Don’t Be a Data Hoarder — Why Data Governance Matters in Marketing

They say data is an asset. I say it, too. If collected data are wielded properly, they can definitely lead to financial gains, either through a revenue increase or cost reduction. But that doesn’t mean that possessing large amounts of data guarantees large dollar figures for the collector. Data governance matters.

They say data is an asset. I say it, too. If collected data are wielded properly, they can definitely lead to financial gains, either through a revenue increase or cost reduction. But that doesn’t mean that possessing large amounts of data guarantees large dollar figures for the collector. Data governance matters, because the operative words in my statement are “wielded properly,” as I have been emphasizing for years through this column.

Plus, collecting data also comes with risks. When sensitive data go into the wrong hands, it often leads to a direct financial burden for the data collector. In some countries, an assumed guardian of sensitive data may face legal charges for mishandling sensitive data. Even in the United States, which is known as the “freest” country for businesses when it comes to data usage, data breach or clear abuse of data can lead to a publicity nightmare for the organization; or worse, large legal settlements after long and costly litigations. Even in the most innocuous cases, mistreatment of sensitive data may lead to serious damage to the brand image.

The phrase is not even cool in the business community anymore, but “Big Data” worked like a magic word only a few years ago. In my opinion, that word “big” in Big Data misled many organizations and decision-makers. It basically gave a wrong notion that “big” is indeed “good” in the data business.

What is “good,” in a pure business sense? Simply, more money. What was the popular definition of Big Data back then? Three Vs, as in volume, velocity and variety. So, if varieties of data in large volumes move around really fast, it will automatically be good for businesses? We know the answer by now, that a large amount of unstructured, unorganized and unrefined data could just be a burden to the holder, not to mention the security concerns listed earlier.

Unfortunately, with the popularity of Big Data and emergence of cloud computing, many organizations started to hoard data with a hope that collected data would turn into gold one day. Here, I am saying “hoarding” with all of the negative connotations that come with the word.

Hoarders are the people who are not able to throw away anything, even garbage. Data hoarders are the same way. Most datasets are huge because the collector does not know what to throw out. If you ask any hoarder why he keeps so many items in the house, the most common answer would be “because you never know when you need them.” Data hoarders keep every piece of data indefinitely for the same reason.

Only Keep Useful Data

But if you are playing with data for business purposes, you should know what pieces of data are useful for decision-making. The sponsor of any data activity must have clear objectives to begin with. Analysts would then find out what kind of data are necessary to meet those goals, through various statistical analyses and cumulative knowledge.

Actually, good analysts do know that not all data are created equal, and some are more useful than others. Why do you think that the notion of a Data Lake became popular following the Big Data hype? Further, I have been emphasizing the importance of an even more concise data environment. (I call it an “Analytics Sandbox.”) Because the lake water in the Data Lake is still not drinkable. Data must get smaller through data refinement and analytics to be beneficial for decision-makers (refer to “Big Data Must Get Smaller”).

Nonetheless, organizations continue to hoard data, because no one wants to be responsible for purging data that may be useful someday. Government agencies may have some good reasons to maintain large amounts of data, because the cost of losing or misplacing data about some terrorist activities is too high. Even in that case, however, we should collectively be concerned if the most sensitive data about us — such as our biometrics data — reside in some government agency’s server somewhere, without clear and immediate purposes. In cities like London or Paris, cameras are on every street corner, linked to facial recognition algorithms. But we tolerate that because the benefit outweighs the risk (so we think). But that doesn’t mean that we don’t need to be concerned with data breach or abuse.

Hoarding Data Gives Brands the Temptation to Be Creepy

If the data are collected by businesses for their financial gains, then the subjects of such data collection (i.e., consumers) should question who gave them the right to collect data about every breath we take, every move we make and every claim we stake. It is one thing to retain data about mutual transactions, but it is quite another to collect data on our movement or whereabouts, unilaterally. In other words, it is one thing to be remembered (for better service and recommendation in the future), but it is another to be stalked (remember “Every Breath You Take” is a song about a stalker).

Have you heard a story about a stalker who successfully courted the subject as result of stalking? Why do marketers think that they will sell more of their products by stalking their customers and prospects? Since when did being totally creepy – as in “I know where you are and what you’re doing right now” – become an acceptable marketing tactic? (Refer to “Don’t Do It Just Because You Can.”)

In fact, even if you do possess such data, in the interest of “not” being creepy, you must make your message more innocuous. For example, don’t act like you are offering an item because you “know” that the target looked around similar items recently. That kind of creepy approach may work once in a while, but let’s not call that a good sales tactic.

Instead, sellers should make gentle nudges. Don’t say “I know you are looking for this particular skin care item.” The response to that would be “Who the hell are you, and how do you know that?” Instead, do say “Would you be interested in our new product for people with sensitive skin?” The desirable response would be “Hey, I was just looking for something like that!”

The difference between a creepy stalking and a gentle nudging is huge, from the receiving end.

Through many articles about personalization, I have been emphasizing the use of model-based personas, as they pack so much information in the form of answer to questions and cover the gap of missing data (as we’d never know everything about everyone). If I may add one more benefit of modeling, it coverts data into probabilities. Raw data is about “I know she is looking for a particular high-end skin care item,” where coverage of such data is seriously limited, anyway. Conversely, model scores are about “Her score for high-end beauty products is 8 out of 10 scale score,” even if we may not even have concrete data about that specific interest.

Now, users who only have access to the model score — which is “dull” information, in comparison to “sharp” data about some verified behavior — would be less temped to say “Oh, I know you did this.” Even for non-geeky types, the difference between “Is” and “Likely to be” is vast.

If converting sharp data into innocuous probability scores through modeling is too much for you to start with, then at least categorize the data, and expose data points to users that way. Yes, we are living in the world of SKU-level product suggestion (like Amazon does), but as a consumer, have you ever “liked” such blunt suggestions, anyway? Marketers do it because such personalization does better than not doing anything at all, but such a practice is hardly ideal for many reasons (Being creepy being one. Refer to “Personalization Is About the Person”).

The saddest part in all this is that most marketers don’t even know how to fully utilize what they collected. I’ve seen too many organizations that are still stuck with using a few popular data variables repeatedly, while hoarding data indiscriminately. Why risk all of those privacy and security concerns, not to mention the data maintenance cost, if that is the case?

Have a Goal for All of That Data

If analytics is part of the process, then the analysts will tell you with conviction, that you don’t need all those data points for certain types of prediction. For instance, why risk losing a bunch of credit card numbers, when the credit card type or payment method is all you need to predict responses and propensities on a customer level?

Of course, the organization must first decide what types of models and predictions are necessary to meet their goals. But that is the beginning part of the whole analytics game, anyway. Analytics is not about answering to some wishful thinking of data hoarders; it should be a goal-oriented activity, with carefully selected and refined data for clear purposes.

A goal-oriented mindset is even more important in the age of machine learning and automation. Because we should never automate bad behaviors. Imagine a powerful marketing automation engine in the hands of data hoarders. Forget about organizational inefficiency. As a consumer, don’t you get a chill down your spine just imagining how creepy the outcome would be? Well, maybe we don’t really have to imagine it, as we all get bombarded with ineffective and not-so-personal offers every day.

Conclusion

So, marketers, have clear purposes in data activities, and do not become mindless data hoarders. If you do possess data, wield them properly with analytics. And while at it, purge pieces of data that do not fit your goals. That “you never know” attitude really doesn’t help anyone. And you are supposed to know your own goals and what data and methodologies will get you there.

GDPR Leads Brands to Better CX

A year ago, most companies had no clue where all of their customer data resided, let alone whether or not it was secure. With the implementation of GDPR, and California’s digital privacy law scheduled to take effect in January 2020, companies have started taking their customer and prospect data, and its security, much more seriously.

A year ago, most companies had no clue where all of their customer data resided, let alone whether or not it was secure. With the implementation of GDPR, and California’s digital privacy law scheduled to take effect in January 2020, companies have started taking their customer and prospect data, and its security, much more seriously.

Most organizations keep their customer data in a customer relationship management (CRM) database. However, prior to GDPR, the information was incomplete, the accuracy of the data was not taken seriously, and the data was not secure due to a lack of business process management and master data management policies.

Based on the interviews I have conducted with IT executives involved in databases, big data, AI/ML and security, there has been a significant change in the past year; whereby, companies are now implementing and enforcing data management best practices and creating data Centers of Excellence. Employees are learning the importance of data and its security.

Given that a well-maintained CRM is necessary to deliver a great customer experience (CX), we can expect to see companies begin taking CX seriously, because they are getting their data in order and their competitors will begin using that data to deliver improved CX. We’re now in a race to see who can use data first and best to improve the CX.

Updated privacy policies and security protocols will increase the opportunity to deliver personalized and relevant information of value. In addition to getting consumers’ explicit permission to communicate with their customers and prospects, organizations will want to enact progressive profiling; whereby, they learn more about each customer or prospect every time they interact with your website or organization. The more you know about a customer, the more relevant you should be able to be to them by providing information of value while anticipating needs and wants.

Organizations need to learn what customers and prospects need and want to make their lives easier. This is key to building a disruptive business and earning a customer for life. Lyft has done this for me. Every time I need to travel to or from an airport, I no longer need taxis, rental cars or parking at the airport. Lyft has made my life traveling much simpler and easier. Lyft has earned a customer for life — or at least until its business model is disrupted.

A good CRM with proper data management processes is beneficial to organizations on several fronts:

1. The CRM serves as the repository for all customer data and enables customer-facing employees to have a 360-degree view of the customer so they understand the customer’s relationship with the company — interactions, products/services bought, considered, feedback. All customer-facing employees are able to see the actions that have taken place and know what actions need to take place in the future based on sales and CX processes.
2. Organizations are able to provide more relevant help and information; thereby, making customers’ lives simpler and easier. Some organizations, e.g. financial institutions, are already using predictive analytics to recommend the “next best action” for the customer to the employee.
3. The CRM can be integrated with calendars and marketing automation software for appropriate follow-up before and after a sale, for nurturing marketing qualified leads (MQLs) to sales qualified leads (SQLs) or to market to “lookalike” prospects.
4. The CRM provides real-time metrics enabling team members to see where prospects and customers are in the sales, post-sales, follow-up or problem/resolution cycle.
5. A sound CRM enables the organization to scale in a thoughtful way with proper data management, security and updates. Leveraging even more data to improve the CX.

How has GDPR affected your organization and its data management practices?

5 Core Marketing Operations Processes to Master

If you are lucky enough to have a marketing operations function at your organization, then you know that an important part of their job is in defining, documenting and refining the core processes that keep the machinery of marketing running well. Let’s narrow the discussion to the top five processes, and cover each of the five in more detail in subsequent posts.

Marketing Operations and Traffic ControlIn last month’s blog post, we covered the final elements of an organizational structure for a center of excellence marketing team. Next stop in our Revenue Marketing journey is to address the fundamental marketing operations processes we need to run a demand generation function efficiently and effectively.

If you are lucky enough to have a marketing operations function at your organization, then you know that an important part of their job is in defining, documenting and refining the core processes that keep the machinery of marketing running well. Let’s narrow the discussion to the top five processes, and cover each of the five in more detail in subsequent posts.

5 Marketing Operations Processes to Rule Them All

Why do we even need marketing process? A process defines a series of actions taken so that we can achieve a particular end. It helps ensure, but not guarantee an outcome that meets our quality goals. With that in mind, here are my top five processes that a marketing center of excellence requires:

  1. Lead management
  2. Reporting and analytics
  3. Data management
  4. Campaign development
  5. Content development

Yes there are many others, and if you feel one of these five should be ousted in favor of something else, please share what that is, and why in the comments below.

1. Lead Management Process

The lead management process outlines the steps for tracking and reporting on leads as they are created and move through a funnel, becoming qualified or disqualified, and eventually passing through any lead development representatives to sales or channel partners.

A typical lead management process includes the following components:

  • Definition of a sales ready lead
  • Definition of the various lead statuses in the CRM defined funnel
  • Design of the lead processing, routing, and related notifications
  • Design of the lead scoring algorithm
  • Development and agreement to a service level agreement between sales and marketing
  • Establishment of funnel metrics

(To learn the Proven Success Formula for Lead Management, download here.)

2. Reporting and Analytics Process

The reporting and analytics process defines who will report on what, when, and for whom. Where will they get the data, and how will the reports be made available? Before you rocket your eyebrows to the ceiling and slam me for stating the obvious consider that the resources for doing reporting in mid-sized organizations are usually limited, and so often the function is decentralized. I.e., many marketing field offices report on their piece only. And without some defined process, templates, definitions, rules, and hand-holding your ability to roll up the reports will be either laborious or impossible.

Reporting and analytics process components:

  • Data sources: defined for all the different data or activity types
  • Report frequency: report timing based on the decision making needs related to that data
  • Owner assignment: Identifying authors and the folks who run the reports
  • Standards: Report presentation norms for different types of reports
  • Media: to be used for delivering and presenting reports (CRM, MAP, Excel, BI, PPT, etc.)
  • Distribution: How to subscribe, unsubscribe, access reports
  • Modifications: Who to call to get new or modified reports
  • Archival: Where all past reports be housed

3. Data Management Process

No this is not solely the job of IT or sales operations. It absolutely includes marketing as both a customer of the data, and a provider of much new data. The best way to corrupt a perfectly fine CRM database is let an untrained person in marketing, with no process, do a 100K contact data import into their marketing automation platform and have it sync over to the CRM. From a marketing perspective here are some of the basic components:

  • List import process and designated, trained, importers
  • Rules for all forms (required fields)
  • Normalization guidelines for lists and form data
  • Governance — defined authorization for what marketing can and cannot do

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.

How to Make a Billion: The Costs of ‘Undeliverable as Addressed’

The USPS recently shared some interesting data on the volume and cost of undeliverable as addressed (UAA) mail. That tab was $1.3 billion in 2010, and that was just the cost to the Postal Service, which has to incorporate these costs into its rate-setting. All this UAA is money down the drain to the mailers—who designed, produced and labeled it and applied its postage—and to the Postal Service that has to deal with its final disposition.

The USPS recently shared some interesting data on the volume and cost of undeliverable as addressed (UAA) mail.

According to the USPS, “Total UAA volume dropped from 9.3 billion pieces (4.71 percent of total mail volume) in FY 1998 to 6.9 billion pieces (4.11 percent of total mail volume) in FY 2010. (This reduction, while significant, falls far short of previous Postmaster General Jack Potter’s goal of reducing UAA mail by 50 percent by 2010.) Historically, UAA mail runs in the range of 4 percent to 5 percent of total mail volume, and the percentages of total volume vary by class of mail. Periodicals mail, for example, has a UAA percentage of about 1.5 percent, while Standard Mail usually runs about 6.75 percent. Interestingly, the volumes of UAA mail that the USPS forwards or treats as waste both experienced declines, but the volume of UAA mail that the USPS returns to sender actually increased.”

All this UAA is money down the drain to the mailers—who designed, produced and labeled it and applied its postage—and to the Postal Service that has to deal with its final disposition.

That tab was $1.3 billion in 2010, and that was just the cost to the Postal Service, which has to incorporate these costs into its rate-setting. Add to this bill the cost of 7 billion pieces that went nowhere near the intended recipient—and that’s a fortune off the bottom line. Some of this is inefficiency. Marketers in particular—primarily who use the Standard Mail category—must do a better job in data hygiene and the use of postal addressing and preparation tools.

It may be helpful, and profitable, for mailers to make sure they are undertaking every feasible effort to keep their mailing lists clean—and to avoid this hefty bill. The Direct Marketing Association has an online tool to help marketers make sure their list hygiene and data management efforts are up to par.

It’s called the Environmental Planner & Optional Policy Generator, and it’s based in part on the DMA’s “Green 15” Environmental Principles. But the green focus is dual in nature. Avoiding mail waste through proper data management also applies green—as in money—back to the bottom line! Consider these suggested activities from this planner to get back some of this billion-plus that are lost to UAA:

________________________________________________________

I. LIST HYGIENE AND DATA MANAGEMENT

Our company continually endeavors to manage data and lists in an environmentally responsible manner with a focus on reducing the amount of duplicate, unwanted and undeliverable mail [to both consumers and businesses]. To achieve our goals in this area [If applicable to the goals and/or nature of your organization, please select one or more of the following options.]:

A. We Maintain Suppression Lists

  • We maintain in-house do-not-market lists for prospects and customers who do not wish to receive future solicitations from us (as required by DMA’s Commitment to Consumer Choice).
  • We maintain a more detailed suppression file that enables customers and prospects to opt off our organization’s marketing lists on a selective basis, such as by frequency or by category.

B. We Offer Notice & Choice

  • We provide existing and prospective customers with notice of an opportunity to modify or eliminate future marketing contacts from our organization in every commercial solicitation (as required by DMA’s Commitment to Consumer Choice).
  • We provide periodic notices and opportunities for prospects to opt in or opt out of receiving future marketing contacts from our organization.
  • We provide customers incentives (such as the offer of a discount on their next purchase) for notifying us of duplicate mailings and incorrect addresses.
  • We offer customers a choice to receive communications from our organization electronically.

C. We Clean Our Lists Prior to Mailing

  • We use the Direct Marketing Association (U.S.) Mail Preference Service (MPS) monthly on all applicable consumer prospecting lists. In addition to use of MPS, we maintain clean, deliverable files by using (Please check all that apply):
    • ZIP Code correction
    • Address standardization
    • USPS National Change of Address (NCOA)
    • Other USPS products such as
      • Address Element Correction (AEC)
      • Delivery Sequence File (DSF)
      • Address Correction Requested (ACR)
    • Predictive models and RFM segmentation
    • Other: (Please specify.)
  • We use the DMA “Deceased Do Not Contact” list to eliminate names of deceased persons from mailings.
  • We use the Foreign Mail Preference Service on applicable mailings to the United Kingdom, Belgium or Germany.
  • We use the mail preference services of other foreign national direct marketing associations, where applicable.
  • We [ encourage/ require] our client mailers to use MPS.
  • We [ encourage/ require] companies and organizations that rent our list of customers to screen consumer names through MPS, and to maintain their own do-not-rent and do-not-mail in-house name suppression lists.

D. We Merge/Purge Our Data

  • We match outside lists against each other to prevent duplicates.
  • We use match definitions in merge/purge that minimize duplicates.
  • We match outside lists against other commercially available suppression files where appropriate.

E. We Test Market Offers

  • We test a sample of a list before mailing or marketing to the entire list.
  • We test different versions of advertising and marketing offers, in mail and other media, to select those offers and media combinations that receive the best response.

For more information, see DMA Environmental Resource Guide, “Mailing List Management: A Key to Waste Reduction,” pages 63-70.

________________________________________________________

Now the entirety of the UAA issue is not attributable solely to less than adequate data management, but it is likely a good portion of it is. We know the DMA Board of Directors—in adopting its first environmental public goal which in part commits to reduce UAA by 25 percent from 2009 to 2013—very much intends for marketers to avoid losing these billions down the line.

The Postal Service is working closely with mailers and, vice versa, to tackle other ways to manage UAA and to reduce its volume. Certainly, Intelligent Mail barcodes will help, with the ability to track mail whereabouts in real time as it moves through the USPS’s processing and handling. “Return to Sender” UAA is the most costly for the Postal Service to handle, because of the return handling costs—that, too, needs attention.

In the very least, marketers also should work with their mail service providers most closely to design mail pieces for postal automation compatibility, to apply proper data management practices (as indicated by DMA, for example), and—as the USPS embarks on its network consolidation effort—to track their mail most precisely through the mail stream. A billion dollars and more are in the balance.

Helpful Links:
DMA First Public Green Goal, concerning List Hygiene

DMA Environmental Planner & Optional Policy Generator

Have a Happy & Profitable Earth Day 2012! A Good Time to Enter the ECHO Awards’ Green Marketing Competition

For the past three years, the Direct Marketing Association has awarded a Special ECHO Award dedicated to incorporating sustainable, environmental concerns in marketing. The award is given NOT for being “green” (which is self-limiting), but for being successful in marketing—read, profitable—and demonstrating environmental performance in the process.

For the past three years, the Direct Marketing Association has awarded a Special ECHO Award in its International ECHO Awards competition dedicated to incorporating sustainable, environmental concerns in marketing: The ECHO Green Marketing Award.

The three winners to date—the United States Postal Service (2009), the World Wildlife Fund (2010), and Consumer Reports (2011)—each have taken the direct marketing process and used the DMA “Green 15” environmental marketing practices and principles to illustrate how marketing activity can be both successful in driving response and interaction, and adhere to best practices for environmental performance. Note, the award is given NOT for being “green” (which is self-limiting), but for being successful in marketing—read, profitable—and demonstrating environmental performance in the process.

Importantly, the award—which is judged by members of the DMA Committee on the Environmental and Social Responsibility, under the auspices of the DMA ECHO Awards Committee—looks to evaluate and recognize the marketing process, and not the product or service being marketed. Thus, the product or service being marketing need not be environmentally focused (though it certainly can be). What the judges look for is the usual hallmarks of an ECHO Award-winning direct-response campaign—strategy, creative, results—and adds a fourth component, adherence to environmental principles which apply to direct marketing. These principles are clearly stated in the DMA Green 15, which articulate list hygiene, paper procurement and use, printing and production, mail design, fulfillment and recycling collection & pollution prevention in everyday direct marketing business decision-making.

To date, each previous winner interpreted this objective in in very different ways. The USPS sought to demonstrate how direct mail advertising can be very environmentally sensitive (and sensible) in its multi-faceted “Environmailist” campaign, targeted at advertising agencies and brands that use the direct mail channel. In Australia, the World Wildlife Fund, working to promote its “Earth Hour” environmental awareness effort, sent carbon-neutral plant spikes via potted plants to office managers around the country to promote greater efficiency in office environments. Last year, Consumer Reports—in promoting subscription to its ShopSmart and Consumer Reports magazines—used the Green 15 to audit each of its business decisions in data management, supply chain engagement, procurement, production, logistics and customer communication, and to apply the principles where they made economic sense or were revenue-neutral.

The deadline for entering the 2012 DMA International ECHO Marketing Award competition is April 25, 2012, with a late deadline of May 2, 2012: http://dma-echo.org/enter.jsp

As brands and agencies enter the Awards, there is an entry field where consideration for the ECHO Green Marketing Award is prompted. If the “yes” box is checked, an additional Green Marketing Award Addendum can be promptly accessed that allows up to 1,000 words to explain how the entry:

  • Employs Innovative Green Tactics & Strategies Employed Throughout the Direct Marketing Process
  • Inspires Action & Making a Difference to the Planet
  • Demonstrates Measurable Environmental Impact of the Campaign
  • … all the while being a successful marketing campaign overall.

Happy Earth Day 2012—and take the time to show others how your brand or your client’s brand is leading the way in incorporating environmental sensitivity in its everyday marketing decision-making—and producing outstanding, profitable results. I’m hopeful I will be writing about your winning campaign once the 2012 winner is announced during the DMA2012 Conference this October in Las Vegas, NV.

Consumer Reports Nets DMA ECHO Green Marketing Award 2011: Lessons for Every Marketer

One of the highlights of the Direct Marketing Association’s 2011 annual conference was the awarding of a special ECHO award to Consumer Reports, the organization behind the magazine of the same name. As a member of DMA’s Committee on the Environment and Social Responsibility (CESR), I was one of the judges of this year’s competition, which looks to honor one marketing organization that has demonstrated environmental performance and sustainable practices in the design and execution of an advertising campaign.

One of the highlights of the Direct Marketing Association’s 2011 annual conference was the awarding of a special ECHO award—the ECHO Green Marketing Award—to Consumer Reports, the organization behind the magazine of the same name. As a member of DMA’s Committee on the Environment and Social Responsibility (CESR), I was one of the judges of this year’s competition, which looks to honor one marketing organization that has demonstrated environmental performance and sustainable practices in the design and execution of an advertising campaign.

What makes the Consumer Reports entry remarkable is its demonstrated adherence to a set of environmental principles and practices known as the DMA “Green 15.” Established by DMA in 2009, the DMA Green 15 provides guidance to marketers on list hygiene and data management, paper procurement, printing and production, and recycling and workplace operations—all in an effort to support the triple bottom line of people, planet and profit.

The campaign itself was a recent subscription offer for Consumer Reports and ShopSmart magazines. The campaign did not sell an environmental product. It did not tout environmental claims. It did not involve environmental causes. Yet it won our discipline’s highest environmental marketing honor. Why? Because the campaign incorporated environmental sensitivity, efficiencies, and cross-company and supply chain engagement into everyday marketing planning and decision-making.

In short, the Consumer Reports effort is a blueprint that all marketers—commercial and non-profit—can replicate in their own everyday marketing.

Consider this excerpt from the entry:

We produced the Winter 2010/11 direct marketing campaign with the goal of strategically supporting the sustainability objectives of meeting our acquisition targets, serving the ongoing needs of consumers, and of being good stewards of the resources we use. Direct Marketing and Publishing Operations departments worked collaboratively guided by our internal Environmental Policy & Vision Statement to identify, implement, and track meaningful environmental choices made throughout the life cycle of the campaign season.

The overall environmental benefits of the choices we made included less energy and materials consumption, more benign manufacturing, and reduced emissions. Additionally, we promoted recycling of direct marketing packages that are recyclable, saved money, upheld response rates, and met our objectives.

The full entry incorporated actions that the Consumer Reports vendors undertook to increase efficiencies and environmental performance, as well as documented gains in paper procurement and use, mail design and production, and recycling and pollution reduction—all with measurements that document positive environmental impacts while achieving financial objectives.

I encourage all marketers to look to the example of Consumer Reports and its adherence to the DMA Green 15. In fact, the long-term sustainability of direct marketing depends on it.

Resources:
Direct Marketing Association’s Green 15 Toolkit for Marketers

With Special Permission, This Year’s DMA International ECHO Green Marketing Award Winner, Consumer Reports.

Editor’s Note: As of Autumn 2011, ConsumersUnion is newly rebranded as Consumer Reports.