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.

Marketing and IT; Cats and Dogs

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

 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The following are the major steps:

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

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

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

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

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

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

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

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

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

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

Email Marketing Redefined: Service With a Side of Sales

The multichannel marketplace has blurred the line between service and sales. People expect to get answers to their questions while they are shopping and on-demand after an order is placed. Redirecting them to another channel or platform for pre-sale and post-order information has a negative effect on the buying experience and long-term loyalty

The multichannel marketplace has blurred the line between service and sales. People expect to get answers to their questions while they are shopping and on-demand after an order is placed. Redirecting them to another channel or platform for pre-sale and post-order information has a negative effect on the buying experience and long-term loyalty.

Unfortunately, technology has changed faster than the corporate organizational chart has adapted. Marketing and operational departments aren’t integrated enough to provide the seamless shopping and service experience that people want. It’s time to make the shift to integrated messaging across all channels, platforms and departments. The email program is the best place to start, because changes are quick and easy.

Transactional emails tend to be matter of fact announcements of order receipt, shipment and issues. They serve the operational side of the business well but do little to directly improve sales. Branding is minimal and the messages are rarely in the same voice used for promotional information. Failure to include marketing service messages is a lost opportunity.

Marketing is a service when it solves people’s problems. Transactional emails are one to one communication. The right combination of marketing and service messages benefit customers by helping them maximize the return from their investments. The key to successful execution is having the correct processes, careful planning, and good application of business rules. When done well, they keep customers informed and motivate them to buy more.

For example, the order confirmation email should thank the customer for the business, provide specific purchase information, and suggest other items that complement the original products.

An email for an order of earrings could offer a matching necklace or an order for a vacuum cleaner might suggest bags and filters. If the operational process allows combining the orders at the same shipping rate, the suggestion to do so creates a sense of urgency. The only catch is that business rules have to be accurate with personal messaging to optimize the return.

Inserting product images with a brief description will bump sales a bit, but it doesn’t have the same effect as: “Thank you for your order of the super suction vacuum cleaner. It will ship tomorrow. Please remember that the filter needs to be changed every month. Add one on to your order by clicking this link before midnight tonight and there will not be an extra shipping charge.” Of course your copy team will do a better job than me, but you get the idea.

Almost every transactional email sent to customers should include a marketing message. The exceptions to this rule are issue-related emails. Following “your item is out of stock until next month” with “buy this to go with your item” won’t win customer loyalty.

To get started with integrated marketing and service emails:

  1. Review your transactional emails. When are they sent? What information do they include? Is there a follow-up after the sale to encourage people to provide feedback? Do you ask people if they like their purchases? Document all of the transactional emails so you will have a starting point.
  2. Identify opportunities for marketing messages. Add-on sales are good for order confirmation emails. “New items just arrived” works well on shipment confirmation messages. Be creative when thinking about how to combine service and sales, it will provide more testing options.
  3. Select the emails and messages to test. Start small and learn quickly. Testing provides the best information for rolling out your program. Use simple business rules and build from that foundation. Complicated processes are recipes for disaster when you are starting an integrated program.
  4. Verify that the offers are deliverable. Promising your customers that you will combine orders when it is operationally impossible creates mistrust with customers and colleagues. Always under promise and over deliver. It surprises customers and minimizes dissatisfaction.
  5. Measure everything. What effect does the new messaging have on sales? Opens? Clicks? Lifetime value? Lifespan? The more you know the better you can create targeted emails that deliver sales and satisfaction.
  6. Revise as needed. Transactional emails are easy to set and forget. They continue to go out day after day without any maintenance required. This tends to make them a low priority. Scheduling regular updates to rework the emails keep them fresh and informative for customers. It optimizes the return.

Take Command of Marketing Data Governance—Because We Have To

The emergence of “big data” as an enterprise concern for many businesses and organizations is, as with most trends, both an opportunity and a concern. I recently was involved in reviewing new and recent Aberdeen Research on “Big Data”—how it is defined, how it is changing information volume (astounding in quantity), variety (both structured and unstructured, with tremendous pressure to integrate and make sense of it), and velocity (pushing the insight, analytics and business rules that flow from such data to lines of business that can best profit from it).

The emergence of “big data” as an enterprise concern for many businesses and organizations is, as with most trends, both an opportunity and a concern.

I recently was involved in reviewing new and recent Aberdeen Research on “Big Data”—how it is defined, how it is changing information volume (astounding in quantity), variety (both structured and unstructured, with tremendous pressure to integrate and make sense of it), and velocity (pushing the insight, analytics and business rules that flow from such data to lines of business that can best profit from it). An infographic that captures some of this research is now posted at Mason Zimbler, a Harte-Hanks Company, which created the visual presentation.

Alongside this current fascination and business trend, perhaps it’s not surprising that members of Congress, both Democrats and Republicans, also are posing questions at the marketing business as to how we collect, buy/sell, rent and exchange data about consumers online and offline, and if there is adequate notice and choice in the process. In the rush to capitalize on Big Data, we need to ensure that we’re collecting and using marketing data for marketing purposes only, and doing so in a manner that is respectful of fair information practices principles and ultimately serves the end-customer, be it consumer or business individual or enterprise. [See Rep. Ed Markey, D-MA: http://markey.house.gov/content/letters-major-data-brokers.]

All too often, privacy adherence is considered a legal matter, or an information technology matter—but I maintain that while these two business areas are important in respecting consumer privacy, it is marketers who have the most to gain (and lose) by smart (or insensitive) information practices. Data is our currency, and we must treat data (our customers as data subjects) as our primary asset to protect. Our method of marketing is in the balance. One or two major privacy mishaps can spoil it for everyone.

Of course, marketing data governance is far more than privacy compliance. Data quality, data integrity, data security, data integration, data validation and data flows within an enterprise all, too, are part of marketing data’s customer intelligence equation. It is in this spirit that the Direct Marketing Association recently introduced its newest certification program for professionals: “The Institute for Marketing Data Governance and Certification,” taught by marketing veteran Peg Kuman, who is vice chair at Relevate Group. The three-day course, which has launched on a two-year, multiple-city tour, is indispensable in understanding how multiple channels, multiple data sources and platforms, customer expectations and business objectives combine to command better understanding, tools and processes for data handling for smart integrated marketing. Forthcoming course dates and registrations are available here: http://www.dmaeducation.org/dm-essentials/marketing_data_governance.php

For three days last month in New York, approximately two dozen professionals from large and small enterprises, both commercial and nonprofit, attended the first seminar. I, too, attended. There were representatives from marketing, public relations, analytics, legal, IT and fundraising, representing brands, agencies and service providers. This group was engaged—providing examples, asking questions and reporting experiences as the curriculum moved along. (For those who don’t know Peg—a former client of mine—she is quite the facilitator.)

Alongside a workbook, I took home some great handouts, too:

  • A sample security policy; a sample information security vulnerability assessment;
  • A security due diligence questionnaire;
  • A sample vendor risk management program vendor questionnaire;
  • The latest copy of the DMA Guidelines for Ethical Business Practice (recently updated with new email append guidelines, by the way) and a bevy of news articles that captures the media’s and public policymakers’ current attention on consumer data in America.

The meat of the course tackled, among other topics:

  • Categorizing data and assigning priority and sensitivity (personally identifiable information, sensitive data and other categories);
  • Mapping data flows and interactions with customers; enhancing data with appended information, and ensuring its use for marketing only;
  • Having a data quality strategy as part of a data strategy;
  • Calculating return on data investment;
  • The emergence of digital, mobile and social data platforms, and how these present both structured and unstructured data collection and insight analysis challenges;
  • Assigning data “ownership”;
  • Calculating and assigning risk regarding security;
  • Monitoring security, investigating potential incidents of a breach, and handling a response to a breach were it to occur (using recent breach response examples of LinkedIn and Epsilon); as well as
  • Laws, ethics and best practices for all of these areas.

One of my concerns is the importation of European-style privacy protection in America, and current fascination with such protections by U.S. regulators and elected officials. That is worth another blog post in itself, but I can assure you that we need to educate politicians about the superiority of self and peer regulation where no consumer harm exists.

Thank you, DMA. Marketing data does not harm. It only creates consumer choice, commerce, jobs and (tax) revenue—and pays for the Internet and other media, too—and it is ridiculous to even entertain government-knows-better regulation of such information through a potential omnibus law in America, or other notions such as a government-mandated “privacy by design” requirement in marketing innovations. (On the other hand, I’m more than happy to see laws pass that protect Americans from potential government abuse of private sector marketing data—Big Brother should not be getting access to marketing data for non-marketing purposes, unless there is a demonstrable greater public good, where subpoenas are served and heard.) Privacy by design is smart business, but only when left to the innovators, not the policymakers.

Which brings me to close—and if you’re still reading this, I congratulate myself for not chasing you away. Big Data (which can incorporate far more than marketing data) goes hand-in-hand with marketing data governance. Whether a Big Data user or not, we all use marketing data everyday as our currency. Protect it. Respect it. Serve it. Govern it. So we can use it.