Data-Centricity, Marketing Dashboards, AI, Transparency, Etc.

You’ll have to forgive me. Forces are conspiring — and it’s giving me a “deer in headlights” feeling. And I’m not alone.

You’ll have to forgive me. Forces are conspiring — and it’s giving me a “deer in headlights” feeling. And I’m not alone.

On the one hand, business organizations are set on data-driven transformation, according to The Winterberry Group’s “The Data-Centric Organization 2018” report, in partnership with the Data and Marketing Association and Interactive Advertising Bureau’s Data Center for Excellence. The report is an update of a previous study, so there is comparison data from 2017 to 2016 from which to spot trends.

The newest report, however, gives some pause on our collective march toward data fluency.

“A majority of marketers are rolling out strategies to support data-centricity … but relatively few say they’ve generated results,” the report concluded. Much of this report of scarcity is timing — nearly half say their strategy is being developed but not yet implemented. As a result, 12.3 percent of the most recent study respondents reported “strategy has been developed, implemented and is delivering results.”

Yet just a year ago, 28.6 percent reported favorably on the same question. Which begs the question, why the drop off? Did we go backward?

Perhaps not. Could it be that the more we (organizations) learn about data-centricity, the more we learn that there’s much more to learn? Think about it, it’s a question of confidence.

Conceptually, we may well understand that a “full view of the customer” is required to enable “customer-focused” business activities. This simple premise from the earliest days of (customer relationship management) CRM presents a whole new set of challenges when considering the volume, velocity and variability of “big data,” mobile data, social data, location data, transaction data, cross-device data and the algorithms that seek to create relevance and drive business and media decisions. Prior to integration, all this data needs to be staged, parsed, cleansed to protect against “garbage-in, garbage-out” outcomes. Enter the forest. The CMO had best hold a compass with the chief technology/information officer.

Add in the insatiable demand for analytics know-how — the “plastics” of our day — where we have tremendous demand, supplemented by artificial intelligence tools that some may or may not understand quite yet, and wow, it’s a recipe for uncertainty. Just what will the marketing dashboard of the future look like?

And we haven’t even talked about policy and ethics considerations: fraud prevention, transparency, governance, security, permissions, preferences and restrictive global regulatory regimes (General Data Protection Regulation and ePrivacy Regulation).

Yet what’s enduring in the data-centric study is industry’s determination to get it right: 44.4 percent of respondents expect that their organization will be “extremely data-centric” by 2019 (two years’ time from the taking of the survey). Truly, what choice do we have but to learn the data to serve the customer?

As the study’s authors report in part, “Experience=Awareness.” It’s easier (and dangerous) to shrug off the impacts of data centricity’s demands, when you’re still at the starting line. Take a few steps, and lo and behold, the complexities and intricacies take focus.

Next post, we’ll examine some study recommendations – a helpful look at people, platforms, partners and process. Awareness may not help a deer in time, but thankfully we can learn from our practice.

A Guide to Hire Next-Gen SEOs

Recently several key figures, who have made enormous contributions to the search industry, have either left (Matt Cutts) or stepped back from day-to-day activities in the companies they have helmed (Danny Sullivan and Rand Fishkin). It is a sign of the times — a generational shift in the industry. Not a generational shift determined by the age of the participants, but one determined by how the industry as a whole has aged and matured.

(Image via
Pioneers forge ahead, settlers are the next wave. We’ve reached the settler stage of SEOs.

Recently several key figures, who have made enormous contributions to the search industry, have either left (Matt Cutts) or stepped back from day-to-day activities in the companies they have helmed (Danny Sullivan and Rand Fishkin). While each of these gentlemen is to be congratulated on all he has accomplished, it is not a sign of impending doom for SEO that they have moved on to other opportunities. It is a sign of the times — a generational shift in the industry. Not a generational shift determined by the age of the participants, but one determined by how the industry as a whole has aged and matured.

SEO has changed and become a different discipline than it was some 20 years ago when I first optimized sites for search engines that are no longer household names — AltaVista, Infoseek, Excite, Yahoo! and others. Everything we did was so new and moving so fast that the industry attracted heat-seekers. What do I mean by heat-seekers? They are those people who want to be right on the edge of new things where the risks are greatest, and there is the most chance for upside reward. They are heavily self-reliant and willing to go off-road intellectually to explore new territory. They are not settlers; that is the next generation. Be mindful that there is nothing wrong with being a settler; our country was made great by hardworking settlers.

As I look back on those early days and try to figure out how to bring the next generation of SEO practitioners along, I cannot help but reflect on how the industry pioneers learned and developed the practice of SEO. When an industry is brand new or emerging, the knowledge base is less important than the minds and ingenuity of the individuals carving the way. As we look to training the next generation of practitioners, we must consider what makes someone able to learn this discipline — not just create it. Here are some of the qualities that make a good SEO.

SEOs Must Have Inquisitive Minds

The ideal candidate for learning SEO must have an active, inquisitive mind and be willing to ferret out answers from mounds of data. SEOs today, even with all of the guidelines for the actual practice of SEO (the roads to success), must be able to map the way for their organizations. This means bringing together multiple strains of information — business goals, marketing goals, technology roadmaps. Today’s SEO does not work in a vacuum, just doing their thing to make the site visible in search. Today’s businesses have developed online and offline marketing programs, all of which must be integrated into the SEO plans.

SEOs Must Be Highly Analytical

When online marketing was new, traditional marketers (myself included) were thrilled to be able to gather so much information on each visitor. From my own experience, the first time I looked at a set of log files and realized what they included, I immediately closed the file and spent a few minutes worrying about whether I was invading someone’s privacy. I felt like a peeping Tom. Today, the analytics packages provide dashboards filled with useful information, which once had to be mined from lengthy raw data reports. The hard part is still extracting meaning from the data, even in its contemporary easy-to-analyze form. This requires strong analytical skills.

SEOs Must Have Strong Communication Skills

To be successful, the SEO must be able to talk tech intelligently with tech teams and speak fluent biz buzz with marketers and managers. As the role of content has grown in search, the SEO can no longer just create content that “works” for search engines but does nothing for the site’s visitors. SEO content must inform and persuade both search engines and the site’s human visitors.


All that is missing from this list of skills and abilities is perhaps the ability to leap over buildings in a single bound or have a spidey-sense for algorithm updates. All gags aside, the SEO of today may not be bushwhacking through totally uncharted terrain, but SEO is still a fascinating, rewarding and difficult discipline. The next generation will have new challenges as the industry continues to mature.

What the Hell Is Happening to Retail?

Retail is having a moment right now. Since the calendar flipped to 2017, we’ve seen more bankruptcies than all of last year. What’s going on?

Retail is having a moment right now.

Since the calendar flipped to 2017, we’ve seen more bankruptcies than all of last year, including some consumer electronics chains like RadioShack (again), hhgregg and Sears. Just recently, hhgregg announced it was going a step further, closing all of its remaining stores and liquidating all products.

The doom-and-gloom news has, of course, extended well beyond the CE industry. Some major stores — JCPenney, Macy’s, Payless, Lululemon, Urban Outfitters and more — have all announced either store closures or seen their stocks tumble to new lows. Ralph Lauren even announced that it was closing its flagship Polo store on Fifth Avenue in New York.

Mind you, all of this is happening at a time when the economy is very much out of the recession phase, consumer spending and confidence are up and the economic outlook has been nothing short of rosy.

So What Gives?

So what the hell is going on? Is this the end of retail? Is the apocalypse upon us?

The answer to any of those questions, as you’d expect, isn’t so straightforward.

Yes, this is a very dark time for retail. A lot of things are up in the air. Brick-and-mortar stores are facing some incredibly difficult times. But that doesn’t mean the entire industry is about to go kaput.

The Atlantic recently published a deep dive into the current state of the retail industry, and its explanation couldn’t have been more accurate — it’s probably the closest thing to a complete answer we’ll be able to find. The analysis points to three main causes for the “Retail Meltdown of 2017”:

  • The rise of online shopping,
  • the existence of faaaaar too many malls,
  • and a major shift in how consumers are spending money.

All of those make perfect sense. No industry understands the impact of online shopping better than the consumer electronics space, which has seen billions of dollars in business go to Amazon. Since 2010, the e-commerce giant’s sales in North America have quadrupled from around $16 billion to more than $80 billion last year. And in 2016, Amazon’s growth alone accounted for more than half of all online sales growth.

There’s also the mobile wallet affect. Since 2010, The Atlantic pointed out, mobile shopping has grown from less than 2 percent of digital spending to 20 percent last year.

As for the declining footprint of malls, there are roughly 1,100 in existence today. That’s down from the peak of about 1,500 (all of which were built between 1956 and 2005). So nearly a third have closed in the last decade. Further, while all of those malls have closed, not a single new one has been built.

Whereas malls used to serve as the cornerstone of local communities, the era of expansion resulted in an oversaturation of malls. When you take into account the number of malls and outdoor shopping centers throughout the U.S. — which brings the total up to more than 7,500 — and break it down by “gross leasable area,” as one research firm did, the U.S. far outpaces the rest of the world.

shopping center study

Lastly, The Atlantic hit on consumer spending trends. Here, it notes that Americans have shifted from a materialistic mindset to one where we’d rather spend money on going out with friends for food and drinks. A fair point.

A Shifting Retail Paradigm

The message in all of this is really geared toward the larger national retail chains. The downturn in physical retail square footage goes to show that, in the era of omnichannel retail, there’s no need to expand your brick-and-mortar footprint in order to boost sales. Rather than needing more locations, companies need a stronger e-commerce strategy. Easier typed out by a blogger than implemented by a retailer.

But what about the smaller stores?

If anything, all of the mall closures are positive for the specialty guys out there. With less competition in those local markets, consumers who still prefer to shop in-person are more likely to turn to you.

But that doesn’t mean you can continue to operate as you always have. Just like we wrote about in our analysis of Staples’ new strategy to drive foot traffic, local shops need to continue to drive home in consumers’ minds why their stores matter. You have to find that personal touch, something that separates you from the national chain, and can entice consumers to come walk through your showroom. It doesn’t have to be a co-working space. Maybe it’s free delivery if they live within so many miles, or same-day installation for orders over a certain price threshold. Heck, maybe it’s even just outstanding personal service from your salesfloor team.

Retail isn’t going anywhere. There’s always going to be a need for a physical place where people can come in and experience a product. The only difference today is that consumers need a little more enticing to get out from behind their computers. The onus is on the retailer to prove its worth in its communities. If you can do that, the “Retail Meltdown of 2017” will mean absolutely nothing to you.

10 Marketing Clichés I’d Challenge to a Fight if I Met Them in the Street

There you are, minding your own business, when it rounds the corner and rams into you full speed: the copywriting cliché. (It didn’t see you coming because it was too busy looking up the percentage of Americans who rely on a mobile device to complete daily tasks, without which we’d have no idea that mobile is important.)

There you are, minding your own business, when it rounds the corner and rams into you full speed: The copywriting cliché. (It didn’t see you coming because it was too busy looking up the percentage of Americans who rely on a mobile device to complete daily tasks, without which we’d have no idea that mobile is important.)

You’ve seen them ’round these parts before, and to put it mildly, you’re sick of it. Time to put up your dukes.

We’re all guilty. I’m certainly guilty. Lock me up in cliché jail and throw away the key, Copper. In fact when I was looking for ideas for this entry, I Googled “marketing clichés” and Google spat back “About 435,000 results (0.53 seconds).” Even this article is a cliché. But, as the slew of tired and overused concepts and phrases is certainly not static, let’s give my own list a go anyway.

Here are a few gems that make me clench my fists. They’re not really in order, save for No. 1 frosting my cookies the most. Disclaimer: I use some of them — frequently. And a lot of them, we continue to use because they work. But there’s always a tipping point, and for these guys, we’re definitely getting there. Consider this an exercise in self-awareness as much as anything else.

10. The latest and greatest resource for today’s [insert industry professional here]
9. With technology rapidly evolving…
8. “Look who’s talking!” to promote a speaker lineup
7. Mobile/Social media/Online is everywhere these days…
6. Grandiose list boasting — ie: “Build your audience with the bestest file out there … no really, honest … THE.BEST.LIST! Ever.”
5. I know you’re busy, so I’ll only take a minute!
4. Before you leave for the weekend/vacation
3. If you only register for ONE event this year, this is the ONE/A fresh, totally unique event
2. A one-stop shop for all your ________ needs
1. Any variation of “How to Market to Millennials”

This last one I just need to just expand on a tiny bit further. Look, I know. I totally get it. From roughly 1980-2000, people continued to have babies, and those babies continued to grow up, and it’s all very scary and brand new.

There’s all kinds of studies about how differently our brains work or how we think about spending money in an unsteady economy, so on and so forth. It’s only natural to feel like there’s a code to crack or a secret language to learn.

If you Google “market to millennials” you’ll get About 13,000,000 results (0.22 seconds). I’d say the industry has it pretty much on lock. And a good portion of these results will discuss us with the clinical tone of a scientist journaling the behavior a newly discovered species of fruit fly.

It gets to be a little much for those of us under the microscope. I promise, we’re just people. Hath not the millennial eyes? If you prick us, do we not bleed?

But I digress — this is possibly a whole other topic for a whole other entry. Meanwhile, tell me which clichés you avoid like the plague! (It’s a cliché that I used “avoid clichés like the plague,” since every English teacher in America has that poster in their classroom. This blog has been a wild trip to meta-land.)

See ya!

Benchmarking: There’s No Such Thing as an Average 2% Response Rate

It seems easy enough to answer the question: How to know if a marketing campaign measures up? But managing client expectations (whether they’re internal or external) is sometimes more fuzzy

It seems easy enough to answer the question: How to know if a marketing campaign measures up?

Often enough, there are predefined business objectives, acceptable margins for profit and cost, and a marketing return on investment that is straightforward enough to calculate. If one is able to know any and all of these markers, then one can know if a marketing campaign, or even a single tactic, is making the grade.

But managing client expectations (whether they’re internal or external) is sometimes more fuzzy. And a marketing execution doesn’t always go according to plan, prompting investigations on what might have gone wrong. (I’m still surprised how testing is underutilized, for example.)

On the happier end of the spectrum, stellar results might prompt a whole other set of questions: “Did we really beat the long-standing control? This campaign performed gang-busters, how does it measure up to efforts of our industry peers? Is this campaign award-worthy?”

As a public relations professional in the world of direct response, I’ve often been asked to help an agency or marketing client understand how good or bad a particular marketing result might be. When the question is about results that are less than expected, there is often internal wrangling about the creative, the list and/or the strategy — any of which might be the culprit. When the results are fantastic, clients often want to know, are we beating whatever the competition may be up to.

In both scenarios, among go-to options are various industry research sources. Anyone who has a subscription to Who’s Mailing What! archive (direct mail, email), or taps eMarketer or Econsultancy (digital and mobile information), or steps up to Gartner, Forrester and the like for subscriptions to qualitative reporting, certainly has access to great data and idea stores.

I personally keep a copy of “DMA Statistical Fact Book” (annually published) and “DMA Response Rate Report” close at hand. The “DMA Response Rate Report’s” 2015 version is recently published, and is available at the DMA Bookstore. Both are understandably Direct Marketing Association top-sellers.

The “DMA Response Rate Report” aggregates data from respondents — providing a true benchmarking resource. And it breaks response data out by media, and by industry (selling cars is not selling clothes) which gives marketers a helpful guide of what to shoot for and expect. It’s worth a whole other post to delve into its insights, but IWCO Direct and SeQuel Response recently offered some. A quick inspection of the report can let marketers know what they might expect from an otherwise well-executed campaign.

And I’m happy to say to some clients, too, as another benchmark, that they should enter the International ECHO Awards. It’s perhaps the best way to be recognized for achievement (beyond the paycheck). With judges inspecting the world’s best in data-driven advertising, an ECHO trophy says that a marketing team, agency or organization knows its stuff. This year’s competition deadline for entering is July 10, and DMA is offering a Webinar on May 19 to give tips and insights from the judges themselves (speaking will be yours truly, joined by fellow Target Marketing blogger Carolyn Goodman of Goodman Marketing Partners and Smithsonian’s Karen Rice Gardiner). Have only five minutes to spare? You can always hear directly from Carolyn here about the entry process.

Enter early and often! I’d love to point to your campaign as a “benchmark” later this year.

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.”

Today’s B-to-B Marketing: It’s a Lot Like Shark Tank

As a marketer, I understand the challenge of reaching business decision makers like me in a fresh and meaningful way, but I will tell you that as a focus group of one, I despise the direction marketers seem to be headed:

As a marketer, I understand the challenge of reaching business decision makers like me in a fresh and meaningful way, but I will tell you that as a focus group of one, I despise the direction marketers seem to be headed:

  • My LinkedIn inbox is now overflowing with invitations to connect to people I don’t know and now choose NOT to connect to because I know they’re going to simply try and sell me something based on their job description/profile.
  • To download a whitepaper of interest requires me to complete a form that includes my phone number, which means dealing with unwanted calls from a bored sales rep.
  • My regular inbox is stuffed with offers from strangers that want to set up meetings, desperate attempts to sell me data from unknown sources, demands that I click links to view the video about revolutionary new technology that will “change the way I do business.”
  • If I express any interest at all in a product (attend a webinar, visit a tradeshow booth, download a spec sheet), I am relentlessly mobbed by emails and phone calls.

I get that sales folks have a job to do, so what’s the answer?

It’s called Lead Nurturing.

An organized and systematic way of building a relationship that will, over time, help turn a cold prospect into a warm prospect… and from a warm prospect into a hot prospect… and ultimately to a sale.

But excellence in lead nurturing seems to be a lost art form as I haven’t been exposed to many companies that are doing it—let alone doing it well.

Best practices suggest that the marketer try to ask just a few questions at the outset of the relationship to try and determine the prospects pain point (the reason for their download or visit to your website or tradeshow booth), and the role the individual plays in the purchase process (influencer, part of a decision making team, final decision maker).

Based on the answers to these and perhaps one or two other pertinent questions that would help you define your lead nurturing strategy (for example, industry or job title/function), leads should be scored and placed into an appropriate lead nurturing system that will help the marketer deliver ongoing content that will be most relevant to that prospect.

Best practices do NOT include asking questions about intent to purchase timeframes (God forbid you answer “in the near future” as that will guarantee an instant follow up call), budget size (really? Do you think I’ll reveal that I have earmarked$100K on a form?).

Lead nurturing programs should include:

  • Additional assets that can be distributed via email: Content can include a competitive review, an article that’s relevant to the prospects vertical industry, research findings, videos that demonstrate how a product works, etc. These should NOT be sales literature but rather help the company position itself as an expert in their field. This in turn, helps build credibility and trust (key components in a B-to-B purchase).
  • Invitations to webinars where a particular topic is explored. Webinars should include speakers from OUTSIDE the sponsoring organization to give the topic value and ensure the attendee isn’t just signing up for a sales pitch.
  • Invitations to breakfast or luncheon roundtable discussions: Bring in a speaker of interest and discuss a topic that is most relevant to your audience (especially if it’s industry specific).

Over the course of time, you’ll be able to ask additional questions / gain additional insights into your prospect pool that will help you become more familiar with them and the problem they’re trying to solve.

After all, don’t we all want to do business with people we know and like? The reality is, it is highly unlikely that I’m ready to buy after one simple download, so stop treating me like a piece of meat that has fallen into a tank full of hungry sharks.

One Size DOES NOT Fit All in B-to-B Marketing

Here’s a painful truth: B-to-B lead generation takes a lot of hard work BEFORE you execute any marketing or sales program. Work smarter, not harder, and follow these six steps to make a real difference:

Here’s a painful truth: B-to-B lead generation takes a lot of hard work BEFORE you execute any marketing or sales program.

Work smarter, not harder, and follow these six steps to make a real difference:

  1. Do your homework. What do you know about your existing customers? Do they fall into any particular vertical industries? What types of job titles do they encompass? It’s doubtful that they’re all C-level executives—chances are your real customers are well down the food chain. Select your top four or five vertical industries, identify their job titles, and plan your next steps with these verticals in mind.
  2. Find prospects that look like your target. Finding the right target is NOT like finding a needle in a haystack, and if you’ve always relied on renting a D&B list, then good luck to you. Think like your targets. Join their industry organizations, attend industry conferences and read their trade publications—increase the breadth and depth of your industry knowledge. Most of these organizations/events make their lists available for rent, and their data is probably more current and accurate at the levels you’re really targeting.
  3. Determine your targets’ pain points. What problem does your product or service solve? It’s probably different by vertical industry and by job title/function. Rent your list and use an outside research firm to contact prospects to help identify the challenges facing them in your particular area of expertise.
  4. Gather sales support assets. Use the information gathered in Step 3 to reposition your product, create new white papers or industry articles aimed at different functional areas within each company. Review existing case studies and determine how you can refresh and repurpose them by vertical industry based on your new found insights. Create assets digitally and in hard copy so you can use them in fulfillment and follow-up efforts.
  5. Create a destination of information. Before you start reaching out to prospects, create an online destination BEYOND your existing web site. Organize your new assets by vertical industry, as most organizations want to know that you understand and have experience in their category. A healthcare company, for example, will probably not have the same challenges as a financial services organization. And it’s most likely that your solution wouldn’t be identical either.
  6. Execute an outreach program. Now that you know your top four or five verticals, you’re ready to tap targets on the shoulder. Create a campaign by vertical target in order to highlight key benefits that are most relevant to that target (you should know what these are as a result of your research in Step 3).

All your outbound communications to each of these job functions within each of your target verticals should be different. The individual in finance, for example, will want to understand ROI while the individual on the technology side might be concerned about how well your product can be integrated into existing technology.

Your research should have already helped you identify the pain they’re facing, so leverage that learning in your communications. Whether it’s the initial contact, the follow up materials, or the landing page, mirror what you’ve heard to make the conversation most relevant from the beginning. Your participation in industry events and conferences should help you establish the correct tone and language in your communications.

B-to-B marketing should never apply a “one size fits all” strategy. The more relevant your communications, and the more you can demonstrate that you understand their particular industry and business challenges by tailoring your solutions, the more likely you are to engage in a meaningful discussion with your target. Listen to feedback and refine your communications accordingly. And yes, the results will be worth it.

Avoiding the One-Night Stand

Stating that all customers are not created equal is hardly an oversimplification. But, just like the pigs in Orwell’s “Animal Farm,” some customers are more equal than others. No company has unlimited resources to equally service or support all its customers. Repeat buying power, the essence of customer loyalty, is everything. Some customers are worth a great deal, some may become more valuable over time, some may be valuable for a brief period but may be easily lured away, and some are never likely to become valuable.

Stating that all customers are not created equal is hardly an oversimplification. But, just like the pigs in Orwell’s “Animal Farm,” some customers are more equal than others. No company has unlimited resources to equally service or support all its customers. Repeat buying power, the essence of customer loyalty, is everything. Some customers are worth a great deal, some may become more valuable over time, some may be valuable for a brief period but may be easily lured away, and some are never likely to become valuable.

At minimum, companies need to segment their customers so they can determine how much longer that customer will remain with them, how much revenue each customer will contribute, how much and what kind of services the customer should receive, and what efforts will be needed to keep them whether they are new, at risk, or even already lost. Also, if a company is changing product or service focus—such as beginning a new customer experience management or frequency marketing program—decisions will have to be made about which customers it wants to retain.

Just as companies are becoming smarter about keeping the customers they want or “firing” less attractive customers through stepped-down services, they have to invest more upfront, at the beginning of the customer life cycle, in learning which potential customers will be the most valuable over time. This goes beyond segmentation. It is almost pre-segmentation.

Here’s a prime example. The business of gaming in Las Vegas, Atlantic City, numerous riverboats, Indian reservations and offshore is built not on a house of cards, but a house of numbers. At Las Vegas casinos like the Rio, those players who gamble $1,000 a day with the Rio, whether they win or not, receive the designation “hosted guests.” These are the kinds of customers the Rio works hard to acquire. Their level of play accords them VIP status, with more “comps” (free dinners, show passes and other gifts). Each hosted guest has an individual staff host assigned to check on them and provide any needed services.

The host is actually a highly paid, personal customer service representative. It’s an important position, which casino operations like the Rio consider pivotal to their success. The hosts cultivate relationships with the players; and VIP players are encouraged to call their hosts before arriving at the casino, so the host can have show tickets, restaurant reservations and suites set up, per the player’s profile.

There’s even a higher echelon of gaming customers—those players who have a $1 million line of credit. They get the best suites and virtually everything the casino has to offer. They’re nicknamed “whales,” and with good reason. At the Rio, this means a suite with 7,000 square feet of space and bathroom sinks with gold-plated faucets. These players are relied upon to bet in the Rio’s secluded back room, called the Salon, where they may play baccarat and roulette with $100,000 chips.

In an industry like gaming, where the level of customer migration is very high, it is imperative that casinos not only keep the players they want but target the right customers in the first place. They do this in a number of ways, including geodemographic profiling for their acquisition. For the high rollers they’ve lost, many of the casinos make an extra effort to get them back, as well.

Advanced companies have begun applying “conversion” models, seeking customers who:

  • Need less direct motivation (incentive) or indirect motivation (promise of support and committed resources) to purchase;
  • Have demonstrated more resistance to claims and attempts to lure them away;
  • Are less price-sensitive;
  • Are more accepting of occasional value delivery lapses and are less likely to accept alternatives if the brand/service is unavailable; and
  • Demonstrate more positive attitudes about “their” brand.

In the retail automotive industry, as another example, potentially loyal new customers take less time making their purchase decisions, consider fewer dealerships, are less price-driven, and rely less on magazine articles and other media and more on previous experience and personal recommendation.

Some years ago, South African researchers Jan Hofmeyr and Butch Rice created an effective conversion model, which helped marketers develop and sustain effective customer loyalty initiatives programs for customers, both new and established. They found that, beyond customer needs and value delivery requirements, companies must understand the potential depth of a customer’s commitment to the supplier. Part of this means identifying the degree of customers’ tangible and intangible involvement with the company. Tangible involvement can include such factors as the actual dollar cost of switching to a competitor. Intangible issues include the emotional strength of the connection or the upset and insecurity created by switching suppliers. The model also measures the degree of attractiveness of competitive brands, based on what these customers want as prioritized elements of value.

Hofmeyr and Rice’s model also enabled them to view their clients’ marketplace in terms of users and non-users. Users can be divided into those who are truly committed and loyal and those who are “convertible”; that is, declining or wavering in their loyalty. Non-users—prospects and previous customers—are divided into potentially convertible and non-available (because they are committed to their current supplier).

Detailed analysis could then be developed for current customers and prospects. The percentage of current customers who are entrenched, or completely loyal, can be identified, as well as those who have moderate loyalty, shallow loyalty, or convertibility (true vulnerability). Non-users, or prospects, could also be identified in a similar manner: those who are available, or highly receptive to a competitive offer; and those who are ambivalent, but who would switch with the right value-based incentive. Other prospects, who have average or strong loyalty to their brand or supplier, are considered unavailable by the model.

The model has been used to plan the amount of advertising and promotional activity required for new customers and prospects, according to their commitment level and potential value. It has been applied in more than 50 countries and for scores of products and services.

On an everyday, or tactical, basis, companies should also always be on the lookout for customers who could represent more of a problem than the revenue they might contribute. Through our own research, we’ve identified seven such types of customers:

  • Non-Complainers—Customers who never express any negative feelings about performance or identify potential areas of improvement may just be hiding their disaffection. Marketing scientist Theodore Levitt has said: “One of the surest signs of a bad or declining relationship with a customer is the absence of complaints. Nobody is ever that satisfied, especially not over an extended period of time.”
  • Over-Complainers—Customers who tend to complain frequently, sometimes irrespective of whether their issues are really consequently or not, can beat down a company’s morale and overtax its support infrastructure.
  • Price Grinders—New customers who pressure their suppliers to lower prices on initial sales in return—they often promise—for future business that may or may not exist.
  • Chronic Defectors—When customers have a history of pulling their business without explanation or warning, this may be a sign that they’ll never be happy with any supplier’s performance. Their volatility and refusal to communicate issues makes them undesirable.
  • Friends in Need—These “quick-jump” customers who want to find new suppliers with great haste often don’t make purchase decisions very well, or they may have economic challenges.
  • Discourteous Slobs—Any customers who are chronically rude and verbally abusive, even though they may not contact their suppliers frequently, can undermine a company’s morale and operations. If they have reason to be upset or annoyed, that’s one thing. Their concerns should, obviously, be addressed and dealt with as quickly as possible. If the negative behavior continues, they’re probably not worth the effort.
  • Misfits—The needs of some new customers may simply not align well with the supplier’s ability to perform. If, for example, 99.9 percent of the deliveries to customers are made during normal business hours and the new customer wants delivery in the middle of the night, unless this customer truly represents a great deal of business, they are probably not serviceable.

If most people are like me—a statement always open to interpretation—virtually every day they will see content or promotional material from long distance telephone companies offering their latest and greatest low cost plans. Typically, they don’t try to find out about my business and personal long distance needs. They just try to push the plan. One of the enduring reasons for the high rates of customer turnover in this industry is the lack of scientific prospect targeting, and attempts to understand potential customers’ tangible and intangible switching issues, done at the outset. Perhaps it’s time for their conversion.