Better Beats Bigger Data, Every Time.

“Better Data” vs. “Bigger Data” ― hopefully, this doesn’t even sound like much of a debate. Yet in many cases, the distinction isn’t so subtle. These two broad descriptions are often easily, and innocently, confused. Let’s try and clear that up a bit.

Oftentimes, marketers we help with marketing database development find themselves in a rush to “build their database.” Virtually all retail organizations today are tasked with “building the database.” CMOs know intuitively that amassing data about customers and prospects has value. One CMO recently gushed, “that data is gold.”

Not surprisingly, given the data was valued like gold ― a precious commodity ― then more must be better. And what came next was somewhat predictable after working with dozens of brands on leveraging their data to achieve scale and performance.

Customer, marketing, operations and finance all rolled into a singular database being constructed by a modest and already overwhelmed IT staff. Years went by before the project was killed ― and creating value with the data was postponed indefinitely.

Here are just a few of the key issues that develop when the focus meanders to “bigger” rather than “better”:

1. The Goal Isn’t Entirely Clear. If you begin with the goal of capturing and storing data, that’s what you’re going to do. Conversely, if you begin with the goal of growing customer value, a discrete set of data points come into focus. How they are captured and organized is clearly informed, and how they will be utilized gets clearer from the start.

Similarly, marketing data can fail to meet the expectation when those expectations were only loosely formed shortly after the result was delivered!

2. The Culture Doesn’t Embrace Making Decisions With Data. Let’s face it. Historically, many organizations embrace decisions from the gut. Intuition and opinions rule, even as talk of using data to inform decisions is the norm. These organizations can only shift from valuing data by the terabyte to valuing data by its financial performance after the top-level decision-makers in the organization embrace data-driven decision-making.

3. Skepticism vs. Materiality. Building on the above, there will be skeptics. Skeptics will challenge if data can or does create business value for the organization. A healthy degree of skepticism is helpful. A “Data Athlete” does bring a healthy curiosity about what data suggests, and how it is captured, transformed and considered. There’s unhealthy skepticism, even if it is innocent in its nature.

A common example of where skeptics combat a data-driven culture is finding examples of incomplete records, or inaccuracies. Another example is illustrating the gap between two systems. These all, at face-value, seem terribly problematic. However, those “gotcha” moments need to be considered in terms of context ― a 2 percent discrepancy between the core financial system and the marketing database is extremely important in the financial database ― and most likely immaterial in the marketing database.

4. Complexity Can Undermine Results. In the first example, an ambition to squash “silos” of disconnected systems was the justification for “biggering” a new database approach. To be certain, there were prior experiences where “siloed” data created frustrations in the CIO’s capabilities he/she extended inside the organization. However, none of these were directly aligned with the CMO’s objective of improving messaging, response and sales.

Bigger data doesn’t just mean more of the same data types ― it often means adding more types of data. The complexity and design of the underlying schema or data model is directly correlated with the sheer number of data fields being captured. So capturing more and more adds to the challenge of making it economically viable to create value with it.

The same can be said for the complexity of the data fields themselves. While leveraging transaction data can be done through reasonably well-understood statistical methods and models, incorporating social signals is typically more challenging. Marketers frequently cite correlation of certain observable social behaviors, (a common example being “likes”) with buying behaviors when causation is what’s needed to discern the economic value and business impact of these types of data.

Given the relationships between disparate data types aren’t always clear (much less actionable), the underlying data models grow murkier as more “fuzzy” data is added to the database. In this context, “fuzzy” refers to the implicit value it has in a structured or statistical application to target messages.

The reports they generate may be highly engaging and interesting (see “Analytics Isn’t Reporting”), even as they reduce the probability of successful outcomes of database marketing.

5. Bigger Isn’t a Silver Bullet: Specialization vs. Generalization. With the advent of a Big Data industry, pundits, generalists and traditional agencies, have all volunteered opinions. This cacophony leads to adding more and more to the mix ― compounding all the issues we’ve already covered herein. This underscores one of the top challenges in utilizing data ― it’s not just the tech, it’s people. There is an overwhelming shortage of talented and experienced individuals who have the marketing, database, technology and analytics experience to convert data to insights and those insights to profits. True Data Athletes are in high demand. Put another way, generalists and opinions don’t cut it. Utilize specialists and pare the scope and expectations of your database marketing to the level of talent you have or can realistically budget to engage.

While there are many challenges to utilizing data to create leverage in your business, the opportunity is clear and expansive.

Here’s the simplified checklist for marketing executives on how to overcome the common challenges in leveraging data to create marketing and business value:

• Begin with a pure business outcome as the first thing you decide. What will success look like after you’ve implemented your database marketing solution?

• Align your data collection methodically with your objectives. If it’s not clearly necessary, throw it away ― or store it somewhere else.

• Be patient and thoughtful upfront ― and capture the data in a schema or data model that supports the kinds of questions and queries you realistically expect to ask and answer of your database.

• Think Small to think Big ― what’s the most important question you can answer? Focus on that first and foremost. Getting more of the most valuable outcome is a big win ― even if you have to tighten or shrink your focus at first.

• Avoid “Boiling the Ocean” ― your likelihood of successful marketing outcomes is highly dependent on the complexity you create along the way, and the quality not quantity of resources you bring to solve the data challenge.

If you’re like most marketers, you’re probably under significant pressure to do more with less, achieve greater scale and drive greater profitability.
Data is not a “tool,” per se, to help you achieve those goals, however. It’s a strategic data asset. When you think “quality” first and “quantity” second, “bigger” takes a back seat to “better” data ― and bigger performance.

Analytics Isn’t Reporting

Today, virtually all organizations have challenges in effectively leveraging analytics to drive business performance. Odds are pretty good that when you read that statement, you thought of at least one example in your organization. Perhaps you thought about the systemic contribution that analytics is making or a frustration you’ve had with analytics performance. If so, you’re hardly alone.

Today, virtually all organizations have challenges in effectively leveraging analytics to drive business performance.

Odds are pretty good that when you read that statement, you thought of at least one example in your organization. Perhaps you thought about the systemic contribution that analytics is making or a frustration you’ve had with analytics performance. If so, you’re hardly alone.

Here’s my home base for thinking about “analytics” in your organization.

“The promise of marketing analytics isn’t esoteric, or abstract — it’s fundamentally simple — analytics generates evidence of problem or opportunity that can be used to drive a specific business impact.”

Yet marketing analytics all too often fails to live up to its full potential. When it comes to the Web, almost a decade after the advent of mass adoption of Web analytics platforms like Google Analytics, engagement and conversion rates are still struggling to make methodical progress forward, and bring the business to materially greater profitability.

One of the biggest errors in strategy is the inadvertent substitution of “reporting,” or even “dashboards,” for a robust analytics process. It helps to first appreciate how subtle that difference is and why it happens:

  1. Analytics Is Interesting. Analytics can be intellectually stimulating, but some individuals and organizations spend too much time in the rapture of how interesting all that data can be. I was recently at an event where a smart young woman had a name badge on that said “I love data” below her name. I was tempted to write “I make money with the data” under my own.

    While I’ll be the first to express a life-long affair with the database and discovering “interesting” things in the data, that’s just not enough. So we have to monitor when analytics isn’t producing the evidence we need to affect change and deliver a business impact. While that can take a tremendous amount of work, the purpose itself must remain clear to create value.

  2. Reports Don’t Always Have the Right Questions Behind Them. Most of us came up in business generating and reading reports. I confess that I remember craving a report we used to call “the blue book” (if you still remember paper). I looked forward to every week when I ran my business line off of it in a large company that razed many a forest generating blue books. Thankfully, they email them now — but these reports are the same static, one-dimensional view of the business, many years later.

    The problem comes when we see our “standard reports” as the answer, even if the question we should be asking has changed.

    When you’re dealing with fickle consumers, and infinite choice is a click away, those questions sometimes change faster than “reporting standards” can realistically keep up with.

  3. The Relevancy Is Gone. Better than 80 percent of the time, I see marketing organizations with ample “stats” on their historical activity — yet they often fundamentally lack a strategic big picture and framework to consistently improve marketing and business decision-making. Frequently, the same organizations struggled with aligning the technical implementation of analytics and metrics required to drive business growth.

  4. Continuous Business Improvement Sometimes Requires a Cultural Shift. Cultural shifts of any size aren’t trivial, of course. I recently attended an all-day digital commerce strategy summit at a large brand I’ve done strategy work with during the past year. Dozens of staff, vendors and executives attended. The ultimate revelation for some of these executives who made the six-figure investment in the event was, “this requires patience, and is very methodical and testing-based” — it took a huge amount of effort, resources and time. To the credit of the executive who sponsored this event, a necessary cultural shift was recognized. While all in attendance knew intuitively about “test-optimize-learn” and had a large investment in their analytics software platform — she recognized that her organization was playing catch-up culturally — an achievement in itself.

5. Prioritization Is Key. Many large and more traditional organizations have very deep roots in a task- and reporting-based culture. This stifles Data Athletes from doing their jobs. Prioritization is key. As the old saying goes, “If everything is a priority, nothing is a priority.” Executive sponsors need to make choices on where to dial effort back; focus can then be applied to build a point of view based on evidence, and the opportunity to create and discover the context of opportunity and problems.

Forward vs. Backward Analysis.
Very frequently, I’ve helped organizations that started analytics processes or programs by looking “backward” at tactical reports; these reports can only show if a past tactic has or hasn’t worked. You cannot tell if a different tactic or mix of tactics would have done better, and by how much. Worse yet, the very volume of these “reports” often obscures the bigger picture. The solution … Look forward.

Analytics Should Be Forward-Looking. It’s driven not only by analyzing the past, but by creating a framework for planning and creating future performance. In other words, what to test, how to test it, and how to use the results of those tests to drive continuous improvements in the business.

In short, analytics done well creates visibility into what you should be doing and suggests the delta with what you are currently doing. Think about the aforementioned necessity for prioritization — Analytics done well helps you set those priorities.

Analytics professionals and and the executive team must all work together according to one principle:

Analytics is the process of identifying truths from data.
These truths inform decisions that measurably improve business performance.

Analytics Must Be Purpose-Driven.
Here’s a simple approach to create focus and align the specific implementation of analytics to serve you and your business growth:

  • Your business’s Purpose drives specific Business Objectives.
  • Those Business Objectives, in turn, inform Goals.
  • Your Goals are tracked via KPIs.
  • The KPIs are continuously compared against Benchmarks.

It’s easy to dive into the weeds, get lost in the data, lose patience with the process, and begin a bottom-up approach. This deceptively simple framework I’ve suggested will help you take a top-down approach to analytics that ensures you are measuring the right things — correctly. When you do, you will become a true analytics-driven organization.

Doing so will help your organization grow faster, more consistently and reliably — and that makes for a valuable and happier organization. Be a Data Athlete, not an analytics nerd — and you’ll make all the difference in your organization.

Channel Collaboration or Web Cannibalization?

Multichannel marketers experience the frequent concern that online is competing with, or “cannibalizing,” sales in other channels. It seems like a reasonable problem for those responsible, for instance, for the P&L of the retail business to consider; same goes for the general managers responsible for the store-level P&L. I like to do something that we “digital natives” (professionals whose career has only been digitally driven) miss all too often. We talk to retail people and customers in the stores, store managers, general managers, sales and service staff.

Multichannel marketers experience the frequent concern that online is competing with, or “cannibalizing,” sales in other channels. It seems like a reasonable problem for those responsible, for instance, for the P&L of the retail business to consider; same goes for the general managers responsible for the store-level P&L.

I like to do something that we “digital natives” (professionals whose career has only been digitally driven) miss all too often. We talk to retail people and customers in the stores, store managers, general managers, sales and service staff. Imagine that … left-brain dominant Data Athletes who want to talk to people! Actually, a true Data Athlete will always engage the stakeholders to inform their analysis with tacit knowledge.

Every time we do this, we learn something about the customer that we quite frankly could not have gleaned from website analytics, transactional data or third-party data alone. We learn about how different kinds of customers engage with the product and their experiences are in an environment that, to this day, is far more immersive than we can create online. It’s nothing short of fascinating for the left-brainers. Moreover, access and connection with the field interaction does something powerful when we turn back to mining the data mass that grows daily. It creates context that inspires better analysis and greater performance.

This best practice may seem obvious, but is missed so often. It is just too easy to get “sucked into the data” first for a right-brain-dominant analyst. The same thing happens in an online-only environment. I can’t count how many times I sat with and coached truly brilliant Web analysts inside of organization who are talking through a data-backed hypothesis they are working through from Web analytics data, observing and measuring behaviors and drawing inferences … and they haven’t looked at the specific screens and treatments on the website or mobile app where those experiences are happening. They are disconnected from the consumer experience. If you look in your organization, odds are you’ll find examples of this kind of disconnect.

So Does The Web Compete with Retail Stores? Well, that depends.
While many businesses are seeing the same shift to digital consumption and engagement, especially on mobile devices, the evidence is clear that it’s a mistake to assume that you have a definitive answer. In fact, it is virtually always a nuanced answer that informs strategy and can help better-focus your investments in online and omnichannel marketing approaches.

In order to answer this question you need a singular view of a customer. Sounds easy, I know. So here’s the first test if you are ready to answer that question:

How many customers do you have?

If you don’t know with precision, you’re not ready to determine if the Web is competing or “cannibalizing” retail sales.

More often than not, what you’ll hear is the number of transactions, the number of visitors (from Web analytics) or the number of email addresses or postal addresses on file—or some other “proxy” that’s considered relevant.

The challenge is, these proxy values for customer-count belie a greater challenge. Without a well-thought-out data blending approach that converts transaction files into an actionable customer profile, we can’t begin to tell who bought what and how many times.

Once we have this covered, we’re now able to begin constructing metrics and developing counts of orders by customer, over time periods.

Summarization is Key
If you want to act on the data, you’ll likely need to develop a summarization routine—that is, that does the breakout of order counts and order values. This isn’t trivial. Leaving this step out creates a material amount of work slicing the data.

A few good examples of how you would summarize the data to answer the question by channel include totals:

  • by month
  • by quarter
  • by year
  • last year
  • prior quarter
  • by customer lifetime
  • and many more

Here’s The Key Takeaway: It’s not just one or the other.
Your customers buy across multiple channels. Across many brands and many datasets, we’ve always seen different pictures of the breakout between and across online and retail store transactions.

But you’re actually measuring the overlap and should focus your analysis on that overlap population. To go further, you’ll require summarization “snapshots” of the data so you can determine if the channel preference has changed over time.

The Bottom Line
While no one can say that the Web does or doesn’t definitively “cannibalize sales,” the evidence is overwhelming that buyers want to use the channel that is best for them for the specific product or service, at the time that works for them.

This being the case, it is almost inevitable that you will see omnichannel behaviors when your data is prepared and organized effectively to begin to see that shift in behavior.

Oftentimes, that shift can effectively equate to buyers spending more across channels, as specific products may sell better in person. It’s hard to feel the silky qualities of a cashmere scarf online, but you might reorder razor blades only online.

The analysis should hardly stop at channel shift and channel preference. Layering in promotion consumption can tell you how a buyer waits for the promotion online, or is more likely to buy “full-price” in a retail store. We’ve seen both of these frequently, but not always. Every data set is different.

Start by creating the most actionable customer file you can, integrating the transactions, behavioral and lifestyle data, and the depth that you can understand how customers choose between the channels you deliver becomes increasingly rich and actionable. Most of all—remember, it’s better to shift the sale to an alternative channel the customer prefers, than to lose it to a competitor who did a better job.

Omnichannel Customers Are 2X as Valuable – How to Make Them Yours

With so many trying to sort out an “omnichannel” marketing strategy, I thought it would make the most sense this month to provide some structure around what it is, the best way to take the “buzz” out of the term, and provide a framework for thinking strategically about this new mandate in marketing and strategy. For starters, here’s a simple idea, or “true north,” you can use to drive your own marketing strategy as you embrace the omnichannel consumer. “Put the Customer First” and build your “omnichannel strategy” around them.

With so many trying to sort out an “omnichannel” marketing strategy, I thought it would make the most sense this month to provide some structure around what it is, the best way to take the “buzz” out of the term, and provide a framework for thinking strategically about this new mandate in marketing and strategy.

For starters, here’s a simple idea, or “true north,” you can use to drive your own marketing strategy as you embrace the omnichannel consumer. “Put the Customer First” and build your “omnichannel strategy” around them.

Let’s remember, connecting with, engaging and finding the right new customers are where customer value is created and realized in omnichannel marketing. Optimizing that value comes through studying and tuning communications, improving your relevance and becoming more creatively authentic, not in the boardroom, but in the eyes of your customer.

Today, marketers appreciate that consumers engage on multiple platforms, devices and channels—the ones they want, when they want. With mobile devices being a spontaneous window into their thoughts and an outlet for their wants and needs as they arise. What’s a bit more subtle and more often missed is the objective and capability to respect the way your customers choose to engage and buy across them in a scalable manner—as it will either fragment their relationship with your brand or galvanize it.

Consider Kohls. Not exactly a high tech player in most folks’ minds. However they now deliver an omnichannel experience that deepens relationships with them. Recently, my wife received a promotion by direct mail (I doubt if she remembers when they asked for her phone number the first time, making the connection between the POS and her online purchases), she had it in hand as she went to the website to browse. Later, she used another promotion from her email right at the POS with her iPhone.

In a single engagement with the brand, she hopped across three channels, not including a customer service call by phone. As a consumer, she didn’t even notice—she just expected it to work.

Similarly, OpenTable will consistently get you to a good restaurant based on where you’ve dined before, and what your current online browsing and mobile location is. You probably do it all the time. Your relationship with that brand hops between mobile, desktop and point of sale effortlessly—but as a consumer, you’re not exactly impressed: You expect it to work.

As a result, effective omnichannel organizations have become “stitched into” the lifestyles of their customers. Moreover, this supports the creation of competitive advantage in the measurable, trackable, digital age.

Omnichannel Means Understanding the Customer
Putting the customer first obviates really knowing and understanding your customer in more meaningful and actionable ways. Not just with an anecdote of the “average customer,” but with legitimate, fact-based methods that are built on a statistical and logical foundation. This is the basis for the “absolute truth” that your omnichannel source is dependent on.

This, too, is no small task for many organizations, but it’s becoming more “doable.” And it has to be—because your competition is thinking and investing in this path, and it’s not a long-term, viable position to not have an actionable strategy to miss the boat on knowing your customer in a way that is valuable, actionable and profitable.

But first, let’s clear up some of the confusion that we’ve been hearing for at least a year now: Is omnichannel more than the buzzword of 2015, or is it something much more important?

Multichannel
At the most basic level, “multi” means many. As soon as you adopted your second or third channel, be it a catalog or an e-commerce website, your organization became a multichannel organization. Multichannel came quickly—as it’s not uncommon that the majority of a customer base has made a purchase across more than one channel—whether you have that resolution or not is another matter, and often requires a smarter approach to collection.

Digital growth is accelerating channel expansion. With the explosion of online and digital channels and the rapid adoption of mobile smartphones, tablets and now wearables, digital can no longer be viewed as a single channel. We now have the merging and proliferation of digital, physical and traditional channels.

Many marketers have experienced as much challenge in juggling an increasing number of channels as there is opportunity. But digital channels, of course, are more measurable and challenge the traditional approaches by bringing a greater resolution and visibility for some, and confusion for others.

Key factors in leveraging, managing, and maximizing those channels include:

  • Competencies developed in the organization
  • Identifying third-party competencies, especially in digital partnerships
  • The culture of the organization
  • Support for change and innovation in marketing
  • The depth of technical capability in an organization

As channel usage expands, data assets “pile up,” though most of the data in its raw format is of limited practical use and less actionable as one would hope. From the inside of dozens of IT organizations, the refrain is common; “We’re just capturing everything right now.” Creating marketing value would require strategists and the business units.

Omnichannel Is the Way Forward
While most organizations are still working through mastering their channels and the data they perpetually generate, the next wave of both competitive advantage and threats have come with them. The customer learns what works for them relatively quickly and easily, adopting new channels and buying where they want, how they want. Those touches are often lower touch, and introduce intermediaries, and are surrounded by contextual advertising, often from competitors.

Omnichannel buyers aren’t just more complex, they are substantially more valuable. We’ve seen them be as much as twice as valuable as those whose relationship is on a single channel. Perhaps this a reflection of the greater engagement with the brand.

Delivering that omnichannel experience will require more thought, focus and expertise than before. It requires the integration of systems, apps and experiences in a way that’s meaningful—to the customer—and that of course requires an integration of the data about those purchases and experiences.

To serve the business, the Omnichannel Readiness Process has six components, each of which require thoughtful consideration:

1. Capture—many organizations are aware that they need to capture “the data.” The challenge here is shifting to what to capture, and what they may be missing. The key challenge is: It’s impossible to capture “everything” without understanding how it can and should be used and leveraged. How that data is captured in terms of format and organization is of great importance.

2. Consolidate—In order to act on the omnichannel reality, we must have all our data in one place. In the ongoing effort to find the balance between cost, speed and value, “silos” have been built to house various data components. Those data sources must be consolidated through a process that is not quite trivial if those data sources are to create value in the customer experience and over the customer lifetime.

3. Enhance—Even after we’ve pulled our data together into an intelligent framework and model, built to support the business needs, virtually every marketer is missing data that consumers generally don’t provide, or don’t provide reliably on a self-reported basis. “Completing the customer record” requires planning and investing in appropriate third-party data. This will be a requirement if we’re to utilize tools and technology to mine for opportunity in our customer base.

4. Transform—much of the data we need to perform the kinds of analysis and create the kinds of communication that maximize response now, and the customer value over time, utilizes the derivation of new data points from the data you already have. Here is one example: Inter-order purchase time. Calculating the number of days between purchases for every customer in your base allows you to see whose purchase cadences are similar, faster, slower or in decline. On average, we’ll derive hundreds of such fields. This is one example of how a marketer can “mine” data for evidence of opportunity worth acting on and investing in.

5. Summarize—The richest view of a customer with the best data in its most complete state is a lot to digest. So to help make it actionable, we must roll it up into logical and valuable cohorts and components. Call them what you will—segments, personas or models—they are derivative groups that have value and potential that you can act on and learn from.

Many marketers traditionally spend 80 percent to 90 percent of their time and effort on getting their data to a point where it serves both the omnichannel customer and their brand. However, marketers can do better with emerging tools and technologies.There is no replacement for solid data strategy that is built around the customer, but efficiencies can be gained that speed time-to-value in an omnichannel environment.

6. Communicate—The prep work has been done, you’ve found the pockets of opportunity, now it’s time to deliver on the expectations the omnichannel customer holds for marketers. At this juncture, we need to quickly craft and deploy messages that resonate in ways consumers will think about their situation and your brand. They must address the concerns they have and the desires and opportunities they tend to perceive.

Omnichannel customers expect you will recognize them for their loyalty and their engagement with your brand at multiple levels, and that those experiences will be tailored in small ways that can make a bigger difference.

They expect your story to better-fit with their own, if not complete it. That sounds like a dramatic promise, but the ability to know your customers and engage them in the way they prefer, and at scale, is upon us.

Keep It Relevant to Your Business
This entire process must include of course, the answers to key business questions about the types of discoveries we’d make and questions we’d answer with it—for example, does the Web cannibalize our traditional channels? (Hint: It surely doesn’t have to).

That said, we’ve learned to start with the most basic questions—and are not surprised when there are no robust answers:

1. How many customers do you have today?

2. Do you have a working definition of a High Value or Most Valuable Customer?

3. If so, how many of those customers do you have?

4. How many customers did you gain this past quarter? How many did you lose?

a. Assuming you know how many you lost, what was the working definition of a lost customer?

5. How many customers have bought more than once?

6. What’s the value of your “average” customer, understanding that averages are misleading and synthetic numbers are not to be trusted? But we can measure where other customers are in terms of their distance from the mean.

7. Who paid full price? Who bought at discount? Who did both? How many of all the above?

8. For those who bought “down-market,” did they trade up?

9. How many times does a customer or logical customer group (let’s call them “segments,” for now) buy? How long, on average, is it between their purchases? And the order sizes, all channels included?

10. All this, of course, gets back to understanding more deeply, “Who is your customer?” While all this information about how they engage and buy from us is powerful, how old are they? Where are they from? What is relevant to them?

Now, even if a marketer could get the answers to all of these questions, how does this relate to this “Omnichannel” Evolution?

Simple. It only relates to your customer. Of course, they are the most important actors in this business of marketing—in fact in the business of business. What this really means is deceptively simple, often overlooked, and awesomely powerful:

Omnichannel Is Singularly Focused on Customers, Not Channels
It’s about the customer, and having the resources, data and insights at your disposal to serve that customer better. Virtually all of your customers are “multichannel” already. Granted, some are more dominantly influenced by a single channel. For example, online through the voice of the “crowd.” But even then, the point of omnichannel only means one thing: Know your customers across all the channels on which they engage with you. Note the chasm between having the dexterity to examine and serve customers across all the channels, and just knowing their transactions, behaviors or directional, qualitative descriptors.

So “knowing the customer” really means having ready access to actionable customer data. Think about it. If your understanding of your customer data isn’t actionable, how well do you really know your customer in the first place?

Considering the 10 questions above, and evaluating the answers in terms of the most important questions about your customers, is a solid starting point.

When you’ve worked through all of these, you’re now ready to create experiences and communications for customers that are not only relevant, but valuable—to your customer and to the business.

When you’re adding value and are channel-agnostic, as you must become, you’ve achieved the coveted omnichannel distinction that market leaders are bringing to bear already.

Not only is this an impressive accomplishment professionally, it surely is—but remember—it’s the customer we have to impress.

Data Athletes in Modern Organizations

Let’s look at the ideas, insights and strategies for becoming what I have termed a “Data Athlete.” This term has evolved during the many years I have been involved with training and developing exceptionally smart creative analysts. These professionals have a high aptitude and passion to solve big data challenges and possess the dexterity to leap from the intellectually engaging problems to the immediately actionable digital media plays that yield a high ROI. I have found smart analysts love this term—they enthusiastically consider it a badge of honor in making it to the major leagues, where they solve complex marketing problems and optimize campaigns.

Let’s look at the ideas, insights and strategies for becoming what I have termed a “Data Athlete.” This term has evolved during the many years I have been involved with training and developing exceptionally smart creative analysts. These professionals have a high aptitude and passion to solve big data challenges and possess the dexterity to leap from the intellectually engaging problems to the immediately actionable digital media plays that yield a high ROI. I have found smart analysts love this term—they enthusiastically consider it a badge of honor in making it to the major leagues, where they solve complex marketing problems and optimize campaigns.

I’m sharing all of these learnings with you, as organizations are under ever greater pressures to change in a world that only grows more digital, and in the process is generating more and more data at a blinding pace. Keeping up will require a shift in thinking about businesses, marketing and data—and of course its value, or lack thereof. This will require you and/or your team to become or be more of a Data Athlete to compete in an ever more digital world.

What is a Data Athlete?
Like any athlete, a Data Athlete is competitive. If you’re striving to become or to be more of a Data Athlete, competitiveness is important. Data Athletes compete with the norm—challenging it and outperforming it. They also challenge all assumptions, opinions and even the data they work with. Nothing’s too sacred not to inquire, challenge and test.

Most importantly, Data Athletes build brands by creating solutions based on the evidence and the impact. They seek to affect change based on the impact it will realistically have. They methodically create the future and its outcomes.

Data Athletes have that internal drive to solve and to accomplish. Contrast this with the kitschy T-shirts at the Google Developers Conference that say “data nerd” (disclosure, I have one myself). Data Athletes aren’t interested in tech for tech’s sake, or data for data’s sake.

Data Athletes Don’t Come From Traditional IT Structures
Traditional IT organizations may have staff entirely comfortable with data, having spent entire careers working with databases—building and maintaining infrastructure, building cubes, reports, integrating systems and data sources, and performing the necessary “care and feeding.” Until very recently however, traditional IT and marketing have organizationally been far apart. Bridging that gap may realistically take years in some organizations. The cultural differences between Athletes and Traditional IT aren’t trivial, and they are well-founded. IT has, for decades, been focused on stability, consistency, repeatability—command and control and gradual cautious change.

Data Athletes, on the other hand, will seek to fail and fail fast, test and learn. They require an environment that is not only tolerant of, but embraces the rigorous, ambitious development of multiple hypotheses informed by customer data, rapid testing of those hypotheses, and speedy implementation of those tests—quickly weeding out the ideas that don’t work through a data-driven system of meritocracy and speed. Gumming up that value creation process through a traditional IT process and “queue” stifles the innovation and positive change. Data Athletes often have engineering backgrounds—and have little patience, as they know the cost of slow and lumbering improvement, or lack thereof.

Not surprisingly, Data Athletes don’t come from traditional IT departments, even though many come from software engineering, front-end development, Web analytics and data science. They bring direct marketing logic and understand how brands are built. They enjoy marketing and they are creative—they challenge marketing that “can’t” be measured and improved.

So while the circa 2015 Data Athletes has a deep appreciation for traditional IT and the back office, they are different from traditional IT in critical dimensions. Data Athletes are typically driven to engage, communicate and connect with the end customer at scale, where traditional IT tends to serve corporate management and internal customers.

So, why is it so difficult to cultivate an environment that nourishes and rewards data athletes? Why are some large organizations with abundant operational reporting capabilities slow to address the evolving needs of the more digital, “big data” marketplace?

Let’s answer these questions and discuss how companies can move the ball downfield with the help of data athletes, our future organizational stars, and thinking about your level of fitness as a more “data athletic” organization.

Here are four major considerations in the era of the Data Athlete as a mission-critical team member:

1. Data Athletes Differentiate Quickly Between Reporting and Analytics
More than 90 percent of the analytics programs I’ve looked at, specifically in Web analytics, are little more than reporting programs. Visits, clicks, time on site, sales, etc. All good. All interesting, and all are short on actionability.

2. Actionability Is The Data Athlete’s Priority
Successful businesses have the habit of tracking progress over time. It’s often driven by the CFO’s office. All rhythms drive from those operational metrics: sales, units sold, turnover, etc. They have reports on top of reports. No small effort or expense is required to make those reports and answer questions based on them. These are good for business. They also can shape a culture, a culture of looking at the same things. A culture of reporting.

A “report-driven” culture isn’t all bad. Maintaining that continuity of reporting over time doesn’t, in itself, address new challenges, new consumer behaviors, the impact of Pinterest on your customer relationships, or the threat of a new intermediary who’s putting pressure on you and driving up your acquisition costs. These things affect those top-level, “operational” numbers driven by that reporting. By the time they really hit the reports hard enough, you’re already behind, which sets up “fire drills” and suffocates marketing strategy. The direction is oftentimes driven by opinions. More about that in a moment.

Reporting by definition is reactive, where analytics is really driving the creation of strategies to affect change.

3. HiPPOs Usually Aren’t Athletes.
This isn’t the “hippo” at least some of you were thinking of …

A HiPPO is the “Highest Paid Person’s Opinion.” You probably know from experience how often the HiPPO in the room has an opinion—and challenging it isn’t easy. Or maybe you are the “HiPPO” in the room, at times. HiPPO-dominated organizations don’t need evidence that data provides. They don’t assess the impact of decisions with data, either.

HiPPOs often come from backgrounds where data and evidence are non-existent or primitive. Their ideas are rarely tested or proven, they are qualitative and only shoot straight from the hip.

In comparing Amazon to JCPenney, Fortune described Amazon’s perspective on HiPPOs as “leaders who are so self-assured that they need neither others’ ideas nor data to affirm the correctness of their instinctual beliefs.” HiPPOs sometimes frown on using data to inform and shape a business, labeling anything that seeks to create business model scalability through the intelligent use of customer data as “analysis paralysis.”

HiPPOs miss the fact that Data Athletes don’t just gorge themselves on data, they actually loath excessive unusable data and the overhead that comes with it.

An Athlete does not believe in data for data’s sake. They know what they need, and what they can do with it.

Instead, they see the HiPPO’s experience and knowledge as a source to shape problem definition. They validate the opportunity and problem with the right data. Without strong and accurate problem definition, it’s hard for anyone to effectively choose what data matters and what can be thrown away.

If you have these smart data athletes in your organization, don’t be a HiPPO and trample them—for when you do, you miss opportunity.

If you hire smart Data Athletes, it’s a business risk to ignore them. When you do, you’re under-leveraging and you’re not learning and growing yourself.

How Does This Help a Marketer?
First, think about your own organization, your own challenges, and evaluate if you’re dominated by HiPPOs or if you’re leveraging Athletes in your organization. It’s hard to debate if you need them anymore—you do, and you will. Partner with the Athletes in your organization, and you’ll begin the process of performing at an advanced level.

In future articles, we’ll discuss more specific strategic approaches and tactical executions that can help you execute and become more of a Data Athlete and introduce this unique type of “athleticism” to your organization.