Marketing and IT; Cats and Dogs

Cats and dogs do not get along unless they grew up together since birth. That is because cats and dogs have rather fundamental communication problems with each other. A dog would wag his tail in an upward position when he wants to play. To a cat though, upward-tail is a sure sign of hostility, as in “What’s up, dawg?!” In fact, if you observe an angry or nervous cat, you will see that everything is up; tail, hair, toes, even her spine. So imagine the dog’s confusion in this situation, where he just sent a friendly signal that he wants to play with the cat, and what he gets back are loud hisses and scary evil eyes—but along with an upward tail that “looks” like a peace sign to him. Yeah, I admit that I am a bona-fide dog person, so I looked at this from his perspective, first. But I sympathize with the cat, too. As from her point of view, the dog started to mess with her, disrupting an afternoon slumber in her favorite sunny spot by wagging his stupid tail. Encounters like this cannot end well. Thank goodness that us Homo sapiens lost our tails during our evolutionary journey, as that would have been one more thing that clueless guys would have to decode regarding the mood of our female companions. Imagine a conversation like “How could you not see that I didn’t mean it? My tail was pointing the ground when I said that!” Then a guy would say, “Oh jeez, because I was looking at your lips moving up and down when you were saying something?”

 

Cats and dogs do not get along unless they grew up together since birth. That is because cats and dogs have rather fundamental communication problems with each other. A dog would wag his tail in an upward position when he wants to play. To a cat though, upward-tail is a sure sign of hostility, as in “What’s up, dawg?!” In fact, if you observe an angry or nervous cat, you will see that everything is up; tail, hair, toes, even her spine. So imagine the dog’s confusion in this situation, where he just sent a friendly signal that he wants to play with the cat, and what he gets back are loud hisses and scary evil eyes—but along with an upward tail that “looks” like a peace sign to him. Yeah, I admit that I am a bona-fide dog person, so I looked at this from his perspective first. But I sympathize with the cat, too. As from her point of view, the dog started to mess with her, disrupting an afternoon slumber in her favorite sunny spot by wagging his stupid tail. Encounters like this cannot end well. Thank goodness that us Homo sapiens lost our tails during our evolutionary journey, as that would have been one more thing that clueless guys would have to decode regarding the mood of our female companions. Imagine a conversation like “How could you not see that I didn’t mean it? My tail was pointing the ground when I said that!” Then a guy would say, “Oh jeez, because I was looking at your lips moving up and down when you were saying something?”

Of course I am generalizing for a comedic effect here, but I see communication breakdowns like this all the time in business environments, especially between the marketing and IT teams. You think men are from Mars and women are from Venus? I think IT folks are from Vulcan and marketing people are from Betazed (if you didn’t get this, find a Trekkie around you and ask).

Now that we are living in the age of Big Data where marketing messages must be custom-tailored based on data, we really need to find a way to narrow the gap between the marketing and the IT world. I wouldn’t dare to say which side is more like a dog or a cat, as I will surely offend someone. But I think even non-Trekkies would agree that it could be terribly frustrating to talk to a Vulcan who thinks that every sentence must be logically impeccable, or a Betazed who thinks that someone’s emotional state is the way it is just because she read it that way. How do they meet in the middle? They need a translator—generally a “human” captain of a starship—between the two worlds, and that translator had better speak both languages fluently and understand both cultures without any preconceived notions.

Similarly, we need translators between the IT world and the marketing world, too. Call such translators “data scientists” if you want (refer to “How to Be a Good Data Scientist”). Or, at times a data strategist or a consultant like myself plays that role. Call us “bats” caught in between the beasts and the birds in an Aesop’s tale, as we need to be marginal people who don’t really belong to one specific world 100 percent. At times, it is a lonely place as we are understood by none, and often we are blamed for representing “the other side.” It is hard enough to be an expert in data and analytics, and we now have to master the artistry of diplomacy. But that is the reality, and I have seen plenty of evidence as to why people whose main job it is to harness meanings out of data must act as translators, as well.

IT is a very special function in modern organizations, regardless of their business models. Systems must run smoothly without errors, and all employees and outside collaborators must constantly be in connection through all imaginable devices and operating systems. Data must be securely stored and backed up regularly, and permissions to access them must be granted based on complex rules, based on job levels and functions. Then there are constant requests to install and maintain new and strange software and technologies, which should be patched and updated diligently. And God forbid if anything fails to work even for a few seconds on a weekend, all hell will break lose. Simply, the end-users—many of them in positions of dealing with customers and clients directly—do not care about IT when things run smoothly, as they take them all for granted. But when they don’t, you know the consequences. Thankless job? You bet. It is like a utility company never getting praises when the lights are up, but everyone yelling and screaming if the service is disrupted, even for a natural cause.

On the other side of the world, there are marketers, salespeople and account executives who deal with customers, clients and their bosses, who would treat IT like their servants, not partners, when things do not “seem” to work properly or when “their” sales projections are not met. The craziest part is that most customers, clients and bosses state their goals and complaints in the most ambiguous terms, as in “This ad doesn’t look slick enough,” “This copy doesn’t talk to me,” “This app doesn’t stick” or “We need to find the right audience.” What the IT folks often do not grasp is that (1) it really stinks when you get yelled at by customers and clients for any reason, and (2) not all business goals are easily translatable to logical statements. And this is when all data elements and systems are functioning within normal parameters.

Without a proper translator, marketers often self-prescribe solutions that call for data work and analytics. Often, they think that all the problems will go away if they have unlimited access to every piece of data ever collected. So they ask for exactly that. IT will respond that such request will put a terrible burden on the system, which has to support not just marketing but also other operations. Eventually they may meet in the middle and the marketer will have access to more data than ever possible in the past. Then the marketers realize that their business issues do not go away just because they have more data in their hands. In fact, their job seems to have gotten even more complicated. They think that it is because data elements are too difficult to understand and they start blaming the data dictionary or lack thereof. They start using words like Data Governance and Quality Control, which may sound almost offensive to diligent IT personnel. IT will respond that they showed every useful bit of data they are allowed to share without breaking the security protocol, and the data dictionaries are all up to date. Marketers say the data dictionaries are hard to understand, and they are filled with too many similar variables and seemingly conflicting information. IT now says they need yet another tool set to properly implement data governance protocols and deploy them. Heck, I have seen cases where some heads of IT went for complete re-platforming of their system, as if that would answer all the marketing questions. Now, does this sound familiar so far? Does it sound like your own experience, like when you are reading “Dilbert” comic strips? It is because you are not alone in all this.

Allow me to be a little more specific with an example. Marketers often talk about “High-Value Customers.” To people who deal with 1s and 0s, that means less than nothing. What does that even mean? Because “high-value customers” could be:

  • High-dollar spenders—But what if they do not purchase often?
  • Frequent shoppers—But what if they don’t spend much at all?
  • Recent customers—Oh, those coveted “hotline” names … but will they stay that way, even for another few months?
  • Tenured customers—But are they loyal to your business, now?
  • Customers with high loyalty points—Or are they just racking up points and they would do anything to accumulate points?
  • High activity—Such as point redemptions and other non-monetary activities, but what if all those activities do not generate profit?
  • Profitable customers—The nice ones who don’t need much hand-holding. And where do we get the “cost” side of the equation on a personal level?
  • Customers who purchases extra items—Such as cruisers who drink a lot on board or diners who order many special items, as suggested.
  • Etc., etc …

Now it gets more complex, as these definitions must be represented in numbers and figures, and depending on the industry, whether be they for retailers, airlines, hotels, cruise ships, credit cards, investments, utilities, non-profit or business services, variables that would be employed to define seemingly straightforward “high-value customers” would be vastly different. But let’s say that we pick an airline as an example. Let me ask you this; how frequent is frequent enough for anyone to be called a frequent flyer?

Let’s just assume that we are going through an exercise of defining a frequent flyer for an airline company, not for any other travel-related businesses or even travel agencies (that would deal with lots of non-flyers). Granted that we have access to all necessary data, we may consider using:

1. Number of Miles—But for how many years? If we go back too far, shouldn’t we have to examine further if the customer is still active with the airline in question? And what does “active” mean to you?

2. Dollars Spent—Again for how long? In what currency? Converted into U.S. dollars at what point in time?

3. Number of Full-Price Ticket Purchases—OK, do we get to see all the ticket codes that define full price? What about customers who purchased tickets through partners and agencies vs. direct buyers through the airline’s website? Do they share a common coding system?

4. Days Between Travel—What date shall we use? Booking date, payment date or travel date? What time zones should we use for consistency? If UTC/GMT is to be used, how will we know who is booking trips during business hours vs. evening hours in their own time zone?

After a considerable hours of debate, let’s say that we reached the conclusion that all involved parties could live with. Then we find out that the databases from the IT department are all on “event” levels (such as clicks, views, bookings, payments, boarding, redemption, etc.), and we would have to realign and summarize the data—in terms of miles, dollars and trips—on an individual customer level to create a definition of “frequent flyers.”

In other words, we would need to see the data from the customer-centric point of view, just to begin the discussion about frequent flyers, not to mention how to communicate with each customer in the future. Now, it that a job for IT or marketing? Who will put the bell on the cat’s neck? (Hint: Not the dog.) Well, it depends. But this definitely is not a traditional IT function, nor is it a standalone analytical project. It is something in between, requiring a translator.

Customer-Centric Database, Revisited
I have been emphasizing the importance of a customer-centric view throughout this series, and I also shared some details regarding databases designed for marketing functions (refer to: “Cheat Sheet: Is Your Database Marketing Ready?”). But allow me to reiterate this point.

In the age of abundant and ubiquitous data, omnichannel marketing communication—optimized based on customers’ past transaction history, product preferences, and demographic and behavioral personas—should be an effortless routine. The reality is far from it for many organizations, as it is very common that much of the vital information is locked in silos without being properly consolidated or governed by a standard set of business rules. It is not that creating such a marketing-oriented database (or data-mart) is solely the IT department’s responsibility, but having a dedicated information source for efficient personalization should be an organizational priority in modern days.

Most databases nowadays are optimized for data collection, storage and rapid retrieval, and such design in general does not provide a customer-centric view—which is essential for any type of personalized communication via all conceivable channels and devices of the present and future. Using brand-, division-, product-, channel- or device-centric datasets is often the biggest obstacle in the journey to an optimal customer experience, as those describe events and transactions, not individuals. Further, bits and pieces of information must be transformed into answers to questions through advanced analytics, including statistical models.

In short, all analytical efforts must be geared toward meeting business objectives, and databases must be optimized for analytics (refer to “Chicken or the Egg? Data or Analytics?”). Unfortunately, the situation is completely reversed in many organizations, where analytical maneuvering is limited due to inadequate source data, and decision-making processes are dictated by limitations of available analytics. Visible symptoms of such cases are, to list a few, elongated project cycle time, decreasing response rates, ineffective customer communication, saturation of data sources due to overexposure, and—as I was emphasizing in this article—communication breakdown among divisions and team members. I can even go as far as to say that the lack of a properly designed analytical environment is the No. 1 cause of miscommunications between IT and marketing.

Without a doubt, key pieces of data must reside in the centralized data depository—generally governed by IT—for effective marketing. But that is only the beginning and still is just a part of the data collection process. Collected data must be consolidated around the solid definition of a “customer,” and all product-, transaction-, event- and channel-level information should be transformed into descriptors of customers, via data standardization, categorization, transformation and summarization. Then the data may be further enhanced via third-party data acquisition and statistical modeling, using all available data. In other words, raw data must be refined through these steps to be useful in marketing and other customer interactions, online or offline (refer to “‘Big Data’ Is Like Mining Gold for a Watch—Gold Can’t Tell Time“). It does not matter how well the original transaction- or event-level data are stored in the main database without visible errors, or what kind of state-of-the-art communication tool sets a company is equipped with. Trying to use raw data for a near real-time personalization engine is like putting unrefined oil into a high-performance sports car.

This whole data refinement process may sound like a daunting task, but it is not nearly as painful as analytical efforts to derive meanings out of unstructured, unconsolidated and uncategorized data that are scattered all over the organization. A customer-centric marketing database (call it a data-mart if “database” sounds too much like it should solely belong to IT) created with standard business rules and uniform variables sets would, in turn, provide an “analytics-ready” environment, where statistical modeling and other advanced analytics efforts would gain tremendous momentum. In the end, the decision-making process would become much more efficient as analytics would provide answers to questions, not just bits and pieces of fragmented data, to the ultimate beneficiaries of data. And answers to questions do not require an enormous data dictionary, either; fast-acting marketing machines do not have time to look up dictionaries, anyway.

Data Roadmap—Phased Approach
For the effort to build a consolidated marketing data platform that is analytics-ready (hence, marketing-ready), I always recommend a phased approach, as (1) inevitable complexity of a data consolidation project will be contained and managed more efficiently in carefully defined phases, and (2) each phase will require different types of expertise, tool sets and technologies. Nevertheless, the overall project must be managed by an internal champion, along with a group of experts who possess long-term vision and tactical knowledge in both database and analytics technologies. That means this effort must reside above IT and marketing, and it should be seen as a strategic effort for an entire organization. If the company already hired a Chief Data Officer, I would say that this should be one of the top priorities for that position. If not, outsourcing would be a good option, as an impartial decision-maker, who would play a role of a referee, may have to come from the outside.

The following are the major steps:

  1. Formulate Questions: “All of the above” is not a good way to start a complex project. In order to come up with the most effective way to build a centralized data depository, we first need to understand what questions must be answered by it. Too many database projects call for cars that must fly, as well.
  2. Data Inventory: Every organization has more data than it expected, and not all goldmines are in plain sight. All the gatekeepers of existing databases should be interviewed, and any data that could be valuable for customer descriptions or behavioral predictions should be considered, starting with product, transaction, promotion and response data, stemming from all divisions and marketing channels.
  3. Data Hygiene and Standardization: All available data fields should be examined and cleaned up, where some data may be discarded or modified. Free form fields would deserve special attention, as categorization and tagging are one of the key steps to opening up new intelligence.
  4. Customer Definition: Any existing Customer ID systems (such as loyalty program ID, account number, etc.) will be examined. It may be further enhanced with available PII (personally identifiable information), as there could be inconsistencies among different systems, and customers often move their residency or use multiple email addresses, creating duplicate identities. A consistent and reliable Customer ID system becomes the backbone of a customer-centric database.
  5. Data Consolidation: Data from different silos and divisions will be merged together based on the master Customer ID. A customer-centric database begins to take shape here. The database update process should be thoroughly tested, as “incremental” updates are often found to be more difficult than the initial build. The job is simply not done until after a few successful iterations of updates.
  6. Data Transformation: Once a solid Customer ID system is in place, all transaction- and event-level data will be transformed to “descriptors” of individual customers, via summarization by categories and creation of analytical variables. For example, all product information will be aligned for each customer, and transaction data will be converted into personal-level monetary summaries and activities, in both static and time-series formats. Promotion and response history data will go through similar processes, yielding individual-level ROI metrics by channel and time period. This is the single-most critical step in all of this, requiring deep knowledge in business, data and analytics, as the stage is being set for all future analytics and reporting efforts. Due to variety and uniqueness of business goals in different industries, a one-size-fits-all approach will not work, either.
  7. Analytical Projects: Test projects will be selected and the entire process will be done on the new platform. Ad-hoc reporting and complex modeling projects will be conducted, and the results will be graded on timing, accuracy, consistency and user-friendliness. An iterative approach is required, as it is impossible to foresee all possible user requests and related complexities upfront. A database should be treated as a living, breathing organism, not something rigid and inflexible. Marketers will “break-in” the database as they use it more routinely.
  8. Applying the Knowledge: The outcomes of analytical projects will be applied to the entire customer base, and live campaigns will be run based on them. Often, major breakdowns happen at the large-scale deployment stages; especially when dealing with millions of customers and complex mathematical formulae at the same time. A model-ready database will definitely minimize the risk (hence, the term “in-database scoring”), but the process will still require some fine-tuning. To proliferate gained knowledge throughout the organization, some model scores—which pack deep intelligence in small sizes—may be transferred back to the main databases managed by IT. Imagine model scores driving operational decisions—live, on the ground.
  9. Result Analysis: Good marketing intelligence engines must be equipped with feedback mechanisms, effectively closing the “loop” where each iteration of marketing efforts improves its effectiveness with accumulated knowledge on a customer level. It is very unfortunate that many marketers just move through the tracks set up by their predecessors, mainly because existing database environments are not even equipped to link necessary data elements on a customer level. Too many back-end analyses are just event-, offer- or channel-driven, not customer-centric. Can you easily tell which customer is over-, under- or adequately promoted, based on a personal-level promotion-and-response ratio? With a customer-centric view established, you can.

These are just high-level summaries of key steps, and each step should be managed as independent projects within a large-scale initiative with common goals. Some steps may run concurrently to reduce the overall timeline, and tactical knowledge in all required technologies and tool sets is the key for the successful implementation of centralized marketing intelligence.

Who Will Do the Work?
Then, who will be in charge of all this and who will actually do the work? As I mentioned earlier, a job of this magnitude requires a champion, and a CDO may be a good fit. But each of these steps will require different skill sets, so some outsourcing may be inevitable (more on how to pick an outsourcing partner in future articles).

But the case that should not be is the IT team or the analytics team solely dictating the whole process. Creating a central depository of marketing intelligence is something that sits between IT and marketing, and the decisions must be made with business goals in mind, not just limitations and challenges that IT faces. If the CDO or the champion of this type of initiative starts representing IT issues before overall business goals, then the project is doomed from the beginning. Again, it is not about touching the core database of the company, but realigning existing data assets to create new intelligence. Raw data (no matter how clean they are at the collection stage) are like unrefined raw materials to the users. What the decision-makers need are simple answers to their questions, not hundreds of data pieces.

From the user’s point of view, data should be:

  • Easy to understand and use (intuitive to non-mathematicians)
  • Bite-size (i.e., small answers, not mounds of raw data)
  • Useful and effective (consistently accurate)
  • Broad (answers should be available most of time, not just “sometimes”)
  • Readily available (data should be easily accessible via users’ favorite devices/channels)

And getting to this point is the job of a translator who sits in between marketing and IT. Call them data scientists or data strategists, if you like. But they do not belong to just marketing or IT, even though they have to understand both sides really well. Do not be rigid, insisting that all pilots must belong to the Air Force; some pilots do belong to the Navy.

Lastly, let me add this at the risk of sounding like I am siding with technologists. Marketers, please don’t be bad patients. Don’t be that bad patient who shows up at a doctor’s office with a specific prescription, as in “Don’t ask me why, but just give me these pills, now.” I’ve even met an executive who wanted a neural-net model for his business without telling me why. I just said to myself, “Hmm, he must have been to one of those analytics conferences recently.” Then after listening to his “business” issues, I prescribed an entirely different solution package.

So, instead of blurting out requests for pieces of data variables or queries using cool-sounding, semi-technical terms, state the business issues and challenges that you are facing as clearly as possible. IT and analytics specialists will prescribe the right solution for you if they understand the ultimate goals better. Too often, requesters determine the solutions they want without any understanding of underlying technical issues. Don’t forget that the end-users of any technology are only exposed to symptoms, not the causes.

And if Mr. Spock doesn’t seem to understand your issues and keeps saying that your statements are illogical, then call in a translator, even if you have to hire him for just one day. I know this all too well, because after all, this one phrase summarizes my entire career: “A bridge person between the marketing world and the IT world.” Although it ain’t easy to live a life as a marginal person.

Copywriting: Stir Emotion, Calm the Mind

Stimulate. Calm. In the direct marketing world, these are two related, but contrasting messaging and copywriting concepts that every marketer and copywriter should master. Why? Because a sure-fire way to get attention from prospective customers is by stimulating emotion. But you don’t want to stimulate emotion and drop the ball there

Stimulate. Calm.

In the direct marketing world, these are two related, but contrasting messaging and copywriting concepts that every marketer and copywriter should master. Why? Because a sure-fire way to get attention from prospective customers is by stimulating emotion. But you don’t want to stimulate emotion and drop the ball there. You must then immediately calm the mind so your prospect’s fears are relieved, allowing them to become engaged with your message, so they will pause long enough for you to introduce them to your solution.

In my most recent column, “Leveraging Fear, Uncertainty and Doubt in Copywriting,” I described how fear paralyzes thinking because it’s an instinctive response from the amygdala, our lizard brain.

But because fear is so overwhelming as a natural response, it shuts off the thinking part of the brain. So while, as a copywriter, you want to stimulate emotion by tapping into fear, uncertainty and doubt, you need to quickly calm the mind so decision-making is unblocked. And you can do that by dangling a carrot in front of your audience to moderate their mood.

Search the Web for “how do you calm the mind” and you’ll get thousands of websites with meditation advice. While you don’t want to steer prospects to meditate—at least in the stereotypical way you think of meditation—you do want your prospect to be calmed enough to focus on your message.

To more fully grasp the connection between stimulating emotion and the need to calm the mind, it may be helpful to take a deeper dive into how our brains respond to stimuli. Your brain is filled with neurotransmitters, and knowing the signals they transmit will help you better understand how the brain functions. For marketers, it’s important that you know how to use these signals to strengthen your messaging.

Neurotransmitters are the brain chemicals that communicate information throughout our brains and bodies. They relay signals between nerve cells, called “neurons.” The brain uses neurotransmitters to tell your heart to beat, your lungs to breathe, and your stomach to digest. They can also affect mood, sleep, concentration, weight and can cause adverse symptoms when they are out of balance.

There are two kinds of neurotransmitters: inhibitory and excitatory. Excitatory neurotransmitters stimulate the brain. Inhibitory neurotransmitters calm the brain and help create balance.

So as a direct marketer, after stimulating emotion you must quickly balance the mood. When you over-stimulate, the inhibitory neurotransmitters can be depleted and instead of focusing on your solution, you leave your prospect focusing on their fear, uncertainty and doubt.

Those inhibitory neurotransmitters—those brain chemicals—include:

  • Serotonin, which is necessary for a stable mood and to balance any excessive excitatory (stimulating) neurotransmitter firing in the brain.
  • Gaba helps to calm and relax us, by balancing stimulation over-firing.
  • Dopamine is a special neurotransmitter because it is considered to be both excitatory and inhibitory. It’s very complex. When it spikes, it can motivate and give a person pleasure. When elevated or low, it can cause focus issues such as not remembering what a paragraph said when we just finished reading it (obviously, not something marketers want to happen when reading our copy).

With a cocktail of brain chemicals swirling around in your prospect’s mind, here are a few ways you can calm your prospect’s mind after stimulating their emotion:

  1. Announce a new discovery
  2. Introduce a solution
  3. Assure with a promise
  4. Promise a reward
  5. Brighten the mood of the message to evoke pleasant memory
  6. Introduce new learning

Stimulate. Calm. With these two initial steps, you’ve grabbed attention and have moderated mood so your prospect desires to hear and read more about you.

Data Deep Dive: The Art of Targeting

Even if you own a sniper rifle (and I’m not judging), if you aim at the wrong place, you will never hit the target. Obvious, right? But that happens all the time in the world of marketing, even when advanced analytics and predictive modeling techniques are routinely employed. How is that possible? Well, the marketing world is not like an Army shooting range where the silhouette of the target is conveniently hung at the predetermined location, but it is more like the “Twilight Zone,” where things are not what they seem. Marketers who failed to hit the real target often blame the guns, which in this case are targeting tools, such as models and segmentations. But let me ask, was the target properly defined in the first place?

Even if you own a sniper rifle (and I’m not judging), if you aim at the wrong place, you will never hit the target. Obvious, right? But that happens all the time in the world of marketing, even when advanced analytics and predictive modeling techniques are routinely employed. How is that possible? Well, the marketing world is not like an Army shooting range where the silhouette of the target is conveniently hung at the predetermined location, but it is more like the “Twilight Zone,” where things are not what they seem. Marketers who failed to hit the real target often blame the guns, which in this case are targeting tools, such as models and segmentations. But let me ask, was the target properly defined in the first place?

In my previous columns, I talked about the importance of predictive analytics in modern marketing (refer to “Why Model?”) for various reasons, such as targeting accuracy, consistency, deeper use of data, and most importantly in the age of Big Data, concise nature of model scores where tons of data are packed into ready-for-use formats. Now, even the marketers who bought into these ideas often make mistakes by relinquishing the important duty of target definition solely to analysts and statisticians, who do not necessarily possess the power to read the marketers’ minds. Targeting is often called “half-art and half-science.” And it should be looked at from multiple angles, starting with the marketer’s point of view. Therefore, even marketers who are slightly (or, in many cases, severely) allergic to mathematics should come one step closer to the world of analytics and modeling. Don’t be too scared, as I am not asking you to be a rifle designer or sniper here; I am only talking about hanging the target in the right place so that others can shoot at it.

Let us start by reviewing what statistical models are: A model is a mathematical expression of “differences” between dichotomous groups; which, in marketing, are often referred to as “targets” and “non-targets.” Let’s say a marketer wants to target “high-value customers.” To build a model to describe such targets, we also need to define “non-high-value customers,” as well. In marketing, popular targets are often expressed as “repeat buyers,” “responders to certain campaigns,” “big-time spenders,” “long-term, high-value customers,” “troubled customers,” etc. for specific products and channels. Now, for all those targets, we also need to define “bizarro” or “anti-” versions of them. One may think that they are just the “remainders” of the target. But, unfortunately, it is not that simple; the definition of the whole universe should be set first to even bring up the concept of the remainders. In many cases, defining “non-buyers” is much more difficult than defining “buyers,” because lack of purchase information does not guarantee that the individual in question is indeed a non-buyer. Maybe the data collection was never complete. Maybe he used a different channel to respond. Maybe his wife bought the item for him. Maybe you don’t have access to the entire pool of names that represent the “universe.”

Remember T, C, & M
That is why we need to examine the following three elements carefully when discussing statistical models with marketers who are not necessarily statisticians:

  1. Target,
  2. Comparison Universe, and
  3. Methodology.

I call them “TCM” in short, so that I don’t leave out any element in exploratory conversations. Defining proper target is the obvious first step. Defining and obtaining data for the comparison universe is equally important, but it could be challenging. But without it, you’d have nothing against which you compare the target. Again, a model is an algorithm that expresses differences between two non-overlapping groups. So, yes, you need both Superman and Bizarro-Superman (who always seems more elusive than his counterpart). And that one important variable that differentiates the target and non-target is called “Dependent Variable” in modeling.

The third element in our discussion is the methodology. I am sure you may have heard of terms like logistic regression, stepwise regression, neural net, decision trees, CHAID analysis, genetic algorithm, etc., etc. Here is my advice to marketers and end-users:

  • State your goals and usages cases clearly, and let the analyst pick proper methodology that suites your goals.
  • Don’t be a bad patient who walks into a doctor’s office demanding a specific prescription before the doctor even examines you.

Besides, for all intents and purposes, the methodology itself matters the least in comparison with an erroneously defined target and the comparison universes. Differences in methodologies are often measured in fractions. A combination of a wrong target and wrong universe definition ends up as a shotgun, if not an artillery barrage. That doesn’t sound so precise, does it? We should be talking about a sniper rifle here.

Clear Goals Leading to Definitions of Target and Comparison
So, let’s roll up our sleeves and dig deeper into defining targets. Allow me to use an example, as you will be able to picture the process better that way. Let’s just say that, for general marketing purposes, you want to build a model targeting “frequent flyers.” One may ask for business or for pleasure, but let’s just say that such data are hard to obtain at this moment. (Finding the “reasons” is always much more difficult than counting the number of transactions.) And it was collectively decided that it would be just beneficial to know who is more likely to be a frequent flyer, in general. Such knowledge could be very useful for many applications, not just for the travel industry, but for other affiliated services, such as credit cards or publications. Plus, analytics is about making the best of what you’ve got, not waiting for some perfect datasets.

Now, here is the first challenge:

  • When it comes to flying, how frequent is frequent enough for you? Five times a year, 10 times, 20 times or even more?
  • Over how many years?
  • Would you consider actual miles traveled, or just number of issued tickets?
  • How large are the audiences in those brackets?

If you decided that five times a year is a not-so-big or not-so-small target (yes, sizes do matter) that also fits the goal of the model (you don’t want to target only super-elites, as they could be too rare or too distinct, almost like outliers), to whom are they going to be compared? Everyone who flew less than five times last year? How about people who didn’t fly at all last year?

Actually, one option is to compare people who flew more than five times against people who didn’t fly at all last year, but wouldn’t that model be too much like a plain “flyer” model? Or, will that option provide more vivid distinction among the general population? Or, one analyst may raise her hand and say “to hell with all these breaks and let’s just build a model using the number of times flown last year as the continuous target.” The crazy part is this: None of these options are right or wrong, but each combination of target and comparison will certainly yield very different-looking models.

Then what should a marketer do in a situation like this? Again, clearly state the goal and what is more important to you. If this is for general travel-related merchandizing, then the goal should be more about distinguishing more likely frequent flyers out of the general population; therefore, comparing five-plus flyers against non-flyers—ignoring the one-to-four-time flyers—makes sense. If this project is for an airline to target potential gold or platinum members, using people who don’t even fly as comparison makes little or no sense. Of course, in a situation like this, the analyst in charge (or data scientist, the way we refer to them these days), must come halfway and prescribe exactly what target and comparison definitions would be most effective for that particular user. That requires lots of preliminary data exploration, and it is not all science, but half art.

Now, if I may provide a shortcut in defining the comparison universe, just draw the representable sample from “the pool of names that are eligible for your marketing efforts.” The key word is “eligible” here. For example, many businesses operate within certain areas with certain restrictions or predetermined targeting criteria. It would make no sense to use the U.S. population sample for models for supermarket chains, telecommunications, or utility companies with designated footprints. If the business in question is selling female apparel items, first eliminate the male population from the comparison universe (but I’d leave “unknown” genders in the mix, so that the model can work its magic in that shady ground). You must remember, however, that all this means you need different models when you change the prospecting universe, even if the target definition remains unchanged. Because the model algorithm is the expression of the difference between T and C, you need a new model if you swap out the C part, even if you left the T alone.

Multiple Targets
Sometimes it gets twisted the other way around, where the comparison universe is relatively stable (i.e., your prospecting universe is stable) but there could be multiple targets (i.e., multiple Ts, like T1, T2, etc.) in your customer base.

Let me elaborate with a real-life example. A while back, we were helping a company that sells expensive auto accessories for luxury cars. The client, following his intuition, casually told us that he only cares for big spenders whose average order sizes are more than $300. Now, the trouble with this statement is that:

  1. Such a universe could be too small to be used effectively as a target for models, and
  2. High spenders do not tend to purchase often, so we may end up leaving out the majority of the potential target buyers in the whole process.

This is exactly why some type of customer profiling must precede the actual target definition. A series of simple distribution reports clearly revealed that this particular client was dealing with a dual-universe situation, where the first group (or segment) is made of infrequent, but high-dollar spenders whose average orders were even greater than $300, and the second group is made of very frequent buyers whose average order sizes are well below the $100 mark. If we had ignored this finding, or worse, neglected to run preliminary reports and just relying on our client’s wishful thinking, we would have created a “phantom” target, which is just an average of these dual universes. A model designed for such a phantom target will yield phantom results. The solution? If you find two distinct targets (as in T1 and T2), just bite the bullet and develop two separate models (T1 vs. C and T2 vs. C).

Multi-step Approach
There are still other reasons why you may need multiple models. Let’s talk about the case of “target within a target.” Some may relate this idea to a “drill-down” concept, and it can be very useful when the prospecting universe is very large, and the marketer is trying to reach only the top 1 percent (which can be still very large, if the pool contains hundreds of millions of people). Correctly finding the top 5 percent in any universe is difficult enough. So what I suggest in this case is to build two models in sequence to get to the “Best of the Best” in a stepwise fashion.

  • The first model would be more like an “elimination” model, where obviously not-so-desirable prospects would be removed from the process, and
  • The second-step model would be designed to go after the best prospects among survivors of the first step.

Again, models are expressions of differences between targets and non-targets, so if the first model eliminated the bottom 80 percent to 90 percent of the universe and leaves the rest as the new comparison universe, you need a separate model—for sure. And lots of interesting things happen at the later stage, where new variables start to show up in algorithms or important variables in the first step lose steam in later steps. While a bit cumbersome during deployment, the multi-step approach ensures precision targeting, much like a sniper rifle at close range.

I also suggest this type of multi-step process when clients are attempting to use the result of segmentation analysis as a selection tool. Segmentation techniques are useful as descriptive analytics. But as a targeting tool, they are just too much like a shotgun approach. It is one thing to describe groups of people such as “young working mothers,” “up-and-coming,” and “empty-nesters with big savings” and use them as references when carving out messages tailored toward them. But it is quite another to target such large groups as if the population within a particular segment is completely homogeneous in terms of susceptibility to specific offers or products. Surely, the difference between a Mercedes buyer and a Lexus buyer ain’t income and age, which may have been the main differentiator for segmentation. So, in the interest of maintaining a common theme throughout the marketing campaigns, I’d say such segments are good first steps. But for further precision targeting, you may need a model or two within each segment, depending on the size, channel to be employed and nature of offers.

Another case where the multi-step approach is useful is when the marketing and sales processes are naturally broken down into multiple steps. For typical B-to-B marketing, one may start the campaign by mass mailing or email (I’d say that step also requires modeling). And when responses start coming in, the sales team can take over and start contacting responders through more personal channels to close the deal. Such sales efforts are obviously very time-consuming, so we may build a “value” model measuring the potential value of the mail or email responders and start contacting them in a hierarchical order. Again, as the available pool of prospects gets smaller and smaller, the nature of targeting changes as well, requiring different types of models.

This type of funnel approach is also very useful in online marketing, as the natural steps involved in email or banner marketing go through lifecycles, such as blasting, delivery, impression, clickthrough, browsing, shopping, investigation, shopping basket, checkout (Yeah! Conversion!) and repeat purchases. Obviously, not all steps require aggressive or precision targeting. But I’d say, at the minimum, initial blast, clickthrough and conversion should be looked at separately. For any lifetime value analysis, yes, the repeat purchase is a key step; which, unfortunately, is often neglected by many marketers and data collectors.

Inversely Related Targets
More complex cases are when some of these multiple response and conversion steps are “inversely” related. For example, many responders to invitation-to-apply type credit card offers are often people with not-so-great credit. Well, if one has a good credit score, would all these credit card companies have left them alone? So, in a case like that, it becomes very tricky to find good responders who are also credit-worthy in the vast pool of a prospect universe.

I wouldn’t go as far as saying that it is like finding a needle in a haystack, but it is certainly not easy. Now, I’ve met folks who go after the likely responders with potential to be approved as a single target. It really is a philosophical difference, but I much prefer building two separate models in a situation like this:

  • One model designed to measure responsiveness, and
  • Another to measure likelihood to be approved.

The major benefit for having separate models is that each model will be able employ different types and sources of data variables. A more practical benefit for the users is that the marketers will be able to pick and choose what is more important to them at the time of campaign execution. They will obviously go to the top corner bracket, where both scores are high (i.e., potential responders who are likely to be approved). But as they dial the selection down, they will be able to test responsiveness and credit-worthiness separately.

Mixing Multiple Model Scores
Even when multiple models are developed with completely different intentions, mixing them up will produce very interesting results. Imagine you have access to scores for “High-Value Customer Model” and “Attrition Model.” If you cross these scores in a simple 2×2 matrix, you can easily create a useful segment in one corner called “Valuable Vulnerable” (a term that my mentor created a long time ago). Yes, one score is predicting who is likely to drop your service, but who cares if that customer shows little or no value to your business? Take care of the valuable customers first.

This type of mixing and matching becomes really interesting if you have lots of pre-developed models. During my tenure at a large data compiling company, we built more than 120 models for all kinds of consumer characteristics for general use. I remember the real fun began when we started mixing multiple models, like combining a “NASCAR Fan” model with a “College Football Fan” model; a “Leaning Conservative” model with an “NRA Donor” model; an “Organic Food” one with a “Cook for Fun” model or a “Wine Enthusiast” model; a “Foreign Vacation” model with a “Luxury Hotel” model or a “Cruise” model; a “Safety and Security Conscious” model or a “Home Improvement” model with a “Homeowner” model, etc., etc.

You see, no one is one dimensional, and we proved it with mathematics.

No One is One-dimensional
Obviously, these examples are just excerpts from a long playbook for the art of targeting. My intention is to emphasize that marketers must consider target, comparison and methodologies separately; and a combination of these three elements yields the most fitting solutions for each challenge, way beyond what some popular toolsets or new statistical methodologies presented in some technical conferences can acomplish. In fact, when the marketers are able to define the target in a logical fashion with help from trained analysts and data scientists, the effectiveness of modeling and subsequent marketing campaigns increase dramatically. Creating and maintaining an analytics department or hiring an outsourcing analytics vendor aren’t enough.

One may be concerned about the idea of building multiple models so casually, but let me remind you that it is the reality in which we already reside, anyway. I am saying this, as I’ve seen too many marketers who try to fix everything with just one hammer, and the results weren’t ideal—to say the least.

It is a shame that we still treat people with one-dimensional tools, such segmentations and clusters, in this age of ubiquitous and abundant data. Nobody is one-dimensional, and we must embrace that reality sooner than later. That calls for rapid model development and deployment, using everything that we’ve got.

Arguing about how difficult it is to build one or two more models here and there is so last century.

If Content Is King, Grammar Is Queen

Growing up in a household with highly disciplined parents, my grammar was always being corrected. Whether it was ending a sentence with a preposition, misplacing a modifier or splitting an infinitive, any conversation could be stopped, at any moment. Now that the marketing world has turned its sights to “content” as a key brand engagement device, I’m hopeful that the grammar police are reinforcing their troops for a ride along. Because from where I sit, brands could use a little disciplinary action. (Yep, just gave myself a smack for starting a sentence with the word “because.” Ouch.)

Growing up in a household with highly disciplined parents, my grammar was always being corrected. Whether it was ending a sentence with a preposition, misplacing a modifier or splitting an infinitive, any conversation could be stopped, at any moment, to make sure I knew the right way to restate my thought (per the English grammar guidelines found in the little book Strunk & White’s “The Elements of Style”).

Yes—dinnertime conversation was often painful.

The lowlight was when my parents told me that my most recent letter home from college was fraught with grammatical errors, and they had seriously considered returning it to me, complete with red pencil corrections. Needless to say, my correspondence home dwindled.

Now that the marketing world has turned its sights to “content” as a key brand engagement device, I’m hopeful that the grammar police are reinforcing their troops for a ride along. Because from where I sit, brands could use a little disciplinary action. (Yep, just gave myself a smack for starting a sentence with the word “because.” Ouch.)

Over the years, I’ve certainly visited thousands of websites, downloaded hundreds of whitepapers and case studies, and, like you, I’ve received lots and lots of emails including sales tips and e-newsletters. I’m still amazed at the lack of grammar skill. Forget the typos—they’re just inexcusable—I mean the basics like “too” instead of “to,” or “between Joe and I” instead of “between Joe and me,” or a simple sentence like this: “If you would like to discuss Social Media with regards to your business further, please feel free to contact me.” Huh?

If you read my blog, you’ll know that I love commas. I think they help the reader pause, consider the point being made, and then continue to absorb the next point. It appears that idea is lost on many writers … or worse, the comma is misplaced. Consider the famous book title “Eats shoots and leaves” versus “Eats, shoots and leaves” or even “Eats, shoots, and leaves.” Personally I like serial commas, but it seems many brands have pushed them aside as part of their brand guidelines and chaos has erupted over the meaning of a sentence. [Editor’s note: Target Marketing adheres to AP Style, as do most publications, and the AP does not endorse serial commas. We apologize for any misunderstanding this may cause about whether to leave your bullets or dinner.]

I’m the first to tell you my personal grammar skills are still not entirely A+ (my parents are nodding), but there are so many proofreaders, grammarians or other online expert sources available (not to mention a nifty little tool in Microsoft Word called ‘Spelling & Grammar’) that there is simply no excuse for any company to be executing marketing materials that are anything less than perfect.

So before you create and publish your next ‘content’ deliverable, consider getting professional help. Here are a few of my favorite editorial review pros:

  • HyperGraphix (www.hgpublishing.com): This guy is smart, fast and CHEAP; Known for proofing tediously long documents on topics that would bore the average reader. Plus he works in two languages (Canadian and American) in case you’re publishing north of the border. He has an online tool that fixes sentences for free (you can’t beat that price), and if you subscribe to his tweets, he provides helpful tips and links to helpful articles.
  • Grammar Girl (grammar.quickanddirtytips.com/): Short, sharp, and to the point, her emails on grammar tips have become part of my morning reading ritual.
  • Bulletproof (www.bulletproofonline.com): Strong proofreading skills and your ideal “brand police” if you share your brand guidelines with them.

If your issue, on the other hand, is content creation, don’t leave that to your sales guy. Cough up the budget for a professional writer—one with the research skills that can thoroughly investigate the topic, identify a point of view for your brand, and write in a voice that matches your brand style. There are hundreds of excellent writers out there who are wincing as they read your materials.

So go ahead—jump on the content bandwagon—and Long live the Queen!

Numbers Don’t Lie: Gen X, Can You Handle the Truth?

If you’re a Gex Xer, chances are since you’ve been in the workforce, for better or for worse you’ve lived in the shadow of the Baby Boomers. They’re the ones who have hired you, fired you … and most certainly always held the best jobs. The more I think about the marketing world, the more I realize that there’s an important undercurrent here, one that will have a tremendous impact on Gen X, and quite possibly Gen Y, as well.

If you’re Gen X, that means you were born in the ’70s, grew up in the ’80s and came of age in the ’90s, or something like that. You grew up listening to music like Van Halen, Run DMC, The Smiths and Nirvana. You went to school, and probably began working sometime during the second Clinton Administration, beginning to pay off your student loans. It was an exciting time to enter the labor force, just as the digital revolution was beginning to take hold.

Like many others in my generation, I entered the labor force in the mid-’90s. My first job was with a marketing firm. I was hired by a Baby Boomer, a nice woman named Stephanie about 20 years my senior. Marketing at the time was still pretty old school, but it was there where I was given my first work PC, set up with my first email address, and taught to surf this new thing called the World Wide Web using what was then the state-of-the-art browser called Netscape.

If you’re a Gex Xer, chances are since you’ve been in the workforce, for better or for worse you’ve lived in the shadow of the Baby Boomers. They’re the ones who have hired you, fired you … and most certainly always held the best jobs. The more I think about the marketing world, the more I realize that there’s an important undercurrent here, one that will have a tremendous impact on Gen X, and quite possibly Gen Y, as well.

You see, last time I talked about a transition that’s taking place in the marketing world, as an older generation of brand stewards gives way to a new generation of digital marketers. I explained this trend was set to accelerate in coming years due to the rapidly changing nature of marketing itself, which is becoming more data driven, technology focused and operational in nature. In case you missed it, you can read about this topic in “3 Ways Rank-and-File Marketers Matter to the C-Suite in a Brave New Marketing World.”

In the marketing world (not in tech, but most definitely in the rest of corporate America), most high-level roles are still staffed by Boomers. What I find very interesting is that for the most part, the vast majority of Baby Boomers (with some notable exceptions, of course) are not especially digital people. Many have learned to live and work in the digital world and quite well, but when I see my dad fumble around on his feature phone I most definitely can see a huge gap.

So the transition I mentioned above will essentially be a passing of the baton, as the Boomers recede from the picture and are replaced by the next generation of marketers. Now here’s where it gets really interesting. According to the U.S. Census Bureau, a Baby Boomer is someone who was born between 1946 and 1964. Ranging in age from 48 to 66, Baby Boomers aren’t getting any younger. Generation X spans the years 1965 to 1983, more or less, while Gen Y is from 1985 to 2003. Now let’s take a look at the size of these three generations:

  • Baby Boomers: 79 million
  • Gen X: 41 million
  • Gen Y: 85 million

What this means is that in the marketing world if you’re a Gen Xer, your time to lead is coming. If you look at the numbers above, you can see there will there be a huge leadership void that will need to be filled as the Boomers retire during the next few years … as a small generation replaces a huge one. The economic crisis during the past for years may have postponed their retirement. But any way you slice it, the Baby Boomers will soon begin retiring more or less en masse during the next few years. When they go, they will leave huge leadership vacuum behind.

But that’s not all. In today’s marketing world, playing a leadership role will require both digital and managerial experience. This means that if you’re a Gen Xer with digital marketing and managerial experience, you’re literally going to be worth your weight in gold in coming years as the generational transition accelerates.

Don’t believe me? Just wait and see. And if you’re not ready to rise to the occasion, guess what? There are 85 million hungry and talented digital natives in Gen Y itching to move up ahead and take your place. If anything, they are the most digital generation yet. At this point, they’re still young and have yet to acquire the years of on-the-job experience it takes to succeed in a high-level marketing job. But give them some time and that will certainly change.

So, Gen X, are you up for the job? To quote Jack Nicholson is the classic 1992 movie A Few Good Men, “Can you handle the truth?” If not, Gen Y will be there waiting in the wings, happy to swoop in and take your place.

Any questions or feedback, as usual I’d love to hear it.

—Rio

3 Ways Rank-and-File Marketers Matter to the C-Suite in a Brave New Marketing World

A couple weeks ago in my post titled “Wanted: Data-Driven, Digital CMOs,” I wrote about the enormous pressure CMOs are finding themselves under as the world digitizes, requiring a new type of leader, one who understands and feels comfortable in the digital space. The result of this changing dynamic has been a dramatic shortening of your average CMO’s tenure. I’m not the first to observe this trend—it’s been covered in many places over the past few months, including this great article from Fast Company. In response to this post, however, many colleagues have asked me “What does this mean for the rank-and-file marketer?” I thought this was an excellent question; one I’ve not seen discussed elsewhere.

A couple weeks ago in my post titled “Wanted: Data-Driven, Digital CMOs,” I wrote about the enormous pressure CMOs are finding themselves under as the world digitizes, requiring a new type of leader, one who understands and feels comfortable in the digital space. The result of this changing dynamic has been a dramatic shortening of your average CMO’s tenure.

I’m not the first to observe this trend—it’s been covered in many places over the past few months, including this great article from Fast Company. In response to this post, however, many colleagues have asked me “What does this mean for the rank-and-file marketer?” I thought this was an excellent question; one I’ve not seen discussed elsewhere.

By any standard, it’s certainly not an easy time to be a marketer. Over the past decade, nearly everything we know has changed, as new technologies have arrived in a dizzying fashion, upending the established order. The result for most firms has ranged from confusion to clarity, from paralysis to paroxysm—very frequently all at the same time! Working in an environment like this is definitely no picnic, as firms flail around like a hurt animal trying to figure out what to do, reducing head count, hiring, outsourcing, in-sourcing, you name it.

It may not be an easy time to be a marketer, but I think it’s a good time. The reason why is that marketing has evolved in four very important ways:

1. Marketing has become data driven—in the digital age, information is power. Contemporary marketing requires learning about who your customers are, what they look like, what attributes and affinities they share, and so on. Success means becoming fluent in the new language of the digital age—understanding what terms like “impressions,” “clicks,” “likes” and “followers” mean. But that’s not all: Success requires a deep understanding of and familiarity with campaign analytics, what they mean and signify, and how to interpret and improve upon them.

2. Marketing is technology-focused—it’s no secret that a large portion of marketers’ budgets are now being allocated to digital. Anyone who’s worked in the digital marketing arena knows that success in the space means understanding the new technology ecosystem. The other major technology trend is the fragmentation of the IT infrastructure as the SaaS/Cloud model gains traction. In this new service model, it’s marketing that’s mostly responsible for buying, using and maintaining these new tools.

3. Marketing is highly operational in nature—unlike the brand strategists of yesteryear, today’s marketing department is almost entirely focused on operations, with a heavy emphasis being placed on creating, testing and launching, tracking and optimizing numerous marketing campaigns across various channels using different tools.

In this new environment, the DNA of the rank-and-file marketer has changed radically, morphing from that of a brand steward into, well, something else entirely. Any way you look at it, today’s marketers are highly trained and qualified specialists, possessing a wide range of skills and knowledge, which can take months, if not years, to master.

Moreover, success in any given marketing role requires a deep understanding of various marketing program details, familiarity with firm’s marketing technology, systems and tools, not to mention the prevailing corporate culture. All in all, it’s a tall order.

Over the years, I’ve consulted with dozens of large firms, and I can tell you firsthand that most marketing leadership stakeholders are not digital people. In other words, the only people in the firm who really “get” what the firm’s marketing department is actually doing are the marketers themselves. Interesting, huh?

So what does this all mean? Well, in coming years I foresee a shift in the balance of power as the old generation of marketers gives way to a new generation of younger digital specialists. Now, of course, one generation passing the mantle to the next is the natural order of things. But, based on what’s going on, I see this trend accelerating dramatically in coming months and years, as those who don’t get it are replaced by those who do.

If you’re a marketer, all if this is undoubtedly good news, meaning you’re not only much more important than you think, but your trip up the proverbial corporate ladder is that much shorter. So go forth, young man (or woman), it’s a brave new world!

Any questions or feedback? As usual, I’d love to hear it.

—Rio

The B-to-B Buying Revolution, and Five Ways Marketers Need to Change Their Game

The Internet has driven dramatic changes in business buying behavior. Just as no one buys a car anymore without first checking prices and features online, business buyers now research and educate themselves online, months—even years—before ever seeing a salesperson. This has big implications for B-to-B marketers.

The Internet has driven dramatic changes in business buying behavior. Just as no one buys a car anymore without first checking prices and features online, business buyers now research and educate themselves online, months—even years—before ever seeing a salesperson. This has big implications for B-to-B marketers.

In the old days-just a few years ago-when business buyers had a problem, they’d call in their vendors for advice on how to solve it. So a sales person was in a nifty position to educate—and influence—the buyer from the earliest stages of the process.

But these days, the sales person has lost control. Buyers don’t really want to talk to vendors until somewhere akin to 70 percent of the way down the road, at the stage of writing RFPs and getting quotes. By then, the possible solutions and the specifications are already set.

But there’s more. Business buying processes are getting longer, and-most important-involving more parties than ever before. The so-called Buying Circle in large enterprise B-to-B-the influencers, specifiers, users, decision-makers-comprises as many as 21 people, according to Marketing Sherpa.

So marketers have to think differently today. First, you need to take an active role in the early stages of the buying process, to ensure that your solutions are front and center, and that you are in the game of influencing buyers as they educate themselves online. Second, you must gain access to each member of the Buying Circle, so you can understand their needs and interests, and deliver relevant messaging to them as they move from stage to stage in their buying journey.

These developments bring front and center five important areas requiring renewed focus from marketers:

  1. Complete and accurate data on customers and prospects. To influence the multiple Buying Circle members, and get to them early, you need to know who they are. Not an easy task, but more essential than ever. Here are some resources for gaining access to prospect data, and keeping your database clean.
  2. A deliberate contact strategy. Beyond blasting out prospecting campaigns, marketers must move toward a series of ongoing outbound messages, via multiple communications channels, to connect with multiple parties, over time. Here’s where marketing automation becomes an important resource for B-to-B marketers.
  3. Active social media outreach. No longer an experiment, social media has become a must-have element of the B-to-B marketing toolkit. A well-written blog, promoted by Twitter and LinkedIn groups, is a good way to start.
  4. A superb website, the core resource for engagement with buyers at all stages of the process. Enhance its interactivity by adding downloadable content in exchange for registration.
  5. A library of content assets. Populate your website with white papers, research reports, videos, how-to guides, technical documents, archived webinars, all written in objective, non-salesy language, to help educate buyers and help influence them toward your solution. Be sure to title the documents with plenty of keywords.

It’s a different marketing world today. But an exciting one, as long as marketers evolve along with buyers as they change the way they work.

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