(This also appears as a guest post on SAS’s Left of the Date Line blog for the Asia Pacific region.)

Later this month, I’ll have the privilege of meeting with groups of CMOs and other marketing leaders across southeast Asia as part of an event tour with SAS Institute. We’ll be visiting Singapore, Malaysia, Indonesia, the Philippines, and India, as well as doing a live webcast for Australia and New Zealand on Tuesday, May 21.

The title of my presentation will be The Emergent CMO: Combining Art & Science in Modern Marketing, and I thought I’d share a sneak preview of what I’ll be talking about.

In a world where everything is connected, hybrids — people who are able to bridge multiple disciplines — wield tremendous power. They see the interrelationships between things that used to be isolated in silos, and they’re able to creatively combine them in innovative new ways.

For instance, I’ve long championed the hybrid role of marketing technologists — technical professionals who apply their talents in the pursuit of marketing. These aren’t just IT people working indirectly on behalf of marketing. They’re technical professionals who actually identify themselves as marketers. They’re genuinely passionate about marketing. And they’re enthusiastically seeking ways to leverage their technical knowledge in the pursuit of breakthrough marketing ideas.

Of course, not everyone in marketing needs to be a marketing technologist.

However, I have come to believe that everyone in marketing should strive to become a broader kind of hybrid: a marketer scientist. Like a warrior poet or a philosopher king, but for modern marketing.

There’s been a lot of talk lately about the art and science of marketing. Some believe that the explosion of data and technology in our field is pushing out the more creative, intuitive, and human aspects of marketing. This is sometimes framed as “science” replacing “art.” I put those terms in quotes because their interpretation varies depending on who you’re talking to and in what context. Generally, people seem to equate left-brain thinking with science and right-brain thinking with art.

But of course, that’s highly oversimplified. As it turns out, real science is an incredibly creative discipline. Einstein was a great scientist because of his imagination, far more than his math. Vice versa, great art can be highly analytical and systematic in its execution. Beethoven’s 9th Symphony is a work of creative beauty, but also one of great precision.

The art and science of marketing is not a trade-off between two opposing forces. Instead, modern marketing is a synthesis of these two worldviews. Marketing must embrace more science, but that doesn’t mean it should abandon its art. We can be both creative and analytical. The key is to appreciate the strengths of these two worldviews and apply them in concert together.

That is the ideal of a hybrid marketer scientist.

To illustrate this, let’s consider eight characteristics of a marketer scientist — four that emphasize the “marketer” half and four that emphasize the “scientist” half:

The marketer half of this hybrid combines:

• Storyteller
• Brand champion
• Experience designer
• Change agent

Marketers have always been storytellers and brand champions, and those responsibilities are more important than ever. Great storytelling must break through the Internet’s content cacophony, where now everyone is a publisher with an industrial-strength printing press. And in the connected world of social media, every touchpoint with every customer can directly contribute to — or detract from — brand equity in a significant way.

Digital has blurred the line between customer communications and customer experiences. Stories are now best told through experiences, and experiences have become the backbone of the brand. Therefore, marketers must take the lead with experience design. Chief Customer Officer. Chief Experience Officer. These are marketing roles.

Because these changes in marketing are so significant — and they’re happening so rapidly — ultimately a marketer must become a change agent, helping the organization as a whole embrace new opportunities and new approaches.

This is especially true with the scientist half of this hybrid, which combines:

• Data analyst
• Experimentalist
• Technologist
• Systems thinker

Marketers must be fluent with using data to make better decisions and to deliver better customer experiences. In particular, we must be able to harness data the way scientists do: providing inspiration for new ideas and — through controlled experiments — proving which of our theories are correct. The modern marketer must be an experimentalist, comfortable with continually trying new ideas on a small scale, with low risk, and then ramping up the demonstrated winners.

Not everyone needs to be a heavy-duty, code-wielding marketing technologist, that’s true. But in a marketing environment where technology powers just about everything, every marketer should at least be willing to embrace technology as a means to an end. Scientists look to technology as better tools to push the frontier of what science is capable of achieving.

Finally, marketers must integrate these different pieces into a unified worldview as systems thinkers. Any one channel, tactic, approach, etc., cannot be viewed as an isolated component, but rather as part of a greater whole. That’s certainly how customers experience our brands. This will require changes to the way in which we organize marketing — adopting new management methodologies such as agile marketing.

Admittedly, this is a lot under one hat.

Rest assured that not every marketer needs to be an expert in each of these eight characteristics. But every marketer should have a little piece of each of these integrated into their thinking. One marketer might be an expert storyteller, but only modestly proficient as using data to help tell those stories and measure which ones resonate best. Another marketer might be an expert technologist, but mostly embrace the role of brand champion in the context of making sure that their technical implementations fulfill brand promises.

Ultimately, being a marketer scientist is less about a specific set of skills and more of philosophy for modern marketing. Marketer scientists balance the art and science of marketing together in harmony.

The post Everyone in marketing should be a marketer scientist appeared first on Chief Marketing Technologist.

Agile marketing has been gaining a lot of steam over the past year, but we’re still in the “early adopter” stage of this movement.

(If you have no idea what agile marketing is, I recently posted a video introduction to agile marketing as well as an essay version.)

Most teams that have adopted agile marketing have had to figure it out on their own, improvising from ideas of agile development and adapting the process as they go along. There are precious few published stories of how real teams have struggled through that process that others could use as a reference and inspiration.

So I was thrilled when David Quinn and Amy Callahan of EMC agreed to share their pioneering experiences with adopting agile marketing in the following Q&A:

To start, can you tell us a little bit about each of your backgrounds and your roles at EMC?

David Quinn, Senior Director, Corporate Marketing Engagement Office

I’ve led a variety of marketing functions for EMC in both web marketing and marketing support. Currently leading the Engagement Office within the corporate marketing organization as we define, deploy and refine new methodologies for success within the marketing organization.

Amy Callahan, Senior Engagement Manager, Corporate Marketing Engagement Office

Coming from a high-tech, account management background, I joined EMC seven years ago, holding various positions throughout web marketing. I volunteered to help build out the “agile pilot” two years ago, which led to my current role as a senior engagement manager.

What motivated you to embrace agile marketing in the corporate marketing team? Where did the inspiration come from?

It was not really a case of “deciding” to utilize agile marketing, but rather a result of addressing real business problems we faced within the corporate marketing organization.

We had been battling a set of parallel issues while trying to take on the vast amounts of work that come into the CM organization. On one hand, the corporate marketing groups were overwhelmed with requests, actions and deliverables that were thrust upon them on a daily basis. With no particular prioritization or synchronization it was difficult to know which activities were critical and which could be addressed later. Also, each group made priority decisions in a vacuum.

With no particular prioritization or synchronization it was difficult to know which activities were critical and which could be addressed later.

At the same time, business owners — those people and groups who needed the corporate marketing teams’ services — were knocking on the doors of the CM teams, telling their story and asking for help — sometimes several times over — as they attempted to cover all the requisite functions.

Needless to say, not everyone heard the same things and, as noted, not everyone prioritized the work in the same fashion. In addition, it was often the case that a business owner simply did not think about a particular area of corporate marketing that could bring value to their project, and therefore certain areas were overlooked.

It was then that our new Senior VP of Corporate Marketing, Jonathan Martin, recognized the need for change. He asked us to look at the problem and consider some radical solutions to the way we conducted our business — inclusive of the agile concepts that we were somewhat familiar with from our prior web experience around software development.

How did you get started? What were some of the challenges you faced in adopting an agile approach, and how did you overcome them?

Because this was uncharted territory and there was no Engagement Office or Engagement function in existence, I looked around my existing organization and asked for volunteers who were looking for a challenge. Fortunately, several people raised their hands and decided that a second job, in addition to their current one, seemed like a good idea. Truthfully, these folks saw an opportunity to solve a real business problem and work on something that was truly cutting edge.

This mini team met daily for a number of hours and took the time to diagnose the real issues. While we really wanted to dig into our effort of “marketizing” the agile development process, the team recognized the gravity of introducing major change into our current environment — therefore sound research needed to proceed action.

Rather than move forward with a new set of ideas, we initially worked with each functional group within CM to get their perspective on the issues at hand. We did the same thing with other parts of the business that were looking to the CM organization for help. It was literally a case of documenting the pain points and presenting it back to the various constituencies to make sure we had captured their thoughts and issues.

In true agile form, we applied a process framework and iterative plan but recognized that we needed to be flexible: try it, learn from it, and adjust as necessary.

Once we completed that effort we began to build a process that incorporated agile methodologies seen in the development world and applied those to the problem at hand. Then it was time to jump in with both feet and give it a try. We picked one existing campaign effort and built a pilot team around those activities as our first real “engagement.” In true agile form, we applied a process framework and iterative plan but recognized that we needed to be flexible — that is, try it, learn from it, and adjust as necessary.

The initial challenges within the pilot, once we scaled the program, were very much the same. The frequency of meetings, albeit only 15 minutes in duration, were frustrating for some of the subject matter experts. The role of Engagement Manager was initially confusing to some, as it was not a typical project manager role, but rather an effort to facilitate and enable a true team atmosphere and effort.

In the end, we can sum up the biggest challenge in one simple word: change. Change is hard, change is uncomfortable and change is typically met with resistance. The initial feedback was not overly positive and not all parties were embracing this new methodology.

We can sum up the biggest challenge in one simple word: change. Change is hard, change is uncomfortable and change is typically met with resistance.

In time, we did overcome the resistance, the backlash, and the frustrations. We were successful for two reasons. We did our homework. We never decided for others what their problems were. We let them define the issues. And when we began to utilize the agile methods, we were able to point back to specific pain points and show just how the new methods addressed those particular issues.

Additionally, our team fully bought into the lean agile concepts. We built out a full process by spending many hours reviewing results and commentary, adapting those thoughts by refining our process, and creating more predictable outcomes.

What are the different roles and responsibilities in your agile marketing teams?

In each engagement there are three types of roles:

1. The Business Owner is in effect any individual or group that comes forward with a project or request that requires the services of multiple corporate marketing functions. They can come from any part of the company with a wide variety of asks, from product launches to campaigns to marketing programs. The Business Owner participates in all the activities throughout the agile process.

2. The Engagement Manager is responsible for driving and enabling the team on a particular project or effort. They function as a front door to marketing and work much like an agency to pull together a virtual team based on the needs of a particular project. They have been trained to advocate for all Corporate Marketing functions so they can offer the Business Owner (the requestor) advice on what groups should be “at the table.” They drive the engagement throughout and pull together all relevant meetings while not inhibiting the one-on-one collaboration between key stakeholders.

3. The Subject Matter Experts (SMEs) make up the third portion of the team. Each relevant functional team assigns a representative to each engagement to represent that team throughout the effort. As part of the engagement effort they are now able to be at the table earlier and concentrate on proactive consultation versus reactive fire drills.

Can you walk us through the process — what are the steps of an agile marketing project with your group?

In its simplest form, the engagement steps consist of:

• A business request
• Business Objectives Meeting
• Iterative Planning Meeting
• Sprint Sets
• Launch
• Demonstration
• Retrospective

The Engagement Manager meets with the requestor to define the marketing backlog and pulls together the key players so that all members hear the same story at the same time. This provides the opportunity for all members of the virtual team to comment and collaborate on additional ideas.

Sprint sets are defined, and the cadence for stand-up meetings is agreed upon and the process begins. It results in continuous output that depletes the existing backlog and allows for ongoing prioritization.

Finally, mandatory demonstration and retrospective meetings provide everyone the chance to see the greater results and to reflect on the engagement as a whole. Valuable insights are brought back to the Engagement Office and fed back into the process as appropriate.

Can you share with us one or two examples of the kinds of projects that have been successful with agile marketing?

Our plan for launching new products has been completely refined using the agile methodology. We built an agile “recipe” for each launch tier that provides teams a track to run on, making the entire effort simpler and completely transparent, while allowing for the subtle changes necessary in each launch effort.

This lean approach has helped to support what we believe is critical thinking: do fewer things better.

Our campaign activities have all gone through the process and have been very successful. The sheer volume of assets and activities associated with each campaign have benefited from the new model. Constant prioritization and the ability to plan activities and deliverables throughout the year has been rewarding to all involved. This lean approach has helped to support what we believe is critical thinking: “Do fewer things better.”

Looking back, what would you say have been the greatest benefits of agile marketing?

The ability to focus and prioritize for the CM Groups — they know how to schedule work based on the overall needs and each group understands the bigger picture without guessing.

Time management. The Business Owners don’t spend days trying to get around to each functional group to present their ask. And while all team members balked initially at the idea of regular stand up meetings, they eventually agreed that frequent short meetings eliminate the need for many long drawn out meetings with various groups trying to get in line with the activities of the others.

The “corporate nod” is just not going fly during a stand-up meeting when you’re on the hook for a deliverable that other team members are waiting on.

Accountability through transparency. Great teams establish good ground rules. It’s easier to challenge people when these rules are well known. A good Engagement Manager can make visible the differences to achieve alignment. The “corporate nod” is just not going fly during a stand-up meeting when you’re on the hook for a deliverable that other team members are waiting on to start their processes. This type of interaction becomes so important, especially in today’s global/remote team world.

If you could offer one key piece of advice to CMOs who are considering adopting agile marketing, what would you tell them?

We would like to offer two:

1. Agile Marketing teams need to act in a manner like their name. There is no one process or solution that is “right.” What works for one company or organization is not necessarily the perfect recipe for another. Be flexible, try it out, and be ready to change. Assume you will have to re-craft your effort and reorganize your process. But don’t be afraid to try it and fail — just take the learnings and apply them quickly. The world will not come to an end. Be agile yourself.

Don’t be afraid to try it and fail — just take the learnings and apply them quickly. Be agile yourself.

2. Understand from the beginning that change is hard. No one wants to change the way they do things. It’s not comfortable. It’s scary. It’s threatening. Understand all of these things. Your teams will feel this way and they will react. That said, if you understand it and show them — versus forcing them — that the change actually benefits them in specific areas they have identified as problems or difficult tasks, they will embrace the change.

Thank you, David and Amy!

The post How EMC successfully adopted agile marketing appeared first on Chief Marketing Technologist.

Terry Kawaja and the brilliant folks at LUMA Partners — a modern investment bank specializing in the intersection of media and technology — have just released their Marketing Technology LUMAscape, shown above.

A few years ago, Terry put together a similar landscape for the more specialized ad tech space, which became the reference document for that industry. It was also what inspired me to create a marketing technology landscape in a similar vein.

However, while my landscape was intended just to help people visualize the sheer breadth and diversity of the marketing technology space, Terry and his team have clearly put a lot more work into illuminating the structure of the industry — for instance, how categories relate to each other into meta-categories.

They also use dashed red boxes to identify firms that have been acquired, but whose technologies are still active in the market. This is very helpful to see which categories have been hotbeds of acquisition and consolidation, which categories are still more independent, and where there is more red ocean competition or more open greenfield.

Nicely done!

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In a world powered by software, the developers who create that software — especially the really good ones — are increasingly the center of influence and power in business. That’s the core thesis of Stephen O’Grady’s brief-but-brilliant, 48-page book, The New Kingmakers: How Developers Conquered the World.

This is highly relevant to marketers and marketing technologists.

The genesis for Stephen’s book was the striking realization that as part of the democratization of technology in organizations — sometimes called the consumerization of IT — the CIO could be the last know which technologies were actually being used.

The most mainstream example, of course, are smartphones and the proliferation of bring-your-own-device (BYOD) trends — whether officially sanctioned or not. But such “shadow IT” has blossomed much further, with software-as-a-service (SaaS) applications that users adopt because they love them — not because they are mandated by IT. This is certainly the case with many popular marketing technology applications.

Stephen traces this revolution to the modern emancipation of software developers.

Not too long ago, software developers were beholden to the corporate hierarchy to perform their trade. Software was expensive. Hardware was expensive. Marketing and distribution of applications was expensive. So developers had to rely on sponsorship from executives with large purses. The balance of power was heavily weighted in the favor of those executives to decide what was going to be developed, on which platforms, and with which tools.

But four forces have converged to turn that model upside down:

1. Open source software. Thanks to the explosion of the open source movement, a large number of software platforms and tools — operating systems, programming languages, web servers, databases, application frameworks, advanced science and math libraries, etc. — are now free. Not only are they free, but their source code is freely available too. If developers don’t like how a particular open source program works, they can modify it — and contribute that modification back to the community. Effectively, they no longer need anyone’s sponsorship to use or deploy best-in-class software.

2. Cloud computing. The other half of the equation was hardware: those with the keys to expensive data centers still had control. But cloud-based platforms and infrastructures — Amazon Web Services (AWS) is the clear leader here — have made state-of-the-art hardware available, on-demand anywhere in the world, to anyone so cheaply that it might as well be free. “Anyone with a $10 bill can rent a 10-machine cluster with 1TB of distributed storage for 8 hours,” noted Flip Kromer, CTO of Infochimps. Heck, in some cases, it actually is free. Now for hardware and world-class Internet infrastructure, developers no longer needed deep-pocketed sponsorship either.

With free software and nearly-free hardware, developers were liberated from oppressive cost structures.

3. The connected economy. The Internet itself has broken down many of the remaining barriers to developers adopting and harnessing new platforms and tools. Blogs, online communities, Q&A sites like StackOverflow enables developers to connect with each other and to learn and share knowledge without expensive training or the constraints of traditional publishing. Shared code repositories like GitHub have made it cheap-or-free for developers to collaborate on projects around the world.

Even more powerful, however, has been the fact that the connected economy has provided a channel for developers to directly market software to users. Software-as-a-service is now accepted as the de facto delivery mechanism for applications — and it’s easy to scale cloud computing hardware as customer demand grows. Search marketing and social media marketing can be done on a shoestring budget, and a good product can gain an enormous boost through word-of-mouth. I’ve written before that engineers are becoming very good marketers in their own way.

4. Seed funding and crowd funding. Despite free or extremely low cost software, hardware, and marketing channels, developers who want to create a commercial application still have some other basic needs — food, water, shelter. Even though the economics of software development have been radically transformed, money is still needed to get a new venture off the ground. However, a new generation of investors have appeared to fulfill that need: seed stage investment funds, startup accelerators, angel investment groups, and crowd funding via services such as Kickstarter. (For a great crowd funding story, read how the Pebble watch project raised $4.7 million on Kickstarter.)

With these four forces, developers were suddenly in control of their own destiny.

At the same time, the world became digital — and software became the centerpiece of almost all business activities. See Marc Andreessen’s editorial in the Wall Street Journal: Why Software Is Eating The World.

In that world, software developers, the modern day mystics who can read and write code, have become the most valuable players in almost every venture. And with their newfound freedom, they’re in a position to be extremely choosey about who they work for and how they work. Happy and inspired software developers can transform a company’s fortunes. Disaffected software developers can abandon a company to irrelevance.

As the cover image on Stephen’s book suggests: software developers, who used to be pawns in the great game of business, are now the new kingmakers.

For marketers, it’s incredibly important to recognize this dynamic because:

• The products we’re marketing are increasingly built under this new power structure.
• The marketing technology applications we’re adopting are products of this dynamic.
• Understanding this helps us grok the revolution underway in the IT department.
• We’re trying to hire, harness, and retain our own marketing technologist developers.
• This is indicative of a broader shift to bottom-up power in modern organizations.

That last point connects to the growth of agile marketing. It’s no coincidence that agile methodologies in software development have increased in popularity in conjunction with the shift of power to developers. With more power at the bottom of the organizational pyramid than ever before, the real competitive advantage will go to companies that are able to adapt their management and culture to take advantage of this bottom-up wellspring of capability.

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As marketers, we want to be better consumers of data. Presented with data and its analysis, we want to be able to judge its accuracy and relevance to our decision making. We want to gauge its ambiguity and uncertainty, even though on the surface we’re being presented with quantified “facts.” We want to detect bias and account for it.

So let’s start with our own statistics in content marketing.

Because, seriously, too many of the stats that are appearing in content marketing these days smell fishy. I don’t want to pick on anyone in particular — there are too many folks doing this to unfairly single out one — so I’ll give you a hypothetical example:

Company X reports that their latest state-of-the-industry survey reveals 72% of marketers are engaging in — or plan to engage in — hamster optimization. Clearly hamster optimization is big! And isn’t that great, because coincidentally Company X just happens to be a hamster optimization provider…

You’ve certainly see examples like that. Some are blatantly biased. Others are a little more subtle. But the content marketing arms race has fueled the fire for many newsworthy-but-questionably-justified kinds of claims like these.

Now, being biased is not necessarily a terrible thing — as long as you disclose your bias and don’t try to sweep it under the rug. Qualify the data on which your statistics are based, so that readers can make a fair assessment of the context and relevance of your findings. After all, you presumably want your readers to trust you. That’s kind of the bigger brand mission with your content marketing in the first place, right?

5 ways to make your statistics more authentic

There are five things you can — and should — do when presenting survey statistics:

1. Note the sample size — how many people participated.
2. Break down the basic firmographics of the participants.
3. Describe how the participants were selected.
4. Include the original question and answer choices.
5. Define nomenclature that may be highly subject to interpretation.

The first, noting sample size, most people already do. If you don’t, start now because it’s an immediate red flag if it’s not stated — and because n = 20 is very different than n = 2,000. Conclusions drawn from small samples are weaker than those from large samples. And while there may still be value in sharing results from undersized samples, the value is more anecdotal than statistical. Play fair and let people know that.

How large should your sample size be — how big should n be? It depends on some slightly technical parameters such as confidence level and confidence interval. But here’s a simple sample size calculator you can use with some basic examples to put you in the ballpark.

The second, breaking down the basic firmographics of participants, is unfortunately less common. Firmographics are things such as the size of participants’ companies in revenue or employees, their geographic region, their industry, whether they’re B2B or B2C, etc. You may also consider including the level of the participants — mid-level managers, senior directors, top executives, etc.

You don’t have to go overboard, but even a little bit of this information goes a long way towards qualifying your results. If all your participants were mid-level managers from enterprises with $100 million or more in revenue, that’s probably a very different story than if your data comes from top executives at small businesses with less than 50 employees.

The third, describing how participants were selected, is the difference between the amateurs and the pros. Any professional research will disclose how the participants were found, enticed, and qualified, usually under the heading “methodology.” Here’s an example from a report on business analytics by Harvard Business Review and SAS:

This is super important because selection bias — a set of characteristics or circumstances that influenced the selection of participants — can profoundly skew the results of a study.

For example, let’s go back to our hypothetical hamster optimization provider, Company X. For their survey, they reach out to their blog subscribers, Twitter followers, and Facebook fans to participate. It should come as no surprise that a sample of that population — people who follow Company X — would have pretty positive views on hamster optimization. (“Hamster optimization rules!”)

The results would likely be quite different if Company X invited participants from a random set of Harvard Business Review subscribers. (“What the heck is hamster optimization?”)

To be honest, selection bias is almost impossible to avoid — especially in industry studies with modest budgets, which is usually the case in guerrilla content marketing. That’s okay. Just disclose your selection methodology so that readers can adjust their interpretation with that bias in mind.

For Company X to pull participants mostly from its own universe, but to report as if their findings represent a more general population, however, would be disingenuous.

If you fear that disclosing your selection methodology could undermine the results of your study — that should be a warning bell — then you might consider ponying up money to find a less biased population to sample. This is one of the services that professional industry analyst firms offer. They’re not free from selection bias either, of course, but their audiences are usually much less biased than the ecosystem around a single vendor.

The fourth, including the original question and answer choices, helps make sure that you, your survey participants, and your content consumers are all talking about the same thing.

If you ask a question like, “Do you use data-driven decision making?” and get Y% who respond “yes” — but then in your report you write, “Y% are data-driven decision makers” — you’re changing the meaning. Participants may have answered the question thinking that they occasionally use data-driven decision making along with other experience-driven approaches. But the statement in your report could be interpreted that those participants predominantly or exclusively use data-driven decision making.

This effect can be subtle or significant. But it’s easy to avoid problems by simply restating the question and answers verbatim. You can add other narrative around that, but you’re clear about what’s data and what’s narrative.

Finally, defining nomenclature that may be highly subject to interpretation, in both the survey and the report, avoids misinterpretations. For instance, if you’re surveying how many marketing teams have a “marketing technologist” on staff, you might want to define who qualifies as a marketing technologist. An IT person working in marketing? A web developer? A marketing automation specialist? Depending on the definition, you may get very different results.

Particularly with so many new terms popping up in our profession, including brief definitions in your study can help reduce the risk of wildly different interpretations impacting the accuracy of your analysis.

This certainly isn’t an exhaustive list of survey and statistical analysis dos and don’ts. But if we could raise the bar on survey-driven content marketing to address these five issues, it would make that content more valuable than a quick flurry of soundbite tweets on Twitter — it would provide information that our readers could actually use in more data-driven decision making.

And it would make you a more credible source in the eyes of your audience — as all good content marketing should.

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The terms “marketing technologist” and “growth hacker” seem to be gaining traction out there.

Voice-based marketing automation provider ifbyphone recently released their annual State of Marketing Measurement Survey for 2013, which included the following results on the evolution of the marketing team:

• 31% have a marketing technologist
• 25% have a growth hacker
• Marketing automation is used more frequently by teams with growth hackers (44%) than teams without growth hackers (26%)

Ifbyphone defines a marketing technologist as “a marketer with significant IT skills.” They define a growth hacker as “a marketer who combines marketing knowledge with a strong technical background to drive growth.”

The survey was conducted online earlier this year with more than 400 respondents across the U.S. who are responsible for marketing within their company. Unfortunately, more detailed firmographics of the participants were not provided, so it’s hard to draw more specific conclusions about the kinds of organizations this data represents (size, industry, B2B vs. B2C, etc.). My guess is that they’re probably mostly smaller firms, since only 1/4 report having a product manager, which is a fairly common role at larger organizations. (Interesting note: for this population, marketing technologists are more common than product managers.)

It also makes it hard to compare against Gartner’s claim that 70% of companies have a chief marketing technologist role — especially since Gartner didn’t publicly provide firmographics of the participants in their survey either.

Hey, marketers and analysts — I love all this research data that is being published in the name of content marketing, but a little more detail on the participants would make this information much more useful, credible, actionable.

Just another humble vote for pragmatic marketing.

But I digress! The one solid conclusion I can draw is that the terms marketing technologist and growth hacker are appearing in more marketing industry surveys and a good number of participants in those surveys report that they’ve adopted those roles in their teams.

It’s a qualitative conclusion, but a good one.

UPDATE: Someone pointed me to the Gartner webinar where they did share some of the firmographics of the participants in their research that weren’t included in the original press release. In regard to the 70% of firms that have a chief marketing technologist role, those were based on 203 respondents from U.S. firms with $500 million or more in revenue. It was a pretty even mix of B2B and B2C companies across a number of different verticals. In their question, they defined a chief marketing technologist role as “the equivalent of a CTO and CIO dedicated to marketing, familiar with all kinds of marketing software, data & analytics, social & mobile platforms, content marketing, web mechanics, digital advertising networks, among other topics.”

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Darling, I don’t know why I go to extremes
Too high or too low there ain’t no in-betweens
— Billy Joel

There’s a common fallacy known as a false dilemma or false dichotomy. It’s where you’re artificially presented with a black-and-white, either-or choice: you’re forced to choose between all of one or all of the other. “You’re either with us or against us!”

It’s a fallacy because, most of the time, you’re not actually constrained to just those two choices. There are many great options in the middle — and often even more outside of that narrowly framed continuum.

In modern marketing, however, I’ve been noticing that more and more issues are being framed as black-and-white, either-or choices. We seem more ready than ever to take a new idea and either reject it completely or embrace it to the point of absurdity. The “reasonable middle” seems to get relatively little voice, even though in most cases, a balanced approach is optimal.

Consider these examples, topics that generate much debate at the extremes but in actuality benefit from a healthy dose of moderation:

Inbound marketing. Inbound marketing (and permission marketing before it) is the native son of the Internet: using good content marketing, organic SEO, social media marketing, and subsequent email-driven nurturing to win customers by fulfilling their quest for knowledge is an incredibly powerful paradigm. Outbound marketing, such as advertising, direct mail, sponsorships, event exhibits, sales calls, and other mainstays of traditional marketing, is on the other end of the spectrum. But while these are very different approaches, however, there’s no reason why a company shouldn’t do both — each to the degree that they provide ROI and have a positive brand impact. There are even a plethora of hybrid tactics, such as pay-per-click (PPC) advertising in search and social media.

Data-driven marketing. Relying solely on gut-based, experience-driven decision-making in marketing is foolish in the digital age. It’s so easy to leverage analytics and experimentation to make better decisions. But going overboard, believing that data has all the answers — what Kate Crawford has labeled data fundamentalism — may be even worse than ignoring data entirely. Data fundamentalism allows you to make incredibly stupid decisions with a high degree of statistical confidence. Again, the sensible answer for most companies is a balance of data analytics and human judgement.

Marketing technology governance. Should marketing leave all technology strategy and management to the IT department? In the digital world, probably not. But does that mean that marketing has to become 100% compartmentalized in all technology it uses? Of course not. There are many reasonable compromises and collaborations between those two poles, where marketing teams develop native technical leadership capabilities while also partnering and coordinating with IT. A chief marketing technologist can be the bridge between marketing and IT, not a crocodile in the moat.

Agile marketing. Traditional management approaches are often top-down, strict command-and-control hierarchies. But in our digitally malleable and rapidly shifting world, such corporate structures struggle to adapt to ever more fluid markets. Agile management approaches, which give a lot of power to bottom-up creativity and talent, are much better suited to this environment. But for most companies, the optimal approach will be a combination of the two. The best agile organizations naturally blend consistent top-down vision and values with bottom-up imagination and insight.

In all these scenarios, it’s not a strict either/or proposition.

You can do great inbound marketing and great outbound marketing. You can embrace data-driven decision making and apply human reasoning and the insights of experience. You can collaborate with IT and have your own marketing technology responsibilities. You can have strong top-down leadership and adopt agile marketing for the parts of your marketing mission that benefit from it.

So why don’t these more balanced approaches get more attention?

Hype cycles, maturity models, and other self-inflicted wounds

I believe there are 7 reasons why the extremes in modern marketing get more air time than they deserve — and by calling them out, I hope it will make it easier to keep them in check:

1. Gartner’s hype cycle.
2. A mental bias for either-or decisions.
3. Maturity models.
4. Up-and-to-the-right graphs.
5. Conflating advocacy with absolutism.
6. False trade-offs — e.g., art/science is not really a trade off.
7. Content marketing escalation.

The first is Gartner’s hype cycle. Marketing actually created the hype cycle, perfecting the art of sensationalizing new technologies to generate buzz, news clippings, and higher valuations. But the peak of inflated expectations — overpromising and underdelivering — inevitably leads to the trough of disillusionment. Many companies (and careers within them) are damaged by this precipitous fall.

The irony is that marketing is now a victim of its own hype cycle dynamics. These days, it’s the marketer who is the target of the hype. The hype for new social media platforms, the hype for big data, the hype for new marketing technologies, etc. And the rate of new cycles being thrown at marketers is now so rapid that, even as they each run their course, most CMOs are being pulled up at least two or three peaks of inflated expectations at any point in time.

But here’s the thing: a hype cycle can only seduce you if you let it. You don’t have to flat out ignore a new technology either. You can take a modest position of skeptical curiosity and a willingness to test the practical uses of something new.

In Chip and Dan Heath’s new book, Decisive, they call attention to a number of psychological quirks in our decision-making instincts. One of them is our tendency to get trapped in narrow frames — such as either-or decisions. This is the false dilemma fallacy.

However, by recognizing this bias, we can consciously broaden the frame of our decision, considering “and” instead of “or” — or exploring entirely different options.

Another culprit of overreaching expectations is the maturity model. No doubt, you’ve seen these: four or five stages that organizations progress through — or supposedly should progress through — in developing a certain capability along a number of dimensions. One of the original maturity models was for software development, CMM (Capability Maturity Model). Since then, there have been an explosion of maturity models, from big data to social media. Even I’m guilty of producing one, a search marketing maturity model five years ago. (Has the statute of limitations expired?)

The problem is that each stage typically promotes advancement on all dimensions of the model — pushing every aspect of the company’s “maturity” further and further into the stratosphere. The implicit assumption is that you should always want to move up to the next stage.

But that may not be the right thing to do. There is a cost to everything, and advancing into the highest levels of a maturity model may have diminishing returns or even negative returns for you. For instance, with that original maturity model for software, CMM, it has been observed that many of the world’s leading software companies — Apple, Microsoft, Symantec — never advanced beyond the first or second stage.

Real maturity is discovering which stage is optimal for your business, or even mixing attributes from different stages — resisting the narrow-framing of a fixed set of homogenized stages (that were likely assembled by someone with their own marketing agenda).

A more generic version of maturity model madness is the up-and-to-the-right graph. So many promotional materials emphasize up-and-to-the-right graphs: do more of X, get more of Y. And who doesn’t want more? More clicks, more leads, more customers, more revenue, more profit, more, more, more.

Which is great — but again, everything has a cost, even if it’s just opportunity cost. The pursuit of “more” without considering what those costs are leads to absurdity. If one blog post a week gets us 10 prospects, and one blog post a day gets us 20 prospects, then we should publish a blog post every second to take over the world! Just because the line on the graph can go higher and further to the right doesn’t necessarily mean you should chase it there — at the expense of other variables that aren’t represented on that graph.

The antidote: common sense and more holistic cost/benefit thinking.

Conflating advocacy with absolutism happens when we’re excited and passionate about something that is clearly good, but we slide from merely embracing it to embracing it at the exclusion of other things. Other things that didn’t need to be excluded. For instance, data-driven decision making is a wonderful thing. Use data as much as you can. But that doesn’t have to be at the exclusion of leveraging experience, intuition, and human judgement — especially in deciding what data to use and in recognizing what’s reasonable to extrapolate from it.

Related to that is the notion of a false trade-off, taking two things and erroneously claiming that there is an inverse relationship between them — that more of one inherently means less of the other. The example I’m thinking of is the debate between art and science in marketing. It’s implied that more science means less art. But that’s certainly not true in the real world: more physics does not mean less poetry.

Of course, in the context marketing, “art” is often meant as more qualitative and intuitive factors, in contrast to “science” as more quantitative and analytical factors. Storytelling through written, spoken, and visual expressions is also considered “art” in marketing (although it’s really more of a craft). Whereas analytics, data mining, and controlled experiments such as A/B testing are considered more “science.”

But still, even with those fuzzy definitions, modern marketing can — and should — synthesize methods from both the “art” and the “science” categories. They’re not necessarily trade-offs. Great science in marketing can direct the application of great art in marketing, and vice versa — for instance, there’s an art to running great marketing experiments, if you can wrap your head around that juxtaposition. And let’s not forget that science itself is a creative endeavor.

Last but not least, content marketing escalation. There’s a heated content marketing arms race underway. Marketers are being pushed to generate more and more content, faster and faster. But there are two deleterious “hype cycle” side effects of this.

First, topics that seem to be trending are rapidly added to content marketing agendas. Quick, we need a white paper on something with big data! This causes an acceleration of content on a topic that can make it seem even hotter, which attracts the attention of more content marketers, and so on, like feedback from a microphone next to an amplifier. In the interest of speed-to-publish, not all of these content pieces are, um, deep or original. Let’s just say that there are a lot of echoes in the shallow end of the pool. But those echoes don’t offer independent corroborating evidence of that trend.

Second, because there is suddenly a flood of content on that new subject, everyone is vying for attention in a crowded field. This can create pressure to produce something that has “shock value” — something surprising or controversial. Such things are often found on the edges of a continuum rather than in the middle, which can artificially skew the perception of the edges being greater than the middle.

Moderate and nuanced articles and reports don’t necessarily make for “sexy” content marketing. But I personally believe that more pragmatic content can be more useful to real prospects. In an environment where we marketers are the consumers of content marketing, not just producers, we can influence this dynamic. We can seek out the real over the sensational and use social signals (or the absence of them) to give our fellow marketers clear feedback on what we really want.

(To be fair, certainly a number of marketers do set a high bar for their content marketing. But as a group, they’re probably at least one standard deviation above the mean. Maybe two.)

Pragmatic marketing is marketing in a Technicolor world

The test of a first-rate intelligence is the ability to hold two opposing ideas in mind at the same time and still retain the ability to function.
— F. Scott Fitzgerald

I’ve said it many times: it’s an exciting but challenging time in marketing. We’re in a period of enormous disruptive innovation, an intellectual and creative melting pot of old and new strategies, tactics, capabilities, and worldviews.

The last thing a marketer should do these days is resist change.

In fact, I believe that most marketers should lean into an “early adopter” mindset. Be willing to learn about and experiment with new ideas as they emerge. This is important not because those new ideas will bear fruit immediately — some will, some won’t, and probably none of them to the degree that their initial streak up the hype cycle might proclaim — but because engaging on the vanguard of such changes helps marketing be more attune to broader shifts in markets and society.

Marketing should be the “early warning” signal for changes that will effect the business. Since adapting to change is probably the most challenging thing for any organization to do, having a head start on a new trend, gaining some early experience and familiarity with it, lays the groundwork to more rapidly embrace that trend at scale — if and when that’s warranted.

Not every trend will blossom. Many will fizzle out. But placing little bets on a number of emerging opportunities lets you hedge against both possible outcomes. Even those that don’t take off can provide some valuable insight into how the world is evolving. Marketers must stay hungry to understand their customers — and that’s always a moving target.

And by continually scouting the frontier and experimenting with new ideas, marketing increases its overall metabolism for change. That is a precious capability in a world of constant and accelerating change. It’s part of why I believe marketing, more than any other group, has the potential to break free from the innovator’s dilemma.

So am I advocating two contradictory principles here?

1. Don’t fall prey to the inflated hyperbole of new ideas.
2. Embrace new ideas with the mindset of an early adopter.

Is this a paradox?

I don’t believe it has to be. We can take the pragmatic approach of balancing both in some reasonable proportion to each other. Granted, that denies us the simplicity of black-or-white positions and deflates the false dilemmas that give rise to so many sexy blog post titles.

But it leaves us with the far more interesting challenge of painting marketing in a vibrant Technicolor world.

The post Pragmatic marketing vs. hype cycles and false dilemmas appeared first on Chief Marketing Technologist.

A couple of months ago, I wrote a column on Search Engine Land titled Why Big Testing Will Be Bigger Than Big Data. A shorter spin-off of my post here on the big data bubble in marketing, its overarching message was that in a world of ever more data, experimentation would inevitably become king.

The message seemed to resonate, and the article was widely shared. However, my suggestion that the number of people empowered to run experiments should be significantly expanded in most organizations raised a number of questions in the comments. How do you build and scale a testing culture in a company?

The column and those questions caught the attention of Andy Pulkstenis, whom I would describe as a Jedi Knight of experimental design. He’s had years of experience running marketing-oriented controlled experiments at Capital One — one of the world’s pioneers in large-scale marketing experimentation programs — and now at State Farm.

Andy’s an enthusiastic champion of developing a “testing culture,” and he gave a popular presentation on the subject at SAS’s Analytics 2012 conference last year. He and I had the opportunity to chat, and he agreed to participate in the following Q&A on testing on the big stage. All the graphics in this post were created by him.

Can you start by telling us a little bit about your background and your role at State Farm?

I have an MS in Statistics from Penn State, and have been working in applied business analytics and predictive modeling for about 18 years now. I spent 8 years working for a firm that did statistical consulting and a lot of SAS, then spent 6 years at Capital One leading statistical analytic teams in a variety of departments. I’ve been at State Farm doing the same thing for the past 4 years.

I dabbled in testing off and on before Capital One, but it was there that I feel I really learned how to do testing. I was fortunate to work directly with a few mentors who had significant experience in testing and DOE and really knew the math behind the machinery as well. Bill Kahn and Tom Kirchoff were instrumental in my development as an experimenter.

What are your arguments for why marketers should engage in testing, specifically controlled experiments?

There are several. But the most basic is that if you are not testing, you never really know what’s driving customer behavior.

If you are not testing, you never really know what’s driving customer behavior.

In response to a business partner proclaiming what they believed to be truth about customer behavior, one of my colleagues said, “Do you know, or do you just think you know?” I’ve stolen that mantra and use it often in my own business discussions. When push comes to shove, observational data (data that did not come from randomized, controlled experiments) generates theories about what may be happening, but to really know causation with certainty requires a controlled experiment.

I sat in a talk a while back where the lecturer discussed observational studies — you know the ones, where they announce on TV that coffee or wine or whatever is bad for you, then the newspaper the next day says a study showed it’s good for you, then next month the same newspaper runs a contradictory story. He said that a group took dozens of observational studies, replicated each one in a controlled experimental environment, and in a whopping 80% of cases failed to reach the same conclusion as the uncontrolled observational study.

80%!

That should send chills down the spine of any business or marketing analyst when you consider that the vast majority of what passes for business analysis today is essentially an uncontrolled, observational data study, since 95% of the data in any large company is uncontrolled observational data. Clean data from an experiment gives clean answers, and if the sample is chosen properly the data can be used as the foundation for spectacular predictive models going forward.

The vast majority of what passes for business analysis today is essentially an uncontrolled, observational data study.

A related question that comes up often is “Why multivariate testing instead of A/B testing?” I think both have their place, but a firm that stops with simple A/B testing is really limiting potential for growth. In an A/B testing direct mail setting, for example, you learn which letter generated higher response, but you don’t learn why it worked because there were too many simultaneous differences between the letters.

Some practitioners try to mitigate that by OFAT (One Factor At a Time) testing — changing only one feature between the letters in the first A/B test, pick the winner, then change only a second feature in a second A/B test, combine that with the learnings from the first, etc., but this also has significant drawbacks (the most obvious is longer time to a decision).

A more detrimental drawback is that to combine conclusions across tests from different time periods makes some pretty bold assumptions about the lack of interactions, the lack of seasonality, and the lack of impact from exogenous factors like the economy or competitive environment. We demonstrated this for partners at State Farm using our internal data, from a real experiment, showing that the combined solution was actually the worst choice of overall strategy. (See graphic below.)

How can a marketer champion testing in their organization?

You need to be a teacher and be willing to proactively sell this idea to anyone and everyone that will listen. I did that for about 2 months (and many audiences) before finding a willing business partner to try this “new” idea. They all liked the idea, but it took a while to find someone willing to go first.

Initially it’s about educating on the advantages of testing compared to observational analysis, sharing examples on how other firms are using it to improve business strategies, and I found value in answering some common arguments I’ve encountered right there in the first presentation, such as “it’s too expensive” or “it seems too complicated compared to what we do today.” I go down the list of about a dozen of them, and answer each one by one. This addresses 95% of the concerns in most groups before you even get to Q&A.

Initially it’s about educating on the advantages of testing compared to observational analysis.

How do you win executive-level buy-in?

Conceptual buy-in is easy, but commitment to let you do this with a real business strategy can be another thing entirely.

Getting the right initial internal example is really important. If it’s too small, people don’t notice and it doesn’t really feel compelling. If it’s too big and errors occur, it becomes a high profile example people can latch onto for why we may not want to do testing. So aiming for an example of moderate impact but smaller and very manageable scope has worked for me. That opens doors to larger opportunities once the internal proof of concept is out there.

Telling people how other companies are improving business results through testing is helpful, but it’s very powerful when you can switch to telling people how other business units in your own firm have used testing to improve business results. Our first “big” multivariate test was 24 strategies, which felt ambitious at the time. Our follow-up in that same business area was over 200.

From your experience, what are the ingredients that make for a successful testing project?

1) The right SME’s representing the following 4 areas: The business, statistical design, implementation, and creative design (the guys making the marketing piece or web page, for example). Leaving any one of those 4 out causes many problems. Drop implementation, and the other guys create a great test that will crash our system. Drop the stats guy and the others can create a great test that doesn’t actually answer the questions accurately. Drop the business guy and the others create a great test that answers questions no one is asking, etc.

Drop the business guy and the others create a great test that answers questions no one is asking.

2) Monitoring, monitoring, monitoring! The work doesn’t end when it goes to implementation. There must be close monitoring of the test from start to finish, because you’d be surprised how many times someone makes a decision to “help out” that can destroy the statistical integrity of the test.

We had one web test where we needed to battle someone in implementation who believed that if someone visits the page, leaves, and comes back, we should continue to randomize which page he sees (versus fixing it to be the originally randomized page for all return visits for that guy). This, of course, completely destroys the ability to interpret the test factors because now they are all jumbled together. Was the response due to page 1? Page 2? The 3rd page he saw? A combination of all 3? We caught this in post-implementation monitoring and were able to correct it early on, only losing a couple of days.

3) Have a clear analysis plan before the test is even designed, including approach and metrics of interest. The analysis plan and statistical design are linked, and ignoring the analysis plan can lead to a design that doesn’t allow you to carry out the analysis as intended.

For example, you may intend to study interactions, but the design may assume no interactions exist. You usually can’t fix something like that on the back end. Maybe you plan to analyze the data at the customer-level, but the implementation platform produces only aggregate data. Best to get this stuff straightened out in the beginning so there are no surprises on the back end.

As you scale up testing in an organization, how can you manage the process to avoid conflicts, such as one test interfering with another?

We’re currently tackling this challenge. It can be helpful to start first with a siloed prioritization approach, within each channel or business line, for example. When the same stakeholders are the ones causing the problems, it’s easier to resolve. We’ve got that handled pretty well in our firm.

The next step is creating a firmwide prioritization process, for those instances where two competing business units want to use the same space at the same time. Some decisions will be easy as usually there is a widespread awareness of which business units trump others or which strategies are more “important” than others.

Failing that, you need to get the associated stakeholders in a room and hammer it out with the company’s best interests at the heart of the conversation. The dimensions I consider include financial impact, strategic impact, complexity (can we squeeze a less important test in first because it’s simple and not really lose much overall as a company?), and the impacts of delaying an effort until later.

If you could offer 30 seconds of advice to CMOs about controlled experimentation in marketing, what would you say?

If you aren’t testing, you are guessing. The guesses may be based on years of good experience and some halfway decent data, but they are still guesses and educated hunches more than verified truth.

If you aren’t testing, you are guessing. The guesses may be based on years of good experience and some halfway decent data, but they are still guesses.

If you want to know, with certainty, you must test. And it’s the new normal. The ones that refuse to embrace testing will find themselves at a severe disadvantage as more and more competitors in all industries start to test. And learn about interactions — they will likely change the way you think about your strategies.

Thank you, Andy — this was awesome!

The post Advice from a Jedi Knight of marketing experimentation appeared first on Chief Marketing Technologist.

If I were a marketer psychologist — that is, a therapist to marketing teams, for which I’m sure there’s a market — I could imagine making a fine living by asking open-ended questions, such as “What does marketing science mean to you?”, and holding up a mirror to people’s responses.

I hear you say data-driven. What’s your earliest memory of working with data?

“Marketing as a science” strikes me as a bit of a Rorschach test for marketers these days. The answers seem to vary tremendously, offering glimpses into an organization’s worldview and culture in our digitized and data-ified world. (To play fair, last month I wrote up my interpretation of that nomenclature inkblot by describing 4 principles of good marketing science.)

So I’ve been eagerly consuming content, wherever I can find it, that champions science in marketing, to appreciate the different perspectives people bring to that concept. The latest piece I’ve read is a new IBM report, Marketing science: From descriptive to prescriptive.

IBM surveyed 358 marketers, across a range of businesses and industries, to examine how they’re using data and scientific methods to impact business outcomes. In the graphic at the top of this post, they classified participants according to (Y-Axis) whether their analytic outcomes were descriptive, predictive, or prescriptive and (X-Axis) whether the influence of their analytical methods spanned marketing/sales, the whole enterprise, or across the entire value chain.

They labeled as “marketing scientists” those who combine predictive or prescriptive analytical outcomes with influence across the enterprise or the entire value chain. Only 23% of those surveyed qualified for the marketing scientist label.

Of course, one doesn’t need to qualify for their definition of a “marketing scientist” in order to be effective at using science in marketing. In fact, later on in the paper, IBM separately analyzes the results of marketers who report how well they apply science in their work. But what’s interesting is that those participants who did cluster into the marketing scientist category had a much higher propensity than “traditional marketers” or “constrained analysts” to use a scientific approach in general:

In other words, there is a strong correlation between those marketers who embrace more advanced techniques (such as prescriptive analytics) and influence across the value chain of the organization with those who are generally more “scientific” in their approach, presumedly even in simpler scenarios.

That makes sense, although — as data science says — correlation does not imply causation.

I add that caveat, because while I appreciate the capabilities of prescriptive analytics and optimization across the entire value chain, I don’t believe that either of those is necessarily a requirement for applying good science in marketing.

There are plenty of opportunities just within marketing/sales for better data-driven decision making and controlled experimentation that can generate terrific returns. I’d caution against setting the bar so high that people are discouraged from more modest marketing science.

I had the chance to talk with Derek Franks, a consultant for the IBM Center for Applied Insights and one of the authors of the report, who confirmed that testing hypotheses and running controlled experiments was, by far, “the most important step.”

“Start by conducting relatively small scale experiments,” Derek suggested. “Develop a hypothesis, test it, and modify it. That approach is much better than randomly walking around, looking for patterns in the data.”

I also asked him about the limits of prescriptive analytics in marketing science. Prescriptive analytics, as written on Wikipedia, automatically synthesizes big data, mathematical sciences, business rules, and machine learning to make predictions and then suggests decision options to take advantage of the predictions. It is the so-called “third phase” of business analytics which includes descriptive, predictive and prescriptive analytics.

While there are certainly scenarios where prescriptive analytics can be effective, I am a little concerned about the risk of overreaching with such techniques. After all, predicting the future is hard, especially when complexities of markets and human behavior are involved. How should marketers manage that?

“The biggest defense against this,” Derek replied, “is having marketing scientists, data scientists within your organization who really understand what they’re doing. They understand the limits of the data that they have access to and are careful to qualify what they can predict with a certain percentage of confidence.”

That’s fair. But I think it’s also important for marketing executives to calibrate realistic expectations. As Derek mentioned in our chat, “Not everyone needs to become a data scientist. But business executives need to be intelligent consumers of data.”

In my opinion, intelligent consumers of data are open-minded, curious, and willing to look at data through more than one lens. They value analytical decision making. But they also bring a healthy skepticism to the limits of the data they’re working with. Finding the right balance between data-driven and experience-driven management may be a big part of where the “art” of marketing still remains.

“The science of marketing isn’t going to replace the art of marketing,” Derek added at the end. “It will enhance the art of marketing. It’s not an either/or proposition at all, but rather complementary approaches.”

What does marketing as a science mean to you?

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