Marketers’ New Year’s Resolution: ‘I Will Give Customers More T-R-A’

The turning of the calendar may mean a new fiscal year for many marketing organizations, but there is one constant that remains paramount for customer-centric enterprises:  TLC (tender loving care) and how we demonstrate such sentiments to our prospects, customers, and donors — whomever applies.

The turning of the calendar may mean a new fiscal year for many marketing organizations, but there is one constant that remains paramount for customer-centric enterprises: TLC (tender loving care) and how we demonstrate such sentiments to our prospects, customers, and donors — whomever applies.

According to its most recent survey of more than 13,400 C-suite leaders, IBM is recommending data users to pursue another approach in their efforts to build consumer trust: T-R-A, as in transparency, reciprocity, and accountability. See the IBM report, “Build Your Trust Advantage: Leadership in the Age of Data and AI Everywhere” (Opens as a PDF)

The report states:

“To satisfy the modern requirement for trust, leading organizations are adopting three basic principles as their guide: transparency, reciprocity, and accountability. Each provides assurance to customers, but is more than good marketing. These principles are the scaffolding that supports the modern enterprise, remade to propagate trust.”

In a time when trust is increasingly harder to earn — and where consumers question the data-for-value exchange — one may think to shun the data quest. But that is not the correct course of action, nor a viable option, at all. Instead, the answer is to triple up efforts — to seek out and ensure higher quality data sources, to ensure chain-of-trust on permissions and consumer controls, and to hold ourselves and data partners accountable for results.

According to IBM, enterprise leaders — “torchbearers” — have fused their data and business strategies as one. “The torchbearers defy data fears, enhancing the trust of customers.”  Eighty-two percent say they use data to strengthen customer trust, compared with 43% of “aspirational” enterprise data users.

So what does T-R-A entail?

Transparency

“Customers demand transparency of data associated with the products and services, and, in the case of personal data, assurances that it’s used in a fair manner and kept safe,” the report states.

Three Keys to Consumer Love: Transparency, Reciprocity and Accountability. | Credit: Pexels.com

And it’s not just about data used in marketing — it’s also about data regarding how products are developed and manufactured, for example, and user reviews and recommendations. Any data that informs the customer journey, and enables the brand promise, really.

Reciprocity

“C-suite executives understand that to get access to data, they have to give something meaningful in return,” the report states. “The challenge? Organizations often don’t know what their customers would consider a fair exchange.”

That’s a fair assessment — as most consumers say they are skeptical about data-sharing benefits; particularly where privacy is concerned. So it is incumbent upon us to discover — probably using data — what truly motivates consumers’ sense of trust and value. I don’t think we do as good a job as we could as brands, and perhaps as an industry, in explaining data’s value to the consumer. Thus, we must do better.

Accountability

“Accountability is synonymous with brand integrity,” the report opined. “To succeed in retaining trust while growing business or expanding into new marketers, marketers need to establish governance and policies to combat cyber risk and protect consumer trust and brand.”

To me, accountability extends beyond data security — and the lawsuits and brand erosion that may follow data breaches. Data governance is closer to the accountability mark: making sure our data supply chains are “clean,” and that they adhere to industry ethics and best practices.

Here’s Wishing You T-R-A in 2020

So I’m hoping my New Year and yours has a lot more T-R-A in the offing. If the consumers equates sharing of data with a loss of privacy, then no one wins — especially the consumer.

 

 

 

Marketing Success Sans ‘Every Breath They Take, Every Move They Make’

Last month, I talked about how to measure success when there are many conflicting goals and available metrics flying around (refer to “Marketing Success Metrics: Response or Dollars?”). This time, let’s start thinking about how to act on data and intelligence that we’ve gathered. And that means we get to touch different kinds of advanced analytics.

Last month, I talked about how to measure success when there are many conflicting goals and available metrics flying around (refer to “Marketing Success Metrics: Response or Dollars?”). This time, let’s start thinking about how to act on data and intelligence that we’ve gathered. And that means we get to touch different kinds of advanced analytics.

But before we get into boring analytics talk, citing words like “predictive analytics” and “segmentation,” let’s talk about what kind of data are required to make predictions better and more accurate. After all, no data, no analytics.

I often get questions like what the “best” kind of data are. And my answer is, to the inquirer’s disappointment, “it depends.” It really depends on what you are trying to predict, or ultimately, do. If you would like to have an accurate forecast of futures sales, such an effort calls for a past sales history (but not necessarily on an individual or transactional level); past and current marcom spending by channel; web and other channel traffic data; and environmental data, such as economic indicators, just to start off.

Conversely, if you’d like to predict an individual’s product affinity, preferred offer types or likelihood to respond to certain promotion types, such predictive modeling requires data about the past behavior of the target. And that word “behavior” may evoke different responses, even among seasoned marketers. Yes, we are all reflections of our past behavior, but what does that mean? Every breath you take, every move you make?

Thanks to the Big Data hype a few years back, many now believe that we should just collect anything and everything about everybody. Surely, cost for data collection, storage and maintenance has decreased quite a bit over the years, but that doesn’t mean that we should just hoard data mindlessly. Because you may be deferring inevitable data hygiene, standardization, categorization and consolidation to future users — or machines — who must sort out unorganized and unrefined data and provide applicable insights.

So, going back to that question of what makes up data about human behavior, let’s define what that means in a categorical fashion. With proliferation of digital data collection and analytics, the definition of behavioral data has expanded considerably.

In short, what people casually refer to as “behavioral data” may include this to measure success:

  • Online Behavior: Web data regarding click, view and other shopping behavior.
  • Purchase: Transactional data, made of who, what, when, how much and through what channel.
  • Response: Response history, in relation to specific promotions, covering open, click-through, opt-out, view, shopping basket, conversion/transaction. Offline response may be as simple as product purchase.
  • Channel: Channel usage data, not necessarily limited to shopping behavior.
  • Payment: Payment and related delinquent history — essential for credit purchases and continuity and subscription businesses.
  • Communication: Call, chat or other communication log data, positive or negative in nature.
  • Movement: Physical proximity or movement data, in store or store area, for example.
  • Survey: Responses to various surveys.
  • Opt-in/Opt-out: Sign-up specific 2-way communications and channel specific opt-out requests.
  • Social Media: Product review, social media posting and product/service-related sentiment data.

I am sure some will think of more categories. But before we create an exhaustive list of data types, let’s pause and think about what we are trying to do here.

First off, all of these data traceable to a person are being collected for one major reason (at least for marketers): To sell more things to them. If the goal is to predict the who, what, when and why of buying behavior, do we really need all of this?

The ‘Who’ of Buying Behavior

In the prediction business, predicting “who” (as in “who will buy this product?”) is the simplest kind of action. We’d need some PII (personally identifiable information) that can link to buying behaviors of the target. After all, the whole modeling technique was invented to rank target individuals and set up contact priority — in that order. Like sending expensive catalogs only to high-score individuals, in terms of “likely to respond,” or sales teams contacting high “likely to convert” targets as priorities in B2B businesses.

The ‘What’ of Buying Behavior

The next difficulty level lies with the prediction of “what” (as in “what is that target individual going to buy next?”). This type of prediction is generally a hit-or-miss, so even mighty Amazon displays multiple product offers at the end of a successful transaction, by saying “Customers who purchased this item are also interested in these products.” Such a gentle push, based on collaborative filtering, requires massive purchase history by many buyers to be effective. But, provided with ample amounts of data, it is not terribly difficult, and the risk of being wrong is relatively low. Pinpointing the very next product for 1:1 messaging can be challenging, but product basket analysis can easily lead to popular combinations of products, at the minimum.

How Well Do You Know Your Customer Data?

Some marketers seem to keep their distance from customer data. When I ask what kind of customer information they are working with, I hear things like, “Oh, Mary is in charge of our data. I leave it to her.” This is unfortunate.

Some marketers seem to keep their distance from customer data. When I ask what kind of customer information they are working with, I hear things like, “Oh, Mary is in charge of our data. I leave it to her.” This is unfortunate. I realize that the marketing profession may attract people who prefer to focus on “softer” functions like research, competitive strategy, and value propositions. But these days, it’s a real disadvantage, professionally and personally, to shun data. So, let me offer some painless steps to up your comfort level.

In this context, I am thinking about customer data at its most basic level: the customer or prospect record, which is usually found in a marketing database or a CRM system. This record contains the contact information, and descriptive and behavioral data elements we know about the customer. For B2B marketers, it will describe the account as well as the individual contacts. This subject arose in my mind recently as I read Steven Hayes’s interesting article called “Do Marketers Really Want to be Data Scientists?” in Oracle’s Modern Marketing blog. Hayes correctly concluded that marketers don’t need to do the science — build the models, run the experiments — but they do need to be familiar with the variables that drive customer behavior, in order to apply the science to marketing decision-making.

So, it behooves marketers to be deeply familiar with the customer record, which is where these variables are housed. This information serves as what I like to call “the recorded memory of the customer relationship,” and it reveals all sorts of insights into the nature of the customers and competitors, what they value, and how to communicate with them effectively.

So, how do you get familiar with your customer records, and make them your friends? Here are three steps to consider.

1. Take Mary — or whoever manages your customer data — out to lunch. Demonstrate your interest in understanding her world, her challenges and her interests. This puts a personal face to the data, and also makes Mary an ally and mentor in your quest.

2. Examine a handful of customer records. You’ll find all kinds of interesting things: What do we know about this person? Any ideas on how better to communicate and sell to him/her? How complete and accurate is the record? What additional data would help you develop even better ideas on how to treat the customer? Set an hour on your calendar every quarter or so, to repeat the process, becoming familiar with records from various types of customers and prospects.

3. Launch an initiative to develop a data strategy for your department or your company as a whole. This means a written policy that identifies the data elements you should collect on each customer, where each element will come from, and how you will use it to drive business value.

I guarantee, if you dive into the data records, your comfort level will rise dramatically. And so will your insight, and your skill as a marketer.

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

Big Data Must Get Smaller

Like many folks who worked in the data business for a long time, I don’t even like the words “Big Data.” Yeah, data is big now, I get it. But so what? Faster and bigger have been the theme in the computing business since the first calculator was invented. In fact, I don’t appreciate the common definition of Big Data that is often expressed in the three Vs: volume, velocity and variety. So, if any kind of data are big and fast, it’s all good? I don’t think so. If you have lots of “dumb” data all over the place, how does that help you? Well, as much as all the clutter that’s been piled on in your basement since 1971. It may yield some profit on an online auction site one day. Who knows? Maybe some collector will pay good money for some obscure Coltrane or Moody Blues albums that you never even touched since your last turntable (Ooh, what is that?) died on you. Those oversized album jackets were really cool though, weren’t they?

Like many folks who worked in the data business for a long time, I don’t even like the words “Big Data.” Yeah, data is big now, I get it. But so what? Faster and bigger have been the theme in the computing business since the first calculator was invented. In fact, I don’t appreciate the common definition of Big Data that is often expressed in the three Vs: volume, velocity and variety. So, if any kind of data are big and fast, it’s all good? I don’t think so. If you have lots of “dumb” data all over the place, how does that help you? Well, as much as all the clutter that’s been piled on in your basement since 1971. It may yield some profit on an online auction site one day. Who knows? Maybe some collector will pay good money for some obscure Coltrane or Moody Blues albums that you never even touched since your last turntable (Ooh, what is that?) died on you. Those oversized album jackets were really cool though, weren’t they?

Seriously, the word “Big” only emphasizes the size element, and that is a sure way to miss the essence of the data business. And many folks are missing even that little point by calling all decision-making activities that involve even small-sized data “Big Data.” It is entirely possible that this data stuff seems all new to someone, but the data-based decision-making process has been with us for a very long time. If you use that “B” word to differentiate old-fashioned data analytics of yesteryear and ridiculously large datasets of the present day, yes, that is a proper usage of it. But we all know most people do not mean it that way. One side benefit of this bloated and hyped up buzzword is data professionals like myself do not have to explain what we do for living for 20 minutes anymore by simply uttering the word “Big Data,” though that is a lot like a grandmother declaring all her grandchildren work on computers for living. Better yet, that magic “B” word sometimes opens doors to new business opportunities (or at least a chance to grab a microphone in non-data-related meetings and conferences) that data geeks of the past never dreamed of.

So, I guess it is not all that bad. But lest we forget, all hypes lead to overinvestments, and all overinvestments leads to disappointments, and all disappointments lead to purging of related personnel and vendors that bear that hyped-up dirty word in their titles or division names. If this Big Data stuff does not yield significant profit (or reduction in cost), I am certain that those investment bubbles will burst soon enough. Yes, some data folks may be lucky enough to milk it for another two or three years, but brace for impact if all those collected data do not lead to some serious dollar signs. I know how the storage and processing cost decreased significantly in recent years, but they ain’t totally free, and related man-hours aren’t exactly cheap, either. Also, if this whole data business is a new concept to an organization, any money spent on the promise of Big Data easily becomes a liability for the reluctant bunch.

This is why I open up my speeches and lectures with this question: “Have you made any money with this Big Data stuff yet?” Surely, you didn’t spend all that money to provide faster toys and nicer playgrounds to IT folks? Maybe the head of IT had some fun with it, but let’s ask that question to CFOs, not CTOs, CIOs or CDOs. I know some colleagues (i.e., fellow data geeks) who are already thinking about a new name for this—”decision-making activities, based on data and analytics”—because many of us will be still doing that “data stuff” even after Big Data cease to be cool after the judgment day. Yeah, that Gangnam Style dance was fun for a while, but who still jumps around like a horse?

Now, if you ask me (though nobody did yet), I’d say the Big Data should have been “Smart Data,” “Intelligent Data” or something to that extent. Because data must provide insights. Answers to questions. Guidance to decision-makers. To data professionals, piles of data—especially the ones that are fragmented, unstructured and unformatted, no matter what kind of fancy names the operating system and underlying database technology may bear—it is just a good start. For non-data-professionals, unrefined data—whether they are big or small—would remain distant and obscure. Offering mounds of raw data to end-users is like providing a painting kit when someone wants a picture on the wall. Bragging about the size of the data with impressive sounding new measurements that end with “bytes” is like counting grains of rice in California in front of a hungry man.

Big Data must get smaller. People want yes/no answers to their specific questions. If such clarity is not possible, probability figures to such questions should be provided; as in, “There’s an 80 percent chance of thunderstorms on the day of the company golf outing,” “An above-average chance to close a deal with a certain prospect” or “Potential value of a customer who is repeatedly complaining about something on the phone.” It is about easy-to-understand answers to business questions, not a quintillion bytes of data stored in some obscure cloud somewhere. As I stated at the end of my last column, the Big Data movement should be about (1) Getting rid of the noise, and (2) Providing simple answers to decision-makers. And getting to such answers is indeed the process of making data smaller and smaller.

In my past columns, I talked about the benefits of statistical models in the age of Big Data, as they are the best way to compact big and complex information in forms of simple answers (refer to “Why Model?”). Models built to predict (or point out) who is more likely to be into outdoor sports, to be a risk-averse investor, to go on a cruise vacation, to be a member of discount club, to buy children’s products, to be a bigtime donor or to be a NASCAR fan, are all providing specific answers to specific questions, while each model score is a result of serious reduction of information, often compressing thousands of variables into one answer. That simplification process in itself provides incredible value to decision-makers, as most wouldn’t know where to cut out unnecessary information to answer specific questions. Using mathematical techniques, we can cut down the noise with conviction.

In model development, “Variable Reduction” is the first major step after the target variable is determined (refer to “The Art of Targeting“). It is often the most rigorous and laborious exercise in the whole model development process, where the characteristics of models are often determined as each statistician has his or her unique approach to it. Now, I am not about to initiate a debate about the best statistical method for variable reduction (I haven’t met two statisticians who completely agree with each other in terms of methodologies), but I happened to know that many effective statistical analysts separate variables in terms of data types and treat them differently. In other words, not all data variables are created equal. So, what are the major types of data that database designers and decision-makers (i.e., non-mathematical types) should be aware of?

In the business of predictive analytics for marketing, the following three types of data make up three dimensions of a target individual’s portrait:

  1. Descriptive Data
  2. Transaction Data / Behavioral Data
  3. Attitudinal Data

In other words, if we get to know all three aspects of a person, it will be much easier to predict what the person is about and/or what the person will do. Why do we need these three dimensions? If an individual has a high income and is living in a highly valued home (demographic element, which is descriptive); and if he is an avid golfer (behavioral element often derived from his purchase history), can we just assume that he is politically conservative (attitudinal element)? Well, not really, and not all the time. Sometimes we have to stop and ask what the person’s attitude and outlook on life is all about. Now, because it is not practical to ask everyone in the country about every subject, we often build models to predict the attitudinal aspect with available data. If you got a phone call from a political party that “assumes” your political stance, that incident was probably not random or accidental. Like I emphasized many times, analytics is about making the best of what is available, as there is no such thing as a complete dataset, even in this age of ubiquitous data. Nonetheless, these three dimensions of the data spectrum occupy a unique and distinct place in the business of predictive analytics.

So, in the interest of obtaining, maintaining and utilizing all possible types of data—or, conversely, reducing the size of data with conviction by knowing what to ignore, let us dig a little deeper:

Descriptive Data
Generally, demographic data—such as people’s income, age, number of children, housing size, dwelling type, occupation, etc.—fall under this category. For B-to-B applications, “Firmographic” data—such as number of employees, sales volume, year started, industry type, etc.—would be considered as descriptive data. It is about what the targets “look like” and, generally, they are frozen in the present time. Many prominent data compilers (or data brokers, as the U.S. government calls them) collect, compile and refine the data and make hundreds of variables available to users in various industry sectors. They also fill in the blanks using predictive modeling techniques. In other words, the compilers may not know the income range of every household, but using statistical techniques and other available data—such as age, home ownership, housing value, and many other variables—they provide their best estimates in case of missing values. People often have some allergic reaction to such data compilation practices siting privacy concerns, but these types of data are not about looking up one person at a time, but about analyzing and targeting groups (or segments) of individuals and households. In terms of predictive power, they are quite effective and results are very consistent. The best part is that most of the variables are available for every household in the country, whether they are actual or inferred.

Other types of descriptive data include geo-demographic data, and the Census Data by the U.S. Census Bureau falls under this category. These datasets are organized by geographic denominations such as Census Block Group, Census Tract, Country or ZIP Code Tabulation Area (ZCTA, much like postal ZIP codes, but not exactly the same). Although they are not available on an individual or a household level, the Census data are very useful in predictive modeling, as every target record can be enhanced with it, even when name and address are not available, and data themselves are very stable. The downside is that while the datasets are free through Census Bureau, the raw datasets contain more than 40,000 variables. Plus, due to the budget cut and changes in survey methods during the past decade, the sample size (yes, they sample) decreased significantly, rendering some variables useless at lower geographic denominations, such as Census Block Group. There are professional data companies that narrowed down the list of variables to manageable sizes (300 to 400 variables) and filled in the missing values. Because they are geo-level data, variables are in the forms of percentages, averages or median values of elements, such as gender, race, age, language, occupation, education level, real estate value, etc. (as in, percent male, percent Asian, percent white-collar professionals, average income, median school years, median rent, etc.).

There are many instances where marketers cannot pinpoint the identity of a person due to privacy issues or challenges in data collection, and the Census Data play a role of effective substitute for individual- or household-level demographic data. In predictive analytics, duller variables that are available nearly all the time are often more valuable than precise information with limited availability.

Transaction Data/Behavioral Data
While descriptive data are about what the targets look like, behavioral data are about what they actually did. Often, behavioral data are in forms of transactions. So many just call it transaction data. What marketers commonly refer to as RFM (Recency, Frequency and Monetary) data fall under this category. In terms of predicting power, they are truly at the top of the food chain. Yes, we can build models to guess who potential golfers are with demographic data, such as age, gender, income, occupation, housing value and other neighborhood-level information, but if you get to “know” that someone is a buyer of a box of golf balls every six weeks or so, why guess? Further, models built with transaction data can even predict the nature of future purchases, in terms of monetary value and frequency intervals. Unfortunately, many who have access to RFM data are using them only in rudimentary filtering, as in “select everyone who spends more than $200 in a gift category during the past 12 months,” or something like that. But we can do so much more with rich transaction data in every stage of the marketing life cycle for prospecting, cultivating, retaining and winning back.

Other types of behavioral data include non-transaction data, such as click data, page views, abandoned shopping baskets or movement data. This type of behavioral data is getting a lot of attention as it is truly “big.” The data have been out of reach for many decision-makers before the emergence of new technology to capture and store them. In terms of predictability, nevertheless, they are not as powerful as real transaction data. These non-transaction data may provide directional guidance, as they are what some data geeks call “a-camera-on-everyone’s-shoulder” type of data. But we all know that there is a clear dividing line between people’s intentions and their commitments. And it can be very costly to follow every breath you take, every move you make, and every step you take. Due to their distinct characteristics, transaction data and non-transaction data must be managed separately. And if used together in models, they should be clearly labeled, so the analysts will never treat them the same way by accident. You really don’t want to mix intentions and commitments.

The trouble with the behavioral data are, (1) they are difficult to compile and manage, (2) they get big; sometimes really big, (3) they are generally confined within divisions or companies, and (4) they are not easy to analyze. In fact, most of the examples that I used in this series are about the transaction data. Now, No. 3 here could be really troublesome, as it equates to availability (or lack thereof). Yes, you may know everything that happened with your customers, but do you know where else they are shopping? Fortunately, there are co-op companies that can answer that question, as they are compilers of transaction data across multiple merchants and sources. And combined data can be exponentially more powerful than data in silos. Now, because transaction data are not always available for every person in databases, analysts often combine behavioral data and descriptive data in their models. Transaction data usually become the dominant predictors in such cases, while descriptive data play the supporting roles filling in the gaps and smoothing out the predictive curves.

As I stated repeatedly, predictive analytics in marketing is all about finding out (1) whom to engage, and (2) if you decided to engage someone, what to offer to that person. Using carefully collected transaction data for most of their customers, there are supermarket chains that achieved 100 percent customization rates for their coupon books. That means no two coupon books are exactly the same, which is a quite impressive accomplishment. And that is all transaction data in action, and it is a great example of “Big Data” (or rather, “Smart Data”).

Attitudinal Data
In the past, attitudinal data came from surveys, primary researches and focus groups. Now, basically all social media channels function as gigantic focus groups. Through virtual places, such as Facebook, Twitter or other social media networks, people are freely volunteering what they think and feel about certain products and services, and many marketers are learning how to “listen” to them. Sentiment analysis falls under that category of analytics, and many automatically think of this type of analytics when they hear “Big Data.”

The trouble with social data is:

  1. We often do not know who’s behind the statements in question, and
  2. They are in silos, and it is not easy to combine such data with transaction or demographic data, due to lack of identity of their sources.

Yes, we can see that a certain political candidate is trending high after an impressive speech, but how would we connect that piece of information to whom will actually donate money for the candidate’s causes? If we can find out “where” the target is via an IP address and related ZIP codes, we may be able to connect the voter to geo-demographic data, such as the Census. But, generally, personally identifiable information (PII) is only accessible by the data compilers, if they even bothered to collect them.

Therefore, most such studies are on a macro level, citing trends and directions, and types of analysts in that field are quite different from the micro-level analysts who deal with behavioral data and descriptive data. Now, the former provide important insights regarding the “why” part of the equation, which is often the hardest thing to predict; while the latter provide answers to “who, what, where and when.” (“Who” is the easiest to answer, and “when” is the hardest.) That “why” part may dictate a product development part of the decision-making process at the conceptual stage (as in, “Why would customers care for a new type of dishwasher?”), while “who, what, where and when” are more about selling the developed products (as in “Let’s sell those dishwashers in the most effective ways.”). So, it can be argued that these different types of data call for different types of analytics for different cycles in the decision-making processes.

Obviously, there are more types of data out there. But for marketing applications dealing with humans, these three types of data complete the buyers’ portraits. Now, depending on what marketers are trying to do with the data, they can prioritize where to invest first and what to ignore (for now). If they are early in the marketing cycle trying to develop a new product for the future, they need to understand why people want something and behave in certain ways. If signing up as many new customers as possible is the immediate goal, finding out who and where the ideal prospects are becomes the most imminent task. If maximizing the customer value is the ongoing objective, then you’d better start analyzing transaction data more seriously. If preventing attrition is the goal, then you will have to line up the transaction data in time series format for further analysis.

The business goals must dictate the analytics, and the analytics call for specific types of data to meet the goals, and the supporting datasets should be in “analytics-ready” formats. Not the other way around, where businesses are dictated by the limitations of analytics, and analytics are hampered by inadequate data clutters. That type of business-oriented hierarchy should be the main theme of effective data management, and with clear goals and proper data strategy, you will know where to invest first and what data to ignore as a decision-maker, not necessarily as a mathematical analyst. And that is the first step toward making the Big Data smaller. Don’t be impressed by the size of the data, as they often blur the big picture and not all data are created equal.

Take Command of Marketing Data Governance—Because We Have To

The emergence of “big data” as an enterprise concern for many businesses and organizations is, as with most trends, both an opportunity and a concern. I recently was involved in reviewing new and recent Aberdeen Research on “Big Data”—how it is defined, how it is changing information volume (astounding in quantity), variety (both structured and unstructured, with tremendous pressure to integrate and make sense of it), and velocity (pushing the insight, analytics and business rules that flow from such data to lines of business that can best profit from it).

The emergence of “big data” as an enterprise concern for many businesses and organizations is, as with most trends, both an opportunity and a concern.

I recently was involved in reviewing new and recent Aberdeen Research on “Big Data”—how it is defined, how it is changing information volume (astounding in quantity), variety (both structured and unstructured, with tremendous pressure to integrate and make sense of it), and velocity (pushing the insight, analytics and business rules that flow from such data to lines of business that can best profit from it). An infographic that captures some of this research is now posted at Mason Zimbler, a Harte-Hanks Company, which created the visual presentation.

Alongside this current fascination and business trend, perhaps it’s not surprising that members of Congress, both Democrats and Republicans, also are posing questions at the marketing business as to how we collect, buy/sell, rent and exchange data about consumers online and offline, and if there is adequate notice and choice in the process. In the rush to capitalize on Big Data, we need to ensure that we’re collecting and using marketing data for marketing purposes only, and doing so in a manner that is respectful of fair information practices principles and ultimately serves the end-customer, be it consumer or business individual or enterprise. [See Rep. Ed Markey, D-MA: http://markey.house.gov/content/letters-major-data-brokers.]

All too often, privacy adherence is considered a legal matter, or an information technology matter—but I maintain that while these two business areas are important in respecting consumer privacy, it is marketers who have the most to gain (and lose) by smart (or insensitive) information practices. Data is our currency, and we must treat data (our customers as data subjects) as our primary asset to protect. Our method of marketing is in the balance. One or two major privacy mishaps can spoil it for everyone.

Of course, marketing data governance is far more than privacy compliance. Data quality, data integrity, data security, data integration, data validation and data flows within an enterprise all, too, are part of marketing data’s customer intelligence equation. It is in this spirit that the Direct Marketing Association recently introduced its newest certification program for professionals: “The Institute for Marketing Data Governance and Certification,” taught by marketing veteran Peg Kuman, who is vice chair at Relevate Group. The three-day course, which has launched on a two-year, multiple-city tour, is indispensable in understanding how multiple channels, multiple data sources and platforms, customer expectations and business objectives combine to command better understanding, tools and processes for data handling for smart integrated marketing. Forthcoming course dates and registrations are available here: http://www.dmaeducation.org/dm-essentials/marketing_data_governance.php

For three days last month in New York, approximately two dozen professionals from large and small enterprises, both commercial and nonprofit, attended the first seminar. I, too, attended. There were representatives from marketing, public relations, analytics, legal, IT and fundraising, representing brands, agencies and service providers. This group was engaged—providing examples, asking questions and reporting experiences as the curriculum moved along. (For those who don’t know Peg—a former client of mine—she is quite the facilitator.)

Alongside a workbook, I took home some great handouts, too:

  • A sample security policy; a sample information security vulnerability assessment;
  • A security due diligence questionnaire;
  • A sample vendor risk management program vendor questionnaire;
  • The latest copy of the DMA Guidelines for Ethical Business Practice (recently updated with new email append guidelines, by the way) and a bevy of news articles that captures the media’s and public policymakers’ current attention on consumer data in America.

The meat of the course tackled, among other topics:

  • Categorizing data and assigning priority and sensitivity (personally identifiable information, sensitive data and other categories);
  • Mapping data flows and interactions with customers; enhancing data with appended information, and ensuring its use for marketing only;
  • Having a data quality strategy as part of a data strategy;
  • Calculating return on data investment;
  • The emergence of digital, mobile and social data platforms, and how these present both structured and unstructured data collection and insight analysis challenges;
  • Assigning data “ownership”;
  • Calculating and assigning risk regarding security;
  • Monitoring security, investigating potential incidents of a breach, and handling a response to a breach were it to occur (using recent breach response examples of LinkedIn and Epsilon); as well as
  • Laws, ethics and best practices for all of these areas.

One of my concerns is the importation of European-style privacy protection in America, and current fascination with such protections by U.S. regulators and elected officials. That is worth another blog post in itself, but I can assure you that we need to educate politicians about the superiority of self and peer regulation where no consumer harm exists.

Thank you, DMA. Marketing data does not harm. It only creates consumer choice, commerce, jobs and (tax) revenue—and pays for the Internet and other media, too—and it is ridiculous to even entertain government-knows-better regulation of such information through a potential omnibus law in America, or other notions such as a government-mandated “privacy by design” requirement in marketing innovations. (On the other hand, I’m more than happy to see laws pass that protect Americans from potential government abuse of private sector marketing data—Big Brother should not be getting access to marketing data for non-marketing purposes, unless there is a demonstrable greater public good, where subpoenas are served and heard.) Privacy by design is smart business, but only when left to the innovators, not the policymakers.

Which brings me to close—and if you’re still reading this, I congratulate myself for not chasing you away. Big Data (which can incorporate far more than marketing data) goes hand-in-hand with marketing data governance. Whether a Big Data user or not, we all use marketing data everyday as our currency. Protect it. Respect it. Serve it. Govern it. So we can use it.