

The sands in the hourglass are now running. The excitement of people who see this potential is palpable and infectious. And, as we've all become attuned to the quickening tempo by which web innovations are embraced and exploited, the incentives are there to act.

The semantic web isn't just inevitable. It is imminent.

In search of computational marketing

Ever since reading that article in The New York Times about Wall Street-like data analysis being applied to Madison Avenue marketing — what I would call computational marketing, as a nod to computational finance — I've been searching for more stories about that idea.

It turned into a bit of a nomenclature expedition.

Computational Marketing

A search for "computational marketing" in Google brings back ~1,000 results, with the first page being dominated by a single SEMPO job posting from January 2007 for a computational marketing internship. ("These opportunities are ideal for mathematics, computer science, operation[s] research, statistics or engineer[ing] students".)

Most of the job description is grunt work that doesn't seem particularly computational ("growing a list of reciprocal links", "mailing list discovery and management", "reverse word of mouth advertising"). What exactly is reverse word of mouth advertising? But then the very last bullet is:

Implementing computational marketing campaigns using distributed architecture

You know, in between rote data entry, have the intern kick that out. Oof. This is a classic argument for why marketing needs a technologist on the management team.

But clearly "computational marketing" has not been a hot term. Bummer, I kind of like it. (Any chance of a rally to make #compmktg a trending topic on Twitter?)

Algorithmic Marketing

So next up I searched for "algorithmic marketing". A mere 131 hits for that term, although the first page of results has a little more substance. One is remarks from a GigaOM interview with Gail Ennis, Omniture's SVP of Marketing, where the interviewer asks if customers are ready for sites that optimize themselves. Ennis replies:

Some customers say, "I don't want the system to do it. I want to control it." And we love them to do that. Particularly as it gets to the keyword spend, it's so complicated to try to optimize keyword rules so people are much more reluctant to say, "I'm going to let an algorithm take that over for me." But some companies, and some marketers within companies, just get it right away: "We're going to throw everything into that autoserve, let the creative serve up, let the algorithms build." [At this year's user conference, Omniture CEO Josh James in his remarks] opened up with this very fact, that algorithmic marketing is ripe for automation right now.

Another is from a Scandinavian (?) company called Poets + Plumbers — isn't that a great name for a digital marketing agency? — that has a post on digital marketing "micro patterns". Talking about the different factors that play a role in how people respond to marketing execution (product, price, positioning, message, method, media), especially in a social network, they point out:

One of the most important aspects of new-school marketers is their ability to analyze data and identify consumer response patterns. This will give the marketer the opportunity to analyze data and identify consumer response patterns. This will give the marketer the opportunity to constant[ly] update and [improve] the marketing process. In an online marketing context, this model can be taken to an extreme by integrating the analysis with an automated response system, thereby creating algorithmic marketing. In the algorithmic marketing setup, the choice of message, method and media is constantly switching, depending upon which combination gives the best effect.

Okay, "algorithmic marketing" isn't yet a widespread term either — but the meaning seems to be heading in the right direction.

Marketing Engineering

My final search was for "marketing engineering" — which somehow sounds less cool to me than the previous terms, so I wasn't expecting much. Lo and behold, a whopping 210,000 results came back.

The top result is a site called Marketing Engineering, run by two Penn State management professors Gary L. Lilien, who apparently coined the term, and Arvind Rangaswamy. The pair have written a couple of textbooks on the subject, Principles of Marketing Engineering and Marketing Engineering: Computer-Assisted Marketing Analysis and Planning.

However, it appears that a lot of the focus of their vision of marketing engineering predates the web. It includes things like cluster analysis for market segmentation, multinomial logit analysis for customer targeting, conjoint analysis for new product design, etc. But the big picture concept that they're advocating seems right on:

Several forces are transforming the nature, scope, and structure of the marketing profession. Marketers are seeing increasingly faster changes in the marketplace and are barraged with an ever increasing amount of information. While many view traditional marketing as art and some view it as science, the new marketing increasingly looks like engineering.

Lilien and Rangaswamy's emphasis is on using software and techniques to help marketers "collect the right information and perform analysis to make better marketing plans, better product designs, and better decisions". They position marketing engineering as decision support, in contrast to the automated execution implied by algorithmic marketing.

The authors make the first chapter of their Marketing Engineering book available for download free, The Marketing Engineering Approach. It's a great read, although again, the application of this to state-of-the-art digital marketing is left as an exercise for the reader.

So I'd say "marketing engineering" has the most momentum behind it. But will it make the leap to the new new marketing, or will the term evolve with the discipline?

Are there other names that capture this intersection of marketing and computer science? (Be nice.)

Or is this simply marketing automation?

Madison Avenue + Wall Street = Computational Marketing

This past Sunday in The New York Times, there was an interesting article talking about the elevation of data and number crunching in advertising: Put Ad on Web. Count Clicks. Revise.

Written mostly from the perspective of ad agencies, advertising is portrayed as undergoing a seismic shift from Mad Men to a mixed mission with equal parts creatives and finance quants (Mad Money?). "Where the data guys were once an afterthought in a marketing presentation, now they are at the core of the online strategy."

The shift to data-based campaigns is forcing marketers to learn new skills and drawing a new breed of worker to Madison Avenue. While most data executives now in the field came from media backgrounds, they are recruiting Wall Street math geniuses because the job requires hourly adjustments in strategy based on numbers.

There's a great section where Matt Greitzer of Razorfish talks a bit about building a data practice inside their agency and the rationale behind it:

With so much information to trade on, several advertising firms are creating their own data-based practices.

"We have, over the last year or so, gotten more and more interested in the ways that you can use data to make advertising more effective online," says Matt Greitzer, the vice president of search marketing and auction-based media at Razorfish, which is building its exchange group.

In addition to what an ad should say, and where and when it should run, advertisers have to figure out how much each ad, or "impression," is worth. The data helps them do that. "You're making, in some cases, real-time decisions about how much to pay for a specific impression," Mr. Greitzer says.

Cool stuff. Although, I'm obliged to point out, the complexity embodied in such dynamic systems — computational, managerial, and from "externalities" — is daunting. Obviously, that's the reason to bring in Wall Street math geniuses. Instead of computational finance, it's the new computational marketing. Instead of financial engineering, it's marketing engineering.

But as excited as I am about those possibilities, um, you know that Wall Street math wizardry sort of ran into a multi-trillion dollar bump recently, right? I had to pause when I read this paragraph in the article:

As data executives continue to build on their research, this arena could resemble Wall Street even more: yield managers could hedge their purchases, buy futures to lock in prices and use other trading strategies. And this type of sophisticated testing and trading will require changes in clients' attitudes.

Definitely something powerful and wonderful in that intersection of math and marketing. But I would suggest that Fooled By Randomness by Nassim Nicholas Taleb be required reading for everyone contemplating that combination.

I wholeheartedly believe that marketing should leverage quants and computer scientists, and incorporate them into its strategic leadership, but mirroring Wall Street too closely would probably be a mistake. There's a new synthesis of these ideas that deserves to be its own new vision.

Data web marketing presentation

A little over a year ago, I wrote a post on marketing in the semantic web ("semantic marketing") that tackled the question of what marketing might be like in the semantic web.

I should say that now, the semantic web is probably better thought of as a "web of data" — analogous to the existing web of human-readable content, but layered with structured and linked data that software can easily process. If you haven't yet seen Tim Berners-Lee's TED presentation about this "next web", it's a great 18-minute orientation to the powerful idea of a data web.

Yet still the question remains: what will it mean for marketing?

In my opinion, this web of data has the potential to enable an entirely new kind of marketing — what I previously called semantic marketing, but I now think is clearer to think of as data web marketing.

Data web marketing is about growing customer relationships, increasing the visibility of your firm, and building brand equity through the production and delivery of data. There are some parallels to search engine optimization (SEO) and mash-up APIs as they're known today — but by being structured, linked, and delivered in a standardized format, data web marketing goes even further.

The data that companies can provide in this new channel can be harnessed in software applications by their customers, their partners, their own vendors, and related interest groups around the world to do business in more efficient and collaborative ways.

Of course, this is a nascent concept. There are few people producing linked data on the web today, and few people that know how to consume it. But remember, in 1994, hardly anyone knew how to build a web site, yet within 5 years, the entire world changed. The web of data has the potential to be as big of a revolution. Given how our world is so rich in untapped reservoirs of data — more and more every day — this data web explosion to make data far more useful seems inevitable.

And, just as marketing took the lead in commercializing the web, I believe the same will happen with the data web.

Next month, I'll be giving a presentation on "Marketing in the Semantic Web" at the 2009 Semantic Technology Conference to address this subject. Here's a sneak preview of the slide deck — the heart of which is a proposal for 7 missions for data web marketing:

Data Web Marketing

View more presentations from sjbrinker.

I'd love your thoughts and feedback.

Google encourages structured data on your web site

Exciting news came out of Google's Searchology event yesterday for the advancement of semantic web adoption: the announcement of Rich Snippets in Google search results.

Google is now starting to pay attention to structured data that is embedded — using either microformats or RDFa — in the web pages it crawls. And now, via these rich snippets, Google will start displaying that structured data in the search results.

At the moment, these rich snippets only apply to content about reviews and people profiles, but Google makes clear that those are just the starting point and that support for other structured data will be added in the future.

To give credit where it's due, this is very reminiscent of Yahoo!'s SearchMonkey features, but Google's approach is more open (doesn't require the development of special search apps). Also, Google is pretty much the 800 pound gorilla, so its support for microformats and RDFa carries more weight.

This helps the search users, who will now get more information content nicely laid out in the search results, instead of just the usual, boring homogenous text excerpt. And it helps the web site publishers who take the time to embed this structured semantic data into their pages by making their results stand out from the crowd.

That mutually rewarding incentive has the potential to spark a virtuous cycle for bringing more structured data into the web.

The stirrings of semantic marketing and SEO++ (data-enhanced search engine optimization) — yay!

Marketing automation and Jurassic Park

I recently saw the movie Jurassic Park again. It's about an island of cloned dinosaurs, intended as a kind of zoo and amusement park, that spirals out of control during a pre-opening inspection. Spiraling out of control is a euphemism for having ravenous pre-historic creatures devouring everyone in sight. (Remind anyone of their last budget meeting?)

It made me think of marketing automation.

One of the characters is a mathematician who specializes in chaos theory, Dr. Ian Malcolm (played brilliantly by Jeff Goldblum). Dr. Malcolm had been a critic of the park from beginning, predicting its collapse because chaos theory says that complex systems, with certain conditions, cannot be tightly controlled — such as the weather, global financial systems, and an ecology of dinosaurs on a 20th century island.

Dr. Malcolm's warning: trying to force an overly simple management structure upon such a complex system is a recipe for disaster.

Complexity in marketing

I am fascinated by how complex marketing has become in the past decade. If you stop and think about the explosion of search marketing, social media marketing, email marketing, e-commerce web sites, mobile marketing, behavioral targeting, and budding semantic web marketing, it's breathtaking.

But to fully appreciate the complexity generated by all this, consider how these new vehicles build upon each other:

• all the original marketing vehicles still exist and require attention;
• there are now all these new marketing vehicles (e.g., web site, search, email, social media, etc.);
• each of these new vehicles has near unlimited granularity (e.g., not unusual for a company to manage thousands of keywords in a search marketing campaign);
• the management of these different vehicles is often highly distributed across many people (e.g., the folks handling search may be far removed from the people running email campaigns);
• there are all sorts of interaction effects, within and between vehicles (e.g., a particular email campaign can trigger new searches — are the resulting messages consistent, or at least compatible?);
• particularly in social media — but also in search — end-users and third parties also drive the outcome, preventing marketing from predictably controlling those vehicles;
• virtual proximity multiplies the number of competitors, across more niches and more regions, and each of those competitors bring all the complexity above from their own marketing into the mix;

It's not just that each new vehicle brings its own complexity to the table; each one simultaneously increases the complexity of all other vehicles too. This causes exponential growth in marketing complexity.

What's interesting about this is that the actual atomic activity associated with these efforts can be quite simple — at least on the surface. What could be more straightforward than bidding on a keyword in Google AdWords, or posting a link to an article on Twitter?

The complexity comes as a result of lots and lots of these simple atoms bubbling up together into larger structures, catalyzing — and being catalyzed by — reactions in other vehicles. Ironically, the very simplicity that makes it so easy to create a single AdWords ad, causes hundreds or thousands of them to sprout up in unpredictable ways, generating a much more advanced kind of complexity.

This generates often surprising emergent behavior in the overall system.

The irony of marketing automation

The great irony of marketing automation is that it is often pitched as a way to simplify this exploding marketing universe, when in fact, it actually introduces a whole new layer of complexity itself.

Marketing automation, as it sounds, attempts to automate routine marketing processes. Marketers put in place software configurations and rules to handle various trigger events in a systematic fashion. For example, a prospect who fills out a lead generation form on a web site might be automatically queued to receive a series of follow-up "lead nurturing" emails over the next several months.

Sounds great, right?

There are two challenges with this, however, neither of which has been given due respect:

First, the model of interactions that are addressed by a marketing automation system are only an approximation of the real-world dynamics at play. This is by necessity — like trying to predict the weather, there are simply far too many variables all interacting together in the real world to accurately capture them all in a computer algorithm. The difference between the approximated model and reality creates a window for error. Over time, that window tends to grow larger. This is a classic phenomenon of the butterfly effect.

For instance, in our simple lead nurturing email example above, what happens if someone sends out a special offer email to all prospects — as a one-off campaign — that accidentally conflicts with the next email in the automated sequence? Ideally, you would want that scenario to be addressed by the automation system, but that might be beyond its model.

Second, marketing automation configurations and rules themselves introduce a new kind of variable into the environment. These modeled activities and the resulting actions they trigger are a whole new set of elements in the mix that have interaction effects with each other — and all the rest of your marketing initiatives.

Again referring to our lead nurturing example, before when there wasn't an automated re-marketing program, you didn't have to worry about one-off email campaigns conflicting with it. Now you do.

For a further dose of irony, the easier marketing automation software makes it to build up more and more automated programs and rules, the more complex the overall environment becomes.

And the example we've been looking at here is a very simple incarnation of marketing automation. In contrast, when you consider what multi-channel management and enterprise marketing management (EMM) software platforms are trying to do at a far grander scale, it's hard not be skeptical — do these folks really think they can keep the dinosaurs penned in nicely bounded cages?

Taming the Tyrannosaurus

Despite my concerns, I'm not against using software to help with this brave new world of marketing. To the contrary, I think there's tremendous opportunity at the intersection of marketing and computer science.

But as we push into this new era, I believe it's critically important that marketers understand more deeply the complexity of their new environment. (What you don't know can kill you!) That way, their strategies and tactics can be crafted with these dynamics in mind. In particular, I expect there will be an rising set of leadership principles in the marketing department around explicitly managing this complexity.

As this complexity begins to be appreciated for what it is, I think it will inspire a qualitatively different kind of software and management architecture to deal with it. It probably won't embody the command-and-control heuristics of old-school marketing management so much as it will leverage the distributed and emergent nature of this new environment.

As Dr. Malcolm would say, "Life will find a way."