Is Data-Driven Decision-Making (3D) at the Heart of Your Marketing Organization?

This dissonance between the realities of reporting and the expectation of P&L accountability led my colleague Justin Yopp and I to develop a straightforward process to transform the marketing department at The Pedowitz Group into a data-driven, decision-making organization.

In last month’s blog post we discussed the first of the five core marketing processes essential to effective and efficient marketing operations — the lead management process. This was a great lead-in (no pun intended) for this month’s post on the reporting and analytics processes. Most firms, when they first dip their toes into the results reporting waters, look to report on their funnel results which depend entirely on having a functioning lead management process.

But, before we start, let’s agree on two definitions.

  • Activities Reports: opens, clicks, visits, CTRs, visitors, duration, bounce, deliverability, etc.
  • Results Reports: New leads, MQLs, SQLs, opportunities, renewals, new customers, bookings, Revenue, CLV

We are here to discuss the processes around producing regular, accurate, useful results reports that enable an organization to make better marketing decisions and evolve into a data-driven decision making organization.

As a CMO, I don’t have much time for activities reports; I need to understand how my department is impacting revenue on a regular basis.

Our learned colleagues at Gartner tell us that 76 percent of marketing organizations are accountable, or share accountability, for a P&L. But Aberdeen tells us that in reality only 19 percent of marketers have comprehensive tracking and reporting practices in place. Wow! There can only be one outcome if this significant gap between expectation of accountability and reality of reporting is not quickly eliminated — CEOs will be calling in United Airlines to accommodate marketing executives.

6 Steps to Build a Results Reporting Function in Marketing Operations

This dissonance between the realities of reporting and the expectation of P&L accountability led my colleague Justin Yopp and I to develop a straightforward process to transform the marketing department at The Pedowitz Group into a data-driven, decision-making organization.

Justin shared that “far too often, we see firms rush out and buy the latest in reporting software as if it were a panacea for all their reporting ailments.” We also see CMOs pursuing vanity metrics – metrics that “prove” the value of marketing instead of empowering better decisions for better performance. If you take nothing else away from this blog, please avoid being fascinated by the latest shiny software object, and do not focus on producing vanity metrics first.

Here is the process we developed for creating a results reporting function.

Recognizing the need for data-driven decision making.
Recognizing the need for data-driven decision making.

Step 1. Decide what you want to measure.

This is not as straight forward as you might imagine. You need to engage with the marketing team, determine their key performance indicators (KPIs), and find out what decisions they want to inform with these metrics. Are they trying to become more efficient, more effective, and redirect resources and budget? Are you going to measure marketing influence, and is there an attribution model for that?

Now prioritize these metrics and KPIs based on impact to the business. Notice we did NOT discuss how to measure them; where to measure them; and broader data, process and system requirements. That comes later!

Step 2. Determine what reports you will need to measure the KPIs and metrics you selected.

With the metrics and KPI requirements in hand, determine what set of reports are required to effectively provide that data. What are the parameters and dimensions that define these reports? In what systems will the reports be generated?

Step 3: Identify the data, processes, and systems that you need to create the reports.

Are the fields present and collecting data currently in the systems you wish to run the reports in? Are new integrations or file transfers required? Do your teams currently update the data in a reliable way? What data import processes need to be redesigned to support these reports? Will your existing technology stack support the reporting you need for effective decision-making? Will you use Excel, a marketing automation platform, a CRM, or some combination of all of these to produce the reports?

Step 4: Commission someone to build the reports.

Don’t send the initial reports to a wide audience because it is highly likely you will uncover unknown data issues and process issues. Also, there is a risk that the reports won’t conform to a widely held view of how things are, and you need to be pretty sure the data and the reports are spot on before you start that battle. You don’t want to lose reporting credibility just as you are getting started.

Step 5: Determine how to interpret the reports to generate meaningful insights.

Reports alone will not provide the answers and insights necessary to make better decisions. You need these reports visualized and contextualized to facilitate analysis. What dimensions should be used to slice the data into something meaningful? What time series should be used to present the data to unveil trends — week over week, month over month, quarter over quarter? Which metrics and KPIs should be presented together? In this step you’re doing the critical work of making your data consumable.

Step 6: Drive the change to becoming a data-driven decision making organization.

Start the cadence of daily, weekly and monthly reporting. Push your teams to provide data evidence to support their claims and decisions. Model this same behavior yourself. Also, be prepared to adapt the reports to what the teams really wanted in order to make decisions (there’s nothing like seeing the report you asked for to help you really understand what you wanted). This regular distribution and consumption of the reports is critical to truly embedding data-driven-decision making into your marketing organization.

Executive Leadership and Support for the Change

Getting teams to the point of analyzing the data and making data-based decisions on a daily basis will not happen just organically. Leadership has to lead in this practice and drive it down into the organization. Provide the training and resources necessary to bring the team up to speed. And allow time for the organization to acclimate to these new expectations.

It is a satisfying experience to attend a marketing status meeting and hear the broader team excitedly talking about changing their immediate plans based on last week’s or last month’s significant shift in a trendline.

Components of Your Results Reporting Process

  • Metrics/KPI requirements — What decisions do they want to make based on the data?
    • What is the right frequency for updating those decisions?
    • What is the context for the data to support a good decision (comparison data?)
  • Data Sources: what data is needed to support those decisions and metrics?
  • Reporting technology: Where are the reports run? Do these systems support the output desired, subscription and distribution, have access to all the data required? Does your data architecture support running the reports you want in specific systems or was reporting a forgotten afterthought?
  • Roles: Who owns, maintains and modifies the reports? Don’t assume that the report author is the same person who is familiar with the data — the data czar. Also the person who interprets the report might be yet a third person — a marketing business analyst. All 3 roles may need to be in the room when an important data-driven decision is being made.
  • Standards: If your report creation is centralized it may not be as big an issue, but if you are global, and want to be able to roll up marketing reports from each of the regions, you have to drive standards for data collection, report building, naming, and presentation. The same business rules (think filters on the data) need to be applied and visible on the printed reports so people know they are comparing apples to apples.
  • Media: What media will you use for delivering which reports? Are they online, emailed, PDF, Excel, Business Intelligence (BI) system, or MAP/CRM?
  • Distribution: How can individuals subscribe and unsubscribe. How can you secure confidential reports? Restrict access and printing?
  • Archival: Many firms don’t have a BI system and as a result some reports, which are snapshots of the results at an instant in time, are the only record of the data at that instant. Ie the same report cannot be re-run a month later, showing the exact results at that prior time for comparison to the current run. As a result there may be a need to archive all “runs” of a report, for comparison purposes in the future. How is this done without a BI? Build your own SQL database or use Excel? Obviously this can be laborious so it deserves attention and planning to keep it manageable.

Guiding Principles for Data-Driven Decision (3D) making

Data-driven decision making requires a set of guiding principles to be effective.

  • The driving purpose of reporting is to enable data-driven-decisions.
  • Do not succumb to ego/vanity reporting.
  • Create reports that empower better decisions at the lowest levels – democratize decision making.
  • Engender an inquisitive and investigative nature in your organization.
  • Leave room for exploration and discovery of patterns.

The 4 foundation pillars of Data-Driven Decision MakingNext Steps

You are probably already getting results reports. So ask yourself:

  • Which of my teams are using these reports, how often, to make better decisions?
  • What decisions are the reports informing?
  • Are they vanity reports or reports for helping improve marketing performance?

If the reports aren’t driving any decisions or monitoring the health of a process, toss them and go to step 1 above! Audit all the reports in this way. Stop creating reports that no one uses for making decisions or confirming normal operation. My colleague Justin Yopp, who originated many of the ideas in this post also coined the phrase “Become a 3D marketing organization” – meaning Data-Driven Decision making. Are you a 3D organization? How did you do it? What were the biggest hurdles? Here are several examples of marketing dashboards that illustrate 3D thinking.

In step three above, we observed that the data and systems had to support our desired reports. This is a perfect segue to the next post on our Revenue Marketing Journey. Next month we will discuss the various parts to a data operations function within marketing operations and the associated processes.

Please feel free to share your experiences becoming a 3D marketing organization and other insights on the above topics in the comments section below or email me at kevin@pedowitzgroup.com.

Nightmares, Ghosts and Terror in Data Land

When you come fresh from a large industry conference — such as DMA’s &Then16 — where you have lots of conversations and learn about lots of pain points, you’re highly motivated to put those winning ideas to work on solutions. Most of these solutions require access to data and handling it responsibly to make smarter marketing decisions — for the ultimate service to customers.

marketing Data graphicWhen you come fresh from a large industry conference — such as DMA’s &Then16 — where you have lots of conversations and learn about lots of pain points, you’re highly motivated to put those winning ideas to work on solutions. Most of these solutions require access to data and handling it responsibly to make smarter marketing decisions – for the ultimate service to customers.

Today, it’s Halloween, so here’s my own nightmare.

I wake up one morning and the entire world collectively lost its mind and governments have mandated a marketplace that’s totally (and only) opt-in for all types of marketing uses that are only helpful to consumers. There’s no more algorithms and no more “discovery.” All commerce must wait and wait and wait until a consumer asks for it. Particularly egregious online.

Marketing collectively goes dumb. Oh, I love pure branding, pure creative — but take data out of the equation, we’re truly back before the dawn of direct marketing. Not 20 years back. Not 50 years back. But 100 years back.

Entrepreneurship is destroyed. There’s no way to tap a niche market. Data is off limits. Everything is opt-in. Opt-in request here, opt-in request there. Think Europe and cookies — ask, ask, ask. Before long, we’re numb. And, except for big business, there’s no budget for blanketing the world with awareness advertising. (And why would even a big brand want to waste so much of its money?)

Websites get clunkier. You can’t even get past the home page without having to click on a permission (again, read Europe). All because some nanny-types who control policy decided consumers are stupid and have to be protected from being tempted to make purchases that generate sales, jobs, tax revenue — and, by the way, happy customers.

Everything becomes more expensive and, without the commercial availability of data, there’s a lot less of “everything.” Why? Because advertising and smart advertising (read, data), finances content, services and conveniences — gone, gone and gone. Nobody in regulatory land bothered to ask who was paying for the Internet. No one ever bothered to understand the economics of the Information Economy. No one ever understood that AdTech, MarTech and data-driven marketing had become one of the greatest of U.S. assets and exports, and Silicon Valley’s (Silicon Beach, Silicon Prairie, Silicon Alley, etc.) highest rewards.

The range and diversity of consumer marketplace choices disappear. Constantly asking for permission becomes deafening. Thus, data eventually wanes and is off limits. There’s no way to derive insights to build better products, no way to devise better services and no way to compete in a healthy, competitive marketplace with a better idea.

The Information Economy is maimed — only a concentrated few, behind huge walled gardens, get to “own” and use the data. We just inflicted upon ourselves the greatest harm. We gave up the golden chalice, handled with care, for a tin cup. Beggars all of us.

Less choice. Less informed. More expensive. And, the consumer is left poorest of all.

It’s Halloween morning. Somebody woke me up.

Relevancy, the Currency of Conversion

More. The marketer’s mandate will always be “more” — more traffic, more sales, more margins. Add to it that in order to get more, we’ll need to test more ideas, try new strategies, new media and mediums — not all of which will work.

Oliver Twist moreMore.

The marketer’s mandate will always be “more” — more traffic, more sales, more margins. Add to it that in order to get more, we’ll need to test more ideas, try new strategies, new media and mediums — not all of which will work.

More ultimately means sales conversion, and there’s a data-driven approach to getting more that leverages a new currency. Not Bitcoin, but relevancy, because relevancy is the “Currency of Conversion.” That conversion currency is based on the intelligent use of data.

Truly accomplishing data-driven success requires focus and simplifying — one of the few constants in business marketing.

Through advising dozens of organizations on the intelligent use of data to inform and improve performance, it is often helpful to come back to some of the fundamentals in thinking about the application of our data to business problems. And while often we focus on the “what” that has to do with data, let’s consider perhaps the most important question — “Why?”

Why Should I Inform Marketing With Data?

While it’s likely considered risky nowadays to lack a data strategy or better yet, a data-driven strategy, we do need to ask why. I’ve been surprised at the lack of fluency even experienced IT people and all kinds of marketers have when asked why they need to invest in data strategies. That’s despite the “reality” that everyone knows they “should.” Let’s deal with that.

  • Reporting. Many organizations still desire better reporting, Key Performance Indicators (KPIs) being the most important. It’s a baseline use of data, and it’s important. So data serves a purpose and provides consistent, specific solutions to the questions “how are we doing?” It’s hard to operate without it, but it should become “table-stakes” in short order.
  • Analysis and Insights. Data, if organized and governed reasonably well, can yield insights. This requires you have an analyst with a big brain to pore through it. The analyst needs to know enough about your business to understand what is relevant and what is not. The analyst must also consider materiality and the difference between correlation and causality.

This last point being an all-too-common mistake. For example, “our customers are rich” so we need to target rich people. Being affluent may be correlated with buying your product, but it may not be causal! We’ve found this example many times when actually statistically testing to see what attributes have the most causal/predictive relationship. For a full study on causality vs. causation, see this piece from Stats.org.

  • Customer Intelligence. Customer Intelligence is the next-level beyond analytics. In CI, we now use purpose-specific algorithms to derive new data and to identify valuable patterns that arise in large amounts of data. It’s fair to call it “the union between marketing and data mining.” Customer Intelligence provides us the answers to questions we don’t ask because we don’t know how to answer them.

The Most Important Reason to Inform Marketing With Data

The low cost of communicating digitally has, in some cases, left relevancy underrated. This is no coincidence. When you spend real money to send a quality, brand-appropriate direct mail piece or even more money on television — you care a lot about relevancy. This message has to be right, it has to be on-brand, it has to resonate. Today, that mass-market TV ad isn’t a winner if it doesn’t “break the Internet.”

But when it’s an email that costs a fraction of a cent to deploy and just a few fixed dollars to create amortization over millions of recipients, we as marketers can get impressively lazy. Relevancy is trumped by low cost and high ROI. Who cares if the message is perceived as irrelevant? The email drop “worked.”

Let’s consider this further.

Let’s say the “less than relevant” drop had an out-sized 35 percent click rate. We know the sender names were likely those they anticipated email from, and the subject line was likely relevant. We can’t know the breakout of which send carried more weight without testing them. But if you subscribe to the school of thought that relevancy isn’t important, then testing probably is irrelevant to you, too. Before you decide “well, of course we think relevancy is important” — think about whether you’re really using it as a principle in your outbound marketing.

Marketing Machines — Possible or Pipedream?

True data-driven marketing is still “just a dream” for many marketers, rather than a reality. Under this vision, systems data-mine autonomously, and present fresh actionable insights at your desktop in the morning.

True data-driven marketing is still “just a dream” for many marketers, rather than a reality. Under this vision, systems data-mine autonomously, and present fresh actionable insights at your desktop in the morning.

For about 99 percent of marketers, this may sound too good to be true — and in all candor, it usually is.

But it is important to know and recognize that the intelligent application of mathematics and statistics, and the creation of purpose-specific algorithms, have been quietly creating value for years now. Yet the typical marketer still struggles to find enough time to get the mail out, or execute well-thought-out website marketing experiments against a control. (see “Analytics Isn’t Reporting”)

So there have never been more skeptics of the legitimate power of the intelligent application of data, even as the C-suite expectations of a data strategy that creates competitive advantage grows. Sound like your experience, industry or career? Sure it does.

But as investment continues to grind higher and competition grows, progress continues to be made.

The Amazon of Data, Is of Course, Amazon.
You may know that Amazon.com elected to release to the public some technology it uses internally in making recommendations and determining what you’d be likely to buy and when. Amazon took the same tool-set it uses and published it on Amazon Web Services. “Pretty neat” you might say …

Mike Ferranti infographic

Because we get so many questions about how Amazon does it, and how all of this actually works, we’ll break down the AWS Machine Learning and Prediction tool-set so that qualified organizations have an idea of what’s possible.

For the purposes of this article, a “qualified organization” is one that has development talent, experience with data and at least a basic working knowledge of statistical methods. Of course, experience developing models is very helpful, as well.

We call these “requirements,” because Amazon’s tools, and every tool like it (Google has a similar tool-set for the Google Cloud Platform) requires significant programming to use. They also have a learning curve for inexperienced developers and organizations that haven’t developed competencies in structuring and transforming their data to a treatment that is readily ingested and workable with these tools.

What AWS Tools Do
AWS offers a “Machine Learning” and “Prediction” tool-set. These are two related components. Machine Learning is used to ingest large amounts of data and identify patterns in that data. A typical example is extracting promotional history and responses, and utilizing it to identify what customers are most likely to respond to a marketing promotion or offer.

When Should You Use Machine Learning and Prediction?
Generally speaking, machine learning works best when a simple “logic-based” algorithm doesn’t work, or doesn’t work consistently. Simple (or even complex) logic defines a set of rules or requirements for a decision the algorithm makes to be determined. This is also called a deterministic or rule-based approach.

If there are a lot of variables, say hundreds or more — you can’t realistically develop “brute force” rules that cover every scenario that you’d need to create value. You may determine a favorite color of a buyer with a simple rule that says if the majority of their purchases are in red, then they like red. But each purchase is influenced by more than just color… there is style, season, price and category of product, material, size and discount, to name a few. As the permutations of these combinations of variables grow more complex, a simple deterministic rule-based approach can break down, and make a prediction that doesn’t work more and more of the time.

If and when business rules begin to collide with one another and discrepancies require more rules to manage these logical collisions, Machine Learning can help sort through your data in ways rule-based algorithms cannot.

“In short, you can’t realistically create or code all the permutations and business logic costeffectively.”

If your data set is very large and the diversity of variables you have is high, any “brute force” approach is destined to fail. Running through a set of rules on a sample of a few thousand cases may still work. Now what if you have millions of raw records? This can be possible even without a multi-million record customer file, given we may be looking at the colors and other attributes of items purchased during a period of years. Machine Learning can help make the task scaleable, and when you’re using Amazon’s computing power to do it, scale becomes the easy part.

Here’s An Overview of How the Prediction Process Works
So here’s an executive-level overview of how we use Machine Learning, and how it works if you build your solution on top of AWS, or Google’s developer APIs.

1. Problem Definition — Begin with The End in Mind: Here’s the step too many really don’t get right. If you’re going to venture into Machine Learning with AWS, or anywhere else, first you must define the core problems or opportunities you wish to pursue. You’ll have to do so describing that which you can observe (through your data) and an “answer” a model is expected to predict.

2. Data Preparation: Your data is going to go into a “training algorithm” where the tools will identify patterns in the data that will ultimately be used to predict the answers you’re looking for on a like dataset. Look at your data before it goes in. Be curious. Do some logical testing on it. If it is not adding up to the common sense “sniff test,” odds are very good it won’t add up later, either.

3. Transformation: Input variables and the answers you seek from models, also called the “target,” are not tidy such that they can be used to train an effective, predictive model. So you have some heavy lifting to do to get the data into new variables, “transforming” it to a more prediction-friendly input. For example, you may have a set of transactions that a customer had with your brand, but you need to summarize that into a count of transactions for that customer, and an average time between purchases. These two new fields will be more predictive and useful. A command of logic and statistics helps make these calls, as does experience.

4. Implement a Learning Algorithm: Your input variables have to be fed into an algorithm that can sort and find patterns in your data — also called a “learning algorithm.” These algorithms are specialized to help establish models (statistical relationships) and evaluate the quality of the models on data that was held out from model building.

5. Run The Model: We generate predictions against a new or holdout sample of the same format of the same source of data. You can’t run this predictive model on the same sample you used to build the model. This begins the iterative process

6. Iterate … Then Do It Again: As is any process where you’re engineering new outcomes for the first time, this process is generally iterative. It’s usually not realistic to expect a killer result on the first pass. You’ll likely massage inputs and training methods a number of times before the output starts looking good. More on what a good output looks like in a future column, though. For now, you need to know that the first product won’t likely be the final product.

The Bottom Line — Easier Still Isn’t Quite Easy for the Average Marketing Organization
While Amazon and Google may be among the easiest websites to use, and have made tremendous contributions to the proliferation of data science by providing structure and programming tools with which organizations can develop new capabilities, using Amazon AWS for Machine Language and Prediction is not for the creative marketer or even the “traditional” Web marketer.

There is also a rising category of upstarts in data-driven and database marketing apps that add intelligence to the process and can provide marketers with a significant head-start in advancing their marketing intelligence.

Data Science requires a combination of technical, mathematics/statistics and marketing/business skills. This combination is in great demand the world over, and so it’s not easy to hire top contributors to implement all of this. But for organizations with the programming bench, or external experienced business partners, tools like AWS and Google Cloud Platform can provide a substantial leap forward in using data to make superior decisions.

Remember, the outputs of the predictive process don’t have to be “right” 100 percent of the time — and they won’t be. They only need to make the numbers break in your favor enough to have a material impact on your revenue and profit now — and over time.

After all, that’s really what the data science discipline is really all about.

New Directions for B-to-B Data-driven Marketing

Okay, we’re in the maelstrom. But what is on the horizon for data-driven marketing? Here are some predictions, culled from interviews with several very bright observers who contributed to my new book, “B2B Data-Driven Marketing: Sources, Uses, Results.”

For us B-to-B marketers, the world is changing about as fast as we can stand. My head spins at the speed with which new tools, applications and approaches arrive on the scene. Where does this all come from? The Internet, of course, whose impact on business buying behavior has changed the game. As a platform for communications, for selling, for just about every element of the marketing arsenal, it is forcing marketers to think more carefully about customer and prospect data.

Okay, we’re in the maelstrom. But what is on the horizon for data-driven marketing? Here are some predictions, culled from interviews with several very bright observers who contributed to my new book, B2B Data-Driven Marketing: Sources, Uses, Results.

  1. More power and influence for marketing:
    The sales function has traditionally held the primary B-to-B revenue responsibility, leaving marketers with a history of frustration at their exclusion from a seat at the senior executive table. That is changing fast, as new tools and measurable communications techniques enable marketing not only to demonstrate financial results, but to take on revenue responsibility. “Salespeople are no longer the only rainmakers,” says Thad Kahlow, CEO of the digital agency BusinessOnLine. “Marketers today have serious revenue targets hanging over their heads.”
  1. B-to-C provides the inspiration:
    B-to-B marketing is rarely the leader in advancing data-driven marketing techniques. But it is adept at watching and taking up the new ideas from consumer marketers that apply to business buying, observes David Myron, editorial director at CRM magazine. One example is experiments with unstructured data, like that from social media, where consumer marketers are making headway. B-to-B marketers will likely search and analyze nuggets not only from social media but also from phone conversation content and email content, to identify buying intent, competitive interest and other actionable insights.
  1. The end of the database “build:”
    Digital marketers today are taking greater advantage of “real-time” data, delivering immediate responses to interactive behaviors between customer and marketer. Increasingly, the ability to manage such data points efficiently will make the traditional marketing database too stagnant and unresponsive to be useful. We are not there yet—the idea is still experimental. But the “always-on” future is beginning to be visible, where your storefront is always available for any kind of customer interaction.
  1. A simpler technology picture:
    Most marketing technologies claim to make marketer’s lives simpler. But at this point, marketing technology has become dizzying in its complexity. In the future, says Nitin Julka, product manager at LinkedIn, more and more of the complexity of running marketing campaigns is going to be automated, in a simpler way, so that marketers can focus on what truly matters—their target audiences, buyer’s journeys, and messaging.
  1. A sensible balance between data, insight and marketing strategy:
    “You can have all kinds of customer data, and still not understand how to communicate persuasively with customers and prospects,” notes Howard J. Sewell, president of Spear Marketing Group. “Software and analytics can’t tell you the what and the why.   We need to respect and harness what the data tells us, but also put it in its place.”
  1. Data is the business:
    The appreciation among stakeholders for the importance of customer data will continue to grow. “Data isn’t something we just have stored over there,” says Frank Cutitta, CEO of the Center for Global Branding, and professor at Northeastern University. “Data is the business. Companies that understand this are ahead of their competitors.”The Internet has been with us since the early 1990s, and by now it has impacted every scrap of B-to-B marketing.   But the next stage of its evolution will be to simply go away, as a thing in itself. Digital marketing will become so mainstream that it will be called simply marketing. We will no longer make a distinction between online and offline. We will understand and interact with customer from all sides, in a seamless whole. And the data will be the enabler of that relationship.

A version of this article appeared in Biznology, the digital marketing blog.

3 Obstacles in the Way of B-to-B Data-driven Marketing

Ask any business marketer about the importance of data, and you’ll get plenty of good answers. “It’s essential,” they’ll say. “Data drives everything we do.” And that’s a good thing, since marketers are under increasing pressure to manage, collect and make use of data, according to a recent CMO Club/Gartner study. But in my experience, answers like this are just lip service. Most B-to-B marketers really struggle to get their arms around the reality of customer and prospect information. There are at least three obstacles standing in their way.

Of particular importance to data and data-driven marketing are these issues:

1. Inattention to Data and the Database: While most senior marketers and other executives will pay lip service to the importance of customer information, it’s rare that they understand what is in their databases, and how to maintain and improve it consistently. Neither do they invest in the resources, human or otherwise, to manage the data properly. As noted by Derek Slayton, CMO of D&B/NetProspex, “Even companies with data scientists on staff tend to ignore the nuts and bolts of minding the database itself. It’s like they have the back pain, but they aren’t doing the exercises that would keep the pain at a manageable level.”

2. Organization and Process: Taking advantage of the power of customer data requires deliberate consideration of goals and measurement systems to manage the desired outcomes of effective data management. Jim Bampos, VP of quality at EMC, recently explained in DMNews that his group transformed their organization around data to enhance the customer experience. They built a business case, established a partnership with their IT counterparts, and created a roadmap for the systems needed for data access and analytics. Bampos credits enabling technology, organizational and process changes for their success in transforming the EMC’s Total Customer Experience program.

3. Everything Old Is New Again: Database marketing, also known as data-driven marketing, is being used across the B2B go-to-market process today — but it’s very likely called something different. It may be “predictive analytics,” or “CRM,” or “Big Data,” or zillion other buzzwords. So classically trained practitioners need to go with the flow and adjust to the new vocabulary. Ken Lomasney, COO of the agency UMarketing LLC provides a handy illustration of this phenomenon. With his clients, Ken never says “marketing database.” Instead, he says “knowledge platform,” to position the tool as something that provides real value, becomes smarter over time, and comprises an important company asset. A repositioning we might all learn from.

If you are reading this article, you are already convinced of the importance of data in B-to-B sales and marketing. As Alex Kantrowitz of Advertising Age puts it, data is the “new oil” that provides insight, efficiency and scale. For this century’s marketers, it is a new form of currency that gives marketing a seat at the executive table, and the ability to drive shareholder value.

A version of this article appeared in Biznology, the digital marketing blog.

 

Hey, Lawmaker: Marketing Moves Today’s Commerce, and Data Moves Today’s Marketing

Members of Congress, and even the White House, seem to forget or ignore that their very own campaigns depended on the flow of information about citizens and individuals and population segments to inform their campaigns. Their respective elections prove that data and marketing in concert are very effective, especially for incumbents. Yet listen to a few among our leaders, and you’d think data-driven marketing is a consumer privacy problem begging for a government solution

I’ll start this blog off with a disclosure: I’m a member of the Direct Marketing Association, serve and have served on various DMA committees, and I count the Digital Advertising Alliance and other data-driven marketing firms among my clients. In short, my livelihood depends on data-driven marketing.

Members of Congress, and even the White House, in good measure, seem to forget or ignore that their very own elections to office depended on the flow of information about citizens and individuals and population segments to inform their campaigns. Their respective elections prove that data and marketing in concert are very effective, especially for incumbents.

Yet listen to a few among our leaders, and you’d think data-driven marketing is a consumer privacy problem begging for a government solution. How they (some of them) ignore 40+ years of self-regulation success in data-driven marketing; U.S. leadership in information technology and its data-driven marketing application (they are not coincidental); and the economic powerhouse of jobs, sales and tax revenue that is created by data exchange for marketing purposes.

Research Proves Our Case … Again
In November, DMA and its Data-Driven Marketing Institute announced “The Value of Data” Study (opens as a pdf), which documented the economic impact: The data-driven marketing economy added $156 billion in revenue to the U.S. economy that fueled more than 675,000 jobs in 2012 alone. (Importantly, the study also provides state-by-state economic impact.) The full study is available here.

This past week, DAA announced results of its own commissioned research which focused on the value of digital advertising derived from data exchange—and its comparison to general ads online. The study reported that availability of cookies to facilitate information transfer increases the average impression price paid by advertisers by 60 percent to 200 percent. Additionally, ads for which cookie-related information was available sold for three-to-seven times higher than ads without cookies. Thus, the invisible hand of the market, once again, proves data’s value. The full study is available at http://www.aboutads.info/resource/fullvalueinfostudy.pdf.

We’ve Got Work to Do … with our Lawmakers
Yet President Barack Obama and Sens. Jay Rockefeller (D-WV) and Ed Markey (D-MA) might have Americans believe that National Security Agency surveillance of U.S. citizens, data breaches at retailers and other organizations, and data exchange to drive marketing is one big roll-up of the same issue.

We know they are not. Spying by government on its own citizens is an important civil liberty issue, and while I’m not a fan of Snowden hiding out, NSA revelations deserve a full debate on its own merits and threats. Data security extends far beyond marketing—and marketers and many lawmakers agree that we need one national data protection and breach notification standard (and not 50+1). Data-driven marketing is not a problem at all, but instead a huge boon to U.S. marketing success that depends on continued innovation and fair use of information principles, which deserves government support (or at least government staying out of the way).

Importing restrictive laws and regimes on data flows for marketing has the potential to ruin American commerce by killing relevance. At a time when consumers are becoming more skeptical of brands, the intelligent use of information to converse with consumers with resonance is a requirement of marketing smart today. Dumb marketing wastes resources, annoys consumers and frankly places us at a disadvantage globally. While culture around regions of the world is unique, I believe our sector-specific approach to privacy regulation based on consumer harm potential (credit, health, financial) is superior to omnibus privacy law (all personal data is the same) and has served our economy well. How terrible to find we have our own lawmakers who seem to fail to grasp the evidence. You can believe DMA, DAA and other advertising organizations are working hard to show policymakers the great value we create in the marketing profession.

Politicians sense moods … and read polling. In my next blog post, I’ll look at some of the perception challenges we face with consumers. Clearly, as much as consumers “consume,” marketing is not all that popular with some of them either. We have work to do with consumers, too.

Creeping Up Fast: DMA13 and Making Plans for Chicago

August 6 marked the mid-point of summer—so now we’re closer to summer’s end than summer’s beginning. It’s as if all the back-to-school advertising wasn’t enough to have us looking forward (except perhaps for schoolchildren). In the world of data-driven marketing, my mailbox reminded me this past week, too, that fall is just around the corner: I received a DMA2013 conference brochure mailer

The other day (August 6) marked the mid-point of summer—so now we’re closer to summer’s end than summer’s beginning.

It’s as if all the back-to-school advertising wasn’t enough to have us looking forward (except perhaps for schoolchildren). In the world of data-driven marketing, my mailbox reminded me this past week, too, that fall is just around the corner: I received a DMA2013 conference brochure mailer (October 12-17, McCormick Place West, Chicago). We’re eight weeks out from DMA2013, which means it’s time to start getting very serious, rather than spontaneous, in making our must-attend conference experience the best it can be. (Yes, I’m already registered—and you should be, too.)

For me, this is when I review the print brochure to dog-ear my go-to sessions based on the session titles, speakers and descriptions, and start the online process at MyDMA2013 (by Vivastream) to pinpoint an attempt at an “aspirational” schedule. I call this aspirational—let’s face it, when we get on site, business conversations inevitably happen, and diversions of all kinds are bound to take place.

However, there are some absolutes in my DMA13 calendar—and I’m hopeful you’ll agree.

1. Give Back
The first item isn’t even about DMA. It’s Marketing EDGE (formerly Direct Marketing Educational Foundation) and its Annual Awards Dinner (separate ticket required). This event has always been a go-to, but it’s also evolved to become the first, best networking opportunity for all of us as we gather at the DMA conference each year. These are the VIPs, roughly 400 leaders and future leaders in our business, and here is an organization where our proceeds bring the best and brightest into our field. What a powerful combination, and an affirmation of the future of data-driven, integrated marketing. Even if you don’t attend the conference, you can sponsor a professor’s attendance and make a donation at the aforementioned link.

2. What’s Next?
On Wednesday, Oct. 16, the day after the exhibit hall closes—I tell my clients that’s when the real learning begins. What do I mean by that somewhat on-its-face silly statement? That’s when the conference attendees—folks who are real serious about learning—are in the session rooms early, taking notes, and becoming better marketing professionals during the last half-day of sessions, and the post-conference workshops and day-and-a-half certifications. On that final day of the main conference, DMA13’s Main Conference Keynote panel at 11 am (all times Central), will feature “What’s NeXt: A Look through the Lens” with Direct Marketing Hall of Famer Rance Crain of Advertising Age interviewing BlueKai and foursquare execs Omar Tawakol and Steven Rosenblatt.

3. Stand Up
I’m a member of DMA for many reasons—but certainly advocacy is one of them. A lot of my clients literally are focused day-to-day on campaign development and implementation in an omnichannel world, and often don’t dwell on the policy implications that affect it. DMA13 offers marketing execs a chance to listen in, catch up and make sure that policy—legal, ethical, best practice—is aligned with our strategy and execution, and that innovation is fostered across all media channels that customers use. Hence, I will be attending DMA President & CEO Linda Woolley’s address “Listen to the Data” (Monday, Oct. 14, 8:45 a.m.) and Spotlight Session on Privacy: “Top 5 Privacy Issues … Revealed” moderated by Ginger Conlon, editor-in-chief of DMNews, with panelists from DMA (Jerry Cerasale), Eloqua (Dennis Dayman) and LoyaltyOne (Bryan Pearson). Responsible data collection and use is clearly under threat from Washington and elsewhere—we need to stand up for ourselves.

4. Inspired and a Party, Too
What’s the best proof point about data-driven marketing’s success—worldwide? If I had the chance to grab a policymaker and make them sit down and see what data-driven marketing can do—I would make him or her attend what I’m hopeful all DMA2013 delegates will attend: the 2013 DMA International ECHO Awards Gala, “Data-Driven Marketing’s Most Important Night” (separate registration required—and well worth it, Tuesday, Oct. 15, 6:30 pm to whenever). I’ve seen a sneak peak of what’s in store for this year’s gala, and this will be not only a Chicago-size party, with a DJ and Comedian Jake Johansen as host, but also truly a celebration of courageous brands, innovative agencies and the marketing strategies, creative executions and outstanding results that leave me—and many others—inspired. Left-brain, data-driven marketing combined with right-brain creative genius—what a combination for brands in both consumer and business-to-business marketing.

That’s enough for now—with more to come. Feel free to post your DMA13 “would be” favorites for blog readers below … and by all means, get yourself and your colleagues registered if you haven’t already. Get a game plan together, the conference is coming fast!

Judging the 2013 ECHOs: A View of Data-Driven Marketing’s Best

Two weeks back, I had the opportunity to judge Rounds 1 and 2 of the ECHOs this year—and while sworn confidentiality requires me to remain mum on actual campaigns I encountered there, I want to comment on the value of judging itself, from my perspective as a public relations practitioner in our field. The ECHOs have been around a long time—since 1929 to be exact. But what really makes me excited to see the campaigns as a judge each year, is that they represent agencies’ and brands’ self-selected choices on what they consider to be award-winning and innovative work

This past year, I had the honor of joining the Direct Marketing Association’s Board of Governors for the International ECHO Awards. That’s my disclaimer.

Two weeks back, I had the opportunity to judge Rounds 1 and 2 of the ECHOs this year—and while sworn confidentiality requires me to remain mum on actual campaigns I encountered there, I want to comment on the value of judging itself, from my perspective as a public relations practitioner in our field.

The ECHOs have been around a long time—since 1929 to be exact. But what really makes me excited to see the campaigns as a judge each year, is that they represent agencies’ and brands’ self-selected choices on what they consider to be award-winning and innovative work based on the three criteria: marketing strategy, creative and results in equal parts. 2013 is no exception. The honors—which will be announced on October 15 in Chicago—will be the world’s best in data-driven marketing. (Breaking News—comedian Jake Johanssen will be this year’s host.)

There are no longer media categories among the entrants—a reflection of how marketing has converged. Instead, channels serve as brand engagement vehicles, and what matters most is their effectiveness in design, dialogue and generating responses to calls for action—from leads, to sales, to audience engagement on a measured scale. So a direct mail piece that is entered may exist (and be judged) alongside entries that represent Web sites, search campaigns, mobile apps, call center efforts, or—most often—integrated marketing campaigns. Again what matters—and only matters—are the strategy, creative and engagement metrics that define marketing effectiveness. Both consumer and business-to-business markets are incorporated.

The categories where entrants are recognized are by industry—15 altogether. You can review the list here.

This is what being an ECHO judge tells me every year:

  1. How are brands and their agencies measuring effectiveness in data-driven marketing? What metrics have they chosen to index or communicate? How is marketing return on investment conveyed? Increasingly, marketing dashboards appear to be in use—with relevant components part of the external results story.
  2. What creative trends are in play? What constitutes break-through creative? What is the unusual and innovative? Where has risk been met with reward? And who (clients and agencies) are being the most courageous worldwide—while also being effective?
  3. How are data being collected, analyzed and—in some cases—visualized? While the entry forms this year were streamlined and don’t have as much budget information in the past—this really has served to heighten visibility on the data, analysis and segmentation techniques being deployed in the strategy.
  4. What is state-of-the-art in data-driven marketing on a global scale? This year, as always, entries were submitted through various partners and submitted to early judging in Denmark, Australia and the United States, comprising dozens of countries in nearly all continents. It is great to see how globally data-driven marketing is practiced—and the creative genius and extraordinary results achieved in both mature and less mature markets.
  5. Finally, judging happens on an individual basis—as a judge you evaluate a campaign, providing your own perspective. But the judging is a collective one—bringing together experienced peers from all over the nation and world. Once the entries and judging scores are in, we do tend to share with each other our impressions of the experience in the aggregate—and meet great people in the process.

In brief, the ECHOs are an idea store for marketing strategists, creative professionals—and the PR folks like me who support my clients in entering awards. I’ve learned not just about how to create great marketing—but how to tell the story behind great marketing. Both count when it comes to crafting an award entry that wins.

You can find out who the winners are firsthand by attending DMA2013 in Chicago, USA, this year (October 12-17, 2013). Make sure to indicate in your registration for a ticket to the ECHO Awards Gala where a separate registration is required: http://dma13.org/registration/

Come October, I’ll definitely be sharing in this blog snippets from some of my favorite campaigns this year!