The (sort of) good news is that you’re not alone. Even veteran marketers are having a tough time finding ROI in the ever-expanding haystack that is today’s multichannel marketing environment.

But the unsettling news is that easy, online tracking tools and a shaky economy have created a C-suite expectation that every dollar spent must be connected to a quantifiable and measurable return.

Consider this: In a recent Ifbyphone survey, four out of five marketing professionals report that they are expected to deliver measurable results and outcomes for their campaigns. Yet only 29% of those marketers indicate that they can effectively measure ROI across all channels.

Sound familiar? It should, because a substantial portion of average ad spend (82%) still resides in offline channels—the kind of channels that can’t be effectively measured with quick and easy online tracking metrics.

When marketers are asked to identify the most difficult types of campaigns to measure, more than half refer to offline channels. Specifically, 33% cited public relations and 27% cited print ads as the most difficult campaigns to track, according to our survey, while only 6% referenced email marketing.

The need to attach accurate ROI to a full range of marketing channels isn’t going away soon. But there are at least three initiatives marketers can implement to improve their ability to identify return on both online and offline marketing campaigns.

1. Fully Use Available Marketing Tools

In many cases, the difficulty in connecting ROI to specific marketing campaigns can be attributed to not using available marketing tools. According to the Ifbyphone survey, the most heavily used marketing tools include Web analytics (48%), email marketing software analytics (47%), lead counts from online contact forms (38%), social media monitoring (30%), and call tracking (27%).

Tools like social media monitoring and call tracking are gaining ground with marketers. While social media monitoring tracks an entirely new channel, call tracking is a way to measure traditional offline and online channels—including pay-per-click.

2. Leverage Opportunities Presented by New Technologies

It has become increasingly important for marketers to maintain an awareness of new tools and emerging technologies, and be prepared to pounce on opportunities to use those technologies in the pursuit of more accurate ROI insights.

For example, Siri (Apple’s new voice-based digital assistant) transfers search functions from clicks to conversations. Instead of typing or clicking search criteria, users simply speak their requests, and the Siri application can intuitively create a connection to a brand.

From a marketing perspective, Siri creates new opportunities to understand the sources of sales leads and connect specific campaigns to measurable brand outcomes in their effort to forge more meaningful connections with consumers.

3. Emphasize the Importance of Measurement Across All Channels

One of the most disturbing findings of Ifbyphone’s survey was the fact that while nearly nine out of 10 CMOs believe that every marketing campaign should be measured, more than a quarter of marketing assistants don’t see the value in marketing measurements.

This apparent disconnect between the C-suite and the front lines has important ramifications for both present and future marketing success. Through a combination of best practices, and proactive leadership and mentoring, executives need to educate tomorrow’s leaders about the role of accurate measurements in successful multichannel marketing programs.

At the end of the day, the best way to improve on marketing successes (and avoid marketing failures) is by emphasizing the need for accurate measurements across all channels. Despite the challenges, marketers and C-suite executives need to be relentless in their pursuit of new methods of evaluating the ROI of both offline and online marketing campaigns.

As CEO and CTO of Ifbyphone, Irv Shapiro is responsible for overall business strategy and corporate leadership. His business success has earned him several awards including as inductee status with the “Chicago Area Entrepreneurship Hall of Fame” and as gold winner in the Executive of the Year category for the Best in Biz Awards.

Measuring social media isn’t all that different from measuring your other marketing activities, but many of us are still looking for a silver bullet metric that will finally prove it is working.

This post will show you why it doesn’t have to be that complicated, how you can leverage the information you can get today, and how you can compare it to numbers that already have established baselines for success.

According to the 2011 Social Media Marketing Industry Report from Social Media Examiner, 90% of marketers indicate that social media is important for their business. However, the No. 1 question marketers have is:  How do I measure the effect of social media marketing on my business?

Measuring social media has become a debated topic because an industry standard hasn’t been widely accepted. It is challenging to get beyond these new social media metrics like the volume of “@replies” or “likes” and translate it to the value those actions have for business. But here’s the big secret: The industry standard for measuring social media DOES exist; you’ve just been looking in the wrong place.

Tip 1: Align Social Media Metrics with Standard Marketing Metrics

Social media isn’t a shiny new thing that needs to be measured in a “special” way. Trying to create special metrics for social media makes it far too complicated for “non-social media folks” to understand. If you want to get to the core of where social media is delivering value, translate its impact on sales (units), revenue, and costs to the organization. If you can’t measure social media’s impact on sales and revenue yet, you definitely can measure its impact on cost. Start there and build your way into the revenue model. This is a sure-fire way to get even the most skeptical executive on the social media bandwagon.

Tip 2: Leverage “Known” Metrics from Other Marketing Channels

Don’t waste your time trying to explain what the value of a Facebook fan is to your management team. Instead consolidate social media metrics into “buckets” of known metrics from other marketing channels like public relations, online advertising and search engine optimization (SEO). Try grouping social media metrics to show cost per impression, cost per click, cost per visitor, cost per lead, cost per call, and cost per inbound link across all social media channels or from an individual channel. These metrics have a history in your company and a baseline of “acceptable” returns that have been established. Once you calculate the numbers, you will quickly know if social media is outperforming another channel or providing a much needed assist to another platform.

Tip 3: Don’t Isolate Social Media Metrics

Social media is still new for many organizations and if you try to show social media metrics by themselves sometimes they don’t look all that impressive alone. However, if you show them side by side with your public relations, online advertising and SEO results you can quickly show where they are helping these other channels.

In a case study I did with Whistler Blackcomb, we were able to show that social media provided a 128% lift in total impressions generated from public relations with less than 1% of the total marketing budget. Total cost per impression decreased by 28%, cost per engagement decreased by 82%, and cost per site visit decreased by 86%. This cross-functional analysis showed that social media was providing the largest impact on the success of the team’s public relations efforts with a miniscule investment when compared to other marketing activities.

Tip 4: Don’t Be Scared to Show Negative Results

The first time you measure social media ROI, it will be negative. Unfortunately, it is the nature of the beast. You can’t improve what you don’t measure, so if you haven’t been watching ROI, you probably haven’t been taking steps to improve it. However, once you start measuring, you will be able to test different strategies to see which ones deliver the best impact. Showing negative results is the first step to showing dramatic positive increases, so don’t shy away from measuring because you are scared of what the results will show.

Tip 5: Cut Bait When You Need To

You are super smart, but unfortunately every strategy you have implemented will not work. Don’t continue with social media tactics that aren’t showing the ability to deliver on the intended goal. Many times we get into this trap of needing to prove that “my” idea will work, and we invest time and resources into underperforming strategies in an effort to satisfy our ego. You need to give strategies enough time to show success, but when they aren’t delivering be quick to move on to something else. As with any marketing initiative, social media is a game of trial and error. You won’t know what works if you don’t keep trying new ideas.

The key to showing where social media is delivering value is to use tried and true metrics that have historical relevance in the company. Unfortunately, it takes too much time to bring management teams up to speed on the new lingo of social media, and your efforts are better spent finding strategies that work. Cut to the chase and provide metrics that matter to the bottom-line. Your career will thank you for it.

Nichole Kelly is the CEO of Full Frontal ROI Consulting, the social media firm that understands your bottom line. She also publishes the measurement blog,  fullfrontalroi.com.

Do you know which of your marketing programs are working and which ones aren’t? If you are like most marketers, you continue to struggle with marketing measurement—despite the abundance of tools to help track success.

Part of the problem, as explained in the infographic below, is that difficult-to-measure channels, such as television and newspaper advertising, will continue to dominate marketing budgets. And problems persist even in the easy-to-measure online world. The emerging field of voice-based marketing automation is working to address these measurement challenges because it is becoming more important to determine the right metrics for campaign measurement.

Ifbyphone, a leader in voice-based marketing automation, assembled statistics on how marketers currently measure ROI. What tools are they using? What key metrics are they tracking?

Use this knowledge to start planning your own measurement initiatives for 2012.

Just because everyone and their online peeps are talking about you that doesn’t necessarily mean that those social media mentions are resulting in more coins in your coffers or a more positive reputation for your brand. So, how do you know if you’re hitting your marketing campaign goals?

The key is to write clear,  measurable objectives right from the get-go. As public relations strategist Geoff Livingston mentioned in his MarketingProfs PRO seminar Measuring and Sustaining Social Success, the basic question in writing a clear goal is: “How many by when?”

Goals of “get more tweets” and “have (number) more Facebook fans” are too vague. And because they aren’t tied into any specific campaign, you can’t measure their effectiveness. To have a goal that you can measure, make sure that it is: specific (you know what you exactly want to do), measurable (you’ve got the right analytics tools in place), attainable (i.e., realistic), results-oriented (you actually accomplish something) and time-bound (you have a deadline). In other words, make sure it’s SMART.

Sound difficult? It’s not when you narrow down the seemingly overwhelming world of metrics to three areas that truly matter: attention, attitudes, and actions.

Livingston suggests that you ask yourself the following questions when creating measurable goals for your social media campaign:

Attention (e.g., fans, traffic, volumes of interest, online mentions)

• What will your loyal clients and would-be customers know?
• What will they come to understand?

Attitudes (overall sentiment regarding your business)

• How will the people you are targeting feel about your brand or business?
• What will they believe about your brand or business?
• Who will they trust?

Actions (business results of online outreach)

• What will your targeted group now do?
• What will they support?
• What will they stop or avoid doing?

Sure, everyone likes to be mentioned positively on social media (who doesn’t like being liked?), but when it comes to straight-up metrics, it’s important to strike a balance between the numbers-only philosophy of the more mathematically inclined and the relationship-only philosophy of the personally inclined. Smart objectives do just that.

Not a PRO member? Sign up today and become a smarter marketer by taking advantage of the thousands of resources we have available!

The global economy may be a wreck, but data volumes keep advancing. In fact, there is so much information competing for our limited attention, companies are increasingly turning to compute power and algorithms to make sense of the madness.

The human brain has its own methods for dealing with information overload. For example, think about millions of daily input the human eye receives and how it transmits and coordinates information with our brain. A task as simple as stepping a shallow flight of stairs takes incredible information processing. Of course, not all received data points are relevant to the task of walking a stairwell, and thus the brain must decide which data to process and which to ignore. And with our visual systems bombarded with sensory input from the time we wake until we sleep, it’s amazing the brain can do it all.

But the brain can’t do it all—especially not with the onslaught of data and information exploding at exponential rates. We need what author Rick Bookstaber calls “artificial filters,” computers and algorithms to help sort through mountains of data and present the best options. These algorithms are programmed with decision logic to find needles in haystacks, ultimately presenting us with more relevant choices in an ocean of data abundance.

Algorithms are at work all around us. Google’s PageRank presents us relevant results—in real time—captured from web server farms across the globe. Match.com sorts through millions of profiles, seeking compatible profiles for subscribers. And Facebook shows us friends we should “like.”

But algorithmic programming can go too far. As humans are more and more inundated with information, there’s a danger in turning over too much “pre-cognitive” work to algorithms. When we have computers sort friends we would “like”, pick the most relevant advertisements or best travel deals, and choose ideal dating partners for us, there’s a danger in missing the completely unexpected discovery, or the most unlikely correlation of negative one. And even as algorithms “watch” and process our online behavior and learn what makes us tick, there’s still a high possibility that results presented will be far and away from what we might consider “the best choice.”

With a data flood approaching, there’s a temptation to let algorithms do more and more of our pre-processing cognitive work. And if we continue to let algorithms “sort and choose” for us – we should be extremely careful to understand who’s designing these algorithms and how they decide. Perhaps it’s cynical to suggest otherwise, but in regards to algorithms we should always ask ourselves, are we really getting the best choice, or getting the choice that someone or some company has ultimately designed for us?

Question:
Rick Bookstaber makes the case that personalized filters may ultimately reduce human freedom. He says, “If filtering is part of thinking, then taking over the filtering also takes over how we think.” Are there dangers in too much personalized filtering?

Leonardo da Vinci was the ultimate jack of all trades, with expertise across many disciplines in science and art. As painter, he was known for the masterpieces listed above and also for dabbling in sketching, sculpting, writing, music, and mathematics. His contributions in science include discoveries in human anatomy, botany, astronomy, and more.

And of course, Leonardo wasn’t the only genius of the past four hundred years as  Galileo, Blaise Pascal, Einstein, and even Bobby Fisher attest. So then, who is today’s great mind—that once in a lifetime cross disciplinary genius able to see the big picture? No one, says author Tim Hartford.

Harford makes the claim in a recent Financial Times article that there will never be another Leonardo da Vinci because today’s thinker is swimming in too much knowledge. The amount of knowledge available today is unparalleled in history. Harford notes that approximately 3,000 scientific articles are published each day, and the rate of scientific papers is quadrupling every 30 years. With all this information, he says, “the percentage of human knowledge that one scientist can absorb is rapidly heading towards zero.”

Leonardo lived during the Renaissance, a period of significant cultural and intellectual achievement. Harford says Leonardo was able to contribute so much because so little was known. It was possible, he says, to make significant leaps in understanding the world around us because there was much to discover, especially for someone like Leonardo who pursued a multidisciplinary approach to knowledge.

Fast forward to today: With so much knowledge available, and more produced every day, Harford questions whether there will ever be another person with the ability to learn, understand, and then forge the necessary connections to produce new insights. In short, he claims there will never be another Leonardo, not because the individuals alive today are sans the requisite brainpower, but instead because there just aren’t enough hours in the day to acquire the knowledge necessary to make significant—i.e. non-incremental—contributions.

With worldwide data volumes growing 59% a year, we definitely live in an information age of plenty. Our hope then seems to lie in the use of technology to help us separate “signal from noise” in analyzing mammoth stockpiles of social, blogs, and other data. Focusing today’s minds on data worth paying attention to is probably one of the few ways we’ll increase our odds of significant scientific advancement.

• Harford says that too much specialization in science and business means it will take us longer to make significant discoveries. Do you agree?
• Do you agree with Harford that indeed there will never be another Leonardo da Vinci?

I suspect a lot of people in the UK have never heard of Post Rank—or at the very least have rarely used the service beyond tinkering. But for more than three years, this has been the bread and butter of blog analytics for both me and the clients I have built and serviced blogs for.

In short, Post Rank attributes a value to each social gesture that a reader may perform on your blog. So, if you look on the left of this blog, there is a list of posts which have attracted a score depending on their popularity. Retweets score differently to bookmarks, comments receive a different score to Facebook shares, and so on. The hugely important distinction to make, is that whilst the values do not necessarily mean anything in any financial or volume-based context we may already be familiar with, the system allows us to set targets and assess something that we are all looking at: attention and authority.

Any post can be widely read—but if nobody re-tweets it, is it in any way influential on that topic? In the same way that Facebook Likes, Retweets, Diggs and so on each represent a modern day “vote” of favor to a piece of content, Post Rank allows you to collate all these votes and provides you with the intelligence to shape your content plan to ensure that it is relevant more often than not, and in being relevant, stands much more chance of being re-posted (and seen) elsewhere.

I have developed over time, a methodology of posting content by very specific content categories (based on conversation monitoring), which is then measured against a Post Rank score for each category of post. Depending on the score of each post, we accurately know what type of content is most responded-to by the audience and can therefore post more content of this type—whatever it is.

Social Search Engine Results Pages

But what does this have to do with the future of search? Look at where search has gone over the last few years and the overwhelming focus has been on addressing the volumes of social content on the web.

• Universal search introduces video (typically from YouTube) and images alongside standard search engine results pages (SERPs).
• Google’s Caffeine update places more influence on delicious (among other things).
• Real-time tweets for popular topics appear alongside “static” SERPs.
• The Google avatar of friends in your network who have shared content that appear in SERPs are visible.

And so on …

The point is, that without any doubt, google’s issue has been one of attributing human “votes” to content. Post Rank fills this gap.

Take a look at its Google Reader filter below, and you can begin to see just how Post Rank’s system can provide such huge feedback on human votes of confidence in content.

I can now filter my Google Reader content according to what Post Rank has been able to determine are Good, Great or Best (or all) score. This means, it is giving me a score based on the human voted-for scores of content that is out there on the web.

Google Reader is giving me the most relevant content as judged by the interactions/votes of my peers and indeed the myriad people who have also “voted” for this content by sharing, rating and bookmarking it.

Granted, it may not be the most recent (and I have to admit, I don’t know how frequent post rank indexes the content, but it seems pretty instant).

A Ready-Made Human Recommendation System

So, in Post Rank, it would appear that Google is acquiring a ready-made system that provides the much-lauded human attribution of search relevance. What it will look like, maybe the above graphic gives us some clues, but I just hope they maintain the service as-is and not let it disappear like they did with Jaiku. Post Rank, please tell us one way or the other!

Increases in the call for transparency are legion. Health inspectors post food safety grades for eating establishments. Websites track lists of political campaign donors. And restaurants redesign bars, kitchens, and more to show patrons how drinks and meals are prepared. All this, in order to give customers a window into processes for product and service creation.

And to be sure, there’s definitely even more opportunity for transparency in product creation, especially in financial services. As an example, Michael Lewis’ Big Short cites how via the securitization process, hundreds of subprime mortgages were packaged up and divvied into “tranches” of investment quality. Through securitization, it was tough to estimate the contents of a particular asset-backed security. One hedge fund manager exclaimed; “I didn’t know what the (expletive) was in the things. You couldn’t do the analysis. You couldn’t say, ‘Give me all the ones with all California in them.’ No one knew what was in them.”

Creating a product with so much complexity that teams of MBAs are necessary to decipher its contents surely is a recipe for confusion. And on the ugly side of things, perhaps that was the intention. Regardless, in an age of social media where a company’s reputation can be destroyed in five minutes or less, this avenue will not ensure long term success.

However, a key question is whether customers really want to peer inside the black box. After all, investment banks had very little difficulty offloading these impenetrable structured products. Plenty of hedge, pension and even sovereign wealth funds lined up to buy these complicated products—and most with no questions asked.

In the analytics market, there’s an adage that business users really don’t care how a particular solution works, just so long as it meets their needs. And while this may be true in some instances, there’s also ample opportunity to enlighten consumers (or in this case application users) as to the “value” received through peeling back the curtain on how a product or service is designed and delivered.

What say you? Do your customers really want transparency? Do they really need to know what’s in the black box?

Your business probably spends a good deal of time and money on driving people to your website using different marketing channels. Now, having massive amounts of visitors every day is good—but if you can’t convert them into customers, then what’s the point?

Would you prefer 100 visits a day with a 10% conversion rate or a 1,000 at 0.1%? I would pick the first. So how do you increase your online conversion rates?

1. Make it easy. Be sure it’s easy for visitors to respond to your call to action or your offering. If you are trying to get visitors to fill in a form for a trial or a demo, make sure the form is simple and easy. You don’t need all 30 fields. Get the ones you need and then fill in the rest during further communications with your visitors. They shouldn’t have to work to provide you with their information.
2. Test, test, test. This is a surefire way to increase online conversions. Test everything—forms, landing pages, anything. Testing will give you information that you can use to improve rates. Over time, this will guarantee higher conversion rates. One thing to keep in  mind when testing: Go slowly, and test only one or two thing at a time, so you know what worked and what didn’t.
3. Offer multiple communication channels. Not everyone likes to use the same communications tool. Some people prefer phone, some choose email, and others might like filling in forms and getting a call-back. Make sure you have the tools in place to handle your prospects’ preferred way of communicating.
4. Know your visitors. Understand who your potential customers are. Understand how they speak, what matters to them, how they prefer to make decisions. Make your content and offerings relevant to them. To provide value, you need to understand what your visitors value.
5. Provide a clear call to action. Make sure visitors know what you want them to do—and that they can actually find your call to action! This means make sure you call to action message is clear and direct, and that your call-to-action placement is prominent.

Alexandre Sagala is the co-founder of Alsamarketing, a provider of automated online marketing software for B2B marketers.

Like my in-box, my mind is filled now with the fabulous tips that she shared about gathering and analyzing data and, most importantly, how to use this information to create more relevant messages for your customers.

I won’t be like an overly long movie trailer and divulge every detail, but I will share a few ideas (from the many) worth mentioning:

• Be brilliant a few times a month. In other words, not every email message that you send out has to be yeehaw fabulous. It’s all right if some emails are better than others. Just make sure that, if you’re sending out emails once a week, you’re amazing at least twice a month.
• Use info that’s in the community. Don’t limit yourself to gathering the ingredients from just one place for your email marketing campaigns. Use your call center, the sales teams, emails, and customer service to create a more personalized email.
• Make sure the data you gather is actionable. Data isn’t useful if you’re not using it for something. And it’s not interesting if you can’t do something with it.
• Collect social media info on your forms. Most people don’t use the same names across their social media and business platforms. If you get the info upfront, it’ll be easier to match up the info and get a more complete picture of your customers.
• Realize that what works with one segment might not work with others. The data you collect might not be relevant to all segments of your customers. It doesn’t negate the value of the info—but know that it may not work for everybody.
• Store legacy data somewhere to access it later. Don’t toss old info. It might come in handy down the road. Just make sure you have it organized and placed somewhere that you can easily access later.

Did you miss this session? Were you at another one? (There’s so many tasty ones to choose from!) If you’re registered, you can check out the online video until May 1.

And if you were there, feel free to share what struck your fancy, too.

Jonah Lehrer, in the December 13, 2010 issue of the New Yorker, authors a stunning article on the increasing ineffectiveness of therapeutic drugs. Medications tested eight to ten years ago on large populations seem to work only half as well in similar trials today. So then, many tests for medications in randomized, double blind clinical trials (considered the gold standard in science) are not repeatable—not even close! Lehrer points out the conundrum: “The test of replicability … is the foundation of modern research. Replicability is how the (scientific) community enforces itself.”

Lehrer mentions this phenomenon isn’t simply relegated to pharmaceuticals. In fact, in vitamin and cardiac stent tests, what was once “known” and proven is now mostly without validation if tested again. “It’s as if our facts were losing their truth,” Lehrer says. “Claims that have been enshrined in textbooks are suddenly unprovable.”

Observation, testing, experimentation, and replication are foundations of scientific process. The issue in a nutshell is explained by Lehrer: “If replication is what separates the rigor of science from the squishiness of pseudo-science, where do we put all these rigorously validated findings that can no longer be proved?”

Some may argue that a simple case of changing populations, or even improvements in psychology account for changes in testing results. Or even that perhaps there was miscalculation, statistical fluke, or other anomaly in previous trials. These are entirely possible explanations, but it is unlikely that hundreds, if not thousands of studies, would be facing the same challenges today. And even so, there might be some statistical difference, but variances of 30-50% are common in repeated experiments, especially those conducted over a period of years. There must be something to this drastic decline.

The “decline effect” is keeping scientists across the globe quiet about whether their experiments can be replicated. One scientist was told by his mentor that his “real mistake was trying to replicate his work” and that he would only set himself up for “disappointment.”

Adding insult to injury, it appears that scientific journals don’t want to publish findings regarding the decline effect. Biologist Leigh Simmons from the University of Western Australia tried to submit results to various publications detailing his difficulty in duplicating his experiments, but “the journals only wanted confirming data,” he says. And that’s ultimately a shame because there is much to learn not just from success, but failure.

This then, may explain why much scientific “evidence” is often contradictory. One year, we learn that coffee is good for health, and then two years later, it’s bad. We find out that hormone replacement therapy for a menopausal woman is recommended one year, only to find out the next that it never should have been recommended in the first place. “The situation is even worse when the subject is fashionable,” Lehrer says.

All told, there is bias, conflict of interest, faulty design, selective publishing, psychology progress and certainly other developments at work. However, the decline effect is a much more significant finding as it “reminds us how difficult it is to prove anything.” Because what is true today may not be true tomorrow, the best result when dealing with “proven” scientific facts is a healthy dose of skepticism and a question everything mindset.

Questions:

• If yesterday’s rigorous and validated findings can no longer be proved, what does this mean for those practicing empirical, fact based decisioning?
• Statistically speaking, the decline effect shouldn’t be happening, but it does. What other reasons explain why facts can lose their “truthiness” over time?

Most of us enjoy listening to stories of overnight sensations such as how an actor or actress suddenly becomes famous or how a salesperson becomes an overnight success. What most of us don’t see, however, is the actor’s laborious ten years of working as a waiter or waitress in Hollywood before gaining a key audition or for the salesperson, the nine failed sales calls before best practices and lessons learned are incorporated into a successful tenth.

Even in the physical world, newspapers commonly entertain with stories of an unexpected avalanche or a twister that appeared out of nowhere. What’s missing from the discussion however, is the complex set of interactions in a given system that led to that single event occurring. Take an avalanche for instance. A myth pervades that a single person yelling on the slopes or the noise from a snowmobile “causes” the avalanche. Behind the scenes however is the process leading up to that key event, such as myriad actions (weather, snowfall, temperature, wind direction, slope angle etc.) that created and ripened the conditions for the avalanche to occur in the first place.

Not even stock market crashes are sudden. In “Why Stock Markets Crash,” author Didier Sornette writes, “(The) underlying cause of a crash (can) be found in the preceding months or years in the progressively increasing build up of market cooperativity or effective interactions between investors.”

In complex systems such as those in which we live and interact, there are often many relationships and interconnections. It is difficult to believe that anything happens in a vacuum, or that there is a single cause for every event. More often than not, “suddenly” actually occurs over long stretches of time where small events build up and lead up (via a process) to an instantaneous transition.

And perhaps this is why the power of process is so important. It’s the process, over time, which creates. Each day, hour, minute, second, sub-second all contribute to the process. It’s why the first chair violinist in New York’s Philharmonic probably isn’t a prodigy who practiced little to achieve concertmaster status. And it’s also why athletes like Tim Tebow are the first in the training room every morning and the last to leave team headquarters. While we definitely shouldn’t discount the role of talent in such situations, hard work definitely pays off over time. There’s definitely much effort that precedes “sudden” success.

A series of events, decisions, actions are usually the predecessor of some key magnifying event. Suddenly, then, rarely is.

Related: Does the 10 Year Rule Apply to Marketing?

Questions:
• Some of the smartest economists in the world failed to predict the “sudden” 2008 market crash. What do complex systems say about our ability to predict the future?
• Is it “the journey or the destination” that counts the most?

An algorithm is simply a step-by-step approach for solving a problem. Think of an algorithm like a formula; it can be complex, or relatively simple in design. Now add compute power from today’s super fast computers coupled with the know-how to design, build, and maintain these formulae and you have a winning combination! Companies across the globe use algorithms to make recommendations (think: If you like this product, you’ll probably also like this), choose optimum delivery routes for packages, and even route calls to agents that can best diagnose a particular problem.

How can an algorithm confer competitive advantage? Depending on the type of business you’re in, it’s easy to see how algorithms can reduce all available choices into the very best options. Take for instance, Google. In the February 22, 2010 issue of Wired Magazine writer Stephen Levy points out, “For years, (Google) has used its mysterious, seemingly omniscient algorithm to, as its mission statement puts it, “organize the world’s information.” Google’s algorithm is constantly tweaked, honed, tested, and improved to better interpret searchers’ requests, no matter how awkward or misspelled, says Levy. And this competitive advantage in its search algorithm has (so far) confirmed a 65% share of the search market for Google.

In a sea of data, algorithms can also help reduce choice overload. Online dating sites often use proprietary algorithms to divine appropriate partner matches based on user inputs such as preferences for race, religion, eye or hair color, and more. eHarmony’s algorithm for example, helps select potential partners based on a 258 question personality test. eHarmony’s algorithm takes too much choice (sea of available singles) and distills/simplifies millions of choices into much more manageable options.

And while companies like eHarmony rely on data input by a user, a new recommendation engine called Wings mines your social media “bread crumbs” left on various websites (including Facebook, Netflix, Twitter, Foursquare and others) to feed into its algorithm to pick a suitable dating partner. A MIT Technology review article on Wings says, “The idea is that the computer’s analysis of your behavior provides a richer analysis than you’d say about yourself.”

More data has been created in past three years than in past 40,000 years, says Teradata CTO Stephen Brobst. Indeed, today and into the near future, companies that can sort through, analyze and utilize this rich trove of data treasure faster (in some cases with blinding speed) than competitors will dominate over those enterprises slow to comprehend this critical transition.

Related: “Social Network Analysis: Hype or Help?” and “The Zero Latency Future is Now”

Questions:

• Are recommendation engines becoming more or less reliable? Think of a website you often use that uses recommendation algorithms. How “close to home” are its choices for you?
• Do you think a computer can discern your tastes in romance better than you can?

A Financial Times article titled “Technology Let’s Buyers Unravel Ethics Behind the Label” cites that ethical spending has increased significantly in the past 20 years. As an example in the United Kingdom, “The amount of spending and investment influenced by ethical considerations almost doubled between 1999 and 2008 to reach £36 billion.” The FT article mentions the growth of fair trade and certified organic products as part of this growth trend. And fellow Daily Fix author Ted Mininni says, these types of goods “are increasingly being added to retail assortments”—and in growing numbers!

However, one of the major challenges for both retailers and manufacturers alike is authenticating ethically sourced products. There’s more to the process than taking the supplier’s word for it.

In the case of organic bananas, the FT article notes, “the food company Dole labels each of its organic bananas with a three-digit number that, when entered on its website, reveals details of the farm where that banana is grown.” Identifying where a banana is sourced, however,  is simple compared to such products as sweaters or t-shirts that pass through multiple suppliers and countries.

In “The Elephant and the Dragon,” author Robyn Meredith confirms that products often “zigzag” through the global supply chain from factory to factory. “A cheap toy may be assembled by parts from 12 different factories,” she says. And something as sophisticated as an automobile might contain five to seven thousand parts.

Fortunately for marketers, the challenge is not beyond the capabilities of today’s technologies. Some progressive companies are engaging in a purposeful effort to build effective policies (including auditing), technologies (supply chain analytics and infrastructure) and processes to track and monitor the extended supply chain.

Of course, implementing a data-driven supply chain infrastructure is only half the battle—supply chain managers, operations personnel and marketers must learn how to use it! Marketers, working alongside operations, will need training on the various tools and systems used to access data for reporting and query purposes. And marketers may also choose to make supply chain data available directly to consumers—similar to Dole’s web portal—thus enabling them to verify product origins for themselves!

Ethical sourcing, trade, manufacturing and retailing will continue to be a hot button for consumers. However, as seen from this article, jumping into this marketplace requires much more than just fancy signage and/or promotion. A real commitment to corporate responsibility and sustainable practices must be much more than lip service; it involves significant investment in people, processes, technology, and strategy. As seen from the complexity in supply chain traceability alone, it’s definitely not an effort a company should take lightly.

Questions:
• Does it matter to you how a product is made? Are you interested in the origins of the products and services you consume?
• The Financial Times article says that companies should make information about their supply chain public—or consumers will do it for them. Do you agree with this statement?

For B2B marketing professionals, understanding what’s going on with your Web properties is as important as building your content, engaging in lead nurturing, and social media participation. If you don’t know what content resonates with your prospects, how are you going improve as you iterate?

The first step is getting your Web analytics setup. Once you have that in place, you need to take the data and put it into a readable, actionable format.

Get started with reporting:

• Know the functionality of your analytics package. It doesn’t matter if you want to track all or a very small amount of information, but knowing the ins and outs of the tool is essential.
• Understand your objectives and KPIs. Based on your strategy, you should define objectives and KPIs (Key Performance Indicators).  Each objective should require an actionable metric to look at, such as a particular type of search traffic. Under that objective, you would have your KPIs, which roll to it.
• Create a schedule to gather specific data to report. It’s always a good idea to gather specific data on your objectives the same time each month, week or day, depending on your needs. If you create a report for stakeholders and others to see, creating this schedule around the reporting time is even better.

Remember: Don’t just send out the data you have gathered. Put it into a format that’s understandable and tells what happening with those objectives your team has agreed upon. Not everyone will read the entire document, but getting the most important information up front helps decision makers who don’t have time to read a long report.

• Create actionable goals that are realistic. Now that you have the report creation process down from the data you gather, it’s time to put those reports to good use. This is where you create goals and outline how you are going to carry out those goals.  When doing any type of marketing forecasting, be sure to predict realistic, grounded goals to set proper expectation with your team.

Samples of how KPIs roll to objectives

We’ve talked about objective metrics and KPIs, but let’s go through some quick examples to help you see the difference.  As the first example, let’s take a look at search engine traffic as the objective metric.

A few KPIs to look at that roll to this objective would be:

• Search engine rankings: Personalized search and other trends have made rankings increasingly less valuable and should always be tracked as a KPI, not the end objective.
• Inbound links: How many, and of what quality, are the inbound links to your website?  Are they growing each month as you implement promotions or remaining flat?
• Number of pages indexed by search engines/pages actually receiving search traffic: How many pages are actually getting indexed by search engines each month?  As you add content, is it getting found via organic keywords?

What about social media KPIs?

Do you have a social presence such as a blog? Social objectives will vary. They depend on what you are looking for; however, some social KPIs to track could include:

• Unique visitors: What’s the traffic to your community and linked sites?
• Amount of subscribers: How many subscribers do you have to your blog? Are they subscribing through RSS or through email updates? More subscribers will lead to more shares, ReTweets, and interactions on and off site.
• Visitors converted: Are your visitors leaving without doing anything on your site? Are they just reading and bouncing? Time to check your subscription CTA and move it to different positions around your content.

How are you measuring accountability of your marketing? If you’re not using web analytics to track information on your site, you are losing out on a proven way to help with making key decisions for your business. It’s good to start out simple and as you and your team become experienced, you can always add more depth to your analytics.

A previous column, “When Less is More in Customer Choice” cited that many marketers believe innovation and competitive differentiation arise from giving customers more choices and options. But through the strategy of offering more choice, marketers may actually end up increasing complexity, costs and causing customers “mental fatigue.” And avoiding mental fatigue is what retailers are after, especially when they learn that simplicity in store layout and merchandising can lead to sales increases!

Cleaner, simpler and less chaotic is the new mantra for retailers. This means removing towering aisles of product twelve feet high, reducing in-aisle displays, and fewer bins of “grab bag” mixed product. Inevitably though, these improvements in the shopping experience will most likely lead to fewer products stocked and potentially a drastic reduction (10-15%) in SKUs.

But how does a retailer choose which products to eliminate, especially when there are so many variables (year-over-year sales comparisons, seasonality, pricing, profitability and trade promotion dollars, etc.) to consider? More than just traditional rules of thumb or guesswork, analytics and experimentation help retailers get this mix right.

Babson College professor Thomas Davenport has identified eighteen analytical trends that are relatively well established among retailers, such as assortment optimization and shelf space allocation, pricing optimization and market basket analysis among others.

These analytical processes help optimize categories and merchandise quantities effectively allowing retailers to “give space back” to shoppers without sacrificing sales and gross profit. In addition, retailers are using analytics to help decide which categories could benefit from private label sales—or store brands—which tend to carry higher margins.

Retailers are also experimenting with control groups to project how remodels will impact sales. Through testing and experimentation, retailers can discern which changes most improve metrics such as customer experience scores, sales, gross margins and inventory reductions.

For some retail managers, the idea of removing product, reducing aisle height, and even giving space back to customers is contrary to “what works.” However, customers are voting with their feet—and subsequently their wallets.

A win-win outcome is possible where customers gain a more satisfying shopping experience via brighter stores, cleaner merchandising displays, more room to maneuver carts in aisles, and less maddening clutter. And retailers benefit with improvements in category and overall store sales, inventory carrying costs and customer satisfaction scores.

Customers are already facing too many choices on a daily basis. More choice isn’t always better and in fact, reducing customer choice may be the best option for your enterprise.

Questions:

• Are there locations that you refuse to enter or shop because of clutter or too many choices?
• Are you willing to pay more for a clutter-free shopping environment?

A previous column “Is the Speed of Decision Making Accelerating?” cited how a century ago, managers could take weeks or days to make important decisions. That’s because before the advent of the telephone, it would take a substantial amount of time for information to travel by courier. Fast forward to the 21st century, most executives now have a mobile device and can be reached at a moment’s notice.

Our global society is moving towards a zero latency world, where the reduction of time between decision and action is drastically reduced. And we need to look no further than Wall Street’s high frequency traders for evidence.

John Plender of the Financial Times recently defined high frequency trading (HFT) as a “type of computerized dealing (that) exploits the millisecond gap between news events and their impact on markets … such trading has expanded rapidly to the point where 60-70% of the trading volume is in U.S. equities. Much of this volume is conducted by a very small number of companies.”

So what’s wrong with HFT? Plender cites potential problems, such as the “ability (for high-frequency traders) to see orders before they are public” and the propensity for high-frequency traders to co-locate servers on the floor of stock exchanges for faster trading (something not available to the average investor). In addition, the race is on where the winner in high-frequency trading can close trades as fast as 250 microseconds—faster than you can blink your eye!

The speed of decision-making is accelerating. In HFT, the trend is unmistakable. Machines are trading with and against each other. They’re moving ahead of individual investors, leaving day traders in the dust. And as a Financial Times article notes, speed isn’t just confined to Wall Street:  “Technology has changed many other big markets around the world and tied them more closely together … Such changes has created winners and losers.”

For marketers, the implications of zero latency are clear. For example, did you know that “robots” are purported to perform text mining on press releases when they hit the wire? With analysis completed in microseconds, advanced algorithms then execute trades based on what they’ve learned. Your company’s equity price could go up or down in seconds, based on the words in your press release!

In a zero latency world, what marketers (and other employees) say, write, tweet, and announce can all be used as fodder by the machines to either raise equity prices or destroy shareholder value. Our ability to react and “fix” our mistakes before they are noticed is greatly diminished. All it takes is a bad press release, poorly written whitepaper or negative analyst report.

And it’s not just PR. To borrow a phrase from Thomas Davenport, companies are now “Competing on Analytics.” Marketers must understand that they are now engaged in an arms race with competitors mining their own (and third-party) data for insights—increasingly by the hour and minute, and then taking action to better connect with customers. Companies without these capabilities will increasingly face mammoth disadvantage.

Zero latency decision-making isn’t the future. It’s now. Are you ready?

Consider the following:
Regarding decision-making, how fast is too fast? What could go wrong at high speeds? What happens when the human element is removed?

Contagion takes many forms in our global economy. Some definitions for contagion include the “spread and transmission of disease, ideas, influence and emotions.” Another aspect of contagion is that it spreads in unimaginable ways. Thus, sometimes what seems like a tiny spark ends up as a full fledged forest fire.

Marketing professionals know that contagion can sometimes be extremely dangerous to our companies, brands and reputations. Therefore, it’s imperative to not only recognize early warning signs of contagion, but to react quickly when contagion spills over. Below are five strategies that can help us plan, prepare, manage and even seize the advantage when contagion strikes.

First, manage your risks. Author Nassim Taleb of Black Swan fame, says the essence of risk management, “lies in maximizing the areas where we have some control over the outcome while minimizing the areas where we have no control of the outcome.”

One method of risk management is to maintain a significant buffer against unforeseen forces. For example, in financial services, companies are required by regulations to maintain a liquid capital cushion to buffer financial losses. Financial services firms also measure their risk exposure via Value at Risk (VAR) modeling or other stress test methods. And while it can be intelligently argued such methods are flawed, for many companies they remain the best tools available for analytical risk management.

Second, calmer heads prevail. When the European debt crisis threatened to engulf nation states in the European Union, German Chancellor Angela Merkel did her very best to stay above the fray. While contagion swirled around Merkel, she maneuvered among constituents, political leaders and central bankers to find a solution that would benefit both Germany and the greater European Union. And though some pundits argued that Merkel perhaps made matters worse by delaying action for weeks, sometimes it pays to gather information and not make hasty decisions.

Third, have a plan. Suppose a crisis hits, have you considered the likely outcomes? This is where scenario planning can help. Scenario planning is the consideration of known facts with plausible alternatives. Think about a given a situation and the top five likely outcomes. Then prepare a plan of action. Afraid you won’t get scenario planning right? Author Bill Ziemba reminds us that it’s darn near impossible to get scenario planning exactly correct. “What is important,” he says, “is to cover the board of possible occurrences. Then you will make sound decisions with risk under control.”

Fourth, consider using overwhelming force to deal with contagion. Sometimes the only way to restore confidence is via “shock and awe” or the use of the “big guns”. Best practices include the recent response of forty-three children’s medicine manufacturers to remove all potentially tainted products from store shelves until contamination causes can be determined. In another example, facing fraud allegations by the Securities Exchange Commission, Goldman Sachs hired Greg Craig, the lawyer who “saved Bill Clinton from impeachment.” Instead of responding in piecemeal to contagion, often it is better to respond in force.

Fifth, when contagion strikes look for opportunities in chaos. When the Wall Street “flash crash” occurred on May 6th 2010, liquidity evaporated from the market and bellwether companies like P&G saw their stock drop 35%, while global consulting firm Accenture’s stock traded briefly for one penny! Meanwhile, on financial news network CNBC, the normally hyperactive and boorish Jim Cramer calmly announced that anyone with modicum of common sense should buy those two stocks. Ultimately, the market corrected itself within a twenty-minute time frame and those who listened and immediately acted on Mr. Cramer’s advice made a mint. Where there’s panic, nervousness or irrational behavior, there’s also likely opportunity for gain—if you’re paying attention!

With today’s global economy tightly linked by capital, labor, and information flows at the speed of light, it’s not uncommon for bad news, nervousness, or outright panic to spill across borders and disrupt even the most stable of companies and industries. Moreover, what may seem like a non-event can quickly transform into a full blown crisis.

Listed above are five strategies when contagion swirls. Do you have others?

I am in the midst of penning a six piece series for MarketingProfs on achieving relevance in direct digital marketing. The series sheds some light on how to achieve relevance, outlines the common barriers to achieving it, and offers five keys to overcoming those barriers. For this post, I am going to break out some of the key takeaways and give you a taste of what’s to come with the rest of the series.

In the first installment of the series, I defined relevance in the realm of direct digital marketing as: Tailoring direct digital marketing communications to customers and prospects, and ensuring that the information, offers, and calls to action are optimized based on a user’s known attributes, behavioral attributes, and past activity.

Now that we have a working definition of relevance, how do we get past the various barriers to implementation?

For many marketers and brands, the most significant hurdles to delivering timely and relevant marketing communications come from inside a marketing organization.  Challenges from within an organization can put limitations on the adoption of technology and best practices, as well as the manpower to create engaging programs and campaigns.

As I mentioned in my second series installment, “Achieving Relevance in Direct Digital Marketing: Overcoming Organizational Constraints,” you may have to shake things up a bit within your organization to overcome these barriers.

Sure, the thought of making organizational changes can be daunting, but the need to make the necessary changes to adopt a relevance-centered direct digital marketing approach is very real.

If you’re feeling intimidated by the thought of where to begin or how to approach the process, take a look at the three most common organizational constraints, and how to overcome them in the second installment of the series.

Barriers from within an organization may be the most significant hurdle to overcome. But, barriers to technology adoption come in a close second.

It is important to remember achieving relevance with your marketing communications requires technology that makes the process more efficient and effective, not painful and prohibitive.

It is also important for marketers to ask themselves, “How involved in the technology selection process am I?” If you’re not very involved, it’s time to grab the reins and aggressively drive technology adoption and consolidation within your organization.

If marketers are being held responsible for a brand’s connection with consumers over the digital channels, they can’t afford to be isolated from the technology evaluation and selection process.

So how do you piece together the technology puzzle to find out what you need, and how do you become more involved in the evaluation and selection of that technology? Take a look at the two critical steps every brand must take when choosing a technology, outlined in the third installment of the series, and what marketers can do to become more involved in the selection and implementation process.

The upcoming fourth installment of the series focuses on the importance of segmentation and targeting in a relevance-centric approach to direct digital marketing. It will be full of actionable ideas and analysis to help you achieve relevance in your direct digital marketing efforts. Articles five and six will focus on how to take an incremental approach, and how to develop a plan for implementing what you’ve learned.  Stay tuned!

What barriers do you encounter when implementing a relevance-based approach? How are you overcoming them?

The presenters of this session discussed case studies for their specific company, so lots of numbers, lots of discussion. Numbers are great, but I also think case studies need to present the HOW and WHY.  How did you get there, why did you take this approach? Leverage the two and balance it.

Each presenter had a top tip, so I break it down below.

Top Tip from each presenter:

“Forget the numbers, focus on engagement.”

Is it just me, or is that a bit naïve? Yes, focus on the engagement and the type of audiene you have. You can’t build strategy and objectives by guessing where the audience is. If you completely forget numbers, a C-Suite is likely to either a.) laugh b.) accept it and later ask for metrics or c.) Ask for the metrics anyway.

Leverage engagement (qualitative metric/soft) and quantitative metrics as a balance. Think of the “engagement thought process” as a proof point for social media, not your solution. It’s a bandage that will fall off. Do you measure it? Yes. Do you leverage it? Yes. Is it the only part? No, and those that only spout only about engagement make me question if they even understand everything about social media. Research and understand the different venues.

“It’s easy to get overwhelmed with platforms. Having a strong framework like the P.O.S.T. method (People, Objectives, Strategy, Technology) helps your plan.”

I liked this point. Marketers latch onto platforms and don’t realize it.  “Social media is a concept, platforms are tools to execute it.” Not every platform is for every person. Are you more lead generation based? Use Twitter or Foursquare. More community based with an online presence/audience already established? Use Facebook, especially with Open Graph now available. Social media is a process that requires several steps, and it doesn’t stopping at people.

(Especially for B2B marketers) The first step in engagement should be asking, “How can we add real value to the conversation?”

Amen. You won’t have engagement if you aren’t providing real value to your audience. How do you find what’s valuable? Create your target demographic and monitor the conversation. What are they talking about? What times are high traffic? What really drives them? Then, test it. Some will succeed, others will fail. You have to know when to evaluate and pull, or when to keep building. Benchmarks in the form of metrics or social measurement are great for this.

“When creating and maintaining your SM program, always be thinking of SEO.”

This is the one area I have little knowledge of (but I’m learning and asking lots of questions of certain friends) so I won’t pretend to have an opinion. Not fair to you guys. What I do know? She says to leverage keywords and really keep track. Thoughts?

Confession: I was stopped 20 million times and almost missed the first session. Way to get off on the right foot, depending on who you ask.

Stuart Foster stated yesterday, “Success is the only metric I care about.” Seems fitting for the first session.

If we aren’t measuring success of programs, objectives and strategy? Seriously, who will care? You aren’t proving zilch.

Are we quantifying what we do today? Depends on who you talk to. Different people have different goals, dependent on their professional role, what industry they are in, and what sector they represent. My thought? One has to be always aware of what trends are happening, and what unique soft/hard metrics you can offer. Make it about what the brand needs to know, not some random metric that makes no sense.

In the profession and the world of marketing, it’s not easy to quantify what we do.

Why?

Some people threw out these answers:

• People don’t understand metrics.
• Lack of data
• Long sales cycle; hard to connect dots with marketing vs. sales

C-Suite is now coming to marketers asking, “So, tell me. You’re spending X amount of money, but what are we getting from it?”

Pretty common and a legitimate concern. Marketers can’t claim a program is effective without facts;  intuition doesn’t mean anything. Use quantitative and qualitative metrics to prove the worth.

The session stated, “Decision make 78% intuitive, 20% fact has switched now to minimally intuitive.”

I agree to a point.  I think intuition drives a lot of beginning foundation for strategy, objectives and tactics. Obviously, you scale back to craft a unique approach to the brand,  but awesome doesn’t exist when you operate through regulations and boundaries.

Another question thrown out:

How do you measure success? (My snarky brain thought: “If I’m kicking butt at building a brand, obviously.”)

An audience member talked about setting goals up front. Honestly? Disagree. Goals is a generic term, and many marketers don’t break it down. A better approach is creating objectives, what tactics will be used to accomplish objectives, then you can set benchmarks to reach overall goals. Think of objectives as the foundation for the goal base, not vague goals as the beginning. Goals need to be attainable.

If you can’t measure it, you can’t manage it. From that? Work with brands to maintain expectations and set benchmarks specific to them. Blanket approaches don’t work, and they never will. Educate to understand the difference between hard and soft metrics, and how you can balance qualitative and quantitative metrics to prove value.

It’s a mesh of the two.

In May 2010’s issue of Wired Magazine, author Clive Thompson laments the poor mathematical literacy of his fellow citizens. For example, he cites people laughing at the concept of global warming as they face some of the harsher winters on record, or the extra-vocal debate on vaccines and possible links to autism. Mr. Thompson would tell us that it’s the trend lines that matter, and we too often look at the trees and miss the forest.

The problem, he says, is that “statistics is hard” and an overall understanding of this important discipline is severely lacking. He says, “If you don’t understand statistics, you don’t know what’s going on, and you can’t tell when you’re being lied to.”

Thompson is correct that statistics are difficult for most of us, and that thinking by the numbers takes training and much effort. It’s also true that one must understand statistical concepts, especially when percentages, populations, and probabilities are bandied about in business and technical press. However, broader acceptance of the power of statistics should be tempered with limitations of this mathematical science.

Before accepting any statistic, study or experiment as gospel, the following should be considered (there may be more…):

1. Assumptions: What are the assumptions underpinning the research? As seen from recent debate on CBO numbers for the U.S. health reform package, assumptions matter tremendously.
2. History: How much historical data was used in the study? What was the time scale? As seen from the 2008 financial crisis, the models used by Wall Street mavens often only took into account 10 years of data in judging the volatility and probability of failure of complex financial instruments.
3. Samples: Are the samples selected randomly? From what populations? Is there enough data for statistical significance?
4. Data Quality: The output is only going to be as good as the quality of data feeding the analysis. Garbage in, garbage out.
5. Survivorship Bias: Author Nassim Taleb points out that “losers are often not in the sample.” Does the analysis include a population of survivors and those who also failed?
6. Falsification and Omission: Yes, in an era of IPCC’s Climate Gate, one needs to ascertain if data are hidden, missing or outliers ignored.
7. Association equals causation fallacy: Correlation does not equate to causation (a common mistake made by marketing and finance executives alike).
8. Proper Application of Statistics: The effective use of statistics by insurance actuaries, scientists, and even casino managers is well-documented. However, real danger results when mathematical concepts are used to denote certainty indecision-making and divining behavior of markets.

Now, please don’t get me wrong. Statistical analysis is very important for many industries (e.g., health care, transportation, and manufacturing). Statistics, however, can give us an illusion of control in a world that’s much more complex than our models suggest. Nassim Taleb, author of the Black Swan likes to remind us that “(real) life isn’t a casino.”

Statistical analysis is definitely a powerful gadget in the business manager’s decision-making toolkit. But one needs to understand the limitations of this science.

After all, Taleb points out that many of today’s statistical models work as though we have “full knowledge of the probability of future outcomes.” And this just isn’t so, especially when it comes to fat tails, or the “ten sigma” event. Indeed, sometimes those rare events have extremely large impacts. Were he alive today, the former captain of the Titanic, E.J. Smith would wholeheartedly agree.

Questions:
• Clive Thompson calls statistics “the language of data.” How important is it for marketers to understand and apply statistical concepts?
• “Lies, damned lies, and statistics” is a phrase popularized by Mark Twain in the context of using statistics to unduly persuade, obfuscate or even swindle. Can statistics get its reputation back? If so, how?

I know we all whisper it, but it’s true.  B2B buyers are liars. They are.  There, I said it.

If you’re in B2B marketing and sales, you probably aren’t surprised by that statement, and you probably suspect it yourself.  But where’s the proof?  How and why do B2B buyers lie? And what are the implications for your demand generation programs?

The “lying” by B2B buyers is a nuanced reality.  It’s not something that buyers do out of spite; it’s something they do both intentionally and unintentionally to better manage the dynamics of their interactions with vendors—to gain control in the buyer/seller relationship.  But if we are going to anticipate and respond to this situation, we must better understand these nuances.

So here’s my take on the nature of the “lies” and how we can respond as marketers.

How and why do B2B buyers lie?

Here are some of the key points to understand about our interactions with B2B buyers:

Buyers are only willing to share a limited amount of information at the initial point of contact with your organization. In the beginning of cultivating the buyer/seller relationship, you don’t know the buyer, and (s)he doesn’t know you.  Yet we as marketers too often ask the buyer to tell his/her complete life story to download a white paper—even though that is the first point of contact you’ve ever had with that buyer.  There is something wrong with this approach.

The results of a recent study indicate that, at initial stages of contact, buyers only provide accurate and complete information on the most basic of information they’re asked to supply for things like white paper downloads.  Only name, email address, industry, company name and job title have high rates (i.e., greater than 50% of the time) of being “always” completed correctly.

What about those questions we use to further qualify potential leads?  According to the same study, technology buyers say they “always” provide accurate answers to custom questions only 29% of the time.  That means 71% of the time there’s some degree of lying going on.

Craig Rosenberg (a.k.a. the ‘Funnelholic) commented on this in a past post on his blog.  He explains it’s just too early in the process of getting to know a buyer to go so deep with qualifying questions:

It’s noble to try, but don’t use reg forms to do the job of your lead qualification or sales team.  You are scaring great prospects off, and are hurting conversion to little benefit.  Use your reg forms to confirm interest, target your market, and get their info.  Gather more data on your second date or your third when you’ve both invested some time.

This point of view is supported by other marketers that have tested the impact on quality and quantity of registration requirements for their own demand generation programs.  “[A] lot of people—more than 75%—DON’T sign up for papers requiring registration, which means the vendor is missing the opportunity to share and disseminate their knowledge,” comments Jay Hallberg, VP of Marketing for Spiceworks in a recent Q&A with the Savvy B2B Marketing blog folks.

So-called “BANT” information for buyers and their organizations increasingly must be discovered on an implicit basis, not asked for on an explicit basis: What does this mean?  BANT stands for budget, authority, needs and timing, and it is a basic set of criteria that nearly all B2B marketing and sales organizations use to assess potential buyers and their organizations.  Implicit data is behavioral data (i.e., the things buyers tell us through their actions).  Explicit data is self-reported data (i.e., the things buyers tell us directly, such as by filling out a form online).  And the issue I’m calling out?  It is simple:  Not only do buyers not want to share this information at first contact, but more than ever buyers often don’t really have accurate, explicit answers to BANT questions, so we have to figure out when/where they’re moving forward on an implicit basis.

Recent research by DemandGen Report found that the B2B buying process is less formalized than ever before.  “More than 8 in 10 respondents said the buying process did not follow a traditional path where a budget was established, criteria outlined and then an RFP distributed to a pre-set list of solution providers,” according to a post by Andrew Gaffney summarizing the report.

B2B buyer organizations are becoming more agile and making more decisions on a non-planned basis.  But this is bad news for our BANT criteria.  A lack of formal process means we can’t really get the information we need when we ask about that process.  So the best indicator of a buyer’s real intentions is his/her behavior (i.e., actually doing the things that indicate they are considering a purchase).  This means that BANT increasingly is something that must be gathered on an implicit basis, and it speaks to the importance of B2B marketers using behavioral factors to better score and route interactions with prospective buyers via marketing automation.

Your prospective, individual buyer is not the sole decision-maker at his/her organization. Even if answered truthfully and accurately, the responses of an individual buyer to questions posed via online forms only constitutes a portion of the buying picture.  Perhaps we can call this “unintentional” lying, but it is the skew that results from one contact inside an organization having his/her perspective on the situation—which may not be consistent with the perspective of the larger group that will approve the final purchase.

The fact is that B2B purchase decisions are not made by a single buyer most of the time.  Increasingly, a complex, savvy buying unit makes B2B purchase decisions inside organizations.  For purchases in the $25K to $99K range, nearly 2/3 of the time there are four or more buyers engaged in the decision, according to data in recent marketing report. For purchases in the $100K to $999K range, 92% of the time there are four or more buyers engaged in the decision.  And it goes up from there.

Buyers aren’t ready to talk to us when they are downloading a white paper, anyway. Any content marketing strategy requires understanding the B2B buying process and rationalizing content and engagement around this process.  Earlier on in the process — when a buyer is first becoming acclimated to, and wrapping his/her head around a problem, it’s more important to help educate and shape the direction of a buyer’s thinking  in a hands-off fashion than it is to capture his/her information yet.  Initial lead capture must be staged a step or two below the point of initial research and consideration.  Lead capture is the launching point for dialogue with a prospective buyer, but it requires having established a growing relationship and having credentialed your organization upstream.

Not only is this good practice, but this is increasingly how buyers expect to act.  The nature of the B2B buyer has changed in a Web 2.0 world, and buyers expect to be able to do more research on their own time and unhindered by proactive vendor interactions.  B2B buyers spend 79% of their time in what Robert Jolles calls the “Acknowledgment” phase in his classic text, Customer Centered Selling.  Acknowledgment phase is the phase where buyers are on the fence, thinking about a problem but not yet ready to act or to pursue a formal buying process.

Buyers need to go through a process of self-education before they move beyond the Acknowledgment phase and before they are ready to become a lead.  So attempts to glean information from them through forms, early on in this process will only lead to buyers providing incorrect information because they want you to leave them alone … until they’re ready to talk to your organization.

How can B2B marketers tune their demand generation programs to anticipate (and overcome) these buyer lies?

The good news is that there are several techniques that have emerged in recent times to help B2B marketers cope with this situation and to gain leverage in their interactions with potential buyers.

Three ideas you can embrace today:

> Require less up front; progressively profile and append data instead: Make your content targeted at the earliest stages of the buying process free.  Don’t require registration.  And when you have that first white paper that requires registration—because it represents the next step in the buying process—just get their email address.  You can continue to send nurture emails and invite prospects to Webinars and events where you can iteratively ask more information from the buyer as they continue their process.  This approach is known as progressive profiling.  You can also append information such as industry data after the fact, using resources from folks like Dunn & Bradstreet.  Such an approach is more appropriate to the rhythm of interaction with buyers in a Web 2.0 world, and it is more appropriate to the give and take that is part of real relationship building.

> Score based on a combination of demographic and behavioral data: Don’t explicitly ask buyers everything you want to know; instead, observe and infer.  If you have found that 49 out of 50 prospects that take X action, such as visiting a specific page on your website, have a high probability of wanting to buy from you, then use that information to increase the buyer’s lead score and have a sales person contact him/her.  A lot smoother than having a check box on your first reg page that says, “Would you like someone to contact you?”

> Combine marketing automation with human contact: Craig Roseberg called this out in his quote.  Don’t expect to learn everything about a prospective buyer through online or automated interactions.  Marketing automation should exist to help power your lead management and content marketing strategies and give you greater operating efficiency, but you still need a combination of smart processes, people and technology to be successful.   A piece of these strategies should include live contact via Webinars, field events, inside sales and field sales interactions.  Use these live points of contact as key steps in your progressive profiling—building this into what you know about your prospective buyer and informing the next steps in your interaction.  Again, technology can weave all of this online and offline insight together, but the buyer facing piece must be a combination of online content and offline interactions.

What do you think?

What are your experiences with buyers “lying,” and how has your company responded in its demand-generation programs?

Marketers have jumped into the social marketing space, but most haven’t had the ability to measure their efforts. People cannot improve what they cannot measure, and with social marketing, it’s no different.

If you’re familiar with the Altimeter frameworks of developing a social strategy based on business objectives, then you’re in good shape, as this research report is the natural extension of the business objectives we put forth:

• Dialog involves starting a conversation and offering your audience something to talk about while allowing that conversation to take on a life of its own
• Advocacy: activation of evangelism, word of mouth, and the spread of information through social technologies.
• Supporting customers may self support each other or companies may directly assist them using social technologies.
• Innovation is the business objective of innovation is an extraordinary byproduct of engaging in social marketing activity.

You can access this report here on MarketingProfs below, or click through to Slideshare to download the report at no cost.

Either way, I’d love your thoughts in the comments below!

I’m about to move from Europe back to the United States. I have to tell the moving guys which books to pack for the sea shipment (which will take nearly two months to arrive at my new home) and which books to pack in our air shipment (which will arrive in a week or so).

This got me thinking: Which of my books are my best resources? Which book would be the most helpful for starting a new company or growing an existing one?

Putting aside the fact that a deserted island may prove challenging to find a customer base, here are my recommendations.

Getting Started

Art of the Start is one of the best first-step books in starting any business. Jump Start Your Marketing Brain scientifically dispels many of our common (mis)beliefs about the right and wrong ways to run a business and perform marketing activities.

The Art of the Start:
The Time-Tested, Battle-Hardened Guide for Anyone Starting Anything
by Guy Kawasaki

Jump Start Your Marketing Brain:
Scientific Advice and Practical Ideas
by Doug Hall

Personal Motivation

Kevin’s Rules of the Red Rubber Ball helps you put your passions in perspective and use them to motivate you. A great story, a great lesson. Steven’s The War of Art kicks your ass and thwarts procrastination like no other book I’ve ever read. David’s The Magic of Thinking Big is the book The Secret for business. A positive and big outlook delivers positive and big results.

Rules of the Red Rubber Ball:
Find and Sustain Your Life’s Work
by Kevin Carroll

The War of Art:
Break Through the Blocks and Win Your Inner Creative Battles
by Steven Pressfield

The Magic of Thinking Big
by David J. Schwartz

Sales & Marketing

A recent purchase for me, Brian’s Be a Sales Superstar contains 21 nuggets of pure sales gold. A must read.

Be a Sales Superstar:
21 Great Ways to Sell More, Faster, Easier in Tough Markets
by Brian Tracy

Idea Generation/Smart Thinking

Every business needs good ideas. Good ideas to start, good ideas to thrive. Sticky Wisdom is one of the best books I’ve read on being creative, brainstorming, and starting a creative revolution.

Sticky Wisdom:
How to Start a Creative Revolution at Work
by ?WhatIf!

Presentation / Influencing Others

Dan’s book The Back of the Napkin is a must-read for anyone who has to influence anyone else. His second, companion book Unfolding the Napkin is a how-to guide that goes deeper than the original book. (If I had only one to pick, it would be this Unfolding the Napkin.)

Unfolding the Napkin:
The Hands-On Method for Solving Complex Problems with Simple Pictures
by Dan Roam

Being Remarkable/Stand Apart From Your Competition

These are the best books on standing out, being remarkable, making yourself differentiated from your competition. Doug talks about figuring out and communicating your overt benefit, Seth is all about going beyond customer expectations, Bill helps you figure out your dominant selling idea, and Marty helps you to zag while everyone else is zigging.

Jump Start Your Business Brain:
The Scientific Way To Make More Money
by Doug Hall

Free Prize Inside!
The Next Big Marketing Idea
by Seth Godin

Why Johnny Can’t Brand:
Rediscovering the Lost Art of the Big Idea
by Bill Schley

Zag:
The Number One Strategy of High-Performance Brands
by Marty Neumeier

Customer Service

Totally Awesome Customer Experiences is totally awesome at helping you create a comprehensive strategy for service and experience at your company. Ben and Jackie wrote the best book on leveraging the power and influence of your best customers.

Creating and Delivering Totally Awesome Customer Experiences
by Gary W. Millet and Blaine W. Millet

Creating Customer Evangelists:
How Loyal Customers Become a Volunteer Sales Force
by Ben McConnell and Jackie Huba

What books am I missing? A cookbook for coconut recipes? What book would you take to your island?

In typically understated fashion, the theme of this conference is “The New Normal.”  Apparently, the world went and changed without consulting us.  Bob Barocci, president of the ARF put it thus, “If we accept the new normal, we have to change the way we think, and that’s hard and scary.  We deny its existence, but can we deny its signals?”

Many of these signals of change are very real, but not really paradigm shifts in and of themselves.  Some examples that came up during the conference are newer waves of immigrants are replacing older ones, consumer spending is down, and digital media fragmentation keeps making it harder for marketers to find captive mass audiences.  Most of this is old news.  What I find fascinating is how the entrepreneurs of marketing research are “thinking different” and responding to these shifts.

There tend to be two schools of thought on how to proceed in the new normal.  Big spenders and traditional media stalwarts keep pursuing the elusive single metric that can be used to dispassionately, automatically, and accurately sort out the entire media universe into a range of good to bad investment choices.  Smaller, more cash-strapped marketers tend to take a very different approach, and simply find whatever metric that suits them for the moment.  In a presentation for Decipher on guerrilla marketing measurement strategies, Kristin Luck encouraged those present to get as creative with their measurement as they are with their marketing.  She makes a convincing argument for using mobile to help track the spread and effectiveness of marketing materials by word-of-mouth.  Luck advocates using tactics like survey widgets in mobile platforms, GPS tracking, pass along pings, tracking mobile bar code coupon redemption, and mobile downloads.  While researchers can spend all day arguing about the continued relevance of metrics like GRPs, it’s clearly important not to let that stop anyone from taking advantage of all the new metrics at our disposal.

Back in the days of analog media, researchers made due with sample data generated by research companies.  The complaint with research was more often that the data was too limited to reflect reality.  These days, census level data tracks billions of people in real time, and now the problem is figuring out what to do with all this data.  Most analytics departments are simply overwhelmed with the amount of data coming in.  It was pointed out during a panel that Walmart collects data on 100 million transactions per hour.  Traditional research methods simply aren’t designed to handle this level of data.

Stepping up to this challenge in a rather innovative way, Discover Card uses text mining software to analyze and quantify the content of their customer service calls and compare the frequency of certain kinds of complaints, and even compare this to the incidence of chatter about the brand on blogs and social networks.  By converting speech to text and analyzing this huge source of data, Discover can get a much more accurate idea about whether a specific complaint is being generated by a few lone individuals, or if it truly is a widespread problem deserving of attention.

Two great examples of how qualitative research is adapting to this new norm of overwhelming data came from CBS’s David Poltrack and Global Park’s Dan Coates.  Both have accepted the fact that “pure” research has its place, but a research program that incorporates itself into the data stream of everyday life makes a lot of sense.  Global Park creates online communities for companies that act as CRM tools to keep customers engaged, while simultaneously mining the communities for product insight and feedback on marketing.  Coates calls this approach “large-scale listening with accuracy”.  He is also quick to point out that it isn’t representative of the general population, but the feedback is authentic in large part because “the community goes on even when the research stops; 95% of communication is repetitive management, 5% is real research probing the community.”  He also points out that this approach can make research more enjoyable for the subject by making survey vehicles less boring.

CBS has taken this approach to a new extreme by opening a research facility in Las Vegas that’s as much about entertaining people as it is about research.  Las Vegas is somewhat unique in that it’s possible to meet someone there who traveled from just about any town in the United States.  At their research facility, they invite the public in to watch new material, running 700+ focus groups over 365 days per year. Poltrack calls this approach “immersion research.”

This combination of new streams of data, new methods of data collection, and more powerful processing tools mean that marketing research, like media itself, is becoming a real-time endeavor.  As software takes up the challenge of collecting and processing data, researchers can focus more on finding insights and affecting business decisions.  Who knew research could be so exciting?

Is this the best projection of market growth — i.e., is this ‘the number’ for 2010?  Beats me — I’m not a full-time industry analyst — but given this data, plus some other recent insights I’ve read, it seems as though 2010 is going to be a big year for marketing automation.

Then it occurred to me:  What do all of these B2B marketers (and some B2C marketers) actually mean by marketing automation?  Is their definition the same as mine?  What is it they think they are investing in — in droves — in 2010?

And I have had similar thoughts while reading recent blog posts by Jep Castelein (a.k.a., “The Lead Sloth”) and David Raab on where marketing automation is headed in 2010.  So I did some thinking about how we define marketing automation.

Strategic Marketing Automation

The problem with definitions is context.  I think many who look at marketing automation lean towards a definition rooted in how marketing automation improves the operational efficiency of a B2B marketing organization.  There is no question that this is a benefit — a significant one — and it often is the initial catalyst that gets a given marketing organization thinking about marketing automation.  In fact, it probably was the catalyst for the initial development of the entire marketing automation segment.  But as with any technology advance — for instance, CRM — having a holistic platform to tackle the operational obstacles changes things.  It opens up totally new capabilities that were perhaps not fully envisioned at the outset.  This is what is occurring with marketing automation … and at a key moment.

Operational efficiency is important, but it is not the greatest challenge B2B marketers are facing today. 

B2B marketers’ greatest challenge lies in responding to a rapidly-changing B2B buyer.  A Web 2.0 world has fundamentally changed the dynamics of buyer-vendor interaction.  B2B buyers not only have greater access to information than ever before, but increasingly, that information comes from industry peers and third parties, versus coming from their vendors or from traditional media outlets.  Charlene Li and Josh Bernoff wrote about this loss of control in their book, Groundswell, and they noted “[t]he groundswell has changed the balance of power.”  Buyers now move forward on their own terms, conducting much of their education on their own (and online) and connecting with a live sales team member later and later in the process.

So given this power shift, B2B marketers must more than ever work to wrap their marketing programs around the B2B buyer — managing dynamic, anticipatory, buyer-driven campaigns and programs and being in the right channel with the right information in the right voice at the right stage of the buying cycle.  It’s a posture that imitates how a live sales person might have engaged with a prospect in a consultative interaction in the past.  But now it’s being driven — out of necessity — by a B2B marketer.  I think of it as ‘mass one-to-one’ marketing or ‘bottoms-up’ marketing, and more than just enabling operational efficiencies, marketing automation is critical to executing against this type of marketing.  In fact, I believe you really can’t do mass one-to-one without some sort of underlying marketing automation platform.

“[C]ompanies have to engage differently with prospects,” commented Lumension SVP of Worldwide Marketing C. Edward Brice in a recent interview on the Savvy B2B Marketing blog.  “They have to stop focusing on the sales cycle and instead focus on the buying cycle. This ultimately means that companies need to understand prospects and customers to a greater extent, including how that individual wants to buy and what information they need at particular points in the buying cycle.”  Marketing automation thus also is critical to observing and understanding the buyer — keeping track of information consumed and using this to better understand and tune outreach — and to closing the loop.  This means having insight into buyers’ ‘implicit’ information-seeking behaviors and being able to link these behaviors to purchasing behavior.

This view of marketing automation — as the key infrastructure for driving buyer-centric, mass one-to-one marketing — is a different definition of B2B marketing automation.  This is what I view as the strategic definition versus the operational definition.  It recasts how we think about automation and its role in modern B2B marketing, and this is important because I believe it is this new role that is driving the rapid growth of the segment — the 70-75% (or whatever number) net growth I cited above.

An Expanded Agenda

Here’s the other impact of the strategic definition:  Once you are thinking about marketing automation in this strategic context, it then becomes clear why recent and upcoming developments in the marketing automation segment make sense.  And why it is natural that marketing automation will increasingly have an expanded agenda.

These developments are all about extending the platform’s ability to deliver buyer-centric, mass one-to-one marketing.  “It’s all about the buyer now,” explains Castelein I his 2010 round-up, above.  And so the natural evolution are features and capabilities that reach further upstream into a buyer’s decision-making process, strengthen our engagement in the middle of this process and reach further downstream into when that process naturally intersects with our sales organization.

This — in my mind — better helps to rationalize the current vector of marketing automation vendors better than any operational definition.

Feedback

How do you define marketing automation?  What are your thoughts on this strategic versus operational view?  And how do you believe B2B marketers will leverage marketing automation platforms and related technology to be more successful in engaging buyers (and in the process, getting their arms around the ‘brave new world’ of B2B marketing) in 2010? 

I’d love to get your feedback.

Neuroscientists have long claimed the act of forgetting is important to the processes of the human mind. Humans have a need to forget especially because each day our brains deal with tons of trivial information and clutter, not to mention hundreds if not thousands of marketing messages.

Therefore, our mental processes must prioritize which facts should have more importance than others–such as ‘where are my car keys?’ versus ‘what did I eat for lunch last Thursday?’ We must forget, because according to neuroscientists, our brains would overload if we captured every detail of our lives.

Yet, unlike the human mind which has a fixed capacity, computer data stores (i.e. disk, tape etc) are getting larger and cheaper to manufacture thereby allowing companies to keep more transactional details very inexpensively.

In fact, thanks to accelerating technological change, companies can now take advantage of less expensive data storage to keep transactional data for longer periods of time–with the ultimate goal of mining data for insights to improve the customer experience.

However, data retention policies of considerable length run head first into concerns from privacy advocates. For example, according to a Washington Post article, online search companies have policies in which they actively keep query data from 3-18 months, and in some instances longer. Their rationale? Online search companies say query data is used to improve their algorithms, optimize search results, and provide advertisers better targeting.

Privacy advocates, however, argue that search queries often contain personal details, and taken collectively can reveal a complete picture of the person using the search engine. Ultimately they say, too much power in the hands of a few key search engines is a privacy nightmare.

To effectively meet customer needs in a very complex and fluid economic environment, companies must be able to collect and analyze data to understand customer behavior, drive better communications and respond to changing customer needs. That said, the benefits of data collection and analysis must coincide with responsible behavior.

Questions:

• Should online companies be required to “forget” what they know about their customers and transactions? If so, what is the cut-off point?
• Should corporations advertise that they quickly “forget”–much as Ask.com has?
• Are consumer privacy concerns regarding data collection policies more bark than bite?

First, let’s start with a fun exercise courtesy of Wilmott Magazine. Let’s look at damage estimates of earthquakes in California from 1970 to 1993 in the table below. Can you make an educated calculation of losses due to earthquakes in 1994?

Taking a look at the distribution of data, notice the low end is “0″ and at high end, the most damage caused was “129″. So what’s your guess?

If you’re like me, you probably guessed wrong. Using the above numbers as an “anchor“, most people would reasonably assume that 1994’s earthquake was either an average of the above numbers or perhaps a bit higher than 129. Maybe you even threw out “129″ as an outlier in the dataset. To be honest, I guessed around “200″.

The correct answer is “2217.2″! FEMA estimates that every year earthquake losses in the United States add up to $4.4 billion a year. But then, some extreme outliers can really skew that number, especially years like 1994 where just the Northridge Earthquake in California alone tallied $20B in damage!

Let’s get back to talking turkeys via a parable from Nassim Taleb, author of the “Black Swan“. Dr. Taleb reminds us that fat, dumb and happy is probably the best way to describe the life of a turkey. They’re fed and nurtured for three years straight. Day after day, they expect the same thing. But then, one fateful day arrives and the “life” of a turkey ends quite abruptly.

Can we accurately predict the future based on reviewing and analyzing historical data? Sometimes, but we have to make assumptions of similar conditions, a normal distribution, and event independence. Complex systems will have none of these characteristics. Dr. Taleb says as much; “Real life isn’t a casino.”

Indeed, the parable of the turkey and the earthquake loss estimation exercise show us that predicting the future in complex systems can be a futile exercise because there are so many unknowns, changing conditions, and inter-connecting relationships. Extreme events that carry a huge impact happen, and some would argue they’re happening a whole lot more often as interlocking financial markets and globalization become commonplace.

Should prediction exercises be avoided? Nassim Taleb would argue otherwise; “We need to start thinking of the inconceivable,” he says. And while we cannot determine the exact probability of tomorrow’s events, we can “get a general idea about the possibility of their occurrence.”

And that’s where scenario planning comes into play. Bill Ziemba, author of the aforementioned Wilmott Magazine article says, “Getting all the scenarios and their probabilities right is impossible and doesn’t matter much anyway. What is important is to cover the board of possible occurrences. Then you will make sound decisions with risk under control.”

The fact is, like the turkey, we just don’t know what tomorrow will bring. So, plan for the five to seven most likely occurrences and then develop contingencies based on those scenarios. French microbiologist Louis Pasteur says it best, “In the fields of observation chance favors only the prepared mind.”

For a turkey, today may appear like any other “normal” day. However, tomorrow could be the chopping block.

Questions:

• Nassim Taleb says, “It is only in very rare circumstances that probability (by itself) is a guide to decision making.” Does this mean that historical data analysis isn’t worth the effort?
• If chance favors the prepared mind, what’s the “next unexpected twist” that marketers should be looking for?

A ZIP + 4 code according to Wikipedia; “uses the basic five-digit code plus four additional digits to identify a geographic segment within the five-digit delivery area, such as a city block, or a group of apartments.” And since the introduction of ZIP + 4 in 1983, this feature has assisted direct marketers (not to mention the USPS) in saving millions of dollars in costs.

While some marketers may decide that ZIP +4 is enough for customer targeting purposes, they’re missing out on a whole level of analysis available from geospatial that can help squeeze more return on investment from scarce marketing dollars.

The term “geospatial” describes a specific type of analytical software combined with geographic data. Going much further than simple ZIP +4 formats, geospatial comes to life via the transformation of a customer address into geographic coordinates (latitude and longitude). With geospatial, a whole host of marketing analytics is now available to marketers.

Examples include:

• Map locations. By overlaying geospatial data types with a mapping tool (such as Google Earth) marketers can pinpoint store or office locations in proximity to customers.
• Calculate distances between locations. Stores, offices or distribution centers can be precisely calculated and then evaluated to examine if they are too close or too far apart. For example, marketers can determine the midpoint between two stores to meet the needs of an under-served customer segment. With geospatial, there’s no guessing, whereas it’s pretty difficult to calculate the distance between locations with ZIP +4.
• Marketing by the mile (or less). Suppose you have a specific store location and you want to market to households 5.1 miles to the north, 2.5 miles to the south, etc; essentially building your own polygon for direct marketing purposes. With geospatial capability, you can do this exercise; ZIP +4–not so much.
• Disaster planning. When an event occurs, let’s say a hurricane is brewing, will you be able to see which stores will be in its path? How about households? How might this event affect your ongoing or future marketing campaigns?
• Risk management. Customer concentration or density analysis (Fig 1) can identify specific areas in which you may be taking on too much risk.

Figure 1. A visual example of density analysis. Source

Some marketers want to know, in comparing ZIP +4 vs. geospatial–which is best? The answer depends on your specific problem, resource constraints, and level of analysis required.

ZIP +4 can help a marketer drill down towards a fairly small area for targeting and the process of converting a mailing list to ZIP +4 is relatively inexpensive. Whereas, adding geo-spatial capabilities usually involves use of an analytical infrastructure (database and hardware), software applications (i.e. data integration and visualization tools), and both technical and business know-how to perform analysis and act upon newly discovered information.

ZIP +4 may work best as an inexpensive way to improve customer targeting. However, as seen from the above marketing examples, geospatial capabilities open a whole host of analytical options for marketers that ZIP +4 just cannot match.

Questions:

• With three billion mobile phone users in the world (and growing) will “location” become an increasingly important component of marketing in the next 3-5 years?
• A business intelligence infrastructure is a necessary pre-cursor to geospatial analysis. What does this say about the skill sets marketers will need in the future to perform such analysis?