How to Outsource Analytics

In this series, I have been emphasizing the importance of statistical modeling in almost every article. While there are plenty of benefits of using statistical models in a more traditional sense (refer to “Why Model?”), in the days when “too much” data is the main challenge, I would dare to say that the most important function of statistical models is that they summarize complex data into simple-to-use “scores.”

In this series, I have been emphasizing the importance of statistical modeling in almost every article. While there are plenty of benefits of using statistical models in a more traditional sense (refer to “Why Model?”), in the days when “too much” data is the main challenge, I would dare to say that the most important function of statistical models is that they summarize complex data into simple-to-use “scores.”

The next important feature would be that models fill in the gaps, transforming “unknowns” to “potentials.” You see, even in the age of ubiquitous data, no one will ever know everything about everybody. For instance, out of 100,000 people you have permission to contact, only a fraction will be “known” wine enthusiasts. With modeling, we can assign scores for “likelihood of being a wine enthusiast” to everyone in the base. Sure, models are not 100 percent accurate, but I’ll take “70 percent chance of afternoon shower” over not knowing the weather forecast for the day of the company picnic.

I’ve already explained other benefits of modeling in detail earlier in this series, but if I may cut it really short, models will help marketers:

1. In deciding whom to engage, as they cannot afford to spam the world and annoy everyone who can read, and

2. In determining what to offer once they decide to engage someone, as consumers are savvier than ever and they will ignore and discard any irrelevant message, no matter how good it may look.

OK, then. I hope you are sold on this idea by now. The next question is, who is going to do all that mathematical work? In a country where jocks rule over geeks, it is clear to me that many folks are more afraid of mathematics than public speaking; which, in its own right, ranks higher than death in terms of the fear factor for many people. If I may paraphrase “Seinfeld,” many folks are figuratively more afraid of giving a eulogy than being in the coffin at a funeral. And thanks to a sub-par math education in the U.S. (and I am not joking about this, having graduated high school on foreign soil), yes, the fear of math tops them all. Scary, heh?

But that’s OK. This is a big world, and there are plenty of people who are really good at mathematics and statistics. That is why I purposefully never got into the mechanics of modeling techniques and related programming issues in this series. Instead, I have been emphasizing how to formulate questions, how to express business goals in a more logical fashion and where to invest to create analytics-ready environments. Then the next question is, “How will you find the right math geeks who can make all your dreams come true?”

If you have a plan to create an internal analytics team, there are a few things to consider before committing to that idea. Too many organizations just hire one or two statisticians, dump all the raw data onto them, and hope to God that they will figure some ways to make money with data, somehow. Good luck with that idea, as:

1. I’ve seen so many failed attempts like that (actually, I’d be shocked if it actually worked), and

2. I am sure God doesn’t micromanage statistical units.

(Similarly, I am almost certain that she doesn’t care much for football or baseball scores of certain teams, either. You don’t think God cares more for the Red Sox than the Yankees, do ya?)

The first challenge is locating good candidates. If you post any online ad for “Statistical Analysts,” you will receive a few hundred resumes per day. But the hiring process is not that simple, as you should ask the right questions to figure out who is a real deal, and who is a poser (and there are many posers out there). Even among qualified candidates with ample statistical knowledge, there are differences between the “Doers” and “Vendor Managers.” Depending on your organizational goal, you must differentiate the two.

Then the next challenge is keeping the team intact. In general, mathematicians and statisticians are not solely motivated by money; they also want constant challenges. Like any smart and creative folks, they will simply pack up and leave, if “they” determine that the job is boring. Just a couple of modeling projects a year with some rudimentary sets of data? Meh. Boring! Promises of upward mobility only work for a fraction of them, as the majority would rather deal with numbers and figures, showing no interest in managing other human beings. So, coming up with interesting and challenging projects, which will also benefit the whole organization, becomes a job in itself. If there are not enough challenges, smart ones will quit on you first. Then they need constant mentoring, as even the smartest statisticians will not know everything about challenges associated with marketing, target audiences and the business world, in general. (If you stumble into a statistician who is even remotely curious about how her salary is paid for, start with her.)

Further, you would need to invest to set up an analytical environment, as well. That includes software, hardware and other supporting staff. Toolsets are becoming much cheaper, but they are not exactly free yet. In fact, some famous statistical software, such as SAS, could be quite expensive year after year, although there are plenty of alternatives now. And they need an “analytics-ready” data environment, as I emphasized countless times in this series (refer to “Chicken or the Egg? Data or Analytics?” and “Marketing and IT; Cats and Dogs”). Such data preparation work is not for statisticians, and most of them are not even good at cleaning up dirty data, anyway. That means you will need different types of developers/programmers on the analytics team. I pointed out that analytical projects call for a cohesive team, not some super-duper analyst who can do it all (refer to “How to Be a Good Data Scientist”).

By now you would say “Jeez Louise, enough already,” as all this is just too much to manage to build just a few models. Suddenly, outsourcing may sound like a great idea. Then you would realize there are many things to consider when outsourcing analytical work.

First, where would you go? Everyone in the data industry and their cousins claim that they can take care of analytics. But in reality, it is a scary place where many who have “analytics” in their taglines do not even touch “predictive analytics.”

Analytics is a word that is abused as much as “Big Data,” so we really need to differentiate them. “Analytics” may mean:

  • Business Intelligence (BI) Reporting: This is mostly about the present, such as the display of key success metrics and dashboard reporting. While it is very important to know about the current state of business, much of so-called “analytics” unfortunately stops right here. Yes, it is good to have a dashboard in your car now, but do you know where you should be going?
  • Descriptive Analytics: This is about how the targets “look.” Common techniques such as profiling, segmentation and clustering fall under this category. These techniques are mainly for describing the target audience to enhance and optimize messages to them. But using these segments as a selection mechanism is not recommended, while many dare to do exactly that (more on this subject in future articles).
  • Predictive Modeling: This is about answering the questions about the future. Who would be more likely to behave certain ways? What communication channels will be most effective for whom? How much is the potential spending level of a prospect? Who is more likely to be a loyal and profitable customer? What are their preferences? Response models, various of types of cloning models, value models, and revenue models, attrition models, etc. all fall under this category, and they require hardcore statistical skills. Plus, as I emphasized earlier, these model scores compact large amounts of complex data into nice bite-size packages.
  • Optimization: This is mostly about budget allocation and attribution. Marketing agencies (or media buyers) generally deal with channel optimization and spending analysis, at times using econometrics models. This type of statistical work calls for different types of expertise, but many still insist on calling it simply “analytics.”

Let’s say that for the purpose of customer-level targeting and personalization, we decided to outsource the “predictive” modeling projects. What are our options?

We may consider:

  • Individual Consultants: In-house consultants are dedicated to your business for the duration of the contract, guaranteeing full access like an employee. But they are there for you only temporarily, with one foot out the door all the time. And when they do leave, all the knowledge walks away with them. Depending on the rate, the costs can add up.
  • Standalone Analytical Service Providers: Analytical work is all they do, so you get focused professionals with broad technical and institutional knowledge. Many of them are entrepreneurs, but that may work against you, as they could often be understaffed and stretched thin. They also tend to charge for every little step, with not many freebies. They are generally open to use any type of data, but the majority of them do not have secure sources of third-party data, which could be essential for certain types of analytics involving prospecting.
  • Database Service Providers: Almost all data compilers and brokers have statistical units, as they need to fill in the gap within their data assets with statistical techniques. (You didn’t think that they knew everyone’s income or age, did you?) For that reason, they have deep knowledge in all types of data, as well as in many industry verticals. They provide a one-stop shop environment with deep resource pools and a variety of data processing capabilities. However, they may not be as agile as smaller analytical shops, and analytics units may be tucked away somewhere within large and complex organizations. They also tend to emphasize the use of their own data, as after all, their main cash cows are their data assets.
  • Direct Marketing Agencies: Agencies are very strategic, as they touch all aspects of marketing and control creative processes through segmentation. Many large agencies boast full-scale analytical units, capable of all types of analytics that I explained earlier. But some agencies have very small teams, stretched really thin—just barely handling the reporting aspect, not any advanced analytics. Some just admit that predictive analytics is not part of their core competencies, and they may outsource such projects (not that it is a bad thing).

As you can see here, there is no clear-cut answer to “with whom you should you work.” Basically, you will need to check out all types of analysts and service providers to determine the partner best suitable for your long- and short-term business purposes, not just analytical goals. Often, many marketers just go with the lowest bidder. But pricing is just one of many elements to be considered. Here, allow me to introduce “10 Essential Items to Consider When Outsourcing Analytics.”

1. Consulting Capabilities: I put this on the top of the list, as being a translator between the marketing and the technology world is the most important differentiator (refer to “How to Be a Good Data Scientist”). They must understand the business goals and marketing needs, prescribe suitable solutions, convert such goals into mathematical expressions and define targets, making the best of available data. If they lack strategic vision to set up the data roadmap, statistical knowledge alone will not be enough to achieve the goals. And such business goals vary greatly depending on the industry, channel usage and related success metrics. Good consultants always ask questions first, while sub-par ones will try to force-fit marketers’ goals into their toolsets and methodologies.

Translating marketing goals into specific courses of action is a skill in itself. A good analytical partner should be capable of building a data roadmap (not just statistical steps) with a deep understanding of the business impact of resultant models. They should be able to break down larger goals into smaller steps, creating proper phased approaches. The plan may call for multiple models, all kinds of pre- and post-selection rules, or even external data acquisition, while remaining sensitive to overall costs.

The target definition is the core of all these considerations, which requires years of experience and industry knowledge. Simply, the wrong or inadequate targeting decision leads to disastrous results, no matter how sound the mathematical work is (refer to “Art of Targeting”).

Another important quality of a good analytical partner is the ability to create usefulness out of seemingly chaotic and unstructured data environments. Modeling is not about waiting for the perfect set of data, but about making the best of available data. In many modeling bake-offs, the winners are often decided by the creative usage of provided data, not just statistical techniques.

Finally, the consultative approach is important, as models do not exist in a vacuum, but they have to fit into the marketing engine. Be aware of the ones who want to change the world around their precious algorithms, as they are geeks not strategists. And the ones who understand the entire marketing cycle will give advice on what the next phase should be, as marketing efforts must be perpetual, not transient.

So, how will you find consultants? Ask the following questions:

  • Are they “listening” to you?
  • Can they repeat “your” goals in their own words?
  • Do their roadmaps cover both short- and long-term goals?
  • Are they confident enough to correct you?
  • Do they understand “non-statistical” elements in marketing?
  • Have they “been there, done that” for real, or just in theories?

2. Data Processing Capabilities: I know that some people look down upon the word “processing.” But data manipulation is the most important key step “before” any type of advanced analytics even begins. Simply, “garbage-in, garbage out.” And unfortunately, most datasets are completely unsuitable for analytics and modeling. In general, easily more than 80 percent of model development time goes into “fixing” the data, as most are unstructured and unrefined. I have been repeatedly emphasizing the importance of a “model-ready” (or “analytics-ready”) environment for that reason.

However, the reality dictates that the majority of databases are indeed NOT model-ready, and most of them are not even close to it. Well, someone has to clean up the mess. And in this data business, the last one who touches the dataset becomes responsible for all the errors and mistakes made to it thus far. I know it is not fair, but that is why we need to look at the potential partner’s ability to handle large and really messy data, not just the statistical savviness displayed in glossy presentations.

Yes, that dirty work includes data conversion, edit/hygiene, categorization/tagging, data summarization and variable creation, encompassing all kinds of numeric, character and freeform data (refer to “Beyond RFM Data” and “Freeform Data Aren’t Exactly Free”). It is not the most glorious part of this business, but data consistency is the key to successful implementation of any advanced analytics. So, if a model-ready environment is not available, someone had better know how to make the best of whatever is given. I have seen too many meltdowns in “before” and “after” modeling steps due to inconsistencies in databases.

So, grill the candidates with the following questions:

  • If they support file conversions, edit, categorization and summarization
  • How big of a dataset is too big, and how many files/tables are too many for them
  • How much free-form data are too much for them
  • Ask for sample model variables that they have created in the past

3. Track Records in the Industry: It can be argued that industry knowledge is even more crucial for the success than statistical know-how, as nuances are often “Lost in Translation” without relevant industry experience. In fact, some may not even be able to carry on a proper conversation with a client without it, leading to all kinds of wrong assumptions. I have seen a case where “real” rocket scientists messed up models for credit card campaigns.

The No. 1 reason why industry experience is important is everyone’s success metrics are unique. Just to name a few, financial services (banking, credit card, insurance, investment, etc.), travel and hospitality, entertainment, packaged goods, online and offline retail, catalogs, publication, telecommunications/utilities, non-profit and political organizations all call for different types of analytics and models, as their business models and the way they interact with target audiences are vastly different. For example, building a model (or a database, for that matter) for businesses where they hand over merchandise “before” they collect money is fundamentally different than the ones where exchange happens simultaneously. Even a simple concept of payment date or transaction date cannot be treated the same way. For retailers, recent dates could be better for business, but for subscription business, older dates may carry more weight. And these are just some examples with “dates,” before touching any dollar figures or other fun stuff.

Then the job gets even more complicated, if we further divide all of these industries by B-to-B vs. B-to-C, where available data do not even look similar. On top of that, divisional ROI metrics may be completely different, and even terminology and culture may play a role in all of this. When you are a consultant, you really don’t want to stop the flow of a meeting to clarify some unfamiliar acronyms, as you are supposed to know them all.

So, always demand specific industry references and examine client roasters, if allowed. (Many clients specifically ask vendors not to use their names as references.) Basically, watch out for the ones who push one-size-fits-all cookie-cutter solutions. You deserve way more than that.

4. Types of Models Supported: Speaking of cookie-cutter stuff, we need to be concerned with types of models that the outsourcing partner would support. Sure, nobody employs every technique, and no one can be good at everything. But we need to watch out for the “One-trick Ponies.”

This could be a tricky issue, as we are going into a more technical domain. Plus, marketers should not self-prescribe with specific techniques, instead of clearly stating their business goals (refer to “Marketing and IT; Cats and Dogs”). Some of the modeling goals are:

  • Rank and select prospect names
  • Lead scoring
  • Cross-sell/upsell
  • Segment the universe for messaging strategy
  • Pinpoint the attrition point
  • Assign lifetime values for prospects and customers
  • Optimize media/channel spending
  • Create new product packages
  • Detect fraud
  • Etc.

Unless you have successfully dealt with the outsourcing partner in the past (or you have a degree in statistics), do not blurt out words like Neural-net, CHAID, Cluster Analysis, Multiple Regression, Discriminant Function Analysis, etc. That would be like demanding specific medication before your new doctor even asks about your symptoms. The key is meeting your business goals, not fulfilling buzzwords. Let them present their methodology “after” the goal discussion. Nevertheless, see if the potential partner is pushing one or two specific techniques or solutions all the time.

5. Speed of Execution: In modern marketing, speed to action is the king. Speed wins, and speed gains respect. However, when it comes to modeling or other advanced analytics, you may be shocked by the wide range of time estimates provided by each outsourcing vendor. To be fair they are covering themselves, mainly because they have no idea what kind of messy data they will receive. As I mentioned earlier, pre-model data preparation and manipulation are critical components, and they are the most time-consuming part of all; especially when available data are in bad shape. Post-model scoring, audit and usage support may elongate the timeline. The key is to differentiate such pre- and post-modeling processes in the time estimate.

Even for pure modeling elements, time estimates vary greatly, depending on the complexity of assignments. Surely, a simple cloning model with basic demographic data would be much easier to execute than the ones that involve ample amounts of transaction- and event-level data, coming from all types of channels. If time-series elements are added, it will definitely be more complex. Typical clustering work is known to take longer than regression models with clear target definitions. If multiple models are required for the project, it will obviously take more time to finish the whole job.

Now, the interesting thing about building a model is that analysts don’t really finish it, but they just run out of time—much like the way marketers work on PowerPoint presentations. The commonality is that we can basically tweak models or decks forever, but we have to stop at some point.

However, with all kinds of automated tools and macros, model development time has decreased dramatically in past decades. We really came a long way since the first application of statistical techniques to marketing, and no one should be quoting a 1980s timeline in this century. But some still do. I know vendors are trained to follow the guideline “always under-promise and over-deliver,” but still.

An interesting aspect of this dilemma is that we can negotiate the timeline by asking for simpler and less sophisticated versions with diminished accuracy. If, hypothetically, it takes a week to be 98 percent accurate, but it only takes a day to be 90 percent accurate, what would you pick? That should be the business decision.

So, what is a general guideline? Again, it really depends on many factors, but allow me to share a version of it:

  • Pre-modeling Processing

– Data Conversions: from half a day to weeks

– Data Append/Enhancement: between overnight and two days

– Data Edit and Summarization: Data-dependent

  • Modeling: Ranges from half a day to weeks

– Depends on type, number of models and complexity

  • Scoring: from half a day to one week

– Mainly depends on number of records and state of the database to be scored

I know these are wide ranges, but watch out for the ones that routinely quote 30 days or more for simple clone models. They may not know what they are doing, or worse, they may be some mathematical perfectionists who don’t understand the marketing needs.

6. Pricing Structure: Some marketers would put this on top of the checklist, or worse, use the pricing factor as the only criterion. Obviously, I disagree. (Full disclosure: I have been on the service side of the fence during my entire career.) Yes, every project must make an economic sense in the end, but the budget should not and cannot be the sole deciding factor in choosing an outsourcing partner. There are many specialists under famous brand names who command top dollars, and then there are many data vendors who throw in “free” models, disrupting the ecosystem. Either way, one should not jump to conclusions too fast, as there is no free lunch, after all. In any case, I strongly recommend that no one should start the meeting with pricing questions (hence, this article). When you get to the pricing part, ask what the price includes, as the analytical journey could be a series of long and winding roads. Some of the biggest factors that need to be considered are:

  • Multiple Model Discounts—Less for second or third models within a project?
  • Pre-developed (off-the-shelf) Models—These can be “much” cheaper than custom models, while not custom-fitted.
  • Acquisition vs. CRM—Employing client-specific variables certainly increases the cost.
  • Regression Models vs. Other Types—At times, types of techniques may affect the price.
  • Clustering and Segmentations—They are generally priced much higher than target-specific models.

Again, it really depends on the complexity factor more than anything else, and the pre- and post-modeling process must be estimated and priced separately. Non-modeling charges often add up fast, and you should ask for unit prices and minimum charges for each step.

Scoring charges in time can be expensive, too, so negotiate for discounts for routine scoring of the same models. Some may offer all-inclusive package pricing for everything. The important thing is that you must be consistent with the checklist when shopping around with multiple candidates.

7. Documentation: When you pay for a custom model (not pre-developed, off-the-shelf ones), you get to own the algorithm. Because algorithms are not tangible items, the knowledge is to be transformed in model documents. Beware of the ones who offer “black-box” solutions with comments like, “Oh, it will work, so trust us.”

Good model documents must include the following, at the minimum:

  • Target and Comparison Universe Definitions: What was the target variable (or “dependent” variable) and how was it defined? How was the comparison universe defined? Was there any “pre-selection” for either of the universes? These are the most important factors in any model—even more than the mechanics of the model itself.
  • List of Variables: What are the “independent” variables? How were they transformed or binned? From where did they originate? Often, these model variables describe the nature of the model, and they should make intuitive sense.
  • Model Algorithms: What is the actual algorithm? What are the assigned weight for each independent variable?
  • Gains Chart: We need to examine potential effectiveness of the model. What are the “gains” for each model group, from top to bottom (e.g., 320 percent gain at the top model group in comparison to the whole universe)? How fast do such gains decrease as we move down the scale? How do the gains factors compare against the validation sample? A graphic representation would be nice, too.

For custom models, it is customary to have a formal model presentation, full documentation and scoring script in designated programming languages. In addition, if client files are provided, ask for a waterfall report that details input and output counts of each step. After the model scoring, it is also customary for the vendor to provide a scored universe count by model group. You will be shocked to find out that many so-called analytical vendors do not provide thorough documentation. Therefore, it is recommended to ask for sample documents upfront.

8. Scoring Validation: Models are built and presented properly, but the job is not done until the models are applied to the universe from which the names are ranked and selected for campaigns. I have seen too many major meltdowns at this stage. Simply, it is one thing to develop models with a few hundred thousand record samples, but it is quite another to apply the algorithm to millions of records. I am not saying that the scoring job always falls onto the developers, as you may have an internal team or a separate vendor for such ongoing processes. But do not let the model developer completely leave the building until everything checks out.

The model should have been validated against the validation sample by then, but live scoring may reveal all kinds of inconsistencies. You may also want to back-test the algorithms with past campaign results, as well. In short, many things go wrong “after” the modeling steps. When I hear customers complaining about models, I often find that the modeling is the only part that was done properly, and “before” and “after” steps were all messed up. Further, even machines misunderstand each other, as any differences in platform or scripting language may cause discrepancies. Or, maybe there was no technical error, but missing values may have caused inconsistencies (refer to “Missing Data Can Be Meaningful”). Nonetheless, the model developers would have the best insight as to what could have gone wrong, so make sure that they are available for questions after models are presented and delivered.

9. Back-end Analysis: Good analytics is all about applying learnings from past campaigns—good or bad—to new iterations of efforts. We often call it “closed-loop marketing—while many marketers often neglect to follow up. Any respectful analytics shop must be aware of it, while they may classify such work separately from modeling or other analytical projects. At the minimum, you need to check out if they even offer such services. In fact, so-called “match-back analysis” is not as simple as just matching campaign files against responders in this omnichannel environment. When many channels are employed at the same time, allocation of credit (i.e., “what worked?”) may call for all kinds of business rules or even dedicated models.

While you are at it, ask for a cheaper version of “canned” reports, as well, as custom back-end analysis can be even more costly than the modeling job itself, over time. Pre-developed reports may not include all the ROI metrics that you’re looking for (e.g., open, clickthrough, conversion rates, plus revenue and orders-per-mailed, per order, per display, per email, per conversion. etc.). So ask for sample reports upfront.

If you start breaking down all these figures by data source, campaign, time series, model group, offer, creative, targeting criteria, channel, ad server, publisher, keywords, etc., it can be unwieldy really fast. So contain yourself, as no one can understand 100-page reports, anyway. See if the analysts can guide you with such planning, as well. Lastly, if you are so into ROI analysis, get ready to share the “cost” side of the equation with the selected partner. Some jobs are on the marketers.

10. Ongoing Support: Models have a finite shelf life, as all kinds of changes happen in the real world. Seasonality may be a factor, or the business model or strategy may have changed. Fluctuations in data availability and quality further complicate the matter. Basically assumptions like “all things being equal” only happen in textbooks, so marketers must plan for periodic review of models and business rules.

A sure sign of trouble is decreasing effectiveness of models. When in doubt, consult the developers and they may recommend a re-fit or complete re-development of models. Quarterly reviews would be ideal, but if the cost becomes an issue, start with 6-month or yearly reviews, but never go past more than a year without any review. Some vendors may offer discounts for redevelopment, so ask for the price quote upfront.

I know this is a long list of things to check, but picking the right partner is very important, as it often becomes a long-term relationship. And you may find it strange that I didn’t even list “technical capabilities” at all. That is because:

1. Many marketers are not equipped to dig deep into the technical realm anyway, and

2. The difference between the most mathematically sound models and the ones from the opposite end of the spectrum is not nearly as critical as other factors I listed in this article.

In other words, even the worst model in the bake-off would be much better than no model, if these other business criterion are well-considered. So, happy shopping with this list, and I hope you find the right partner. Employing analytics is not an option when living in the sea of data.

Don’t Do It Just Because You Can

Don’t do it just because you can. No kidding. … Any geek with moderate coding skills or any overzealous marketer with access to some data can do real damage to real human beings without any superpowers to speak of. Largely, we wouldn’t go so far as calling them permanent damages, but I must say that some marketing messages and practices are really annoying and invasive. Enough to classify them as “junk mail” or “spam.” Yeah, I said that, knowing full-well that those words are forbidden in the industry in which I built my career.

Don’t do it just because you can. No kidding. By the way, I could have gone with Ben Parker’s “With great power comes great responsibility” line, but I didn’t, as it has become an over-quoted cliché. Plus, I’m not much of a fan of “Spiderman.” Actually, I’m kidding this time. (Not the “Spiderman” part, as I’m more of a fan of “Thor.”) But the real reason is any geek with moderate coding skills or any overzealous marketer with access to some data can do real damage to real human beings without any superpowers to speak of. Largely, we wouldn’t go so far as calling them permanent damages, but I must say that some marketing messages and practices are really annoying and invasive. Enough to classify them as “junk mail” or “spam.” Yeah, I said that, knowing full-well that those words are forbidden in the industry in which I built my career.

All jokes aside, I received a call from my mother a few years ago asking me if this “urgent” letter that says her car warranty will expire if she does not act “right now” (along with a few exclamation marks) is something to which she must respond immediately. Many of us by now are impervious to such fake urgencies or outrageous claims (like “You’ve just won $10,000,000!!!”). But I then realized that there still are plenty of folks who would spend their hard-earned dollars based on such misleading messages. What really made me mad, other than the fact that my own mother was involved in that case, was that someone must have actually targeted her based on her age, ethnicity, housing value and, of course, the make and model of her automobile. I’ve been doing this job for too long to be unaware of potential data variables and techniques that must have played a part so that my mother to receive a series of such letters. Basically, some jerk must have created a segment that could be named as “old and gullible.” Without a doubt, this is a classic example of what should not be done just because one can.

One might dismiss it as an isolated case of a questionable practice done by questionable individuals with questionable moral integrity, but can we honestly say that? I, who knows the ins and outs of direct marketing practices quite well, fell into traps more than a few times, where supposedly a one-time order mysteriously turns into a continuity program without my consent, followed by an extremely cumbersome canceling process. Further, when I receive calls or emails from shady merchants with dubious offers, I can very well assume my information changed hands in very suspicious ways, if not through outright illegal routes.

Even without the criminal elements, as data become more ubiquitous and targeting techniques become more precise, an accumulation of seemingly inoffensive actions by innocuous data geeks can cause a big ripple in the offline (i.e., “real”) world. I am sure many of my fellow marketers remember the news about this reputable retail chain a few years ago; that they accurately predicted pregnancy in households based on their product purchase patterns and sent customized marketing messages featuring pregnancy-related products accordingly. Subsequently it became a big controversy, as such a targeted message was the way one particular head of household found out his teenage daughter was indeed pregnant. An unintended consequence? You bet.

I actually saw the presentation of the instigating statisticians in a predictive analytics conference before the whole incident hit the wire. At the time, the presenters were unaware of the consequences of their actions, so they proudly shared employed methodologies with the audience. But when I heard about what they were actually trying to predict, I immediately turned my head to look at the lead statistician in my then-analytical team sitting next to me, and saw that she had a concerned look that I must have had on my face, as well. And our concern was definitely not about the techniques, as we knew how to do the same when provided with similar sets of data. It was about the human consequences that such a prediction could bring, not just to the eventual targets, but also to the predictors and their fellow analysts in the industry who would all be lumped together as evil scientists by the outsiders. In predictive analytics, there is a price for being wrong; and at times, there is a price to pay for being right, too. Like I said, we shouldn’t do things just because we can.

Analysts do not have superpowers individually, but when technology and ample amounts of data are conjoined, the results can be quite influential and powerful, much like the way bombs can be built with common materials available at any hardware store. Ironically, I have been evangelizing that the data and technology should be wielded together to make big and dumb data smaller and smarter all this time. But providing answers to decision-makers in ready-to-be used formats, hence “humanizing” the data, may have its downside, too. Simply, “easy to use” can easily be “easy to abuse.” After all, humans are fallible creatures with ample amounts of greed and ambition. Even without any obvious bad intentions, it is sometimes very difficult to contemplate all angles, especially about those sensitive and squeamish humans.

I talked about the social consequences of the data business last month (refer to “How to Be a Good Data Scientist“), and that is why I emphasized that anyone who is about to get into this data field must possess deep understandings of both technology and human nature. That little sensor in your stomach that tells you “Oh, I have a bad feeling about this” may not come to everyone naturally, but we all need to be equipped with those safeguards like angels on our shoulders.

Hindsight is always 20/20, but apparently, those smart analysts who did that pregnancy prediction only thought about the techniques and the bottom line, but did not consider all the human factors. And they should have. Or, if not them, their manager should have. Or their partners in the marketing department should have. Or their public relations people should have. Heck, “someone” in their organization should have, alright? Just like we do not casually approach a woman on the street who “seems” pregnant and say “You must be pregnant.” Only socially inept people would do that.

People consider certain matters extremely private, in case some data geeks didn’t realize that. If I might add, the same goes for ailments such as erectile dysfunction or constipation, or any other personal business related to body parts that are considered private. Unless you are a doctor in an examining room, don’t say things like “You look old, so you must have hard time having sex, right?” It is already bad enough that we can’t even watch golf tournaments on TV without those commercials that assume that golf fans need help in that department. (By the way, having “two” bathtubs “outside” the house at dusk don’t make any sense either, when the effect of the drug can last for hours for heaven’s sake. Maybe the man lost interest because the tubs were too damn heavy?)

While it may vary from culture to culture, we all have some understanding of social boundaries in casual settings. When you are talking to a complete stranger on a plane ride, for example, you know exactly how much information that you would feel comfortable sharing with that person. And when someone crosses the line, we call that person inappropriate, or “creepy.” Unfortunately, that creepy line is set differently for each person who we encounter (I am sure people like George Clooney or Scarlett Johansson have a really high threshold for what might be considered creepy), but I think we can all agree that such a shady area can be loosely defined at the least. Therefore, when we deal with large amounts of data affecting a great many people, imagine a rather large common area of such creepiness/shadiness, and do not ever cross it. In other words, when in doubt, don’t go for it.

Now, as a lifelong database marketer, I am not advocating some over-the-top privacy zealots either, as most of them do not understand the nature of data work and can’t tell the difference between informed (and mutually beneficial) messages and Big Brother-like nosiness. This targeting business is never about looking up an individual’s record one at a time, but more about finding correlations between users and products and doing some good match-making in mass numbers. In other words, we don’t care what questionable sites anyone visits, and honest data players would not steal or abuse information with bad intent. I heard about waiters who steal credit card numbers from their customers with some swiping devices, but would you condemn the entire restaurant industry for that? Yes, there are thieves in any part of the society, but not all data players are hackers, just like not all waiters are thieves. Statistically speaking, much like flying being the safest from of travel, I can even argue that handing over your physical credit card to a stranger is even more dangerous than entering the credit card number on a website. It looks much worse when things go wrong, as incidents like that affect a great many all at once, just like when a plane crashes.

Years back, I used to frequent a Japanese Restaurant near my office. The owner, who doubled as the head sushi chef, was not a nosy type. So he waited for more than a year to ask me what I did for living. He had never heard anything about database marketing, direct marketing or CRM (no “Big Data” on the horizon at that time). So I had to find a simple way to explain what I do. As a sushi chef with some local reputation, I presumed that he would know personal preferences of many frequently visiting customers (or “high-value customers,” as marketers call them). He may know exactly who likes what kind of fish and types of cuts, who doesn’t like raw shellfish, who is allergic to what, who has less of a tolerance for wasabi or who would indulge in exotic fish roes. When I asked this question, his answer was a simple “yes.” Any diligent sushi chef would care for his or her customers that much. And I said, “Now imagine that you can provide such customized services to millions of people, with the help of computers and collected data.” He immediately understood the benefits of using data and analytics, and murmured “Ah so …”

Now let’s turn the table for a second here. From the customer’s point of view, yes, it is very convenient for me that my favorite sushi chef knows exactly how I like my sushi. Same goes for the local coffee barista who knows how you take your coffee every morning. Such knowledge is clearly mutually beneficial. But what if those business owners or service providers start asking about my personal finances or about my grown daughter in a “creepy” way? I wouldn’t care if they carried the best yellowtail in town or served the best cup of coffee in the world. I would cease all my interaction with them immediately. Sorry, they’ve just crossed that creepy line.

Years ago, I had more than a few chances to sit closely with Lester Wunderman, widely known as “The Father of Direct Marketing,” as the venture called I-Behavior in which I participated as one of the founders actually originated from an idea on a napkin from Lester and his friends. Having previously worked in an agency that still bears his name, and having only seen him behind a podium until I was introduced to him on one cool autumn afternoon in 1999, meeting him at a small round table and exchanging ideas with the master was like an unknown guitar enthusiast having a jam session with Eric Clapton. What was most amazing was that, at the beginning of the boom, he was completely unfazed about all those new ideas that were flying around at that time, and he was precisely pointing out why most of them would not succeed at all. I do not need to quote the early 21st century history to point out that his prediction was indeed accurate. When everyone was chasing the latest bit of technology for quick bucks, he was at least a decade ahead of all of those young bucks, already thinking about the human side of the equation. Now, I would not reveal his age out of respect, but let’s just say that almost all of the people in his age group would describe occupations of their offspring as “Oh, she just works on a computer all the time …” I can only wish that I will remain that sharp when I am his age.

One day, Wunderman very casually shared a draft of the “Consumer Bill of Rights for Online Engagement” with a small group of people who happened to be in his office. I was one of the lucky souls who heard about his idea firsthand, and I remember feeling that he was spot-on with every point, as usual. I read it again recently just as this Big Data hype is reaching its peak, just like the boom was moving with a force that could change the world back then. In many ways, such tidal waves do end up changing the world. But lest we forget, such shifts inevitably affect living, breathing human beings along the way. And for any movement guided by technology to sustain its velocity, people who are at the helm of the enabling technology must stay sensitive toward the needs of the rest of the human collective. In short, there is not much to gain by annoying and frustrating the masses.

Allow me to share Lester Wunderman’s “Consumer Bill of Rights for Online Engagement” verbatim, as it appeared in the second edition of his book “Being Direct”:

  1. Tell me clearly who you are and why you are contacting me.
  2. Tell me clearly what you are—or are not—going to do with the information I give.
  3. Don’t pretend that you know me personally. You don’t know me; you know some things about me.
  4. Don’t assume that we have a relationship.
  5. Don’t assume that I want to have a relationship with you.
  6. Make it easy for me to say “yes” and “no.”
  7. When I say “no,” accept that I mean not this, not now.
  8. Help me budget not only my money, but also my TIME.
  9. My time is valuable, don’t waste it.
  10. Make my shopping experience easier.
  11. Don’t communicate with me just because you can.
  12. If you do all of that, maybe we will then have the basis for a relationship!

So, after more than 15 years of the so-called digital revolution, how many of these are we violating almost routinely? Based on the look of my inboxes and sites that I visit, quite a lot and all the time. As I mentioned in my earlier article “The Future of Online is Offline,” I really get offended when even seasoned marketers use terms like “online person.” I do not become an online person simply because I happen to stumble onto some stupid website and forget to uncheck some pre-checked boxes. I am not some casual object at which some email division of a company can shoot to meet their top-down sales projections.

Oh, and good luck with that kind of mindless mass emailing; your base will soon be saturated and you will learn that irrelevant messages are bad for the senders, too. Proof? How is it that the conversion rate of a typical campaign did not increase dramatically during the past 40 years or so? Forget about open or click-through rate, but pay attention to the good-old conversion rate. You know, the one that measures actual sales. Don’t we have superior databases and technologies now? Why is anyone still bragging about mailing “more” in this century? Have you heard about “targeted” or “personalized” messages? Aren’t there lots and lots of toolsets for that?

As the technology advances, it becomes that much easier and faster to offend people. If the majority of data handlers continue to abuse their power, stemming from the data in their custody, the communication channels will soon run dry. Or worse, if abusive practices continue, the whole channel could be shut down by some legislation, as we have witnessed in the downfall of the outbound telemarketing channel. Unfortunately, a few bad apples will make things a lot worse a lot faster, but I see that even reputable companies do things just because they can. All the time, repeatedly.

Furthermore, in this day and age of abundant data, not offending someone or not violating rules aren’t good enough. In fact, to paraphrase comedian Chris Rock, only losers brag about doing things that they are supposed to do in the first place. The direct marketing industry has long been bragging about the self-governing nature of its tightly knit (and often incestuous) network, but as tools get cheaper and sharper by the day, we all need to be even more careful wielding this data weaponry. Because someday soon, we as consumers will be seeing messages everywhere around us, maybe through our retina directly, not just in our inboxes. Personal touch? Yes, in the creepiest way, if done wrong.

Visionaries like Lester Wunderman were concerned about the abusive nature of online communication from the very beginning. We should all read his words again, and think twice about social and human consequences of our actions. Google from its inception encapsulated a similar idea by simply stating its organizational objective as “Don’t be evil.” That does not mean that it will stop pursuing profit or cease to collect data. I think it means that Google will always try to be mindful about the influences of its actions on real people, who may not be in positions to control the data, but instead are on the side of being the subject of data collection.

I am not saying all of this out of some romantic altruism; rather, I am emphasizing the human side of the data business to preserve the forward-momentum of the Big Data movement, while I do not even care for its name. Because I still believe, even from a consumer’s point of view, that a great amount of efficiency could be achieved by using data and technology properly. No one can deny that modern life in general is much more convenient thanks to them. We do not get lost on streets often, we can translate foreign languages on the fly, we can talk to people on the other side of the globe while looking at their faces. We are much better informed about products and services that we care about, we can look up and order anything we want while walking on the street. And heck, we get suggestions before we even think about what we need.

But we can think of many negative effects of data, as well. It goes without saying that the data handlers must protect the data from falling into the wrong hands, which may have criminal intentions. Absolutely. That is like banks having to protect their vaults. Going a few steps further, if marketers want to retain the privilege of having ample amounts of consumer information and use such knowledge for their benefit, do not ever cross that creepy line. If the Consumer’s Bill of Rights is too much for you to retain, just remember this one line: “Don’t be creepy.”

3 Steps to an Effective LinkedIn Profile for Sales Reps

Tired of getting so few leads from your LinkedIn profile, investing in LinkedIn Sales Navigator or needing to generate leads with email faster? You’ll need more than a pretty photo on your profile. You need a summary section that creates urges in prospects—provoking them to connect, email or call.

Tired of getting so few leads from your LinkedIn profile, investing in LinkedIn Sales Navigator or needing to generate leads with email faster? You’ll need more than a pretty photo on your profile. You need a summary section that creates urges in prospects—provoking them to connect, email or call.

Make sure prospects viewing your LinkedIn profile take an action and are producing leads for you. But first, ne sure your or your team’s profile is structured to:

  1. Create an urge for what customers’ want most in the Headline space;
  2. Spark buyers’ curiosity about what you can do for them in the Summary;
  3. Direct that curiosity—give them an irresistible reason to act.

These steps are the low-hanging fruit. Don’t just know them, do them. Every word, video, Powerpoint, PDF whitepaper and link on your profile can help buyers develop an irresistible urge to solve their problems or reach a goal—through you. But only if you take a minute to design it to. The best place to start is your Professional Headline.

Fire up your browser. Compare your profile against the checklist below. Check off each one as you implement these proven, effective steps.

STEP 1: Create an Urge to Read via Your Headline
Like it or not, headlines control our world. If you’re not getting to the point and sparking curiosity in a matter of seconds you’re not going anywhere. Just like an effective cold call or elevator pitch.

Use your profile’s Professional Headline space to display information that creates an urge to discover whatever is most important to them. Don’t list your title or job position. Make sure your professional headline presents:

  • what you do,
  • who you do it for and, if possible, and
  • elude to how you do it that creates distinction.

If possible, hint at why buyers should choose you. Make your why clear but not totally complete. Leave off the details. This creates an urge to scroll down to the next section: Your profile’s Summary.

For example, turnaround and acquisition expert, Carter Pennington, says he “maximizes shareholder value of troubled companies.” Mando Villareal names his target market and says he helps them “reduce cost increase efficiency & automate deliverables.”

In both cases, structuring words this way helps prospects wonder, “I wonder how he does that?” It creates an urge to scroll down and learn more about the seller.

Wondering where to start yourself? Use what you already know is most important to your prospective buyer. Don’t be clever. Instead, push your prospects’ buttons.

Trent Smith is a “Trusted advisor to attorneys who want to grow their practice.” He knows there isn’t an attorney on the planet who doesn’t want to grow their practice. In a moment, I’ll show you how Trent exploits this urge in various sections of his profile.

Remember: Use your Professional Headline space to create an urge to discover more about what makes you someone worth paying attention to. Be bold. Grab your prospect, fast.

STEP 2: Ditch the Resume, Go for a Reaction
Your LinkedIn profile is a tool to get prospects curious about what you can do for (not sell) them. Because once they’re curious, they’re more likely to react—to act. Since your Summary section is often “above the fold” (is seen before anything else) it’s the best place to start sparking reactions.

The idea is to quickly make statements that cause customers to become excited, unsure, eager or even a bit scared. This is different than reciting information about yourself, resume style. Showing customers, “I have a better way,” telling them you have short-cuts they desire or making a bold claim helps you:

  • prove to be worth listening to (grabs the prospect) and
  • position yourself to make a big claim.

Every B-to-B seller has a big claim that plays on the desire of buyers—no matter what you’re selling. It’s believable, credible and needed. So use it. Your LinkedIn profile is a great place to

  • set up the claim
  • make it and
  • create an urge to act on the reaction your claim creates.

For example, Gerry Blaum makes the claim he’ll save Fortune 1000 clients $500,000 in health care over-spending and connect them with better service providers. If he cannot he’ll give clients his fee back. He says, “we only get paid when we save money for our clients.”

Gerry makes his claim in dramatic form. To keep it believable and credible, he reveals how he gets paid. This encourages HR executives at some of the world’s largest companies to wonder, “how, exactly, does Gerry accomplish this?”

Be careful to balance. You don’t want to make a claim that is unbelievable. Or a promise that gives away too much, too fast. Only make claims that sound believable and help buyers develop hunger for all the details. You’re going for a reaction, or an irritation—not total satisfaction.

The idea is to scratch the buyers’ itch-stopping short of offering full relief.

To more fully relieve their itch (or help them reach a goal) they need to take an action. This is just one way to effectively spark connections, email conversations or phone calls with prospects. Shoot me an email or comment below and I’ll share more examples.

STEP 3: Make a Direct or Subtle Call to Action
Give ’em what they want. Whether you’re a job-seeker, marketer or sales rep, your LinkedIn profile should contain “exit points.” Spots where a call to action should be placed—driving prospects away from your profile, toward your landing page, telephone or email inbox.

Toward shortcuts, tips, advice, pain relief, clarity on a fuzzy (yet important) issue or confirmation of nagging fears—whatever they want most.

Make sure you use calls to action to the fullest. Here are quick tips on how to make effective LinkedIn profile calls to action.

You cannot use HTML or links in the Summary section. But you can place calls to action inside it. Creating clearly identifiable sub-sections and headlines gives you the chance to make calls to action.

Look at how Gerry Blaum executes it. It’s easy to scan with the eye, grabbing the essence of each “chunk” of copy.

Stick to the basics. In a few words, use sub-sections inside the Summary to describe:

  • What you do & who you do it for
  • Why the prospect should care (how you do it differently than everyone else)
  • How & WHY customers should contact you (email, Facebook, Twitter, phone, Web site, etc.)

Give ’em what they want. Prove to them, quickly, you’ve got what they want.

Use trigger words to encourage action. Use phrases like:

  • Get all the details
  • Call me, email me
  • Discover fresh tips
  • See examples here
  • Start here (this one is very powerful believe it or not!)

Although you cannot use HTML here, readers will take advantage of links your provide.

Your target audience will visit your Web URL by cutting & pasting or right-clicking. In some Web browsers (like Chrome) users can jump to your Web site by highlighting the URL, right-clicking and immediately visiting your site.

Trent Smith uses his Contact and Summary sections to speak directly to prospects:

If you want visitors to say, “Wow! I’ve got to talk to this attorney right now!” then get website strategies for attracting clients at:

Of course, there are subtle, indirect approaches that are also effective. Choosing the specific approach often depends on your target market, type of decision-maker(s), sales cycles, complexity of what you’re selling etc. For example, Challenger sellers will need to take a much different, educational approach.

If you’re interested in taking first steps on everything I’ve presented today this free video training will get you started in just 12 minutes. Otherwise let’s chat in comments below!

1-Trick Ponies and Customer Loyalty Behavior

About 30 years ago, Paul Simon wrote a song entitled “One-Trick Pony.” The song describes a performing pony that has learned only one trick, and he succeeds or fails with the audience based on how well he executes it. As Simon conveys in the lyrics: “He’s got one trick to last a lifetime. It’s the principal source of his revenue.”

About 30 years ago, Paul Simon wrote a song entitled “One-Trick Pony.” The song describes a performing pony that has learned only one trick, and he succeeds or fails with the audience based on how well he executes it. As Simon conveys in the lyrics: “He’s got one trick to last a lifetime. It’s the principal source of his revenue.”

For a long time, I’ve seen this song and its message as something of a metaphor for what challenges many companies endeavoring to create customer loyalty behavior and more effective customer loyalty programs.

A key reason companies have a difficult time achieving stronger customer loyalty is they fail to provide full value and emotional relationship fundamentals. They focus on satisfying customers exclusively through basic rational and functional benefits, which is often too benign and passive an approach to create lasting value.

Mostly, they emphasize single-element or minimal element tactical approaches with customers, such as pricing, merchandise, loyalty cards or points-based programs, without determining (either before programs are launched or after they are up and running) whether this is sufficient motivation for building a long-term relationship. Smart marketers know, for instance, being a low-cost provider can be a trap and that only overall perceived value will prevail. In the United States, chain discount retailers like Caldor, Bradlees, Jamesway, Value City, Ames and Filene’s are either in trouble or have gone out of business, while Target, Costco and Walmart, with strong brand equity and high perceived value, have sustained.

Being a low-cost provider means that brand and customer strategies get little emphasis, and they require little investment. Let’s be honest. Cutting costs seems safe. The downside is it usually does not produce much loyalty (customer or staff), strategic differentiation or profitability.

In a 1980 Harvard Business Review article by William Hall (written, parenthetically, about the same time Simon wrote “One-Trick Pony), he reported study results comparing companies that competed on differentiated customer value vs. companies that competed principally on cost. On any important measure—return on equity, return on capital, and annual revenue growth—companies delivering both rational and relationship value beat the price competitors every time.

Customers can almost always locate cheaper products or services. Ultimately, they will invest a greater share of their purchase dollars with suppliers who create stronger emotional bonds and deliver superior perceived value. Competing on price, or any other single dimension, may pull away customers from other suppliers in the short run, but it will be difficult to keep them for long. Price is rarely a sufficient “barrier to exit,” and is more often an invitation to churn.

The same thing often holds true for incentive programs. Many consumers participate in programs like supermarket bonus clubs and airline frequent flyer programs, but they aren’t particularly effective at producing greater loyalty for any one airline or any one supermarket chain. Customers are often members of several programs, and the most active users tend to be those who would have been frequent purchasers, anyway. The incentive and reward structure more often benefits the already loyal rather than increasing loyalty. Gift programs, travel, dining, entertainment, merchandise, and cash award programs, and other plateau and pre-selected response stimulus programs are having an increasingly difficult time breaking through the clutter to provide unique, differentiated customer value.

Some of the online incentive programs have positively increased transactions, mostly among younger, female and active surfing potential buyers. To keep these incentive promotions from being one-trick ponies, they must be carefully targeted to the right consumers and at the right time. These programs must have four effective elements: ability to attract prospects to the website and, once there, to generate consideration, preference, and purchase. Getting infrequent buyers to purchase more often, or frequent buyers to place larger orders through the use of incentives, will hinge on how well companies leverage their customers’ profiles. Even more basic, it must be well-understood what customers perceive as value and what it will take to optimize their repeat purchases. The essentials for bricks and mortar product and service providers are virtually the same.

Generic, cookie-cutter and “me, too” discounts or incentives don’t do particularly well at increasing overall customer “share of wallet,” because they don’t sufficiently reward the customer for their enhanced purchase activity over time. All that’s really required to meet the customer halfway is infusion of some targeted, personalized elements to the incentive program to make them more attractive and beneficial.

The first step is to segment customers who should receive different incentives. This can be done through both basic data analysis and applied, or pilot, customer research. For example, for large customers who purchase infrequently, the company might have determined that, if they offer special discounts made within the near future, say 60 or 90 days, these customers would find that attractive. Customers who purchase frequently but in low volume amounts might be offered a discount on their next order, so long as it is larger than their last order. The array of potential loyalty program offerings can be customized based on identified needs.

What about incentives for customers who are both frequent and large volume purchasers? Well, start by saying “thank you” to them. Few things are more appreciated than thanks, and few companies express their gratitude as much as they should. Many forget to thank their customers altogether. This is especially critical for Web-based companies, or ISPs and cable companies, where the purchase experience is frequently virtual rather than personal. Thanked customers are more likely to go out of their way to provide positive referrals and testimonials.

Paul Simon’s song lyrics conclude: ” … the bag of tricks it takes to get me through my working day.” Companies would be well-served to have a bag of experience and customer loyalty tricks, using disciplined research and customer data to identify them, rather than relying on only one—price—to get them through.

Are DMA Conference Exhibitors Reinventing, Too?

From 1987 to 2008, I had attended every DMA conference, always enjoying the experience of reconnecting with long-time associates, hearing presentations to continually learn, and meeting with vendors who might be good resources for clients. Last week I found myself looking at the conference through a different lens as I walked the exhibit hall to learn from the exhibitors.

It had been five years since I last attended a DMA annual conference. I decided to return last week. If there were a score card of how direct marketing service providers are reinventing what they sell to end-user companies, one measure of that could be taken from the exhibit hall.

From 1987 to 2008, I had attended every DMA conference, always enjoying the experience of reconnecting with long-time associates, hearing presentations to continually learn, and meeting with vendors who might be good resources for clients.

Last week I found myself looking at the conference through a different lens as I walked the exhibit hall to learn from the exhibitors.

The first thing that astounded me was the shrinkage of the exhibit hall. The program listed 241 exhibitors. While I don’t have access to the number of exhibitors from, say, a decade ago, it feels like it was about one-third the size that it used to be.

The second thing that struck me was the type of exhibitors who were there. I’d generally divide into one of three camps:

  1. Traditional direct marketing vendors, mostly supporting direct mail. The convention program listed 112 exhibitors self-identified as in the Direct Mail and Print Services category. Add in some of the dozens of firms supporting Data Management (who weren’t already listed under Direct Mail and Print Services) and easily over half—perhaps two-thirds—of the exhibitors supported traditional direct mail marketing channels.
  2. Technology companies offering online services to direct marketers accounted for a significant representation as well. An exact count is difficult to infer because of vendors listing themselves under multiple categories including Affiliate Marketing, Content, E-commerce, Mobile, Online Advertising, Real-Time Automated Technologies, Search and Social, but the representation was strong. These are firms that, in my opinion, generally did a poor job of communicating how they support direct marketers. As I spoke to several of them, they glowed over their technology but didn’t connect their technology to how it would generate response. It feels like they want to attract business from direct marketers, but they don’t speak our language. Many technology companies seem to be in love with their buzz words on their booths, but failed to give the passer-by any clue of what their technology would do for me to build sales. At the expansive exhibit of one of the most recognized software companies in America, I quickly spotted three typographical errors on their big screens. Their exhibit booth staff was also the least friendly and willing to explain what they offer direct marketers.
  3. Vendors that effectively blended offline and online. Only a few exhibitors, it seemed, truly attempted to be a one-stop shop where offline could be linked with online media. Those exhibitors were the ones doing business at the conference. They were the ones who were the most positive about returning next year. In one case, a long-time DMA conference exhibitor who has reinvented his service offerings, said last week’s conference was the best ever for them. This traditional direct mail services provider had teamed up with a technology firm so their booth felt like two spaces, but they seamlessly referred clients to each other. More importantly, they linked online technology with the ability to use direct mail for specialized messaging.

It appears there is work to be done by many vendors to update their services to keep up with what direct marketers must do to survive. And technology companies have a lot of work to do to understand the nuances of direct marketing. For vendors who want to grow and prosper in this field, if they haven’t already, they need to reinvent just like the direct marketing customers who they want to serve.

Marketing: It’s the New IT

Spring is here and change is in the air for marketers in the way they consume technology. Big change. Not incremental or run-of-the-mill change. We’re talking a paradigm-busting tectonic shift that’s going to change the way that companies are structured. And when the dust settles, things will never be the same again, for Marketing or IT.

Spring is here and change is in the air for marketers in the way they consume technology. Big change. Not incremental or run-of-the-mill change. We’re talking a paradigm-busting tectonic shift that’s going to change the way that companies are structured. And when the dust settles, things will never be the same again, for Marketing or IT.

What do I mean? What I mean is we’re on the ground floor of a transformational process in which marketing replaces IT as the stewards of the Marketing Technology Infrastructure. At the end of this process, marketing will own and manage vast majority of IT’s responsibilities, as they relate to marketing functions. This is going to happen—sooner than you might think—as a result of several parallel trends that are already underfoot in the business world.

  • Emergence of robust and easy-to-use SaaS marketing technologies—the proliferation of tools like Constant Contact, Eloqua, SalesForce and Marketo give marketers access to incredibly powerful plug-and-play solutions that can be used with virtually no internal IT support. Because they’re delivered using the SaaS model, all updates and tech support are managed by the vendor. Talk about a marketer’s dream …
  • Development of secure and dependable cloud storage and computing infrastructure—as little as five years ago, companies could never have imagined moving their precious data outside the organization’s firewall. Oh, how times have changed! Numerous security breaches combined with improved cloud technology and falling prices for storage have turned the tables on this argument. Why go through the cost and hassle of maintaining your own databases if you don’t need to? For many companies, this is already a rhetorical question.
  • Standardization of Web-service-based API architecture—Now that API technology has grown up, so to speak, we have a universally agreed-upon language (XML) and set of standards (SOAP/REST) developers can use to tie disparate systems together. Building on point No. 1, APIs are a quick and effective way to pass information back and forth between various platforms. What’s more, a new generation of developers has grown up that’s fluent in this ecosystem, and companies are taking advantage by staffing up big time. Within the next couple years, you’ll never again hear, “We don’t have an API developer on staff.”
  • Validation of the “Platform” model for development—why build a platform when you can use someone else’s? What’s more, why try to build a better mousetrap yourself when you can leverage a network of thousands or tens of thousands of developers who are willing to give it stab? This is the power and promise of the platform model. Over the next few years, the marketing space will be increasingly dominated by large platforms who create ecosystems their clients can tap into for cutting-edge capabilities, and developers can leverage to line their pockets. By 2020, I think it’s safe to say that if you’re a developer, you’ll either be working at a platform, developing apps for one, or building tools and methodologies that pass information back and forth between them. So if you like to code, get with the platform program, and quick!

Because the relationship between IT and Marketing could be described as “frosty,” at best, I think it’s safe to say that, overall, this will be a welcome change for most CMOs. In my experience, marketing departments tend to feel that IT is understaffed, distracted and overall not a strong partner for the marketing team to rely on. If anything, the adversarial nature of this relationship will serve to accelerate the overall trend of many IT functions dissolving into marketing department’s purview.

But what’s most interesting about this process is that it will not be limited to the marketing department. Think about it. Other departments consume technology as well, right? That means it’s going to happen in parallel throughout the entire enterprise organization: Finance, Accounting, Purchasing, Procurement … They will all go through the same transformation, as software is procured from SaaS service providers, and data storage and database management is migrated to the cloud. We’re talking comprehensive and organization-wide transformation.

I’ve already seen the beginnings of this process within many of my client’s organizations. In a previous post, The Great Marketing Data Revolution, I touched upon the incredible transformation organizations are being forced to make as they deal with and try to make sense out of with the deluge of unstructured marketing data they are collecting every day, which is often referred to as “Big Data.”

For many companies, the ultimate Big Data strategy involves a Master Data Management (MDM) solution for collecting, aggregating, matching and storing this vast pool of information. While supported by IT, MDM initiatives tend to be marketing projects, as most of the data is collected and used by marketing. MDM/Big Data solutions tend to be cloud-based and take advantage some, if not all, of the four points I addressed above.

Now what’s going to happen to IT, you might ask? If you’re working in IT, don’t fret. Your department won’t disappear. But its role will undoubtedly change with the times. Instead of focusing on product development and infrastructure maintenance, IT will instead focus on identifying the right players to engage with, testing, auditing and supporting the process—not to mention providing API technologists to help tie systems together. And, possibly, developing specialized tools to help fill in gaps the marketplace has overlooked.

If you’re a developer, this means that you’re going to need to redefine your skills to align them to the needs of the marketplace. And the good news is you probably have a few years to get it sorted out. Still, things will undoubtedly change and—once the proverbial tipping point is reached—they’ll change awfully fast.

So I hope this all makes sense. I do have a feeling this may be a controversial topic for many readers—especially those in IT. If you have any questions, comments or feedback, please let me know in your comments.

Jonathan Salem Baskin, Author of “Branding Only Works on Cattle,” to Keynote InterACT!

I wanted to let you in on some exciting news: Jonathan Salem Baskin, author of “Branding Only Works on Cattle” and “Bright Lights and Dim Bulbs”, and CMO strategy columnist for Ad Age, has just be named the opening keynote speaker for the InterACT! conference, an educational forum for marketers, brand owners and service providers on cross-media direct marketing opportunities and strategies.

I wanted to let you in on some exciting news: Jonathan Salem Baskin, author of “Branding Only Works on Cattle” and “Bright Lights and Dim Bulbs”, and CMO strategy columnist for Ad Age, has just be named the opening keynote speaker for the InterACT! conference, an educational forum for marketers, brand owners and service providers on cross-media direct marketing opportunities and strategies.

In his presentation, called “David Ogilvy’s Revenge: The Return of Marketing Realism,” Baskin will offer InterACT! attendees a big-picture perspective on the marketing realism emerging from the convergence of new and old media, and what the opportunities will be in 2011.

Baskin, who has been called “a merry iconoclast,” “lucid and cutting,” “groundbreaking” and “the new bad boy of branding,” looks beyond the
gimmicks and hype to uncover what really works when communicating with the marketplace. Currently, Baskin wears multiple hats as a global brand strategist, leading a global network of partner companies in The Baskinbrand Alliance and managing North American business for the global marketing consultancy Futurelab. Along with his books and columns, he also writes the popular blog Dim Bulb, co-hosts a regular podcast, Socializing Media, and is a member of the board of advisors for Social Media Today.

InterACT! is a two-day, two-track program designed for marketers, agencies and custom publishers, as well as print/marketing service providers interested in improving how they interact with customers and prospects. It’s the first event that will demonstrate how to integrate print, online, mobile and social media to deliver interactive marketing campaigns that drive business results. The event is scheduled for August 10-11 at the Crowne Plaza Chicago O’Hare in Rosemont, Ill.

To follow InterACT! on Twitter, visit:

To register, click here:

Hope to see you all there!