Chicken or the Egg? Data or Analytics?

I just saw an online discussion about the role of a chief data officer, whether it should be more about data or analytics. My initial response to that question is “neither.” A chief data officer must represent the business first.

I just saw an online discussion about the role of a chief data officer, whether it should be more about data or analytics. My initial response to that question is “neither.” A chief data officer must represent the business first. And I had the same answer when such a title didn’t even exist and CTOs or other types of executives covered that role in data-rich environments. As soon as an executive with a seemingly technical title starts representing the technology, that business is doomed. (Unless, of course, the business itself is about having fun with the technology. How nice!)

Nonetheless, if I really have to pick just one out of the two choices, I would definitely pick the analytics over data, as that is the key to providing answers to business questions. Data and databases must be supporting that critical role of analytics, not the other way around. Unfortunately, many organizations are completely backward about it, where analysts are confined within the limitations of database structures and affiliated technologies, and the business owners and decision-makers are dictated to by the analysts and analytical tool sets. It should be the business first, then the analytics. And all databases—especially marketing databases—should be optimized for analytical activities.

In my previous columns, I talked about the importance of marketing databases and statistical modeling in the age of Big Data; not all depositories of information are necessarily marketing databases, and statistical modeling is the best way to harness marketing answers out of mounds of accumulated data. That begs for the next question: Is your marketing database model-ready?

When I talk about the benefits of statistical modeling in data-rich environments (refer to my previous column titled “Why Model?”), I often encounter folks who list reasons why they do not employ modeling as part of their normal marketing activities. If I may share a few examples here:

  • Target universe is too small: Depending on the industry, the prospect universe and customer base are sometimes very small in size, so one may decide to engage everyone in the target group. But do you know what to offer to each of your prospects? Customized offers should be based on some serious analytics.
  • Predictive data not available: This may have been true years back, but not in this day and age. Either there is a major failure in data collection, or collected data are too unstructured to yield any meaningful answers. Aren’t we living in the age of Big Data? Surely we should all dig deeper.
  • 1-to-1 marketing channels not in plan: As I repeatedly said in my previous columns, “every” channel is, or soon will be, a 1-to-1 channel. Every audience is secretly screaming, “Entertain us!” And customized customer engagement efforts should be based on modeling, segmentation and profiling.
  • Budget doesn’t allow modeling: If the budget is too tight, a marketer may opt in for some software solution instead of hiring a team of statisticians. Remember that cookie-cutter models out of software packages are still better than someone’s intuitive selection rules (i.e., someone’s “gut” feeling).
  • The whole modeling process is just too painful: Hmm, I hear you. The whole process could be long and difficult. Now, why do you think it is so painful?

Like a good doctor, a consultant should be able to identify root causes based on pain points. So let’s hear some complaints:

  • It is not easy to find “best” customers for targeting
  • Modelers are fixing data all the time
  • Models end up relying on a few popular variables, anyway
  • Analysts are asking for more data all the time
  • It takes too long to develop and implement models
  • There are serious inconsistencies when models are applied to the database
  • Results are disappointing
  • Etc., etc…

I often get called in when model-based marketing efforts yield disappointing results. More often than not, the opening statement in such meetings is that “The model did not work.” Really? What is interesting is that in more than nine times out of 10 cases like that, the models are the only elements that seem to have been done properly. Everything else—from pre-modeling steps, such as data hygiene, conversion, categorization, and summarization; to post-modeling steps, such as score application and validation—often turns out to be the root cause of all the troubles, resulting in pain points listed here.

When I speak at marketing conferences, talking about this subject of this “model-ready” environment, I always ask if there are statisticians and analysts in the audience. Then I ask what percentage of their time goes into non-statistical activities, such as data preparation and remedying data errors. The absolute majority of them say they spend of 80 percent to 90 percent of their time fixing the data, devoting the rest to the model development work. You don’t need me to tell you that something is terribly wrong with this picture. And I am pretty sure that none of those analysts got their PhDs and master’s degrees in statistics to spend most of their waking hours fixing the data. Yeah, I know from experience that, in this data business, the last guy who happens to touch the dataset always ends up being responsible for all errors made to the file thus far, but still. No wonder it is often quoted that one of the key elements of being a successful data scientist is the programming skill.

When you provide datasets filled with unstructured, incomplete and/or missing data, diligent analysts will devote their time to remedying the situation and making the best out of what they have received. I myself often tell newcomers that analytics is really about making the best of what you’ve got. The trouble is that such data preparation work calls for a different set of skills that have nothing to do with statistics or analytics, and most analysts are not that great at programming, nor are they trained for it.

Even if they were able to create a set of sensible variables to play with, here comes the bigger trouble; what they have just fixed is just a “sample” of the database, when the models must be applied to the whole thing later. Modern databases often contain hundreds of millions of records, and no analyst in his or her right mind uses the whole base to develop any models. Even if the sample is as large as a few million records (an overkill, for sure) that would hardly be the entire picture. The real trouble is that no model is useful unless the resultant model scores are available on every record in the database. It is one thing to fix a sample of a few hundred thousand records. Now try to apply that model algorithm to 200 million entries. You see all those interesting variables that analysts created and fixed in the sample universe? All that should be redone in the real database with hundreds of millions of lines.

Sure, it is not impossible to include all the instructions of variable conversion, reformat, edit and summarization in the model-scoring program. But such a practice is the No. 1 cause of errors, inconsistencies and serious delays. Yes, it is not impossible to steer a car with your knees while texting with your hands, but I wouldn’t call that the best practice.

That is why marketing databases must be model-ready, where sampling and scoring become a routine with minimal data transformation. When I design a marketing database, I always put the analysts on top of the user list. Sure, non-statistical types will still be able to run queries and reports out of it, but those activities should be secondary as they are lower-level functions (i.e., simpler and easier) compared to being “model-ready.”

Here is list of prerequisites of being model-ready (which will be explained in detail in my future columns):

  • All tables linked or merged properly and consistently
  • Data summarized to consistent levels such as individuals, households, email entries or products (depending on the ranking priority by the users)
  • All numeric fields standardized, where missing data and zero values are separated
  • All categorical data edited and categorized according to preset business rules
  • Missing data imputed by standardized set of rules
  • All external data variables appended properly

Basically, the whole database should be as pristine as the sample datasets that analysts play with. That way, sampling should take only a few seconds, and applying the resultant model algorithms to the whole base would simply be the computer’s job, not some nerve-wrecking, nail-biting, all-night baby-sitting suspense for every update cycle.

In my co-op database days, we designed and implemented the core database with this model-ready philosophy, where all samples were presented to the analysts on silver platters, with absolutely no need for fixing the data any further. Analysts devoted their time to pondering target definitions and statistical methodologies. This way, each analyst was able to build about eight to 10 “custom” models—not cookie-cutter models—per “day,” and all models were applied to the entire database with more than 200 million individuals at the end of each day (I hear that they are even more efficient these days). Now, for the folks who are accustomed to 30-day model implementation cycle (I’ve seen as long as 6-month cycles), this may sound like a total science fiction. And I am not even saying that all companies need to build and implement that many models every day, as that would hardly be a core business for them, anyway.

In any case, this type of practice has been in use way before the words “Big Data” were even uttered by anyone, and I would say that such discipline is required even more desperately now. Everyone is screaming for immediate answers for their questions, and the questions should be answered in forms of model scores, which are the most effective and concise summations of all available data. This so-called “in-database” modeling and scoring practice starts with “model-ready” database structure. In the upcoming issues, I will share the detailed ways to get there.

So, here is the answer for the chicken-or-the-egg question. It is the business posing the questions first and foremost, then the analytics providing answers to those questions, where databases are optimized to support such analytical activities including predictive modeling. For the chicken example, with the ultimate goal of all living creatures being procreation of their species, I’d say eggs are just a means to that end. Therefore, for a business-minded chicken, yeah, definitely the chicken before the egg. Not that I’ve seen too many logical chickens.

Why Model?

Why model? Uh, because someone is ridiculously good looking, like Derek Zoolander? No, seriously, why model when we have so much data around? The short answer is because we will never know the whole truth. That would be the philosophical answer. Physicists construct models to make new quantum field theories more attractive theoretically and more testable physically. If a scientist already knows the secrets of the universe, well, then that person is on a first-name basis with God Almighty, and he or she doesn’t need any models to describe things like particles or strings. And the rest of us should just hope the scientist isn’t one of those evil beings in “Star Trek.”

Why model? Uh, because someone is ridiculously good looking, like Derek Zoolander? No, seriously, why model when we have so much data around?

The short answer is because we will never know the whole truth. That would be the philosophical answer. Physicists construct models to make new quantum field theories more attractive theoretically and more testable physically. If a scientist already knows the secrets of the universe, well, then that person is on a first-name basis with God Almighty, and he or she doesn’t need any models to describe things like particles or strings. And the rest of us should just hope the scientist isn’t one of those evil beings in “Star Trek.”

Another answer to “why model?” is because we don’t really know the future, not even the immediate future. If some object is moving toward a certain direction at a certain velocity, we can safely guess where it will end up in one hour. Then again, nothing in this universe is just one-dimensional like that, and there could be a snowstorm brewing up on its path, messing up the whole trajectory. And that weather “forecast” that predicted the snowstorm is a result of some serious modeling, isn’t it?

What does all this mean for the marketers who are not necessarily masters of mathematics, statistics or theoretical physics? Plenty, actually. And the use of models in marketing goes way back to the days of punch cards and mainframes. If you are too young to know what those things are, well, congratulations on your youth, and let’s just say that it was around the time when humans first stepped on the moon using a crude rocket ship equipped with less computing power than an inexpensive passenger car of the modern days.

Anyhow, in that ancient time, some smart folks in the publishing industry figured that they would save tons of money if they could correctly “guess” who the potential buyers were “before” they dropped any expensive mail pieces. Even with basic regression models—and they only had one or two chances to get it right with glacially slow tools before the all-too-important Christmas season came around every year—they could safely cut the mail quantity by 80 percent to 90 percent. The savings added up really fast by not talking to everyone.

Fast-forward to the 21st Century. There is still a beauty of knowing who the potential buyers are before we start engaging anyone. As I wrote in my previous columns, analytics should answer:

1. To whom you should be talking; and
2. What you should offer once you’ve decided to engage someone.

At least the first part will be taken care of by knowing who is more likely to respond to you.

But in the days when the cost of contacting a person through various channels is dropping rapidly, deciding to whom to talk can’t be the only reason for all this statistical work. Of course not. There are plenty more reasons why being a statistician (or a data scientist, nowadays) is one of the best career choices in this century.

Here is a quick list of benefits of employing statistical models in marketing. Basically, models are constructed to:

  • Reduce cost by contacting prospects more wisely
  • Increase targeting accuracy
  • Maintain consistent results
  • Reveal hidden patterns in data
  • Automate marketing procedures by being more repeatable
  • Expand the prospect universe while minimizing the risk
  • Fill in the gaps and summarize complex data into an easy-to-use format—A must in the age of Big Data
  • Stay relevant to your customers and prospects

We talked enough about the first point, so let’s jump to the second one. It is hard to argue about the “targeting accuracy” part, though there still are plenty of non-believers in this day and age. Why are statistical models more accurate than someone’s gut feeling or sheer guesswork? Let’s just say that in my years of dealing with lots of smart people, I have not met anyone who can think about more than two to three variables at the same time, not to mention potential interactions among them. Maybe some are very experienced in using RFM and demographic data. Maybe they have been reasonably successful with choices of variables handed down to them by their predecessors. But can they really go head-to-head against carefully constructed statistical models?

What is a statistical model, and how is it built? In short, a model is a mathematical expression of “differences” between dichotomous groups. Too much of a mouthful? Just imagine two groups of people who do not overlap. They may be buyers vs. non-buyers; responders vs. non-responders; credit-worthy vs. not-credit-worthy; loyal customers vs. attrition-bound, etc. The first step in modeling is to define the target, and that is the most important step of all. If the target is hanging in the wrong place, you will be shooting at the wrong place, no matter how good your rifle is.

And the target should be expressed in mathematical terms, as computers can’t read our minds, not just yet. Defining the target is a job in itself:

  • If you’re going after frequent flyers, how frequent is frequent enough for you? Five times a year or 10 times a year? Or somewhere in between? Or should it remain continuous?
  • What if the target is too small or too large? What then?
  • If you are looking for more valuable prospects, how would you express that? In terms of average spending, lifetime spending or sheer number of transactions?
  • What if there is an inverse relationship between frequency and dollar spending (i.e., high spenders shopping infrequently)?
  • And what would be the borderline number to be “valuable” in all this?

Once the target is set, after much pondering, then the job is to select the variables that describe the “differences” between the two groups. For example, I know how much marketers love to use income variables in various situations. But if that popular variable does not explain the differences between the two groups (target and non-target), the mathematics will mercilessly throw it out. This rigorous exercise of examining hundreds or even thousands of variables is one of the most critical steps, during which many variables go through various types of transformations. Statisticians have different preferences in terms of ideal numbers of variables in a model, while non-statisticians like us don’t need to be too concerned, as long as the resultant model works. Who cares if a cat is white or black, as long as it catches mice?

Not all selected variables are equally important in model algorithms, either. More powerful variables will be assigned with higher weight, and the sum of these weighted values is what we call model score. Now, non-statisticians who have been slightly allergic to math since the third grade only need to know that the higher the score, the more likely the record in question is to be like the target. To make the matter even simpler, let’s just say that you want higher scores over lower scores. If you are a salesperson, just call the high-score prospects first. And would you care how many variables are packed into that score, for as long as you get the good “Glengarry Glen Ross” leads on top?

So, let me ask again. Does this sound like something a rudimentary selection rule with two to three variables can beat when it comes to identifying the right target? Maybe someone can get lucky once or twice, but not consistently.

That leads to the next point, “consistency.” Because models do not rely on a few popular variables, they are far less volatile than simple selection rules or queries. In this age of Big Data, there are more transaction and behavioral data in the mix than ever, and they are far more volatile than demographic and geo-demographic data. Put simply, people’s purchasing behavior and preferences change much faster than family composition or their income, and that volatility factor calls for more statistical work. Plus, all facets of marketing are now more about measurable results (ah, that dreaded ROI, or “Roy,” the way I call it), and the businesses call for consistent hitters over one-hit wonders.

“Revealing hidden patterns in data” is my favorite. When marketers are presented with thousands of variables, I see a majority of them just sticking to a few popular ones all the time. Some basic recency and frequency data are there, and among hundreds of demographic variables, the list often stops after income, age, gender, presence of children, and some regional variables. But seriously, do you think that the difference between a luxury car buyer and an SUV buyer is just income and age? You see, these variables are just the ones that human minds are accustomed to. Mathematics do not have such preconceived notions. Sticking to a few popular variables is like children repeatedly using three favorite colors out of a whole box of crayons.

I once saw a neighborhood-level U.S. Census variable called “% Households with Septic Tanks” in a model built for a high-end furniture catalog. Really, the variable was “percentage of houses with septic tanks in the neighborhood.” Then I realized it made a lot of sense. That variable was revealing how far away that neighborhood was located in comparison to populous city centers. As the percentage of septic tanks increased, the further away the residents were from the city center. And maybe those folks who live in scarcely populated areas were more likely to shop for furniture through catalogs than the folks who live closer to commercial areas.

This is where we all have that “aha” moment. But you and I will never pick that variable in anything that we do, not in million years, no matter how effective it may be in finding the target prospects. The word “septic” may scare some people off at “hello.” In any case, modeling procedures reveal hidden connections like that all of the time, and that is a very important function in data-rich environments. Otherwise, we will not know what to throw out without fear, and the databases will continuously become larger and more unusable.

Moving on to the next points, “Repeatable” and “Expandable” are somewhat related. Let’s say a marketer has been using a very innovative selection logic that she came across almost by accident. In pursuing special types of wealthy people, she stumbled upon a piece of data called “owner of swimming pool.” Now, she may have even had a few good runs with it, too. But eventually, that success will lead to the question of:

1. Having to repeat that success again and again; and
2. Having to expand that universe, when the “known” universe of swimming pool owners become depleted or saturated.

Ah, the chagrin of a one-hit-wonder begins.

Use of statistical models, with help of multiple variables and scalable scoring, would avoid all of those issues. You want to expand the prospect universe? No trouble. Just dial down the scores on the scale a little further. We can even measure the risk of reaching into the lower-scoring groups. And you don’t have to worry about coverage issues related to a few variables, as those won’t be the only ones in the model. Want to automate the selection process? No problem there, as using a score, which is a summary of key predictors, is far simpler than having to carry a long list of data variables into any automated system.

Now, that leads to the next point, “Filling in the gaps and summarizing the complex data into an easy-to-use format.” In the age of ubiquitous and “Big” data, this is the single-most important point, way beyond the previous examples for traditional 1-to-1 marketing applications. We are definitely going through massive data overloads everywhere, and someone better refine the data and provide some usable answers.

As I mentioned earlier, we build models because we will never know the whole truth. I believe that the Big Data movement should be all about:

1. Filtering the noise from valuable information; and
2. Filling the gaps.

“Gaps,” you say? Believe me, there are plenty of gaps in any dataset, big or small.

When information continues to get piled on, the resultant database may look big. And they are physically large. But in marketing, as I repeatedly emphasized in my previous columns, the data must be realigned to “buyer-centric” formats, with every data point describing each individual, as marketing is all about people.

Sure, you may have tons of mobile phone-related data. In fact, it could be quite huge in size. But let me turn that upside down for you (more like sideways-up, in practice). Now, try to describe everyone in your footprint in terms of certain activities. Say, “every smart phone owner who used more than 80 percent of his or her monthly data allowance on the average for the past 12 months, regardless of the carrier.” Hey, don’t blame me for asking these questions just because it’s inconvenient for data handlers to answer them. Some marketers would certainly benefit from information like that, and no one cares about just bits and pieces of data, other than for some interesting tidbits at a party.

Here’s the main trouble when you start asking buyer-related questions like that. Once we try to look at the world from the “buyer-centric” point of view, we will realize there are tons of missing data (i.e., a whole bunch of people with not much information). It may be that you will never get this kind of data from all carriers. Maybe not everyone is tracked this way. In terms of individuals, you may end up with less than 10 percent in the database with mobile information attached to them. In fact, many interesting variables may have less than 1 percent coverage. Holes are everywhere in so-called Big Data.

Models can fill in those blanks for you. For all those data compilers who sell age and income data for every household in the country, do you believe that they really “know” everyone’s age and income? A good majority of the information is based on carefully constructed models. And there is nothing wrong with that.

If you don’t get to “know” something, we can get to a “likelihood” score—of “being like” that something. And in that world, every measurement is on a scale, with no missing values. For example, the higher the score of a model built for a telecommunication company, the more likely that the prospect is going to use a high-speed data plan, or the international long distance services, depending on the purpose of the model. Or the more likely the person will buy sports packages via cable or satellite. Or the person is more likely to subscribe to premium movie channels. Etc., etc. With scores like these, a marketer can initiate the conversation with—not just talking to—a particular prospect with customized product packages in his hand.

And that leads us to the final point in all this, “Staying relevant to your customers and prospects.” That is what Big Data should be all about—at least for us marketers. We know plenty about a lot of people. And they are asking us why we are still so random about marketing messages. With all these data that are literally floating around, marketers can do so much better. But not without statistical models that fill in the gaps and turn pieces of data into marketing-ready answers.

So, why model? Because a big pile of information doesn’t provide answers on its own, and that pile has more holes than Swiss cheese if you look closely. That’s my final answer.

Cheat Sheet: Is Your Database Marketing Ready?

Many data-related projects end up as big disappointments. And, in many cases, it is because they did not have any design philosophy behind them. Because many folks are more familiar with buildings and cars than geeky databases, allow me to use them as examples here.

Many data-related projects end up as big disappointments. And, in many cases, it is because they did not have any design philosophy behind them. Because many folks are more familiar with buildings and cars than geeky databases, allow me to use them as examples here.

Imagine someone started constructing a building without a clear purpose. What is it going to be? An office building or a residence? If residential, for how many people? For a family, or for 200 college kids? Are they going to just eat and sleep in there, or are they going to engage in other activities in it? What is the budget for development and ongoing maintenance?

If someone starts building a house without answering these basic questions, well, it is safe to say that the guy who commissioned such a project is not in the right state of mind. Then again, he may be a filthy rich rock star with some crazy ideas. But let us just say that is an exceptional case. Nonetheless, surprisingly, a great many database projects start out exactly this way.

Just like a house is not just a sum of bricks, mortar and metal, a database is not just a sum of data, and there has to be design philosophy behind it. And yet, many companies think that putting all available data in one place is just good enough. Call it a movie without a director or a building without an architect; you know and I know that such a project cannot end well.

Even when a professional database designer gets involved, too often the project goes out of control—as the business requirement document ends up being a summary of
everyone’s wish lists, without any prioritization or filtering. It is a case of a movie without a director. The goal becomes something like “a database that stores all conceivable marketing, accounting and payment activities, handling both prospecting and customer relationship management through all conceivable channels, including face-to-face sales and lead management for big accounts. And it should include both domestic and international activities, and the update has to be done in real time.”

Really. Someone in that organization must have attended a database marketing conference recently to get all that listed. It might be simpler and cheaper building a 2-ton truck that flies. But before we commission something like this from the get-go, shall we discuss why the truck has to fly, too? For one, if you want real-time updates, do you have a business case for it? (As in, someone in the field must make real-time decisions with real-time data.) Or do you just fancy a large object, moving really fast?

Companies that primarily sell database tools often do not help the matter, either. Some promise that the tool sets will categorize all kinds of input data, based on some auto-generated meta-tables. (Really?) The tool will clean the data automatically. (Is it a self-cleaning oven?) The tool will establish key links (by what?), build models on its own (with what target data?), deploy campaigns (every Monday?), and conduct result analysis (with responses from all channels?).

All these capabilities sound really wonderful, but does that system set long- and short-term marketing goals for you, too? Does it understand the subtle nuances in human behaviors and intentions?

Sorry for being a skeptic here. But in such cases, I think someone watched “Star Trek” too much. I have never seen a company that does not regret spending seven figures on a tool set that was supposed to do everything. Do you wonder why? It is not because such activities cannot be automated, but because:

  1. Machines do not think for us (not quite yet); and
  2. Such a system is often very expensive, as it needs to cover all contingencies (the opposite of “goal-oriented” cheaper options).

So it becomes nearly impossible to justify the cost with incremental improvements in marketing efficiency. Even if the response rates double, all related marketing costs go down by a quarter, and revenue jumps up by 200 percent, there are not many companies that can easily justify that kind of spending.

Worse yet, imagine that you just paid 10 times more for some factory-made suit than you would have paid for a custom-made Italian suit. Since when is an automated, cookie-cutter answer more desirable than custom-tailored ones? Ever since computing and storage costs started to go down significantly, and more so in this age of Big Data that has an “everything, all the time” mentality.

But let me ask you again: Do you really have a marketing database?

Let us just say that I am a car designer. A potential customer who has been doing a lot of research on the technology front presents me with a spec for a vehicle that is as big as a tractor-trailer and as quick as a passenger car. I guess that someone really needs to move lots of stuff, really fast. Now, let us assume that it will cost about $8 million or more to build a car like that, and that estimate is without the rocket booster (ah, my heart breaks). If my business model is to take a percentage out of that budget, I would say, “Yeah sure, we can build a car like that for you. When can we start?”

But let us stop for a moment and ask why the client would “need” (not “want”) a car like that in the first place. After some user interviews and prioritization, we may collectively conclude that a fleet of full-size vans can satisfy 98 percent of the business needs, saving about $7 million. If that client absolutely and positively has to get to that extra 2 percent to satisfy every possible contingency in his business and spend that money, well, that is his prerogative, is it not? But I have to ask the business questions first before initiating that inevitable long and winding journey without a roadmap.

Knowing exactly what the database is supposed to be doing must be the starting point. Not “let’s just gather everything in one place and hope to God that some user will figure something out eventually.” Also, let’s not forget that constantly adding new goals in any phase of the project will inevitably complicate the matter and increase the cost.

Conversely, repurposing a database designed for some other goal will cause lots of troubles down the line. Yeah, sure. Is it not possible to move 100 people from A to B with a 2-seater sports car, if you are willing to make lots of quick trips and get some speeding tickets along the way? Yes, but that would not be my first recommendation. Instead, here are some real possibilities.

Databases support many different types of activities. So let us name a few:

  • Order fulfillment
  • Inventory management and accounting
  • Contact management for sales
  • Dashboard and report generation
  • Queries and selections
  • Campaign management
  • Response analysis
  • Trend analysis
  • Predictive modeling and scoring
  • Etc., etc.

The list goes on, and some of the databases may be doing fine jobs in many areas already. But can we safely call them “marketing” databases? Or are marketers simply tapping into the central data depository somehow, just making do with lots of blood, sweat and tears?

As an exercise, let me ask a few questions to see if your organization has a functioning marketing database for CRM purposes:

  • What is the average order size per year for customers with tenure of more than one year? —You may have all the transaction data, but maybe not on an individual level in order to know the average.
  • What is the number of active and dormant customers based on the last transaction date? —You will be surprised to find out that many companies do not know exactly how many customers they really have. Beep! 1 million-“ish” is not a good answer.
  • What is the average number of days between activities for each channel for each customer? —With basic transaction data summarized “properly,” this is not a difficult question to answer. But it’s very difficult if there are divisional “channel-centric” databases scattered all over.
  • What is the average number of touches through all channels that you employ before your customer reaches the projected value potential? —This is a hard one. Without all the transaction and contact history by all channels in a “closed-loop” structure, one cannot even begin to formulate an answer for this one. And the “value potential” is a result of statistical modeling, is it not?
  • What are typical gateway products, and how are they correlated to other product purchases? —This may sound like a product question, but without knowing each customer’s purchase history lined up properly with fully standardized product categories, it may take a while to figure this one out.
  • Are basic RFM data—such as dollars, transactions, dates and intervals—routinely being used in predictive models? —The answer is a firm “no,” if the statisticians are spending the majority of their time fixing the data; and “not even close,” if you are still just using RFM data for rudimentary filtering.

Now, if your answer is “Well, with some data summarization and inner/outer joins here and there—though we don’t have all transaction records from last year, and if we can get all the campaign histories from all seven vendors who managed our marketing campaigns, except for emails—maybe?”, then I am sorry to inform you that you do not have a marketing database. Even if you can eventually get to the answer if some programmer takes two weeks to draw a 7-page flow chart.

Often, I get extra comments like “But we have a relational database!” Or, “We stored every transaction for the past 10 years in Hadoop and we can retrieve any one of them in less than a second!” To these comments, I would say “Congratulations, your car has four wheels, right?”

To answer the important marketing questions, the database should be organized in a “buyer-centric” format. Going back to the database philosophy question, the fundamental design of the database changes based on its main purpose, much like the way a sports sedan and an SUV that share the same wheel base and engine end up shaped differently.

Marketing is about people. And, at the center of the marketing database, there have to be people. Every data element in the base should be “describing” those people.

Unfortunately, most relational databases are transaction-, channel- or product-centric, describing events and transactions—but not the people. Unstructured databases that are tuned primarily for massive storage and rapid retrieval may just have pieces of data all over the place, necessitating serious rearrangement to answer some of the most basic business questions.

So, the question still stands. Is your database marketing ready? Because if it is, you would have taken no time to answer my questions listed above and say: “Yeah, I got this. Anything else?”

Now, imagine the difference between marketers who get to the answers with a few clicks vs. the ones who have no clue where to begin, even when sitting on mounds of data. The difference between the two is not the size of the investment, but the design philosophy.

I just hope that you did not buy a sports car when you needed a truck.

Copywriting for Social Media Marketing: 3 Best Practices for 2014

Effective copywriting for social media marketing was the game changer in 2013. Still trying to prove ROI on social media? Make 2014 the year you stop obsessing over measuring trivial stats—and start generating leads with social media. Do it without sacrificing brand integrity or annoying prospects. Use these three, proven social media copywriting best practices.

Effective copywriting for social media marketing was the game changer in 2013. Still trying to prove ROI on social media? Make 2014 the year you stop obsessing over measuring trivial stats—and start generating leads with social media. Do it without sacrificing brand integrity or annoying prospects.

Use these three, proven social media copywriting best practices.

Yes, You CAN Sell on Social Media
“People who say it cannot be done should not interrupt those who are doing it,” said George Bernard Shaw. He’s right.

Effective social media and content marketing attracts, engages and takes customers on journeys to better places—where they decide how, when and were to get there. Effective copywriting for social media powers each stage of the “attract, engage, nurture” process.

Effective copywriting for social media is all about helping customers:

  • believe there is a better way (via your short-form social comments)
  • realize they just found part of it (on your longer-form blog) and
  • act—taking a first step toward what they want (giving you a lead)

Best Practice No. 1: Re-think the Role of Your Blog
“Gating” your best knowledge and tips is less and less effective each year. For example, buyers are becoming more likely to offer fake or “un-attended” email addresses in exchange for your whitepaper. What’s the answer? Long-er form content that proves you are worth a real relationship to the buyer.

According to sources like InfusionSoft (and my own experience!) buyers are registering less and less. Why? Because competitors are increasingly giving-away their best knowledge. Where?

Blogs.

We cannot keep forcing readers to give up their contact and purchase intent details in exchange for our content marketing assets. So my first social media copywriting best practice for you is strategic.

Becoming a better social media copywriter starts with the right strategy.

Converting readers to leads demands the best copywriting on social platforms plus effectively written, long-form content. Your best tips, tricks and advice that helps customers achieve a desired goal, avoid a risk or solve a problem.

Your blog is the content marketing hub. It is where your short-form social media copywriting directs prospects. Facebook and Google+ updates. LinkedIn group discussions, status updates, company page posts, your LinkedIn profile call to action.

Social media drives visitors to blog content that proves you’re worth a real email address!

Best Practice No. 2: Follow a Process, Not Just Passion
Don’t get caught up in the “show you’re human” and “tell a good story” nonsense. Having personality and being interesting is the entry fee. It’s essential. The force multiplier is an effective copywriting for social media process.

Start here. Write a solution (answer) to a problem (question) your target market needs solved on your blog. Follow these guidelines to make sure your words get acted on-prospects see your call to action and ACT on it.

1. Get right to-the-point
When you write be like a laser. Don’t make readers wait for the solution. Hit ’em with it in the first paragraph. Give them everything up front at a high level. Then, in the body of your article …

2. Reveal slowly
When it comes to all the juicy details of your remedy take it slow. Slow enough to encourage more questions—to create curiosity in the total solution. When you do this, make sure you …

3. Provoke response by leveraging the curiosity you just created
Yes, be action-oriented and specific. But avoid being so complete in your blog, LinkedIn or Google+ post that readers become totally satisfied with your words.

Remember to:

  • start with customers pains, goals, fears, ambitions or cravings in mind … and …
  • structure blog posts to teach, guide or answer in ways that …
  • create hunger for more of what we have to offer (a lead generation offer).

Focus on following this structure. Form the habit. You can do it!

Best Practice No. 3: Get back to basics
I know it sounds trite, but hear me out. There is one copywriting tip (habit) that consistently produces new business using social media. It’s an old direct response marketing rule.

Give customers a clear, compelling reason to act immediately—resolve, experience or improve something important to them.

This is why your blog is so critical.

At the most basic level, customers need help:

  • believing there is a better way
  • realizing they just found it (on your blog) and
  • acting-taking a first step toward what they want (giving you a lead)

Blog or video content that makes customers respond does one thing really well: It answers questions in ways that makes potential buyers think, “Yes, yes, YES … I can take action on that. That will probably create results for me. Now, how can I get my hands on more of those kinds of insights/tips?”

This is the key to using a blog to sell. This simple idea is the difference between blogging for sales and starving! Make it your goal. Good luck in 2014.

7 Ways to Polish Your Video Concept and Message

Whether you’ve been using video to market your product or services for a long time or are just getting started, there is always the question of content: “What do I do a video about”? Let’s face it, there are more bad video’s on YouTube than good ones. This week’s article will focus on seven steps to polish your idea process.

Whether you have been using video to market your product or services for a long time or are just getting started, there is always the question of content: “What do I do a video about?” Let’s face it, there are more bad videos on YouTube than good ones. This may be due to so many people not giving enough thought or planning time to the video for it to be successful. With a little planning and serious thought, you can still have a successful marketing campaign for your video production.

Let’s focus on the first question that you need to ask yourself to kick off your pre-production procedure: What is your Message?

How will you present your message? Are you excited about your topic? If you aren’t, how in the world will the viewers be excited? Consider the obvious: Does it fit in with your demographics? Is it going to resonate with your viewer?

Once you figure out what your message is, consider how you will present that message. Should you have a spokesperson? Should you deliver the message yourself? Should it be serious or humorous? Is it in line with what is trending? Does it have a strong enough message that your viewers will be compelled to do what you want them to do?

Don’t be afraid to try something different. Humor is always great as long as it’s funny. Do you have a team of people who will give you honest opinions when they critique what you’re doing? Is this educational? Is it informative? Does it touch their emotions? Does your video solve a problem or answer the viewer’s questions?

Remember, once you upload something to YouTube, it’s going to be there forever. Regardless of whether you set it to private; if anyone looks hard enough, they can find it. More importantly, is your video’s concept sustainable? This is where the well thought-out game plan is more important. A video that has an interesting message or solves someone’s problem will have the longevity and continuity of being watched, even years after it’s first posted.

Do you have a special announcement to make? Publicity videos are a great way to get your message out there with a soft-selling approach. A message from the CEO is also affective and can get your message out there quickly. Just remember that videos that last over 60 seconds tend to get boring.

Now that you have a few great pointers to consider, start with a list of 10 video ideas. Share with the critics who will most reliably give you a real opinion. Once you’ve narrowed down the idea, create the message. Research the message on line to see what others are saying about it. Script it or storyboard it out, and then plan carefully.

  1. Ask some of your employees or staff to develop ideas based on common themes they see or hear from your market
  2. Read journals, blogs and trade magazines that are related to your business
  3. Search the Internet for video’s that are similar and copy from the masters
  4. Write 10 ideas, then draw from a hat
  5. Watch the news to check for trends
  6. Ask your clients directly
  7. Develop a video production journal

Executing the idea is the hardest part. If you do your research and have a well thought-out plan, then you will surely succeed. Don’t be afraid to try new ideas. Some will work, while others will not. It’s better to try than to fail wishing because you didn’t try, as long as your video is focused.

‘Big Data’ Is Like Mining Gold for a Watch – Gold Can’t Tell Time

It is often quoted that 2.5 quintillion bytes of data are collected each day. That surely sounds like a big number, considering 1 quintillion bytes (or exabytes, if that sounds fancier) are equal to 1 billion gigabytes. … My phone can hold about 65 gigabytes; which, by the way, means nothing to me. I just know that figure equates to about 6,000 songs, plus all my personal information, with room to spare for hundreds of photos and videos. 

It is often quoted that 2.5 quintillion bytes of data are collected each day. That surely sounds like a big number, considering 1 quintillion bytes (or exabytes, if that sounds fancier) are equal to 1 billion gigabytes. Looking back only about 20 years, I remember my beloved 386-based desktop computer had a hard drive that can barely hold 300 megabytes, which was considered to be quite large in those ancient days. Now, my phone can hold about 65 gigabytes; which, by the way, means nothing to me. I just know that figure equates to about 6,000 songs, plus all my personal information, with room to spare for hundreds of photos and videos. So how do I fathom the size of 2.5 quintillion bytes? I don’t. I give up. I’d rather count the number stars in the universe. And I have been in the database business for more than 25 years.

But I don’t feel bad about that. If a pile of data requires a computer to process it, then it is already too “big” for our brains. In the age of “Big Data,” size matters, but emphasizing the size element is missing the point. People want to understand the data in their own terms and want to use them in decision-making processes. Throwing the raw data around to people without math or computing skills is like galleries handing out paint and brushes to people who want paintings on the wall. Worse yet, continuing to point out how “big” the Big Data world is to them is like quoting the number of rice grains on this planet in front of a hungry man, when he doesn’t even care how many grains are in one bowl. He just wants to eat a bowl of “cooked” rice, and right this moment.

To be a successful data player, one must be the master of the following three steps:

  • Collection;
  • Refinement; and
  • Delivery.

Collection and storage are obviously important in the age of Big Data. However, that in itself shouldn’t be the goal. I hear lots of bragging about how much data can be collected and stored, and how fast the data can be retrieved.

Great, you can retrieve any transaction detail going back 20 years in less than 0.5 seconds. Congratulations. But can you now tell me whom are more likely to be loyal customers for the next five years, with annual spending potential of more than $250? Or who is more likely to quit using the service in next 60 days? Who is more likely to be on a cruise ship leaving the dock on the East Coast heading for Europe between Thanksgiving and Christmas, with onboard spending potential greater than $300? Who is more likely to respond to emails with free shipping offers? Where should I open my next store selling fancy children’s products? What do my customers look like, and where do they go between 6 and 9 p.m.?

Answers to these types of questions do not come from the raw data, but they should be derived from the data through the data refinement process. And that is the hard part. Asking the right questions, expressing the goals in a mathematical format, throwing out data that don’t fit the question, merging data from a diverse array of sources, summarizing the data into meaningful levels, filling in the blanks (there will be plenty—even these days), and running statistical models to come up with scores that look like an answer to the question are all parts of the data refinement process. It is a lot like manufacturing gold watches, where mining gold is just an important first step. But a piece of gold won’t tell you what time it is.

The final step is to deliver that answer—which, by now, should be in a user-friendly format—to the user at the right time in the right format. Often, lots of data-related products only emphasize this part, as it is the most intimate one to the users. After all, it provides an illusion that the user is in total control, being able to touch the data so nicely displayed on the screen. Such tool sets may produce impressive-looking reports and dazzling graphics. But, lest we forget, they are only representations of the data refinement processes. In addition, no tool set will ever do the thinking part for anyone. I’ve seen so many missed opportunities where decision-makers invested obscene amounts of money in fancy tool sets, believing they will conduct all the logical and data refinement work for them, automatically. That is like believing that purchasing the top of the line Fender Stratocaster will guarantee that you will play like Eric Clapton in the near future. Yes, the tool sets are important as delivery mechanisms of refined data, but none of them replace the refinement part. Doing so would be like skipping guitar practice after spending $3,000 on a guitar.

Big Data business should be about providing answers to questions. It should be about humans who are the subjects of data collection and, in turn, the ultimate beneficiaries of information. It’s not about IT or tool sets that come and go like hit songs. But it should be about inserting advanced use of data into everyday decision-making processes by all kinds of people, not just the ones with statistics degrees. The goal of data players must be to make it simple—not bigger and more complex.

I boldly predict that missing these points will make “Big Data” a dirty word in the next three years. Emphasizing the size element alone will lead to unbalanced investments, which will then lead to disappointing results with not much to show for them in this cruel age of ROI. That is a sure way to kill the buzz. Not that I am that fond of the expression “Big Data”; though, I admit, one benefit has been that I don’t have to explain what I do for living for 10 minutes any more. Nonetheless, all the Big Data folks may need an exit plan if we are indeed heading for the days when it will be yet another disappointing buzzword. So let’s do this one right, and start thinking about refining the data first and foremost.

Collection and storage are just so last year.

Manage Your Team, and Answer Important Questions While You Travel

Did you realize that you have a way to communicate with your team right in your back pocket? True or False: Only wealthy companies use video and film production? Statement: It’s impossible to be two places at once. Did you realize that even while you’re traveling you could answer questions, and keep your team informed? If you travel heavily for your company and are an executive or leader, this article will help you by offering some new communication solutions

Did you realize that you have a way to communicate with your team right in your back pocket? True or False: Only wealthy companies use video and film production? Statement: It’s impossible to be two places at once. Did you realize that even while you’re traveling you could answer questions, and keep your team informed? If you travel heavily for your company and are an executive or leader, this article will help you by offering some new communication solutions.

The types of video production companies use now vary considerably. Anything from sales presentations, corporate communications, customer service, tutorials and internal communications are media treasures.

These types of videos can be there to serve both the client and your employees. The other forms of video production include staff training, employee orientation, safety procedures, promotional video and financial reports. The key point to remember here is they can be viewed on several different devices—iPad, computer, and, of course, mobile phone.

Video can be used as a heavy-duty communication machine even while you’re traveling the tundra. Utilizing video platforms like Skype, Livestream and Google+ Hangouts will put you in front of your employees so you can continue to disperse your companies propaganda, even while miles away. This allows your employees to be not only informed, but to have an emotional connection to you as if you are still present, even when absent.

Some types of video production can cost next to nothing to create. For example, Instagram, Vine, Skype, Facetime and Google + Hangouts. These are simple to use and can be viewed individually or as a group; which allows you to continue to lead your team even if it’s in a busy airport. These platforms give you the ability to promulgate to a tailored crowd. You can choose to speak to one person, several or the entire staff.

The other benefit here is that you can be in several places at once. I bet you wish you could clone yourself so that you can be everywhere at the same time. With telegenic devices, you are able to be in multiple locations, which can save you time and money.

HR Professionals are finding these assets invaluable to effectively inform their troops and train their employees on important factors such as safety, company policies and procedures. The same message is given each time to each individual, allowing more control over the communiqué distributed among the new and existing hires.

While any of these types of television programs would be effective and work, here are some more advanced ideas for the use of video in communicating to your present crowd. Use a thumbnail video in your email signature. This could be a general message from the CEO, President or possibly an HR Supervisor.

One of the best devices that I’ve seen this used with is a USB stick. Placing your corporate mini movie on this type of device is sure to get people interested in what’s on it. We can’t help but be curious when a gadget is in the palm of our hands.

What’s the best way to get started by utilizing these simulcast luxuries? This would be some solid hypothesis; Ask the people that have the most questions directed to them at your company. Have them write up to 10 topics that these videos could address. Do this with the answers to those questions, and Voila!, you have a script created for your first production.

Next, decide who will be your audience. Directly address them individually or within the group. Make the dialogue interesting, as if you were right there in the same room—because technically you are.

Then decide what the best way to distribute this message should be. Should it be Live? Do you want to ensure that they will see it? Do you want this to be measurable and traceable? Consider the style as well. Do you want it to be comical, motivational or serious in nature? A financial report to your stock holders may need to be handled with kid gloves, while a safety video that is going to be viewed by the group and needs to be remembered, and comedy can often be more memorable, even on serious subjects.

I hope that this discussion has sparked a few new ways for you to interface with your peers. If anything, perhaps it’s helped answer the question of how can you communicate with the team while abroad? Either way, I’m sure you will remember that the use of video isn’t always obvious but still effective.

Any further discussion or ideas to be added can be sent to me at egrey@hermanadvertising.com.

A Weird, But Effective Shortcut to Generate Sales Leads on LinkedIn

See what I just did? You chose to read this article—probably because the headline provoked curiosity. It’s one of the oldest tricks in the book, the basis of effective copywriting. True, there is no silver bullet for generating sales leads on LinkedIn. However, there is one habit that consistently brings my students and me more success generating leads online: Giving customers a reason to click and take action—relieve that nagging pain or take a step toward an exciting goal.

See what I just did? You chose to read this article—probably because the headline provoked curiosity. It’s one of the oldest tricks in the book, the basis of effective copywriting. True, there is no silver bullet for generating sales leads on LinkedIn. However, there is one habit that consistently brings my students and me more success generating leads online: Giving customers a reason to click and take action—relieve that nagging pain or take a step toward an exciting goal.

Yes, creating curiosity that lures customers to act seems like an obvious strategy. So, are you and your team doing it?

Engagement Is NOT the Goal: It’s the Entry Fee
At the simplest level these are our goals:

  • Grab attention, hold it long enough to…
  • provoke engagement in ways that…
  • earns response (generates a lead).

Will you agree with me? If you don’t get response to content placed on LinkedIn, you’re wasting precious time.

Will you also agree engagement is not the goal on LinkedIn? I know we’ve been told it is. It feels strange saying it’s not. But engagement is the beginning of a courtship process.

Whether it happens on your profile or inside LinkedIn groups, engagement is the entry fee. It’s your chance to create irresistible curiosity—or let your customer click away.

LinkedIn can be a big time-saver. It can scale your ability to generate leads. But only if you adopt a successful paradigm, one where engagement is the beginning, not the end. I’m talking about a world where it’s easy to get response—using a system to get customers curious.

3 Steps to Generating Leads on LinkedIn
Here are my best tips on structuring what to say and when—so you create hunger for more details in potential buyers. Remember, intense curiosity is the goal.

The idea is to give prospects temporary satisfaction. When you post updates, engage in LinkedIn groups or dress up your profile, answer customers’ questions in ways that satisfy. However, make sure your answers cause more questions to pop into their heads. That’s when you’ll hit ’em with a call to action that begins the lead generation journey.

Here’s where to start—either on your profile or in a LinkedIn group where prospects can be found: Answer a question your target market needs answered in a way that focuses on a nagging pain or fear. The idea is to directly or indirectly signal, “this discussion will help you overcome _____” (insert fear or pain).

If responding to an existing question make your comment suggest, “I’m here with a new point-of-view” or “I’m here with a fresh, new remedy to that pain.”

When you communicate follow these guidelines:

  1. Get right to-the-point. When you start or contribute to a LinkedIn group discussion be like a laser. Don’t make readers wait for the solution. Hit ’em with it. However start by…
  2. Revealing slowly. When it comes to all the juicy details of your remedy take it slow. Slow enough to encourage more questions—to create curiosity in the total solution. When you do this, make sure you are…
  3. Provoking response by leveraging customers’ curiosity.

Yes, be action-oriented and specific. But avoid being so complete that readers become totally satisfied with your words.

Make Your Answers Generate More Questions
Think of this like a successful dating encounter. Masters of the courtship process have always known the secret to creating intense curiosity: Being a little mysterious. Suggesting “I’ve got something you might want.” Holding a little information back. Strategically timing the sharing of information.

We’re trying to get the other person to be curious about us. So the best way to spark curiosity is to answer questions in direct ways that satisfy—but only for the moment. Answers should generate more questions … spark more curiosity in what we are all about.

Of course, we need to be credible. We cannot risk playing games with the other side. Yet being a little mysterious is fair play. It encourages more questions. This is how to generate leads on LinkedIn.

In business it works the same. Your ability to start generating sales leads on LinkedIn will be determined by an ability to answer questions in ways that provoke more questions from the buyer. Good luck!

Bad Thing! Or Why Segmentation by Consumer Attitudes May Be Dangerous

For years, B-to-B and B-to-C marketers have relied on attitudinal segmentation research to help them group their current customer base, and potential customers as well, for communication, promotion, marketing and experience initiatives. The thesis has been that, by asking a small, but meaningful, set of attitudinal questions, they would be able to develop an index, algorithm or framework equation that ranked these consumers by propensity to buy, both near-term and long-term.

For years, B-to-B and B-to-C marketers have relied on attitudinal segmentation research to help them group their current customer base, and potential customers as well, for communication, promotion, marketing and experience initiatives. The thesis has been that, by asking a small, but meaningful, set of attitudinal questions, they would be able to develop an index, algorithm or framework equation that ranked these consumers by propensity to buy, both near-term and long-term.

These frameworks—they’re arithmetic, so we can’t call them “models”—typically include questions regarding the importance of elements like value for money, acting with the consumer’s interests in mind, credit and payment terms, having knowledgeable employees, offering products which will meet the consumer’s needs, and the like. From these questions, basic segment categorization can be determined; and, once these three, four or five segments are established, we’ve often seen marketers go on to build assumptive plans and conduct further, more detailed, research around them.

The goal of these approaches is to produce attitudinal segments, which the questions can predict with high accuracy, often in the 80 percent or 90 percent range. This creates what economists would call a “post hoc ergo propter hoc” situation, Latin for “after this; therefore, because of this.” It is a logical fallacy, essentially saying that A occurred (the responses to the attitudinal questions); and then B occurred (the cuts, or segments, of consumers). Thus, A caused B. Once the B, or segment creation, stage has been established, further fallacies, such as creating reliable marketing, operational and experiential strategies around these supposed propensities, can be built. It’s a classic situation, where correlation is thought to be the same as causation. As your economics or stat professors may have told you, correlation and causation are far from being identical concepts.

As a consultant and analyst, I’ve seen this result of this application of research and analytics play out on a firsthand basis on multiple occasions. Here’s a recent one. A client in the retail office products market had been using an attitudinally derived element importance question framework for small business market segmentation purposes. The segment assumptions went unquestioned until followup qualitative research was conducted to better shape and target their planned marketing and operational initiatives. Importance of certain products and reliable service were identified in the research as key areas of focus and opportunity for the office products retailer; but, in the qualitative research, power of both focus areas appeared, anecdotally, to be consistent across all segments. And, even though implied supplier roles were suggested to build purchases, this was much more “leap of faith”-based on the established quantitative research segment personas than actual qualitative research findings.

There are related issues with what we can describe as quasi-behavioral measures, such as single question metrics (likelihood to recommend to a friend or colleague or the amount of service effort required on the part of a consumer); or traditional customer loyalty indices (where future purchase intent is included, but also attitudinal questions such as overall satisfaction). It’s not that they don’t offer some segmentation guidance. They do—on a macro or global level; but they tend to be less effective on a granular level, especially where elements of customer touchpoint experience are involved.

And, they tend to have limitations as predictors of segment behavior, a key business outcome for marketers and operations management. When compared to research and analysis techniques, such as customer advocacy and customer brand-bonding, which are contemporary, real-world frameworks built on actual customer experience—high satisfaction scores, high index scores and high net recommendation scores produced likely future purchase results (in studies across multiple industries) which were often 50 percent to 75 percent lower than advocacy or brand bonding frameworks. I’d be happy to provide proof for anyone interested in reviewing the findings.

So, that’s the scenario. The challenge, and potential danger, for marketers and those responsible for optimizing customer experience is that these attitudinal and quasi-behavioral questions are just that—attitudes and quasi-behaviors. Attitudes are fairly superficial feelings, and tend to be both tactical and reactive. And, because they are so transitory, their predictive value is often unstable and unreliable. Quasi-behaviors are also open to many similar challenges. More importantly, attitudes and quasi-behaviors are not behaviors, such as high probability downstream purchase intent based on actual previous purchase, evidence of positive and negative word-of-mouth about a brand based on prior personal experience, and brand favorability level based on experience. These are especially valuable in understanding competitive set, and they have real, and very stable, predictive and analytical value for marketers.

As Jaggers, the lawyer, said to Pip in Charles Dickens’, “Great Expectations,” take nothing on its looks; take everything on evidence. There’s no better rule.” For marketers, that’s excellent shorthand for taking everything on behavior, and perceptions based on documented personal experience, rather than attitudes and quasi-behaviors.

3 Steps to an Effective LinkedIn Sales Strategy

“How much time do I need to invest in prospecting on LinkedIn each day?” The answer may surprise you. Getting more response, and earning leads, means developing a LinkedIn sales strategy that sets aside the time investment question. Instead, focus on applying an exceptional, proven approach to LinkedIn. Make sure everything you do on LinkedIn has one goal in mind: getting prospects hungry for more details, answers, short-cuts or satisfying experiences.

“How much time do I need to invest in prospecting on LinkedIn each day?” The answer may surprise you. Getting more response, and earning leads, means developing a LinkedIn sales strategy that sets aside the time investment question. Instead, focus on applying an exceptional, proven approach to LinkedIn.

Make sure everything you do on LinkedIn has one goal in mind: getting prospects hungry for more details, answers, short-cuts or satisfying experiences.

Put response at the heart of your LinkedIn sales strategy using a better idea: Make everything you put on LinkedIn create irresistible curiosity in what you (or your team) can do for prospects. Make what you say, and how you say it, foster hunger inside prospects. Then give them a way to act on it. Here’s how to do it in three simple steps.

The Argument for a Better Way
Nothing says “ordinary” like the approach most of us are taking to LinkedIn profiles and groups. Hey, I’ve been there. I know what does not work: posting my latest blog article in groups and putting all kinds of bells and whistles on my profile.

Yet “LinkedIn experts” (most of whom never held a sales job!) say putting videos and multimedia is the key to success. No, it’s not.

Videos, multimedia and words that grab attention, hold it and give prospects a reason to become a lead is one of a handful of keys to success.

Generating leads is not about video, Powerpoint decks or links to your blog. Your success relies on how (or if!) you structure these tools to create response-leads!

Step 1: Attract Prospects by Provoking Responses
Here’s where to start. The idea is to start LinkedIn group discussions (or answer questions inside existing discussions) in ways that provoke questions and create opportunities to generate leads. The same strategy can be applied on your profile page.

LinkedIn is filled with people just like you. They have problems to solve or goals to reach. They’re ambitious. They’re hungry.

They need your help.

Your potential customers are craving better ways to:

  • avoid risks
  • compete better or create market distinction
  • make faster, smarter decisions

Start by kicking off a magnetic LinkedIn group discussion that gives them what they want. Use this practical formula:

  • Focus on a nagging pain prospects are suffering from,
  • quickly suggest a specific, genuinely new/unheard of remedy and …
  • ask for group members to give feedback on it.

Use your discussion title and first sentence of the description to appeal to the emotional frustration of prospects. Then say, “I know how to solve this problem” (and make that pain go away). Appeal to the emotional end result prospects are longing for.

For example, in the Linked Strategies group I asked, “Why isn’t LinkedIn generating LEADS for me?” in my title. My description presented a dramatic take on the issue, suggested a compelling solution to the problem and invited others to comment on it.

When writing the description of your discussion you’re trying to encourage prospects think, “That sounds important for me to understand. I wonder what, exactly, he/she means by that?”

Present your remedy in a way that encourages readers to ask for more details. Leave out most of the important details. You’ll get to them in Step No. 2.

Step 2: Getting Prospects Hungry for Your Answers
Remember the last time you needed something fixed urgently? You were impatient. LinkedIn users are the same. So get right to the point when starting a LinkedIn group discussion.

Don’t make readers wait for the solution you promised.

However, when it comes to ALL the juicy details of your remedy take it slow. How slow?

Slow enough to encourage more questions. Be specific. Be action-oriented. But avoid being so complete that readers become totally satisfied with your words.

The idea is to satisfy the curiosity of group members for the moment.

The success of your LinkedIn leads strategy hinges on holding the attention you worked so hard to get. But you have another goal: Creating hunger for an increasing number of “the details.”

As the discussion unfolds, keep revealing more-and-more tips and advice … BUT do it in ways that:

  • prospects can act on yet also …
  • leads them to ask more-and-more questions of you … and …
  • creates hunger for a BIG SHORT-CUT to what they want.

That short-cut will be a free video tutorial, whitepaper, checklist or e-book that you will trade in exchange for contact information. I call these “knowledge nuggets.”

This is where you get a business lead!

Step 3: Make Calls to Action That Give Prospects Choice
The final step of your new LinkedIn leads strategy involves making simple calls to action. This gives everyone a place to put all that pent up hunger for your “knowledge nugget.”

Here’s how: As you continue to reveal more-and-more there will be a point where it feels natural to offer prospects a short-cut. Think of it as giving them access to a bunch of the answers they’re craving in one fell swoop.

This is where you link to an elegant, focused opt-in lead form page on your website. I recommend doing this once and absolutely no more than twice within a given discussion.

WARNING: Don’t be crass, but do be direct. You’ve worked hard to get here. All that is needed is a clear, text-based call to action that is:

  • casual in tone (are not pushy) and suggestive (“this might help you if you are serious about _____”)
  • in context with how the discussion is flowing
  • promises free, step-by-step instructions, a way to learn a new skill, avoid a risk, make a decision etc.

Here’s a trick I find to be VERY effective: Tell them that the decision is theirs.

Present the call-to-action confidently. Let prospects know you’re doing this because it will help them … BUT … be sure to reaffirm your prospects’ freedom to choose. Doing this indirectly says to them: “I am not threatening your right to say no. You have free choice.”

Want exceptional lead generation results from LinkedIn? Stop practicing ordinary tactics and dallying over how much time to invest in a LinkedIn sales strategy. Most sales people experience ordinary results on LinkedIn because they don’t know about an exceptional approach. This one. Let me know how it works for you in comments!