Digital Marketing Strategy Involves Knowing When to Seek an Outside Perspective

In deciding how to tackle a marketing problem, you should consider whether insider expertise, an outside perspective, or a combination of the two will lead to the best possible outcome.

Tackling digital marketing tasks — as with just about any other business task — can lead you to solutions under your own roof or to bringing in outside help.

Primary motivators in that decision-making process are likely to be cost, of course, or expertise. Do you have someone with the necessary skills and enough bandwidth to take on the project?

There should be another consideration, as well: would an outside perspective provide value that an in-house resource can’t?

The situations below provide some possible paths with which you might approach your own marketing conundrums, even if they aren’t an exact match for these examples.

Website Architecture and Navigation

For all but the most complex of websites, structure and navigation look deceptively simple. (Most sites with overly complex navigation could probably be better organized.)

And of course, most of us spend a fair amount of time on the web, so we feel we can tell a good experience from a bad one.

But just because your team members have their opinions doesn’t mean they can translate them into a useful set of recommendations that fit your website’s needs. That’s where applicable experience becomes valuable, though that experience doesn’t necessarily require an outside perspective.

The real value an outside perspective provides is an ability to more easily view your message and content in the same way your target audience will. An outside expert is not saddled with the deeply ingrained knowledge that any well-versed marketing employee will have.

An outside perspective here can mean the difference between a site that is set up to mimic your firm’s org chart or business units, and a site that is organized to appeal to each of your most important audience segments and gather the information they’re likely to be most interested in.

Content Strategy and Content Development

But an outside perspective doesn’t always win the day. For example, when it comes to content development, we encourage our clients to devote skills and resources needed to generate content themselves, in-house. Nobody will ever know your business as well as your own team does, though a long-term “permalancer” can come very close. In that case, though, they’re not really providing an outside perspective. Quite the opposite; they are outsiders who are essentially assimilating your culture.

There are exceptions, as with problem areas that seem like they should be producing a positive marketing ROI but are not. An outside perspective can be all that’s needed to make the adjustment that get the results rolling in.

Same Old Wine in a Brand New Jar

(Bonus points if you can name the song from which that line comes. Hint: It’s by The Who, but it’s not one of their big hits.)

Finally, there are instances where the combination of an outside perspective and inside knowledge are an unbeatable combination. We see this during the discovery process we run before website design or coding get underway.

As we seek out input from a wide range of stakeholders, we get an incredible range of perspectives, from the marketing team, as you’d expect, but also from top-level executives, as well as entry-level customer-facing employees.

An outside perspective alone wouldn’t provide any great value, but when combined with great insights from the inside team, the outcomes are incredibly powerful. The outside expert may not add any new thinking; they simply help the internal team view the insights they themselves have from another angle.

These three paths — inside knowledge only, outside perspective, and the two working together — should all be considered as you address your everyday marketing tasks, as well as the thorny issues we all face from time to time. Putting the right kind of team together is an important part of crafting the best solution.

7 Reasons It’s Tough to Change Decisions

With each passing day, voters’ decisions are being made up, and not a lot is going to change their minds — no matter how much is poured into political ads. Changing minds is also a problem for direct marketers. The minds of our prospects are often made up before we have a chance to stimulate their emotion and present our message. Envelopes aren’t opened and are pitched. Emails go away in a click. The mind is …

FrustratedWith each passing day, voters’ decisions are being made up, and not a lot is going to change their minds — no matter how much is poured into political ads. Getting people to change decisions is also a problem for direct marketers. The minds of our prospects are often made up before we have a chance to stimulate their emotion and present our message. Envelopes aren’t opened and are pitched. Emails go away in a click. The mind is made up.

There are plenty reasons why our copy and creative hit roadblocks. Some days our message just doesn’t connect. We have to work smarter, and know that changing the mind is often an uphill climb. So today I offer seven reasons why it’s often tough to change a decision, along with ideas you can use to overcome each area of resistance.

  1. Childhood Experiences: At an early age, like a sponge, we start taking in information, all a part of life experiences. We take away feelings about many things. We form opinions to keep us safe. It’s the primitive brain. So make sure you consider how your product or service reassures and keeps your prospect safe.
  1. Long-Term Memory: Deep-seated long-term memories stick with a person for their entire lives. To minimize a bad memory, another memory must be created to neutralize it. It’s a tall order to change a memory of any kind. But if you’re going to get through, you must create a new positive memory, especially if you need to overcome a bad memory.
  1. Perception Rules: For some people, changing an ingrained perception is impossible, even if their perception is wrong. And when you probe more deeply, most people won’t recall why their perception rules exist in the first place. This one is tough to overcome, so acknowledge to your prospect that you’re challenging their perception.
  1. Internal Conflict: Reason and emotion are in opposition to each other. Emotion most often wins. You must interpret your offer for the metaphorical left brain, setting you up to win over with emotion in the right brain.
  1. Regretting a Past Decision: People reflect on past decisions that disappoint. Regret and remorse set in. A person’s gut reaction is usually a product of bias. You need to assure, likely in a guarantee, that you stand by your product and make things right, if necessary, so your prospect doesn’t dredge up past regrets.
  1. Intuition: Intuition is activated before our minds consciously understand, based on stored emotional memories that a person keeps secret in their sub-conscious. Therefore intuition often guides decision making without much conscious deliberation. Keep your prospect focused on your message and set up a logical flow so intuition doesn’t creep in and move your prospect off your message.
  1. Noise: With the noise of competitive marketing messages across media at every turn, the mind becomes confused and numbed, which results in sticking with a past decision. That’s why you must stand out. Have a strong unique selling proposition and use stories to solidify new long-term memory.

Like it or not, the human mind works in mysterious ways. Today more than ever, it’s tough to change a mind.

As for candidates, those who have led in polls and snag voter commitments early solidify their position. How? By expressing positions that make the voter feel good, whether the position is credible or not. Ultimately, whoever attracts the most raving fans wins, because it’s the candidate that makes them feel good about the future who voters will support. There’s a lesson here for marketers, too.

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.

It’s All About Ranking

The decision-making process is really all about ranking. As a marketer, to whom should you be talking first? What product should you offer through what channel? As a businessperson, whom should you hire among all the candidates? As an investor, what stocks or bonds should you purchase? As a vacationer, where should you visit first?

The decision-making process is really all about ranking. As a marketer, to whom should you be talking first? What product should you offer through what channel? As a businessperson, whom should you hire among all the candidates? As an investor, what stocks or bonds should you purchase? As a vacationer, where should you visit first?

Yes, “choice” is the keyword in all of these questions. And if you picked Paris over other places as an answer to the last question, you just made a choice based on some ranking order in your mind. The world is big, and there could have been many factors that contributed to that decision, such as culture, art, cuisine, attractions, weather, hotels, airlines, prices, deals, distance, convenience, language, etc., and I am pretty sure that not all factors carried the same weight for you. For example, if you put more weight on “cuisine,” I can see why London would lose a few points to Paris in that ranking order.

As a citizen, for whom should I vote? That’s the choice based on your ranking among candidates, too. Call me overly analytical (and I am), but I see the difference in political stances as differences in “weights” for many political (and sometimes not-so-political) factors, such as economy, foreign policy, defense, education, tax policy, entitlement programs, environmental issues, social issues, religious views, local policies, etc. Every voter puts different weights on these factors, and the sum of them becomes the score for each candidate in their minds. No one thinks that education is not important, but among all these factors, how much weight should it receive? Well, that is different for everybody; hence, the political differences.

I didn’t bring this up to start a political debate, but rather to point out that the decision-making process is based on ranking, and the ranking scores are made of many factors with different weights. And that is how the statistical models are designed in a nutshell (so, that means the models are “nuts”?). Analysts call those factors “independent variables,” which describe the target.

In my past columns, I talked about the importance of statistical models in the age of Big Data (refer to “Why Model?”), and why marketing databases must be “model-ready” (refer to “Chicken or the Egg? Data or Analytics?”). Now let’s dig a little deeper into the design of the “model-ready” marketing databases. And surprise! That is also all about “ranking.”

Let’s step back into the marketing world, where folks are not easily offended by the subject matter. If I give a spreadsheet that contains thousands of leads for your business, you wouldn’t be able to tell easily which ones are the “Glengarry Glen Ross” leads that came from Downtown, along with those infamous steak knives. What choice would you have then? Call everyone on the list? I guess you can start picking names out of a hat. If you think a little more about it, you may filter the list by the first name, as they may reflect the decade in which they were born. Or start calling folks who live in towns that sound affluent. Heck, you can start calling them in alphabetical order, but the point is that you would “sort” the list somehow.

Now, if the list came with some other valuable information, such as income, age, gender, education level, socio-economic status, housing type, number of children, etc., you may be able to pick and choose by which variables you would use to sort the list. You may start calling the high income folks first. Not all product sales are positively related to income, but it is an easy way to start the process. Then, you would throw in other variables to break the ties in rich areas. I don’t know what you’re selling, but maybe, you would want folks who live in a single-family house with kids. And sometimes, your “gut” feeling may lead you to the right place. But only sometimes. And only when the size of the list is not in millions.

If the list was not for prospecting calls, but for a CRM application where you also need to analyze past transaction and interaction history, the list of the factors (or variables) that you need to consider would be literally nauseating. Imagine the list contains all kinds of dollars, dates, products, channels and other related numbers and figures in a seemingly endless series of columns. You’d have to scroll to the right for quite some time just to see what’s included in the chart.

In situations like that, how nice would it be if some analyst threw in just two model scores for responsiveness to your product and the potential value of each customer, for example? The analysts may have considered hundreds (or thousands) of variables to derive such scores for you, and all you need to know is that the higher the score, the more likely the lead will be responsive or have higher potential values. For your convenience, the analyst may have converted all those numbers with many decimal places into easy to understand 1-10 or 1-20 scales. That would be nice, wouldn’t it be? Now you can just start calling the folks in the model group No. 1.

But let me throw in a curveball here. Let’s go back to the list with all those transaction data attached, but without the model scores. You may say, “Hey, that’s OK, because I’ve been doing alright without any help from a statistician so far, and I’ll just use the past dollar amount as their primary value and sort the list by it.” And that is a fine plan, in many cases. Then, when you look deeper into the list, you find out there are multiple entries for the same name all over the place. How can you sort the list of leads if the list is not even on an individual level? Welcome to the world of relational databases, where every transaction deserves an entry in a table.

Relational databases are optimized to store every transaction and retrieve them efficiently. In a relational database, tables are connected by match keys, and many times, tables are connected in what we call “1-to-many” relationships. Imagine a shopping basket. There is a buyer, and we need to record the buyer’s ID number, name, address, account number, status, etc. Each buyer may have multiple transactions, and for each transaction, we now have to record the date, dollar amount, payment method, etc. Further, if the buyer put multiple items in a shopping basket, that transaction, in turn, is in yet another 1-to-many relationship to the item table. You see, in order to record everything that just happened, this relational structure is very useful. If you are the person who has to create the shipping package, yes, you need to know all the item details, transaction value and the buyer’s information, including the shipping and billing address. Database designers love this completeness so much, they even call this structure the “normal” state.

But the trouble with the relational structure is that each line is describing transactions or items, not the buyers. Sure, one can “filter” people out by interrogating every line in the transaction table, say “Select buyers who had any transaction over $100 in past 12 months.” That is what I call rudimentary filtering, but once we start asking complex questions such as, “What is the buyer’s average transaction amount for past 12 months in the outdoor sports category, and what is the overall future value of the customers through online channels?” then you will need what we call “Buyer-centric” portraits, not transaction or item-centric records. Better yet, if I ask you to rank every customer in the order of such future value, well, good luck doing that when all the tables are describing transactions, not people. That would be exactly like the case where you have multiple lines for one individual when you need to sort the leads from high value to low.

So, how do we remedy this? We need to summarize the database on an individual level, if you would like to sort the leads on an individual level. If the goal is to rank households, email addresses, companies, business sites or products, then the summarization should be done on those levels, too. Now, database designers call it the “de-normalization” process, and the tables tend to get “wide” along that process, but that is the necessary step in order to rank the entities properly.

Now, the starting point in all the summarizations is proper identification numbers for those levels. It won’t be possible to summarize any table on a household level without a reliable household ID. One may think that such things are given, but I would have to disagree. I’ve seen so many so-called “state of the art” (another cliché that makes me nauseous) databases that do not have consistent IDs of any kind. If your database managers say they are using “plain name” or “email address” fields for matching or summarization, be afraid. Be very afraid. As a starter, you know how many email addresses one person may have. To add to that, consider how many people move around each year.

Things get worse in regard to ranking by model scores when it comes to “unstructured” databases. We see more and more of those, as the data sources are getting into uncharted territories, and the size of the databases is growing exponentially. There, all these bits and pieces of data are sitting on mysterious “clouds” as entries on their own. Here again, it is one thing to select or filter based on collected data, but ranking based on some statistical modeling is simply not possible in such a structure (or lack thereof). Just ask the database managers how many 24-month active customers they really have, considering a great many people move in that time period and change their addresses, creating multiple entries. If you get an answer like “2 million-ish,” well, that’s another scary moment. (Refer to “Cheat Sheet: Is Your Database Marketing Ready?”)

In order to develop models using variables that are descriptors of customers, not transactions, we must convert those relational or unstructured data into the structure that match the level by which you would like to rank the records. Even temporarily. As the size of databases are getting bigger and bigger and the storage is getting cheaper and cheaper, I’d say that the temporary time period could be, well, indefinite. And because the word “data-mart” is overused and confusing to many, let me just call that place the “Analytical Sandbox.” Sandboxes are fun, and yes, all kinds of fun stuff for marketers and analysts happen there.

The Analytical Sandbox is where samples are created for model development, actual models are built, models are scored for every record—no matter how many there are—without hiccups; targets are easily sorted and selected by model scores; reports are created in meaningful and consistent ways (consistency is even more important than sheer accuracy in what we do), and analytical language such as SAS, SPSS or R are spoken without being frowned up by other computing folks. Here, analysts will spend their time pondering upon target definitions and methodologies, not about database structures and incomplete data fields. Have you heard about a fancy term called “in-database scoring”? This is where that happens, too.

And what comes out of the Analytical Sandbox and back into the world of relational database or unstructured databases—IT folks often ask this question—is going to be very simple. Instead of having to move mountains of data back and forth, all the variables will be in forms of model scores, providing answers to marketing questions, without any missing values (by definition, every record can be scored by models). While the scores are packing tons of information in them, the sizes could be as small as a couple bytes or even less. Even if you carry over a few hundred affinity scores for 100 million people (or any other types of entities), I wouldn’t call the resultant file large, as it would be as small as a few video files, really.

In my future columns, I will explain how to create model-ready (and human-ready) variables using all kinds of numeric, character or free-form data. In Exhibit A, you will see what we call traditional analytical activities colored in dark blue on the right-hand side. In order to make those processes really hum, we must follow all the steps that are on the left-hand side of that big cylinder in the middle. Preventing garbage-in-garbage-out situations from happening, this is where all the data get collected in uniform fashion, properly converted, edited and standardized by uniform rules, categorized based on preset meta-tables, consolidated with consistent IDs, summarized to desired levels, and meaningful variables are created for more advanced analytics.

Even more than statistical methodologies, consistent and creative variables in form of “descriptors” of the target audience make or break the marketing plan. Many people think that purchasing expensive analytical software will provide all the answers. But lest we forget, fancy software only answers the right-hand side of Exhibit A, not all of it. Creating a consistent template for all useful information in a uniform fashion is the key to maximizing the power of analytics. If you look into any modeling bakeoff in the industry, you will see that the differences in methodologies are measured in fractions. Conversely, inconsistent and incomplete data create disasters in real world. And in many cases, companies can’t even attempt advanced analytics while sitting on mountains of data, due to structural inadequacies.

I firmly believe the Big Data movement should be about

  1. getting rid of the noise, and
  2. providing simple answers to decision-makers.

Bragging about the size and the speed element alone will not bring us to the next level, which is to “humanize” the data. At the end of the day (another cliché that I hate), it is all about supporting the decision-making processes, and the decision-making process is all about ranking different options. So, in the interest of keeping it simple, let’s start by creating an analytical haven where all those rankings become easy, in case you think that the sandbox is too juvenile.