Why Behavioral Science Is Critical to Marketing and Research

What if we could identify consumers’ underlying emotions or motivations to improve our understanding of whether they were actually going to purchase a product? Over the past few years, marketing and research has been digging into the “why” behind behaviors to get even deeper, below the surface of the insights we deliver. The goal is to help brands better understand the true drivers of consumers’ behavior — and it all starts with behavioral science.

What if we could identify consumers’ underlying emotions or motivations to improve our understanding of whether they were actually going to purchase a product? Over the past few years, marketing and research has been digging into the “why” behind behaviors to get even deeper, below the surface of the insights we deliver. The goal is to help brands better understand the true drivers of consumers’ behavior — and it all starts with behavioral science.

What Is Behavioral Science?

Behavioral science isn’t a new industry, but within the past few years is something of an emerging topic in marketing and research. At its core, behavioral science and the research that results from it, seeks to understand the many aspects related to someone’s habits or decision-making. Most importantly, as we noted, it helps to understand why people make certain decisions.

If you think of that in the context of our marketing and product strategies, it’s clear why behavioral science plays a role in market research. There are a variety of methods that can get close to truly understanding consumer behavior, but much of them can fail to capture empirical evidence — sensory information captured through observations and the documentation of behaviors through experimentation.

As a result, the importance and rise of behavioral science in marketing and research is no small subject. Just in the past year, there have already been numerous events discussing behavioral science specific to gathering and analyzing data to understand why consumers make decisions — but marketers and researchers, by and large, are still figuring out how to leverage it.

Leveraging Behavioral Data

Big data can be used as a possible solution for at least two reasons. First, it gives us access to more data than ever before, including data based on actual behavior from purchasing, web analytics, subscriptions, and more. As a result, big data can reduce the struggles we sometimes have with differences between stated versus observed behavior.

Second, there are big data sources that allow us to understand motivations of our consumers by examining the big 5 personality traits for millions and millions of people. By understanding different personalities, we can begin to realize if being “extroverted” or “conscientious” drives consumers’ purchasing. Some suggest that behavioral science and the resulting data on motivations behind decision making will be the new normal for market research. We agree that understanding what people don’t tell us in surveys is as important as what they do. Together, these two types of data give us a more well-rounded picture of consumer behavior, and with the right methodology, you can gain this knowledge quickly.

In a specific use case, a brand was looking to understand their target audience for a new product innovation. They had hypothesis’ about what this audience would look like, and likely could have gained that knowledge through standard quantitative research. However, by incorporating an approach that combines survey data and big data, they were able to understand who their audience was, but also what would motivate them to purchase this particular new product. The moral of the story? Consumers are more than just the people that buy your product.

Experience Design Benefits Greatly From Behavioral Data

Human-centered design thinking has influenced much of the way that companies think about user and customer experience, and for the better. Because customer experience is becoming an important vehicle through which brand propositions are communicated today, it is worth examining if the way we design customer experiences can be improved. Particularly, is there a way to better integrate data and analytics into design thinking?

Human-centered design thinking has influenced much of the way that companies think about user and customer experience, and for the better. Because customer experience is becoming an important vehicle through which brand propositions are communicated today, it is worth examining if the way we design customer experiences can be improved. Particularly, is there a way to better integrate data and analytics into design thinking?

A well-designed customer experience offers many benefits, such as:

  • increasing the productivity of users and service efficiency.
  • Making solutions easier to use and, therefore, reducing support costs
  • Increased accessibility and reducing discomfort and stress
  • Signature experiences that convey and re-enforce the brand proposition

In order to achieve these results, most experience design processes begin with deep empathy, which entails physically observing, interviewing and surveying customers to uncover unmet needs and pain points.

These methods often help uncover significant opportunities to improve the customer services. Just as often, however, they take companies down unprofitable journeys and fail to identify growth opportunities.

For example, Spirit airlines probably ignores every stated customer desire except price (in most cases), yet it has a very strong business model. Can you imagine the market research that says customers don’t care about on-time arrival, service or cabin comfort and want to be nickeled and dimed for every possible amenity? An examination of behavioral data, however, would show that there is a large market of travelers who consistently shop for the cheapest flight, regardless of service, brand and reputation, and Spirit has learned to cater to this segment very well.

In my view, most experience design projects fail to bring in behavioral data and resultingly miss the bigger opportunity. I have observed many customer experience projects that try desperately to empathize with the customer, but fail to examine if this is the customer they want and what their purchase and usage behaviors truly reveal.

Sometime back, my team and I were asked to identify key factors driving retention and renewal behavior among auto and home insurance customers. Certainly, survey-based feedback was helpful and identified areas of dissatisfaction, such as complicated billing, poor claims experiences and unexplained rate increases. Individual customer interviews yielded even more interesting satisfaction drivers, such as financial trust and need for honest advice. However, looking at behavioral data, such as the types of policies purchased, tenure of the policies and household makeup actually uncovered the deepest insights. Although this is now common knowledge in the insurance industry, customers who bundle auto and home policies are much less likely to switch. Therefore, most insurance carriers try to offer an Auto-Home discount. Other behaviorally observed factors, such as the level of coverage selected and signing up for auto pay are also significant predictors of retention. Surprisingly, none of these factors bubbled up directly in customer interviews or surveys. Furthermore, factors derived from the behavioral data explained 70 to 80 percent of the attrition in any given year.

Despite this example, it would be very wrong to assume that human-centered design principles do not work or that some of the methods employed to develop user/customer empathy are bunk. However, I would say that interviews and experience audits are only one source of customer insight; mining customer behavioral data is another powerful source of customer insights. A well-thought-out experience design should have the benefit of both.

The Keyword in ‘Customer Journey’ Is ‘Customer’

The keyword in “Customer Journey” is “customer,” not “journey.” In fact, in this Omni-channel world, the word “journey” doesn’t even do much justice to what that journey study should be all about; there is no simple linear timeline about any of it anymore.

The keyword in “Customer Journey” is “customer,” not “journey.” In fact, in this omnichannel world, the word “journey” doesn’t even do much justice to what that journey study should be all about; there is no simple linear timeline about any of it anymore.

We often think about the customer journey in this fashion: awareness, research, engagement, transaction, feedback and, ever-important, repeat-purchase. This list is indeed a good start.

However, if you look at this list as a consumer, not as a marketer, do you personally go through all of these steps in this particular order? On a conceptual level, yes, but in the world where everyone is exposed to over five types of screens and interactive devices every day, old-fashioned frameworks based on linear timelines don’t always hold water.

I, as a consumer, often do research using my phone at the place of purchase. I may feel rewarded even before any actual purchase. I may provide feedback about my “experience” before, during or after a transaction. And being a human being with emotions, my negative feedback may not be directly correlated to my loyalty to the brand. (Actually, I am writing this piece while flying on an airline with which I have a premiere status, and to which I often provide extremely negative reviews.)

People are neither linear nor consistent. Especially when we are connected to devices with which we research, interact, transact and complain anytime, anywhere. The only part that is somewhat linear is when we put something in the shopping basket, make a purchase, and keep or return the item. So, this timeline view, in my opinion, is just a good guideline. We need to look at the customer journey from the customer’s angle, as well.

Understanding customer behavior is indeed a tricky business, as it requires multiple types of data. If we simplify it, we may put the key variables into three major categories. For a 3-dimensioal view (as I often do in a discussion), put your left hand out and assign each of the following dimensions to your thumb, index finger and the middle finger:

  • Behavioral Data: What they showed interest in, browsed, researched, purchased, returned, subscribed to, etc. In short, what they actually did.
  • Demographic Data: What they look like, in terms of demographic and geo-demographic data, such as their age, gender, marital status, income, family composition, type of residence, lifestyle, etc.
  • Attitudinal Data: Their general outlook on life, religious or political beliefs, priorities in life, reasons why they like certain things, purchase habits, etc.

One may say these data types are highly correlated to each other, and more often than not, they are indeed highly correlated. But not exactly so, and not all the time. Just because one keeps purchasing luxury items or spending time and money on expensive activities, and he is enjoying a middle-age life style living in posh neighborhood, we can’t definitely claim that he is politically conservative. Sometimes we just have to stop and ask the person.

On top of that, what people say they do and what they actually do are often not the same. Hence, these three independent axis of data types to describe a person.

If we have all three types of data about a person, prediction of that person’s intention — or his journey for commercial purposes or otherwise — will become incredibly accurate. But, unfortunately for marketers, asking “everyone about everything” simply isn’t feasible.

Even the most thorough survey is based on a relatively small sample response. One great thing about traditional primary research is that we often get to know who the respondents are. On the other hand, if we rely on social media to “listen,” we get to have opinions from far more people. But the tricky part there is that we don’t get to know who is speaking, as PII (personally identifiable information) is heavily guarded by the social media handlers. Basically, it isn’t easy to connect the dots completely when it comes to attitudinal data. (Conversely, connecting the dots between the behavioral data and demographic data is much simpler, provided with a decent data collection mechanism.)

Now let’s go back to the timeline view of the customer journey for an initial framework. Let’s list the key items in a general order for a simpler breakdown (though things may not be totally linear nowadays), and examine types of data available in each stage. The goal here is to find the point of entry for this difficult task of understanding the “end-to-end” customer journey in the most comprehensive way.

Listing typical data types associated with these entries:

  1. Awareness: Source (where from), likes/followings, clicks other digital trails, survey results, social media data, etc.
  2. Research: Browsing data, search words/search results, browsing length, page/item views, chats, etc.
  3. Engagement: Shopping basket data, clicks, chats, sales engagements, other physical trails at stores, etc.
  4. Transaction: Product/service (items purchased), transaction date, transaction amount, delivery date, transaction channel, payment method, region/store, discounts, renewals, cancelations, etc.
  5. Feedback: Returns, complaints and resolutions, surveys, social media data, net promotor score, etc.
  6. Repeat-purchase: Transaction data summarized on a customer level. The best indicator of loyalty.

Now, looking back at the three major types of data, let’s examine these data related to journey stages in terms of the following criteria:

  • Quality: Are data useful for explaining customer behaviors and predicting their next moves and future values? To explain their motives?
  • Availability: Do you have access to the data? Are they readily available in usable forms?
  • Coverage: Do you have the data just for some customers, or for the most of them?
  • Consistency: Do you get to access the data at all times, or just once in a while? Are they in consist forms? Are they consistently accurate?
  • Connectivity: Can you connect available data on a customer level? Or are they locked in silos? Do you have the match-key that connects customer data regardless of the data sources?

With these criteria, the Ground Zero of the most useful source in terms of understanding customers is transaction data. They are usually in the most usable formats, as they are mostly numbers and figures around the product data of your business. Sometimes, you may not get to know “who” made the purchase, but in comparison to other data types, hands-down, transaction data will tell you the most compelling stories about your customers. You’ll have to tweak and twist them to derive useful insights, but the field of analytics has been evolving around such data all along.

If you want to dig deeper into the “why” part of the equation (or other qualitative elements), you would need to venture into non-transactional, more attitudinal data. For the study of online journey toward conversion, digital analytics is undoubtedly in its mature stage, though it only covers online behaviors. Nonetheless, if you really want to understand customers, start with what they actually purchased, and then expand the study from there.

We rarely get to have access to all of the behavioral, demographic, and attitudinal data. And under those categories, we can think of a long list of subcategories, as well. Cross all of that with the timeline of the journey — even a rudimentary one — and having readily usable data from all three angles at all stages is indeed a rare event.

But that has been true for all ages of database marketing. Yes, those three key elements may move independently, but what if we only get to have one or two out of the three elements? Even if we do not have attitudinal data for a customer’s true motivation of engagement, the other two types of data — behavioral, which is mostly transaction and digital data, and demographic data, which can be purchased in large markets like the U.S. — can provide at least directional guidance.

How do you think the political parties target donors during election cycles? They at times have empirical data about someone’s political allegiance, but many times they “guess” using behavioral and demographic data along with modeling techniques, without really “asking” everyone.

Conversely, if you get to have access to attitudinal data of “some” people with known identities, we can build models to project such valuable information to the general population, only using a “common” set of variables (mostly demographic data). For instance, we may only get a few thousand respondents revealing their sentiment toward a brand or specific stances (for example, being a “green” conscience customer). We can use common demographic variables to project such a tendency to everyone. Would such a “bridging technique” be perfect? Like I mentioned in the beginning, no, not always. Will having such inferred information be much better than not knowing anything at all? Absolutely.

Without a doubt, understanding the customer journey is an important part of marketing. How else would you keep them engaged at all stages of purchases, leading them to loyalty?

The key is not to lose focus on the customer-centric side of analytics. Customer journey isn’t even perfectly sequential anymore. It should be more about “customer experience” regardless of the timeline. And to get to that level of constant relevancy, start with the known customer behaviors, and explain away “what works” in all channel engagements for each stage.

Channel or stage-oriented studies have their merits, but they won’t lead marketers to a more holistic view of customers. After all, high levels of awareness and ample clicks are just good indicators of future conversions; they do not instantly guarantee loyalty or profitability. Transaction data tend to reveal more stable paths to longevity of customer relationship.

You may never get to have explicit measurements of loyalty consistently; but luckily for us, customers vote with their money. Unlock the transaction data first, and then steadily peel away to the “why” part.

I am not claiming that you will obtain the answer to the “causality” question with just behavioral data; but for marketing purposes, I’d settle for “highly correlated” elements anytime. Because marketing activities can happen successfully without pondering upon the “why” question, if actionable shortcuts to loyalty are revealed through sold transaction data.

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.

Behavioral Targeting Industry Needs Further Delineation

I received an interesting press release the other day from ValueClick Media that recapped a recent behavioral targeting panel that took the stage at the Hard Rock Hotel in Chicago.

The panel featured an industry analyst (David Hallerman, senior analyst, eMarketer), a behavioral targeting product expert (Joshua Koran, vice president, targeting and optimization, ValueClick, Inc.), a brand marketer (Julian Chu, Director of Acquisition Marketing, Discover) and an interactive agency executive (Sam Wehrs, Digital Activation Director, Starcom).
 

I received an interesting press release the other day from ValueClick Media that recapped a recent behavioral targeting panel that took the stage at the Hard Rock Hotel in Chicago.

The panel featured an industry analyst (David Hallerman, senior analyst, eMarketer), a behavioral targeting product expert (Joshua Koran, vice president, targeting and optimization, ValueClick, Inc.), a brand marketer (Julian Chu, Director of Acquisition Marketing, Discover) and an interactive agency executive (Sam Wehrs, Digital Activation Director, Starcom).

What I found most interesting about the release was that fact the group discussed and agreed on the need for delineation between the different approaches to behavioral targeting.

“While it is important to understand the difference between retargeting – which Hallerman referred to as “reactive” – and the more complex models, the panel agreed it is also critical to understand the differences within the more sophisticated group of behavioral targeting approaches, and Joshua Koran shared three designations: “clustering,” “custom business rules” and “predictive attributes,” the release said.

The “clustering” approach assigns each visitor to one and only one segment while the “custom business rules” approach offers marketers the ability to target visitors who have done X events in Y days, with Boolean operators of “and.” “or,” and “not.” Finally, the “predictive attributes” approach automates the assignment of interest categories based on the visitor activities that best correlate with performance; thus, the system is continuously learning to identify multiple interest attributes per visitor.

Another notable takeaway was the need for a focus on the customer experience and the corresponding importance of demonstrating value to customers when serving behaviorally targeted ads.

According to the release Julian Chu offered three questions marketers must address to make behavioral targeting a valuable experience for customers instead of merely serving the ads, which would unavoidably become customer annoyance: How are you going to do it? Where is it going to happen? What is going to happen at that time?

Presented as part of ValueClick Media’s ongoing Media Lounge education event series, this event – The Changing Behavioral Targeting Landscape – as well as the discussion itself underscored the importance of education relative to this increasingly important online advertising technique.

Food for thought!