But We Have To Be Willing To Listen – And Take Action – To Benefit
Unless you’ve been living under a rock – and a pretty big one, at that – you’ve probably caught wind of the massive hype surrounding Big Data these days. Suddenly, the answers to all that ails us seem deceptively close, provided we can aggregate enough data (size DOES matter when it comes to data, it seems) and computing power, similar to Deep Thought, the fictional supercomputer made famous in the Douglas Adams novel The Hitchhiker’s Guide to the Galaxy.
And, as with hype cycles before, business and the investor community are clamoring to make massive investments in anything related to Big Data.
Data and analytics practitioners have long understood the value of data-driven insights. Regardless if marketing and advertising (which happens to be my field of focus), supply chain planning, demand forecasting, credit decisioning, insurance underwriting, fraud detection and prevention, or countless other areas, the collection and analysis of large pools of data have been the key to success for years.
So Why Don’t We Leverage Data More?
There are many day-to-day, more mundane examples where statistical analysis can help identify anomalies or spot outright crime.
An example where survey data would have helped.
One good example, from right where I happen to live, is the 2009 Atlanta Publics Schools cheating scandal. For readers unfamiliar, here’s a quick overview: in 2008 an investigative reporter from the Atlanta Journal Constitution noticed that some schools, primarily those within the Atlanta Public Schools system, had made rather impressive improvements in their standardized test performance. Upon closer analysis these gains were found to be not only impressive, but also statistically improbable. And, indeed, further analysis revealed the one of the largest cheating scandals in the history of the United States, with 178 educators from 44 schools suspected of altering tests.
But it took nearly three years for anyone to take note.
So why is quantitative analysis not applied regularly to more areas? At a minimum, it would seem that areas like government contracting, Medicare/Medicaid disbursements, or controlled prescription drugs would seem to be prime candidates for statistical analysis to identify fraud, waste, and abuse. These transactions are conducted electronically and controlled or regulated by the government, making the data easily accessible.
And, as it turns out, all of these areas appear rife with fraud and other abuses, costing the taxpayer as much as $300 billion annually. Moreover, once the data available is analyzed – even at just a very high level – outliers that warrant further review are easy to spot:
While these data points alone may not prove malfeasance, they clearly call for further analysis. One would think that, in these times of fiscal belt-tightening and austerity, citizens and lawmakers alike would demand they be investigated.
Unfortunately, one would be largely wrong.
So Why Don’t We Listen?
Why then, is the data ignored all too often, even in cases where it is readily available and speaks for itself, clearly warranting further investigation? There are several reasons for this, though none very good:
Humans are a predictable bunch and often prone to putting our own interests first. Contractors defrauding the government, pharmacists running pill mills, or doctors ripping off Medicare all share a common pursuit: money. There’s a reason it is said to be the root of all evil. Even the Atlanta schools cheating scandal, which jeopardized the educational success of thousands of children, ultimately came down to bonus payments for teachers and principals.
So, if doctors are willing to break their Hippocratic oath and educators will risk harm to the children in their care for monetary gain, who can we trust? Trust the data.
When misdeeds come to light, the most common defense brought by those accused is that they simply didn’t know it was happening. When that fails, some may admit to having known but insist they were unaware of the damage caused. The leaders at Atlanta Public Schools actually suppressed a report – one they had commissioned and paid for themselves (via tax dollars, mind you) – because the report findings didn’t suit their needs.
This sort of selective hearing – or outright willful ignorance – is commonplace, but should never be an acceptable excuse.
Resistance to Change
It is said that the most dangerous – and expensive – phrase in business is “We’ve always done it this way”. People love stability. They hate – and will resist – change. Especially if it entails effort. And, if sticking to the familiar ways just so happens If it happens to also be personally lucrative – and provide job security and status – even better.
More money for less effort and risk is a powerful incentive.
Insight Without Action = Status Quo
So, while analyzing data and drawing conclusions is valuable, it is not by itself sufficient to bring about needed change. Ultimately, someone has to not only have the gumption to ask the right questions and the analytical skills to draw the right conclusion; they then also need the courage to speak up and call the emperor naked.
Management – and our society – should actively seek to create an environment where employees – or common citizens – feel empowered to express dissent and challenge widely held assumptions, without fear of retribution or backlash. But we should also insist on seeing the data to verify the claims made. And, once validated, we should act. Decisively, and with confidence.
Anything less is little more than a kabuki dance with data.
MediaPost has a great cartoon in today’s entry of Stan Mack’s Real MAD – True Tails from Inside the Ad Biz series.
In it, we see a hapless AdTech salesperson trying to offer solutions to a rather clueless brand CEO. Despite his perfectly clear – and rather concise, albeit somewhat jargon-laden – value proposition, the CEO points out that he is not a “techie” and thus requires a “simpler” version of our protagonist’s pitch. The second and third attempts – now largely sanitized of any “techie” terms, but also increasingly without real insight or differentiation – end similarly fruitless.
I’m Not a Techie!
Finally, our our salesperson gives his conversation partner the bottom line in easily understood terms. And, while his final summary is likely (we hope!) a true representation of the final outcome of his offer, the CEO really has nothing substantive to evaluate this. In the end, all he hears is what everyone else is promising.
At this point – one I have reached far too many times, unfortunately – the message is adequately simplified for the CEO to consume, though also homogenized to a point of having no substance. And, once the conversation reaches this signal/noise ratio, the only way to differentiate an offering or break through the clutter, is with more amplification. At this point, the competition is no longer based on merit or results, but boils down to who has the bigger bullhorn, the most reach, the biggest marketing and expense budget. This is what slows true innovation.
The fact is, CEOs – and certainly CMOs and their brethren CDOs – NEED to be “techies” to some degree, since today’s business relies on technology to work. And, let’s be frank, understanding the role of technology and its pivotal importance in modern business practices, is hardly the exclusive domain of “techies” these days.
Similarly, C-level executives can no longer excuse their ignorance of vital technical components by claiming their domain is “the big picture”. Unfortunately, all too many seem not to have gotten the e-mail. Perhaps they’re still waiting on a memo, complete with the new cover sheets. Or, they’re simply preoccupied with today’s TPS reports.
PART 1 – Getting from Data to Actionable Insight
This is the first white paper in a two-part series exploring how data is revolutionizing digital marketing. This document examines some of the difficulties business faces in meeting the data challenge and what steps must be taken to effectively leverage data-centric methods to gain actionable insights from the huge volumes of data now at our disposal.
PART 2 – Using Data-Centric Insights to Drive Effective Media Strategy builds on this document and examines how business can successfully leverage the insights gained from their analysis to power and optimize marketing operations in the quest for greater ROI in digital media.
Why read this paper?
Big data is big business these days. Research firm IDC forecasts that big data services and technology will grow at a 27% compound annual growth rate (CAGR) to $32.4 billion through 2017 – about six times the growth rate of the overall information and communication technology market. And the performance improvements, increased revenue, and operational savings promised by effectively leveraging big data are expected to dwarf that number. As companies scramble to implement big data strategies, marketing has been one business function where the potential benefits of increased efficiency loom particularly large. And much is at stake here. Industry experts like eMarketer and Forrester predict global spending on digital adverting will grow about 15% annually and top $200 billion in the next five years. Achieving even a small increase in efficiency could therefore deliver huge ROI.
Software vendors have flooded the market with countless products, all promising to help meet the big data challenge. Many are capable tools that perform well in their respective areas but address only part of the challenge. They can also be difficult to implement, hard to integrate, and often require deep technical and analytical skills to configure and operate. Today’s marketer needs solutions that are complete, integrated, easily implemented and intuitive to operate, requiring a minimal learning curve, if they are to deliver on the big data promise.
But most organizations will require more than tools and technology to succeed. Many lack a data-centric culture and will struggle to attract and retain talent with the deep analytical skills needed. And beyond mere talent, companies will need to change operational procedures and structure workflows and procedures to optimize the use of data-driven insights.
Big Data Is About Data, Not Size
But first let’s take a closer look at why the big data revolution is so significant and, while offering significant promise, also presents some unique and vexing challenges.
The challenge with big data isn’t just its size, but also its lack of structure, diversity and fluid nature. These factors, more than sheer size, make it hard to aggregate, integrate, manage and analyze.
While storage and processing capacity have increased greatly and costs have plummeted, there are other factors that limit our ability to manage, index and cross-reference large data sets. It used to be that companies collected data primarily as a part of their daily transactions and stored it in structured databases. This data was used mainly to track operations or forecast sales, inventory and the like. It was structured, well understood and relatively easy to manage and analyze. With the explosion of data sources, we have seen not only the volume of available data increase drastically, but also the very nature of the data itself change. With more and more business processes, operations and stakeholder interactions digitized, business now has data on just about every customer interaction, including click-streams from Website visits, search activity, mobile apps, digital advertisements, social media content streams, videos and countless more. And, as the “Internet of Things” becomes a reality and digital technology invades our homes, our cars and even our bodies (Google recently announced a contact lens with embedded microchip and sensor to monitor glucose levels for diabetes patients in real-time and send the data to an application in the cloud), virtually everything we do will generate data.
Analysts estimate that fifty billion sensors will be connected to the Internet by 2025, each one adding to the data pool available for analysis. While our refrigerators may not be networked (yet), our televisions, home security and thermostats are (note Google’s recent $3.4 billion acquisition of Nest) and all are generating data points. Companies will soon not only be able to collect information about every conversation people are having about their brand, but also monitor interactions with their products anytime and any place they occur.
But that alone will not make them better marketers. In fact, it could do just the opposite. Few executives lament a shortage of data as holding them back. Instead, like modern versions of Coleridge’s Ancient Mariner, they are awash in data, yet thirsting for true insight. Adding even more data – without corresponding improvements in the ability to organize, analyze and interpret it – could well prove counter-productive, leading to analysis paralysis or providing false cover for bad decisions. What harried business leaders seek are simplicity, inspired guidance and clear, actionable insight. And no amount of data can provide this on its own.
Getting from Data to Actionable Insight
Henry Ford once famously proclaimed that if he had asked people what they wanted, they would have said faster horses. And there would have been ample data to support this request. More skilled at engineering than equine breeding, Ford didn’t take this raw data at face value though. Instead, he analyzed it carefully, interpreted it creatively, asked deeper questions and innovated based on the insights he gained from his analysis. And herein lies the crux of the matter: it’s not the data itself, nor its size, that provides the value. Instead, it’s the insight we derive from it and the actions we take based this insight. Data is just a tool, a resource, nothing more than an inert ingredient until we add the catalyst. As Hamlet observed, “there is nothing either good or bad, but thinking makes it so”. Whilst he was musing on things unrelated to data analysis, his ideas are just as valid and applicable here: without human reflection, analysis and thinking, we will drown in data while actionable insight remains beyond our grasp.
So how do we get from raw data to actionable insight? How do we harness this flood of information to understand our customers better and take our business to the next level? The late Dr. Russell Ackoff, once described as the “Einstein of modern problem solving”, shows us the way in his famous article, “From Data to Wisdom”.
In it Professor Ackoff states “Data is raw. It simply exists and has no significance beyond its existence.” We have to enrich it by providing context, relevance and relationships, at which point it becomes information. Take the statement of “we sold 1,000 units last year”, a classic example of raw data that tells us very little. Are sales of 1,000 units good or bad? Is it more than the year prior, indicating an upward trend, or are we falling behind? Were the sales profitable, indicating a healthy business? Are we out-performing our competitors, indicating a comparative advantage? These contextual aspects are what give the data meaning, and thus render it into more useful information.
But information alone provides limited benefit, as it lacks purpose. While it may be interesting to note that your company doubled profits last year while sales increased by 20% to 1,000 units – more than twice your closest competitor – it still doesn’t give us any understanding of how or why this happened. Only by aggregating enough information, to where we can discern patterns and are able to draw conclusions, does it become knowledge. Acquiring this knowledge is valuable, for it gives us insight into how things work but it is still not actionable. It tells us that something works and how, but we don’t understand yet why – or, more importantly, how we can take action to influence it in a desired way.
Getting from raw data to insight you can use.
For knowledge to become actionable insight, we also need an understanding of causality, the why. Achieving a true understanding requires us to distill data, aggregate it into information and then knowledge, and then actually synthesize new knowledge from it. To build on our example, if the analysis of our data shows us that the regions that performed best – the ones that contributed not only the most to our overall sales growth but also the most profitable sales – all had sales people that had completed the new sales training program, then it stands to reason that this was a major contributor. We can then reasonably assume that if we were to train the salespeople in our other regions, they might also see an increase in overall sales as well as profit. With this new insight we now have an understanding of how certain regions perform better as well as why this is the case: their salespeople are better trained. This enables us to take action and train the rest of our salespeople. The analysis of subsequent data should then, presumably, show an increase in performance in these regions as well. This is a classic example of data-driven optimization: gathering data, aggregating and analyzing it to gain actionable insight, acting on it and then measuring the results to validate the effectiveness of our actions.
And, once we do this often enough, with enough data from across the enterprise, and marry these new insights up with our industry experience, professional judgment, and our understanding of business principles, we arrive at what Professor Ackoff referred to as wisdom. Wisdom, in his use of the word, refers to the ability to anticipate outcomes, to predict certain results based on historical data. It also enables experimentation by providing the basis for developing new hypotheses that can enable new business ventures, product extensions and new approaches that can be defined, developed, executed and then tested and measured.
Meeting Today’s Challenges
Professor Ackoff developed this approach back in the 1980s and published his insights in 1989. Back then our data sources consisted mainly of transactional systems commonly employed in business at the time, systems that managed sales, supply chain functions, financial systems and such. Reflecting this, there was far less data, it was clearly delineated by source, well-structured and stored mostly in relational databases. All of this made it relatively easy to collect, aggregate, refine and analyze. The biggest challenge faced by business at the time was the lack of integrated systems, which meant that the data had to be collected from numerous separate systems, converted into common formats, normalized, harmonized, and prepared for analysis.
These are all the same challenges we still face today. With the explosion of data sources and the corresponding increase in volume and complexity, however, they have grown exponentially.
Technology Only Goes So Far
Luckily, new technology has emerged to help us with our efforts. We have seen vast increases in our ability to store, manipulate and process data thanks to tremendous increases in computing power over the last decade. These have been further enhanced by developments like NoSQL databases, Hadoop and other distributed data storage and processing frameworks, new standards like PMML, advanced analytics platforms, and many others. Combined, these factors give us an unprecedented ability to collect, store, manipulate and process data electronically. The one aspect that has not increased significantly though, remains vital today: skilled, inspired human analytical skill.
Without careful analysis and inspiration we simply cannot advance from knowledge to actionable insight and beyond, no matter how big our data pools are or how powerful and sophisticated our technology might become. Professional analysts, systems designers, and programmers are also needed to design and develop the tools, platforms and programs needed to make the data volumes manageable, devise the logic by which we can process and analyze it and then present the results in a clear and actionable manner.
As companies and brands embark on big data projects, the shortage of skilled analytical talent is frequently cited as one of the biggest impediments they face, along with a lack of integrated technology platforms that are easy-to-use and yet flexible and powerful enough to be able to meet the needs of business. Developing talent and changing mindsets to become more data-savvy will take time, of course. Meanwhile, the AdTech market is scrambling to answer the call for better technology and provide integrated platforms able to meet the needs of digital marketers, freeing them up to focus on what they do best: develop effective targeted campaigns.
The Market Responds
With so much at stake and the potential rewards so large, the advertising technology sector has seen a flood of investment in recent years. The result are digital marketing LUMAscapes littered with dozens of startups, most less than five years old. And, as always happens following huge waves of innovation, necessary consolidation has begun. Weaker entities are being acquired and integrated by players that are stronger or have deeper pockets. And signs of promise are emerging, with several vendors now offering complete integrated solution platforms able to target, execute, monitor, analyze and optimize efforts across various media, channels and touch points.
And, recognizing the need for consulting services, campaign strategy and integration, a number of companies and agencies now offer targeted services, often coupled with platform solutions that combine several third-party components into a bundled offering. These service providers promise to get marketers up and running quickly, providing external staff where needed and helping to shorten the learning curve.
The next few years will be an exciting time in digital marketing, as brands and agencies alike embrace data-centric approaches for greater accuracy and efficiency. And, presumably, better business results.
Big Data, Analytics and Programmatic Buying Are No Panacea
Harvard Business Review recently devoted much of their March 2013 print issue, including the cover, to digital advertising and the way big data and analytics are transforming the field. You can read an intro to the piece here, but will have to subscribe to get the full article.
The Promise of Smart Advertising
The article discusses how today’s data-centric marketing is not only able to precisely target ad placements based on demographic, psychographic, behavioral, and a host of other data, but can then also reliably track and attribute subsequent consumer behavior to exactly the advertising that triggered it. This insight is then used to further tune campaigns, optimize media spend, targeting, and placement on an ongoing basis.
Usually, all of this is automated. Media is bought via programmatic platforms and campaigns are optimized in real time based on results achieved, driven by powerful algorithms and without human intervention. According to the HBR article – and countless ad-tech vendors’ marketing claims – this approach promises the equivalent of laser-guided precision ad units that hit the mark each time, at precisely the right place and time for maximum effect and minimal cost. HBR describes this new advertising landscape similar to the precision-guided munitions employed by our military. Perhaps an apt comparison, as they seem to share a common flaw: both require a change in approach and neither seems to work quite as promised in less than ideal settings. Allow me to elaborate.
Measuring is no Substitute for Managing
Digital advertising has always claimed to be more measurable than its traditional, offline brethren. While billboards, print, and broadcast media were forced to rely on crude (and often manipulated) metrics like circulation, Nielsen ratings, GRPs, traffic counts and the like, digital media could always point to its inherently interactive nature. And this claim could be substantiated by “hard” data, including click-through rates and paths, engagement, time-on-site and more.
Proper Ad Targeting, Context, and Placement are Crucial
(Image source: imgur.com)
Even better, when a product was purchased online or a lead generated, this action would commonly be credited to the media that drove the consumer to the conversion point, a process commonly referred to as last-click attribution. It seemed the only blind spot for digital media was the inability to clearly attribute offline purchase decisions to media consumed (and ads viewed) online, where both traditional and digital media appeared to be on equal footing. And, given that consumers were increasingly consuming media online and in digital formats, yet brands were still disproportionately allocating spend to traditional, all that remained was for marketers to shift spend so that online media got its fair share and all would be right. Wrong.
For one, it turns out that not everyone peddling eyeballs online is totally honest and a lot of those clicks and views are not by actual consumers but, instead, automated bots that generate phantom traffic. As a result, much of what brands spend on digital ads is wasted on non-existent views and fraudulent clicks. And it’s not just amateurs getting taken here. Even large, presumably sophisticated advertisers like AT&T, BMW, and McDonalds, running what I assume to be sophisticated (and expensive) campaigns, are falling victim to this and, at times, losing up to 80% of their spend to fraud according to one report.
But, click-fraud and bot-traffic aside, there remain other issues with today’s state of digital advertising and it is still far from the perfect, well-oiled machine portrayed in the HBR. For one, online display ads are notoriously weak in building brand awareness and show weakness even for direct-response advertising. A recent study illustrates this and data shows that consumers are statistically more likely to survive a plane crash than click on a banner ad. Of course, campaigns vary greatly in terms of effectiveness, but you get the gist. Beyond display advertising (banners and such), similar issues surface in paid search, social media, and the latest trend to emerge, so-called “native” advertising.
My Reality Belies Market Claims
At the heart of these issues seem to lay poor segmentation and targeting, along with the crude, heavy-handed execution of many of the campaigns. For all the much-vaunted data that Google and Facebook have on consumer behavior and the supposed sophistication of today’s marketers, I continue to be perplexed by the ads I am (mis-)served on a daily basis. Any parent who has had their teenage daughter borrow their computer for 30 minutes and then paid for this with countless ads for One Direction’s new tour or the spring sale at Forever 21 knows what I’m talking about. These re-targeted ads – which occur when you visit a site but then fail to buy (or even when you DO buy, ‘cause, y’know, you might have missed something, right?) – are becoming increasingly popular with marketers, despite a growing backlash by Internet users.
Marketing Still Requires Marketers
This is not to say that digital advertising isn’t the way forward. Nor that smart, targeted ads guided by data-intensive insight don’t hold great promise, for both are clearly true.
It’s just that, for now, they are not yet delivering on their promise of identifying the right consumers and serving them the most relevant ads at the most appropriate time. Big data, analytics and ad-technology will clearly play a key role in getting us to there. But, technology alone will not do the trick. While it is clearly necessary, it is simply not sufficient on its own. As programmatic buying and RTB (Real-Time Bidding) platforms gain traction and big data becomes an even a bigger deal in guiding marketing (hard to imagine in the current hype-cycle, right?), advertisers, their agencies, and consultants will need to ensure that the human component is given equal consideration. Business processes, marketing departments, campaign design, and media spend all need to be adapted to the new reality. Some even claim that the rise of technology-centric marketing calls for an entirely new member of the C-suite, the Chief Digital Officer or CDO. While this is a debate for another time, this much is clear: investments in hiring, training, and consulting services must accompany those made in systems and technology if they are to be effective. Technology, no matter how refined and sophisticated, is never the panacea promised by vendors. We saw this with ERP systems, EAI/BPM, and a host of other initiatives that all required several years and multiple investment cycles in both IT and human capital, along with structural changes to organizations and processes, to deliver on their promise.
Solid marketing fundamentals, effective strategy, strong creative, and execution still matter, and I fear there’s just no app for that. New techniques and tools will require marketing teams and brands to change their thinking, acquire new skills, and develop effective approaches. Same as it ever was, one might quote one of my favorite 80s bands.
The Marine Corps has a saying that, when things get serious, it doesn’t matter if you’re a cook, a truck driver, clerk or radio operator. When the shit hits the fan, everyone’s a rifleman. As a result, every Marine, regardless of military occupational specialty, is trained in basic rifle marksmanship and required to regularly qualify with their primary weapon, currently the M-16A4 rifle.
The rationale being that, since modern warfare is marked by fluid, often unpredictable battle lines and that even rear echelon support troops may find themselves in armed combat at a moment’s notice, every Marine must be proficient in basic marksmanship. As we have seen in Iraq and Afghanistan, there are no more “front lines” in today’s battlefield.
US Marines with their primary weapon.
Photo courtesy of the USMC.
The Internet – The Primary Weapon for Modern Business
A similar case can be made for a basic understanding of technology, in particular Internet technology, in the context of modern business. Whether you’re an accountant, supply chain manager, banker, salesperson, or marketer, the Internet is an integral part of your job.
Technology is everywhere, and the Web continues to upend and disrupt nearly every aspect of business. Organizations at the forefront of this trend are integrating processes and systems all along the value chain and one is hard-pressed to find roles not dramatically affected by this. GE is now employing social computing to improve their industrial manufacturing processes and is integrating sensors that use TCP/IP and wireless protocols to optimize the efficiency of wind turbines. From procurement to logistics, over sales planning, POS, accounting, billing, payments and customer service, formerly disparate systems are increasingly integrated via networks built on Internet standards. And, in most cases, these systems are being accessed primarily via the Web (or Web-based technologies), often through a multitude of channels, including smart phones and other mobile devices. This not only streamlines business processes, reduces errors and cuts cost, but also increases speed and transparency.
Marketing in the Digital Age
One area that has been affected dramatically by the Internet is marketing. Today’s marketer works in an environment that is fundamentally different in just about every aspect from what he may have encountered as recently as a decade ago. With the rise of digital platforms, marketing has become far more complex, more fluid, and, importantly, more measurable. And yet many of today’s marketers have failed to evolve and often lack the technical skills and fundamental understanding of the Internet needed to most effectively employ the tools at their disposal.
It used to be that IT was a separate department that other functional areas called upon when they needed their PC connected to the printer or a piece of software installed. Accountants, sales people, supply chain managers, and, yes, marketers would go about their daily business blissfully unaware of what made the network run or how exactly their e-mail got to where it needed. Information technology was considered a cost center, not a source of competitive advantage or differentiation. All that has changed and many companies now – from retailers, to publishers, financial services companies, and consulting firms – derive a significant portion of their revenue from online activities. Digital technology is now so pervasive, affecting so many aspects of business in such fundamental ways, that many experts question if it even makes sense anymore to have IT as a separate department. Instead, technical expertise and digital fluency are now such an integral component of just about any business function, that these skills need to permeate each and every part of the org chart.
A Series of Tubes
When the late Alaska Senator Ted Stevens described the Internet as “a series of tubes” back in 2006, he was roundly ridiculed by pundits and members of the digerati. Ironically, he was charged with oversight and regulation of the Internet at the time. But ask a random sample of marketing folks to explain the basic building blocks of the Internet – things like HTTP, TCP/IP, HTML and similar – and you just might find that the late Senator had a better grasp of things back then than many marketers can muster now. And, while many eagerly employ industry buzzwords like Big Data, real-time analytics, business intelligence, cross-media publishing and more, few can describe what they actually mean and even less could write an SQL query or build and launch a simple Website.
Now many of you may interject that there is no need to understand the technical underpinnings of these tools and concepts in order to use them. And, if your job is merely to write content for your Website or place media buys, you may be right. To a point at least. But if your role goes even just a little beyond that – and, let’s face it, most do – you’d be dead wrong. For even those simple tasks require at least a basic understanding of SEO, a grasp of how your content management system works, or how media buys are priced dynamically based on variable CPM rates, how to identify or avoid click-fraud, and a multitude of other factors that affect success.
If you’re in an only slightly more responsible role, you may be called upon to research and identify new marketing automation software, or help assess the cost, time, and effort to integrate your CRM platform with the new e-mail marketing system. Without a basic grasp of how these work, all you have to go on is the information provided by vendors or consultants. How would you know if an estimate is too high or, as often happens, unreasonably low? There are common reasons why most IT projects come in over budget, are completed late or fail altogether and, next to poor planning and project management (and I would argue that both of these are related to technology as well), a lack of technical understanding by key decision makers is often a root cause.
Trust – But Verify
For how can you or your team evaluate a proposal or tool without understanding both the business requirements and the technical criteria needed for it to work, especially when the two are so closely intertwined? If I had a dollar for every time I’ve heard a vendor tell me their system is “basically plug-and-play” and offers all you could ever want or need “out-of-the-box” with only some “minimal configuration”, I’d be a wealthy man. The fact is, IT is complex. And it has to be in order to solve the complex business problems that need to be addressed. Sure, the CRM system your company is considering looked easy and worked flawlessly during the vendor demo, but how much time and effort was put into preparing that slick presentation? And just because your consultant – who, let’s remember, is paid by the hour – says that adapting the system to your processes and integrating it into your environment is “straightforward”, doesn’t make it so.
But bad IT purchase decisions and failed projects are but one symptom of inadequate technical knowledge within many organizations, albeit a very visible one. Much more prevalent – and insidious – are persistent under-performance and missed opportunity, which can often go undetected for years. The effects can be corrosive and will often lead to a vicious circle that feeds on itself and becomes self-reinforcing as more tech-savvy team members become frustrated and seek opportunities with organizations that “get it” and value their expertise.
Survival is Optional
The fact is, technical competence is increasingly important, regardless if you’re a project manager, analyst, or even a sales or account director. The days when a fellow like Tom Smykowski could get by on just “people skills” are over, today’s interconnected business calls for more. Much more. But, worry not, your company will most likely adapt and survive. The question many marketing professionals should ask though is whether they will. Younger, more tech-savvy employees are coming up quick. I was reminded of this just the other day by my 12-yo daughter. Previously, I had learned of her holiday wish-list from a letter to Santa written in crayon. This year’s list, which included a request for an iPad and a new smart phone, came to me via e-mail containing a link to Google Docs. And, just to be sure “Santa” – not always known for his technical prowess – got it right, she had included links to the exact products on retail Websites and photos of the desired items on Pinterest and Instagram.
Online collaboration via Google Docs, integrated with social media and delivered using the Internet. All this by a 12-yo child in seventh grade. I work with senior digital marketing leaders at Fortune-500 companies nearly every day and many of them would not be able to pull this off without help.
Attend any digital marketing conference and you quickly realize who the belle of the ball is these days. Everyone, from agencies, management consultants, publishers, to technology vendors, seems to be obsessed with mobile. Everyone except business, that is. For while mobile currently makes up around 16% of total Internet traffic, and its rapid growth promises to fundamentally change the web, its share of advertising spending and overall investment is significantly lower. This indicates that business may, once again, be late to recognize a major shift.
We see this reflected in the lower advertising CPM for the mobile Internet, which is currently only about 20% of desktop CPM. This is often justified by pointing out that average revenue per user (ARPU) is similarly lower for mobile users. I contend this is the same circular logic that led to the initial under-investment by brick-and-mortar business back in the 90s, when the Internet first became mainstream. This myopia on the part of business allowed many traditional players to become dis-intermediated by the likes of Amazon, eBay and Zappos. Others lost market share to nimbler competitors who adapted more quickly. The current rise of mobile may well do the same.
For let’s face it, mobile Internet users are just like desktop users, only more so. As in more educated, more urban, more influential, younger, and more affluent. So why would CPM and ARPU trail by such significant margin? Well, for one, the mobile Internet experience is currently far different surfing the Web at our desk. Even putting network limitations such as coverage and latency aside – assuming for instance that we are connected via Wi-Fi, as most tablet users are – the difference in user interface and screen size of a phone or tablet demands a different approach.
Business and advertisers alike have been slow to adapt to this new landscape and they most commonly fail due to:
Not Offering a Mobile-Optimized Experience at all
Many businesses, including even large retailers, still do not offer an online experience optimized for mobile users. I have experienced this personally in just the last week when I tried to access a national retailer site using my smart phone.
Failure to Correctly Identify Mobile Users
Even companies that have invested in a mobile site often fail due to poor browser/device detection. Incorrectly identifying mobile users and sending them to the desktop site negates any effort and investment made in developing the mobile-optimized experience.
Not Considering a Connected, Multi-Device World
Business needs to understand that users connect and share information across various devices throughout the day and they need to take this into account and plan for it. If a mobile user posts a link to Facebook using his phone, this link may later be accessed by users on a variety of devices. Hard-coding the posters device choice into the link – as I had happen to me when a friend shared this CNN article earlier today – and then taking me to the mobile site even though I am accessing it on my desktop is simply unacceptable and will lead to frustration and drop-off.
Unsuitable Back-End Systems and Data Structures
Some companies may adapt their front-end UI for mobile users but fail to consider other UX aspects. Back-end systems, data structures and cumbersome check-out processes often need to be adapted for smaller screens. A huge look-up table may work (somewhat) on the desktop, but scrolling through a list of 20-30 options proves cumbersome on a phone.
Failure to Truly Optimize for the Mobile Use Case
Mobile users often have different needs and goals than those behind a desk. For one, they’re mobile (as in away from home), so a store locator driven by ZIP code won’t work if you don’t know where you are. Similarly, mobile users are often more task-oriented and will have little patience for superfluous content or fancy imagery.
Business should recognize that the current difference in CPM and ARPU for mobile users is caused by a poor mobile user experience and ads that are simply not designed appropriately. The sooner digital marketers and eCommerce leaders accept that mobile users are not inherently different from desktop users – but that they do have different needs dictated by their device – the sooner they will adjust and refocus their investment in the mobile Internet to reflect the reality of its rapid growth.
Some readers will find themselves somewhat confused by the title of today’s post. Many others will have recognized instantly the significance of it. Others yet might be intrigued and eager to learn more. Some will quickly leave my blog – I expect abandonment and drop-off to be a bit higher for this entry, but I also expect deeper engagement with those readers who are in the know. Even here knowledge of context makes all the difference.
If you’re still with me then you probably know the significance of the number 42. If you don’t, you’re probably wishing I would just go ahead and explain it. Either way, you’re probably wondering what the hell this has to do with digital marketing or interactive technology.
Well, continuing my musings on metrics and analytics that started with this post over the weekend and, subsequently, with my thoughts on effective analysis this past Wednesday, today’s entry takes a deeper look at the importance of contextual understanding
So, to clear the air and level the playing field, 42 is, as many know, the Answer to the Ultimate Question of Life, The Universe, and Everything, as unveiled in Douglas Adams’ now classic book, The Hitchhikers Guide to the Galaxy.
I chose 42 as the title of today’s post for a number of reasons. First, I like that Adams chose such a simple, yet obscure answer to one of the most profound questions ever asked. It reminds me of many of today’s marketers, who similarly seek simple answers to often immensely complex problems. How else to explain their fixation on things like Klout scores, PageRank, Facebook friends, Twitter followers and similar metrics that, while neat, fail to capture true value? We see this phenomenon offline, in other parts of our lives, as well. Consider FICO scores, BMI numbers and GDP data, which, while providing a valid single data point, cannot possibly encapsulate the complexity of our lives, our creditworthiness, health or wellbeing as a nation.
Second, I like that while Deep Thought returns an answer so seemingly simple, it took 7½ million years to compute and check it, by which time nobody remembers the actual question or understands the significance of the answer. Sound familiar? Marketers, senior executives, board members, “the market”, and countless others are forever looking for a simple metric to capture all aspects of complex issues, so let’s give them what they ask for. Even if we may not really understand the questions, nor grok the answers.
Finally, in a nod to SEO, I figured using the Hitchhikers Guide and 42 as a side topic would boost my blog’s search performance and drive traffic to my site. Sure, it’s poorly targeted and mostly irrelevant traffic, but hey, it’s traffic nonetheless and it’s something that’s measurable.