For Measurement-Oriented Marketers: The Best of ‘Here’s What Counts,’ 2019

Over the past year, “Here’s What Counts” opined on several topics. But the ones that gained the most traction involved Gen Z’s views on privacy, social media data collection, and 1:1 marketing.

Over the past year, “Here’s What Counts” opined on several topics. But the ones that gained the most traction involved Gen Z’s views on privacy, social media data collection, and 1:1 marketing.

The most popular post, “Have We Ruined 1:1 Marketing? How the Corner Grocer Became a Creepy Intruder,” was reposted on LinkedIn by Don Peppers, co-author of the book, “1:1 Marketing.”  The idea grew out of an assignment I gave my students at Rutgers School of Business in Camden, N.J. The students had to compare the 1996 version of database marketing, as described by Arthur Hughes in the introduction to his watershed book, “The Complete Database Marketer,” with the current state of online direct/database marketing. Hughes likened a marketing database to the Corner Grocer, who kept mental notes on his customers’ names, personal preferences, and family connections. Specifically, the students had to tell me how marketing technology innovations have enhanced database marketing since 1996.

The Takeaway:

While they concede that the targeted ads they experience are usually relevant, several of them noted that they don’t feel they have been marketed to as individuals; but rather, as a member of a group that was assigned to receive a specific digital advertisement by an algorithm. They felt that the idealized world of database marketing that Hughes described in 1996 was actually more personal than the advanced algorithmic targeting that delivers ads to their social media feeds.

It’s not surprising that Gen Zers expect a more personalized marketing experience. As I wrote in “Gen Z College Students Weigh-in on Personal Data Collection — Privacy Advocates Should Worry.”

Some Gen Zers don’t mind giving up their personal data in exchange for the convenience of targeted ads and discounts; others are uneasy, but all are resigned to the inevitability of it.

Student comments included:


“I do not feel it is ethical for companies to distribute our activities to others. Despite my feelings on the situation, it will continue — so I must accept the reality of the situation.”


“… I feel as though consumers gain the most from this value exchange. Marketers can do pretty much whatever they want with the information that they collect, but they do not really ‘gain’ from this exchange, until people actually purchase their products …  Even if this exchange allows marketers to play with people’s vulnerabilities, it is ultimately consumers’ choice on whether or not they want to buy something.”

 And, in response to a New York Times article about Smart TVs spying on people, one student expressed:


“Marketers are gaining money and information through various means and have the ability to do so without risk, because consumers are not going to read [a] 6,000-word privacy policy just to be able to work a television.”

Lest we think that the younger generation is alone in eschewing concerns about privacy, take a look at “Getting Facebook Sober: What Marketers Should Know About Consumers’ Attitudes and Social Data.”

While people claim to be concerned about privacy, they’re not willing to pay for it.  A Survey Monkey poll done for the news site Axios earlier this month shows that three-fourths of people are willing to pay less than $1 per month in exchange for a company not tracking their data while using their product — 54% of them are not willing to pay anything.

As we charge into 2020, we need to carefully consider how the data we give up so willingly is used to manipulate not only our purchasing behavior, but our beliefs and values. In the post, “A Question for Marketers: Is it Social or Is it Media?” I recount Sasha Baron Cohen’s speech at the Anti-Defamation League (ADL) calling Facebook “the greatest propaganda machine in history.”

I sent The Guardian’s publication of Cohen’s speech to my children, two of whom have given up their Facebook accounts. My daughter replied, “Did you learn about this on Facebook? If so, irony is dead.”

Actually, I did. RIP, irony.

Have We Ruined 1:1 Marketing? How the Corner Grocer Became a Creepy Intruder

When Don Peppers and Martha Rogers wrote “The One to One Future: Building Relationships One Customer at a Time” in 1993, the Internet was a mere twinkle in Al Gore’s eye. But direct marketers felt excited about 1:1 marketing, and even vindicated.

When Don Peppers and Martha Rogers wrote “The One to One Future: Building Relationships One Customer at a Time” in 1993, the Internet was a mere twinkle in Al Gore’s eye. But direct marketers felt excited, and even vindicated, about the promise of a future where data-driven personalization would deliver the right message to the right customer at the right time.

But now that it’s here, are consumers happy with it?

Recently, I had the students in my direct marketing course at Rutgers School of Business read the introduction to “The Complete Database Marketer” by Arthur Hughes, which was published in 1996 when only 22% of people in the U.S. had Internet access. In the intro entitled “The Corner Grocer,” Hughes explains how database marketing can connect marketers with their customers with the same personal touch that the corner grocer had by knowing all of his customers’ names, family members, and usual purchases.

The students then had to compare the 1996 version of database marketing, as described by Hughes, with the current state of online direct/database marketing, where data collection has been enabled by e-commerce, social media, and search engine marketing.

  • What marketing innovations has technology enabled that didn’t exist before?
  • How has online marketing enhanced the concept of database marketing?
  • How have new marketing techniques and technologies changed consumer behavior?
  • How has social media affected direct/data-driven marketing for the marketer and the consumer?
  • What are some of the fundamental differences between the challenges and opportunities that today’s online marketers face vs. those that the 1996 database marketer faced?

Most of these digital natives were born after Hughes’s book was published. The students experience digital marketing every day, and they’ve seen it evolve over their lifetimes. While they concede that the targeted ads they experience are usually relevant, several of them noted that they don’t feel they have been marketed to as individuals; but rather, as a member of a group that was assigned to receive a specific digital advertisement by an algorithm. They felt that the idealized world of database marketing that Hughes described in 1996 was actually more personal than the advanced algorithmic targeting that delivers ads to their social media feeds. Hughes told the tale of Sally Warner and her relationship with the St. Paul’s Luggage Company that started with returning a warranty card and progressed with a series of direct mail and telemarketing. For example, knowing that Sally Warner had a college-bound son, St. Paul’s sent a letter suggesting luggage as a graduation gift. Hughes describes the concept of database marketing:

“Every contact with the customer will be an opportunity to collect more data about the customer. This data will be used to build knowledge about the customer. The knowledge will be used to drive strategy leading to practical, directly personal, long-term relationships, which produce sales. The sales, in turn, will yield more data which will start the process all over again.”

But Arthur couldn’t foresee the data collection capabilities of Google, Facebook, Instagram, and Amazon. Instead of the friendly corner grocer, database marketers have become a creepy intruder. How else could an ad for a product my wife had searched for at Amazon on her laptop generate an ad for the same product in my Instagram feed? (Alright, I will concede that we use the same Amazon Prime membership, but really?) We don’t have a smart speaker in the house, and I dread to think about how much creepier it could become if we did.

Recently, while visiting someone who has a Google Home assistant, I asked about the level of spying they experienced in exchange for the convenience of having voice-activated control over their household lights and appliances. They responded by asking, “Google, are you spying on us?”

The smart speaker replied, “I don’t know how to answer that question.”

Have we ruined 1:1 marketing?

Do you know how to answer that question? Tell me.

DTC Brands — How Data Fluency Enabled a Digital Disruption

My small apartment building’s lobby is a testament to these changing behaviors — there’s barely any room for the incoming DTC brands and related subscription economy shipments, daily. UPS, Amazon, FedEx and USPS — and their contractor networks — are delivering the goods that pile up. No drones just yet.

One of the entrepreneurial wonders of the 21st Century economy is actually not a very new concept at all. Direct-to-the-consumer (DTC) brands have been around since the first mail-order catalogues. Names such as LLBean, Orvis and Lands’ End revolutionized remote selling, as they understood the power of data and measurement in building these enterprises, by earning customer loyalty through superior products and customer service, and generating lifetime value.

So perhaps it’s only natural that in an increasingly digital, social and mobile world where data enables such direct connections more fluidly and products can be personalized at-scale DTC startups would come to be powerful brands in their own right. Bonobos, Casper and hundreds of others are rising to disrupt consumption and create new patterns of consumer behavior for even the most everyday product. Just this week, Rent the Runway officially became the newest unicorn in the venture capital investment world.

My small apartment building’s lobby is a testament to these changing behaviors there’s barely any room for the incoming DTC and related subscription economy shipments, daily. UPS, Amazon, FedEx and USPS  and their contractor networks  are delivering the goods that pile up. No drones just yet.

If You Can’t Beat Them …

Most retailers today report that their biggest threat comes from DTC brands (see Figure 1). Yes, Amazon and private labels also are leading concerns … but the truth is that building a business with seamless data flows enables the customer, and not the product, to be front-and-center. Brands that embrace customer-centricity, and have the customer data directly, cull the benefits.

Figure 1.

DTC brands
Credit: eMarketer, 2019. Used with Permission.

When database marketing and customer relationship management came of age, we knew that pesky problems such as data silos, legacy systems, senior executive buy-in and lack of data bench strength were crippling. Where entrepreneurs love data and have great products and service, those hurdles don’t exist.

No wonder traditional brands are quickly starting up or buying their own DTC brands and relationships. There’s power in data, and having first-party data relationships with consumers even as third-party data, and perhaps a few social influencers, enable discovery and facilitate connection – has brought about the mail-order bonanza of the digital age.

Physical retailers are not powerless in this mix after all, point-of-sale transactions still rule, and hybrids are flourishing (online to offline, buy online pick up in store). It’s how quickly these stores can integrate POS and transaction data with other forms of advertising data, and even serve as data-sharing coops with the brands they carry, to serve customers better. It’s about more relevance and more personalization. We haven’t heard the last roar from Main Street, Big Box and shopping malls. They’ll need to tap data’s power in similar fashion to go back on offense.

Big Data, Little Intelligence

Data gathering techniques are getting more and more sophisticated. Databases are growing bigger and bigger. There are new data mining tools, techniques and dashboards everywhere you turn. So why is it that so many marketers fail to have a database marketing strategy in place?

Little Data Business ConceptData gathering techniques are getting more and more sophisticated. Databases are growing bigger and bigger. There are new data mining tools, techniques and dashboards everywhere you turn.

So why is it that so many marketers fail to have a database marketing strategy in place?

Yesterday, I got three pieces of direct mail in my home mailbox from Farmers Insurance:

  • One was addressed to my mother-in-law, and she died many years ago. Considering she never lived at our address, never had her name on our mortgage, never registered a vehicle at our address, you’d think — at a minimum — Farmers might use birthdate as one of their selection variables. If they did, I think they’d consider suppressing a woman who was nearly 100. Oh, and let’s not forget the death Masterfile that’s available.
  • The second piece was addressed to my husband, but they had matched his name to the name of my old consulting business that ceased to exist in 1995.
  • The final letter was addressed to my husband.

Each of the packages came from a different Farmers agent — and despite the notation of “Visit me, I’m local,” none of them were remotely close to us. According to Google maps, they ranged from 50 minutes to 1 hour away, yet another Google search indicated there was a Farmers agent within 15 minutes of my home.

There were multiple phone numbers on the creative: One for the “local” agent and a toll-free one for Farmers (I guess they were concerned that my “local” call might incur long distance charges!).

What was most interesting is that two out of three of these packages did not include a way to respond via email. I could visit a corporate website and get a quote, but considering the time invested in personalizing the letter, providing an image of the Agent, including a detailed map showing the Agent’s location, and two phone numbers, this key response channel was omitted.

Finally, what happened to de-duping? Or assigning agent’s a territory where “most likely” prospects would reside? Or using big data to help agents figure out how and where to fish for leads while maintaining a strict recency flag?

I’m continually puzzled that marketers still fail to connect all the dots given all the tools in their marketing toolbox. Perhaps Farmers field marketing needs to go back to Farmers University for that data course they slept through.

How to Reach Your Customers at Home or at Work

Have you ever wished you knew more about your customers’ consumption habits? I have just learned about a new service from the San Antonio-based database marketing company, Stirista, that offers a way to link an individual’s consumer record with your corresponding business record.

unity-1763669_640Have you ever wished you knew more about your customers’ consumption habits? I have just learned about a new service from the San Antonio-based database marketing company, Stirista, that offers a way to link an individual’s consumer record with your corresponding business record. With StiristaLINK, you can enhance your business contacts with a personal email address, social media handles, home address, phone number, demographics and personal interests, vastly enhancing your understanding of your business contacts.

On the flip side, consumer marketers can use this capability to broaden the profile of their targets by providing additional insight — where they work, their titles, schools attended, past employers, their LinkedIn URL and perhaps even their work email address.

StiristaLINK’s files are pretty sizable: 20 million B-to-B profiles are enhanced with consumer information, while 55 million consumer emails are linked to a B-to-B profile.

I’ve been thinking about some of the things a marketer could do with this new capability. The use cases seem endless:

  • Enrich business and consumer profiles for better targeting and segmentation.
  • More touchpoints: Target consumers during business hours. Reach business people at home.
  • Access hard-to-reach segments (e.g.: Say you’re selling graduate business education and want to find 20-somethings who still don’t have an MBA).
  • Expand your universe of display and social media advertising targets.
  • Offer business people consumer products based on their work status. For example: Insurance companies can offer health insurance to workers in companies that are known to have decided to issue vouchers to their employees.

I asked Stirista’s CEO Ajay Gupta about the technology behind the links. He explained that Stirista already had a massive database of B-to-B and consumer records. To create the linkage, they took two innovative approaches. First, they matched the Twitter handles in the business record to those in the consumer database. To validate the match, they conducted research into a sample of the linked records, and found a 97 percent accuracy rate.

Next, Stirista engineers developed a creative approach to inferring current employment among consumer records that happen to have unusual names, geo-coding them by home address and matching that to the same unusual name in a nearby company.

As an example, let’s look at the case of Ajay Gupta himself. While there are scores of Ajay Guptas in the New York region, there happens to be only one in San Antonio. So the engineers could reasonably conclude that the Ajay Gupta working for Stirista is the same as the consumer Ajay Gupta who lives in San Antonio.

Of course, the technique does not work for the John Smiths, but it did add another layer of names to the file.

An early adopter of StiristaLINK was Weight Watchers, which was looking to improve its marketing to HR directors. Despite high brand awareness, selling Weight Watchers group packages had become increasingly difficult. The service is offered as a free benefit to companies, and employees receive a discounted rate when they join through their firms. But with more competition for their attention, HR directors were less and less motivated to pick up the deal and offer it to their employees.

The breakthrough came with the application of StiristaLINK to identify HR professionals and senior managers in the target companies whose consumer profiles indicated a personal interest in fitness and health. Stirista used a menu of about 20 keywords — cycling, exercise and softball, among others — to identify likely prospects and email them with a message about the importance of weight as a part of employee health and productivity. And the response rate boomed.

For B-to-B marketers, the most immediate benefit of this capability is reaching larger custom audiences. Most Twitter, Facebook and Google AdWords custom ad selection is based on the personal email address that was collected on sign up. By adding consumer data to your audience build, you’ll improve your reach dramatically.

There’s seemingly no end to the new data-driven marketing innovations these days.

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

Election Polls and the Price of Being Wrong 

The thing about predictive analytics is that the quality of a prediction is eventually exposed — clearly cut as right or wrong. There are casually incorrect outcomes, like a weather report failing to accurately declare at what time the rain will start, and then there are total shockers, like the outcome of the 2016 presidential election.

screen-shot-2016-11-17-at-1-03-34-pmThe thing about predictive analytics is that the quality of a prediction is eventually exposed — clearly cut as right or wrong. There are casually incorrect outcomes, like a weather report failing to accurately declare the time it will start raining, and then there are total shockers, like the outcome of the 2016 presidential election.

In my opinion, the biggest losers in this election cycle are pollsters, analysts, statisticians and, most of all, so-called pundits.

I am saying this from a concerned analyst’s point of view. We are talking about colossal and utter failure of prediction on every level here. Except for one or two publications, practically every source missed the mark by more than a mile — not just a couple points off here and there. Even the ones who achieved “guru” status by predicting the 2012 election outcome perfectly called for the wrong winner this time, boldly posting a confidence level of more than 70 percent just a few days before the election.

What Went Wrong? 

The losing party, pollsters and analysts must be in the middle of some deep soul-searching now. In all fairness, let’s keep in mind that no prediction can overcome serious sampling errors and data collection problems. Especially when we deal with sparsely populated areas, where the winner was decisively determined in the end, we must be really careful with the raw numbers of respondents, as errors easily get magnified by incomplete data.

Some of us saw that type of over- or under-projection when the Census Bureau cut the sampling size for budgetary reasons during the last survey cycle. For example, in a sparsely populated area, a few migrants from Asia may affect simple projections like “percent Asians” rather drastically. In large cities, conversely, the size of such errors are generally within more manageable ranges, thanks to large sample sizes.

Then there are human inconsistency elements that many pundits are talking about. Basically everyone got so sick of all of these survey calls about the election, many started to ignore them completely. I think pollsters must learn that at times, less is more. I don’t even live in a swing state, and I started to hang up on unknown callers long before Election Day. Can you imagine what the folks in swing states must have gone through?

Many are also claiming that respondents were not honest about how they were going to vote. But if that were the case, there are other techniques that surveyors and analysts could have used to project the answer based on “indirect” questions. Instead of simply asking “Whom are you voting for?”, how about asking what their major concerns were? Combined with modeling techniques, a few innocuous probing questions regarding specific issues — such as environment, gun control, immigration, foreign policy, entitlement programs, etc. — could have led us to much more accurate predictions, reducing the shock factor.

In the middle of all this, I’ve read that artificial intelligence without any human intervention predicted the election outcome correctly, by using abundant data coming out of social media. That means machines are already outperforming human analysts. It helps that machines have no opinions or feelings about the outcome one way or another.

Dystopian Future?

Maybe machine learning will start replacing human analysts and other decision-making professions sooner than expected. That means a disenfranchised population will grow even further, dipping into highly educated demographics. The future, regardless of politics, doesn’t look all that bright for the human collective, if that trend continues.

In the predictive business, there is a price to pay for being wrong. Maybe that is why in some countries, there are complete bans on posting poll numbers and result projections days — sometimes weeks — before the election. Sometimes observation and prediction change behaviors of human subjects, as anthropologists have been documenting for years.

Customer Value: Narrowcasting vs. Broadcasting

The traditional model for customer acquisition has essentially been a broadcast approach, reaching a large audience generally descriptive of the customer base. Contrast this with what is sometimes described as “narrowcasting.”

Virtually every brand we’ve met with in the last few months is hungry for new customers: The war for the customer is on. For more on growing your customer base, consider reading “Bigger is Better: How to Scale Up Customer Acquisition Smarter,” which is an article we published recently about how to grow your customer base.

Many organizations are hooked on customer acquisition. That is, in order to hit sales plans for the organization, new customers will be required in large numbers. It’s about as easy to kick the “acquisition addiction” as it is to kick any other for most brands. Try going without coffee suddenly, and see how your head feels. It’s not very different from reducing a business’s dependence on customer acquisition as a means to achieving revenue and profit targets.

Organizations that need ever larger numbers of new customers to achieve growth goals eventually will find the cost of acquiring incremental net new customers can become prohibitive.

Broadcast vs. Narrowcast
The traditional model for advertising and customer acquisition has essentially been a broadcast approach, reaching a large audience that is generally descriptive of the customer who a brand believes to be a fit. Contrast this with what is sometimes described as a “narrowcasting” strategy. Narrowcasting uses customer intelligence to understand a great number of discrete dimensions that a consumer possesses and can leverage statistical methods to validate the accuracy and predictiveness of targeting customers through these methods.

The chart below, depicting the value of customers acquired through traditional broadcast capabilities upfront and over time helps illustrate why “broadcast” strategies for customer acquisition alone aren’t enough.

Research for Mike Ferranti blog

Broadcast Acquisition Strategies Lack Focus on Customer Value
Large numbers of customers have been acquired in a trailing 13-month window – lots of them. The challenge is this cohort of customers has been acquired without adequate consideration of the right target.

Consider the fact that the target customer value of average or better customers is around $500. In the example above, the marketer has acquired a large number of customers who are lagging in their economic contribution to the business. While the customer acquisition metrics may look good, this was a large campaign and produced several hundreds of thousands of customers over its duration – the average value of those customers is quite low indeed.

Low Customer Value Manifests Itself, Even if Acquisition Volume Is High
When sales targets are rising, it becomes harder to justify the high cost of customer acquisition if the customers previously acquired are underperforming. This leads to a very common bind marketers are placed in. The only way to “make the number” is to acquire more and more.

The most competitive and high quality businesses steadily acquire and have a robust customer base whose economic contribution is materially higher. Consequently, profits are higher, and we have a fundamentally better business.

Oftentimes, “broadcast” advertising approaches define the target with a single criteria like age, income or geography. This can be effective, especially when the media is bought at a good value. However, “effective” is almost always defined as “number of customers acquired.” This of course is a reasonable way to judge the performance of the marketing – at least by traditional standards.

There is another way to measure the success of the campaign that is only just beginning to be understood by many traditional “broadcast” marketers: customer value. The chart above shows that this cohort of acquired customers had relatively low economic value.

Root Causes of Low Customer Value
What are the causes of low value? It would be fair to start with the ongoing marketing and relationship with the customer. Bad service could keep customers from returning. Poor quality could lead to excessive returns. Over-promotion could drive down value. Getting the message and frequency wrong could lead to underperformance of the cohort. These are all viable reasons for lower value that need to be rationally and methodically ruled out prior to looking elsewhere.

Therefore, if operational issues are not clear – either through organizational KPI tracking, or simply by monitoring Twitter — then a marketing professional needs to start looking at three things.

  1. The Target (and Media)
  2. The Offer (and Message)
  3. The Creative

Given the target is historically responsible for up to 70 percent of the success of advertising, this is the first place a professional data-driven marketer would look.

Target Definition Defines the Customer You Acquire, and It Drives Customer Value.
A fact that is often overlooked is that target definition means not just focusing efforts and advertising spent on consumers who are most likely to convert and become customers, but it also defines what kind of customers they have the potential to become.

In conversations with CMOs, we often discuss “the target customer” or the “ideal customer” they wish to introduce their brand to. The descriptions of course vary by the brand and the product. Those target definitions are often more qualitative in nature. In fact, only about 30 percent of CMO’s we engage with regularly are focused on using hard data to define their customer base. While these are helpful and create a vocabulary for discussing and defining who the customer is, those primarily qualitative descriptors are often sculpted to align with media descriptors that make targeting “big and simple.”

“While simplifying is good business, when simplicity masks underlying business model challenges, a deeper look will ultimately be required, if not forced on the organization.”

While we would not refute a place for those descriptors of a valued consumer, they do fall short of true target definition. Ideally, the process of defining the customer who a brand wishes to pursue must begin with a thorough inventory of the customers it already has, and a substantial enhancement of those customer records which provides vibrant metrics on affluence, age, ethnographic, urbanicity, purchasing behaviors, credit history, geo- and demo-graphics, net worth, income, online purchasing, offline purchasing and potentially a great deal more.

3 Database Marketing Strategy Takeaways

An old friend in business called me to share her “crisis of confidence” in using her pricey new database/CRM/analytics system. She had led the organization through a major investment in “cleaning, compiling and organizing” the data to make it more “usable.” It was a herculean task,

An old friend in business called me to share her “crisis of confidence” in using her pricey new database/CRM/analytics system. She had led the organization through a major investment in “cleaning, compiling and organizing” the data to make it more “usable.” It was a herculean task, and she was proud of her accomplishment – but she was struggling to produce a material outcome beyond project completion.

The Business Problem
After wrestling with their data and building reports for another six months, there was a sinking feeling, one you may have even experienced yourself … for all the effort – where was this going? How is it driving the business? Are we making better decisions for it – or are our decisions just different? How will we justify the investment and produce returns? The catch all “infrastructure spending” was tossed around briefly.

After looking at a handful of reports and documents, I had many questions for her. Not surprising for an exceptionally successful executive like her, she gave me some fair and honest answers. One response she used more than once was, “We don’t know.”  That’s not trivial. In many organizations, it’s risky to think it, much less say it. Sometimes the best answer is “I don’t know” – but surrounded by data and smart people, it’s not entirely unreasonable for some folks to feel uncomfortable with a candid “I don’t know.”

Get Comfortable with ‘Not Knowing’
Just saying “I don’t know” can be the first step in solving the problem – so long as you’re wed to the fact that you “just know” you can’t even take the next step, which is problem definition, because you don’t have a problem if you “just know” something is good, important or even working – data and evidence aside. Take the challenge – I guarantee you this small act will spark more ideas, action and solutions for the de minimis time it takes than anything else you can do.

Take Away No. 1:
If you don’t know something, say so. Say it out loud, even. It will help in emotionally and logically moving on to defining the problem.

By now, I’m probably close to losing a few folks who are reading this. “I don’t know” is not something they’re comfortable with. If you are one of them remember, “I don’t know” is not where the process ends – it’s very often where the solution we’re hungry for begins to reveal itself.

Mike Ferranti blog pullquote

Problem Definition: It’s 90 Percent of the Problem
In discussing what the problem really was, we found another common issue. The problem wasn’t well-defined in the first place. The problem was essentially to “clean up the data” and to “have organization.” While that was a good thing to do, it didn’t solve any business problem. The manifestation was they now had a big (expensive) bucket of data that was judged to be better, or more valuable, than it was beforehand.

How was it better? The answer was it was more organized and more clean. Did it answer any specific questions? After looking closer, we saw it did. But did it actually begin to solve any specific problem? This was less obvious.

Here is where problem definition is so important. The problems that were defined as “organization” and “cleaning” weren’t business problems. They were symptoms of a data capture process that didn’t work, and that process came from a lack of a clear strategy.

Boiling the Ocean: Solutions That Are All Things
The specific problems being experienced were many and diverse. Focus was low. This was, in large part, a solution that was intended to do all things for all people.

I’m a direct marketer. I started my career in software development. I have a great appreciation for large systems and for what is commonly known as the “data warehouse” – a large database system that often starts with financial system data. Warehouse solutions often contain every cost in the enterprise, every operations metric, inventory, logistics, marketing, human resources and more.

But surely these are not “function-specific” solutions. In the vast majority of cases they are starting points, and they are not solutions to the problem the marketer has in selling one more widget. Those solutions need to be borne of a very specific set of marketing problems, and utilize a specific set of data – and in a specific format and data model – to actually solve them. That marketing-specific solution would likely need substantial transformation if taken from that “warehouse” solution. And when complete, it would virtually be a whole new dataset, altogether.

Take Away No. 2:
Ask yourself, are we taking a “Boil The Ocean” approach?

Boil the oceanAfter some discussion, we aligned that she had surely accomplished a lot, and that we could now access and view data about many things in the organization, including in marketing. But there were no specific capabilities that would speed the time-to-value present, and it was hard to make progress. Also, the data my friend’s organization quite reasonably thought was its most important failed to highlight the huge differences between the value of customers over the longer term. That created a strategic problem. The organization was trying to fix the long-term and its strategic business problems by looking at the wrong data and taking the wrong actions. These are very bright people with a compelling rationale for their course of action.

In the end, “Boil The Ocean” approaches are short on strategy, or are built on a strategy so grandiose, they become difficult or impossible to execute.

The Root Causes of These Strategic Challenges
So ultimately, how do such quality organizations go down an inefficient path like this? It ultimately comes down to a skills gap. What must change? It’s the skill set in marketing.

In the digital age, there are two major skill sets that we must buy, hire or develop in our organizations. Neither is trivial in marketing, and neither is possible without patience and focus.

Skill Set No. 1: Technology, Logic, Data
Marketers have traditionally come from a promotional and creative background. The big idea was always the highest-valued commodity. Today, things are changing faster – and permanently.

Marketers today are consistently spending more of their time with technologists, developers and data designers. The logical problem-solving skills by these folks are very different from those proposed by professionals with a creative or project management background. They need to solve problems that are not even being discussed on the way to solving the problems that are.

Because most organizations have some expertise with technology, and work with technology providers, the key takeaway here is that marketing data-specific applications require a different set of tech experience. Working with marketing data for marketing outcomes is unlike working with other types of data – the experience your IT department has working with finance or logistics data isn’t as useful as marketing “purpose-specific” data and technology experience.

Skill Set No. 2: Math
Barring some advanced direct marketers, marketers don’t always come from a math background. Only now are VPs beginning to have development, math and statistics experience. In an age of analytics, and now with the advent of tools and technologies to leverage large data sets, a solid understanding of math and basic statistics is becoming increasingly important.

Here’s an example to help make the point about the comfort level of using math and basic statistics to think about data:

You’re looking at the incomes and affluence of a customer base. With 1,000 members in the group, we have an average income of $100,000. That’s pretty telling, you might say.

We get more data, and the 1,001st customer is added to the sample – it’s Warren Buffett (net worth $67 billion). How useful is that average now?

There are many expressions for this common scenario – where outliers in your data can skew your numbers. From this come the expressions “The average lies” or “the tyranny of the averages.” Surely, the average isn’t the best number – though it’s a shortcut and a starting point. But it’s best to compare it to the median before taking too much faith in it – and a distribution histogram might tell an even better story about the composition of your customers’ incomes.

histogramDoing so will show if they are truly random … and follow a Gaussian distribution (AKA, normal distribution) and you can imagine this histogram with Warren Buffett added to the sample.

The takeaway here is the concepts required to evaluate and think about data require experienced and trained analysts. And those trained and experienced in evaluating marketing data are also required.

In the end in the scenario I described with my friend and client at the beginning of this column, her organization built a very large system, finished it on time and about at budget. But what the company invested in and created had some fundamental shortcomings. It was not a “purpose-specific” marketing solution – and it was conceived by a competent IT organization that was tactically adept – and strategically adrift.

Takeaway No. 3: Marketing Must Drive Marketing Outcomes
Marketing must drive marketing outcomes. Due to the discomfort that marketing often has with technology, math and statistics, key strategic decisions are quietly left to IT, a vendor or to chance.

This, of course, jeopardizes the results early. Marketing leadership can always ask good business and marketing questions and hold IT and technical resources accountable to prescribe only solutions that have a clear and simple strategy for achieving those goals.

The Bottom Line
Begin your database marketing endeavor with the “end in mind” by describing what success would look like in business terms. The decisions you’ll need to make will be easier.

If the discussions turn technical early, and they often do, ask business- and outcome-oriented questions to steer the conversation back on track. Maintain a focused strategy from the start and don’t let inexperience with math, programming or database science derail you. Get the help you need – and that means individuals and teams who know not only their discipline, but can answer the questions that matter most to you.

When you follow these strategic guideposts, your next big data-driven initiative will pay dividends now and into the future.

What Does a Data Marketer Look Like?

The currency of nearly all marketing today is data. Ten years ago, we might have said much the same of digital marketing, and all the email, display, social, search, and mobile that’s came forward from it.

The currency of nearly all marketing today is data.

Ten years ago, we might have said much the same of digital marketing, and all the email, display, social, search, and mobile that’s came forward from it.

Twenty years ago, we could have said the same of database marketing and customer relationship management.

And wind back—measurability and accountability, the hallmarks of direct marketing—always have relied on data. We may have called it lists back in the day—but data are what lists have become. The inherent value of data is to know the shared attributes among the data elements and to use that knowledge.

Without a doubt, the “marketing of data” has evolved and transformed as much as marketing itself. Every day in our world, it’s not enough to have contact details on people, or any number of the hundreds of demographic, psychographic, contextual, social and behavioral overlays that may be available, we also need analytics power.

Recent research from The Winterberry Group underscores this point: data is now an $11 billion business in America, and that includes analytics services revenue. I recall an unofficial guestimate of a $2 billion data market back in the early 1990s, when that meant a North American directory of 30,000 plus response and compiled lists available for rental and exchanges.

Next month, the Data Innovators Group will host its annual Data Innovator of the Year Award dinner in New York. This year’s honoree is Auren Hoffman, CEO of LiveRamp (now owned by Acxiom), who says his mission “to connect data to every marketing application.” And so it shall be… Soon.

But who is going to all make it work? Let’s welcome the data marketer and the data scientists and strategists they employ.

Still, too many brands keep customer data in siloes. And while responsibly using offline data with online data is fast coming down the pike, marketing organizations need people in place who can help clients navigate the brave new world of data management platforms, data quality strategies, programmatic media exchanges, big data and small data, and all the algorithms that drive this important “stuff” often in real time. A list sale exists largely no more. Instead data is a pathway to opportunity, a challenge overcome, by way of a data-to-insights-to-strategy recommendation, and a discipline for testing and data quality that leads brands (and their agencies and data marketer partners) to succeed.

It’s more difficult than ever to be a successful data marketer, but our field is producing the partners that businesses, brands and chief marketing officers need. Now if we could just go find a few.

Thank you to the Hudson Valley Direct Marketing Association for enabling my participation at its recent “Meet the Masters” event. Ryan Lake (Lake Group Media), Mark Rickard (Rickard Squared) and Rob Sanchez (Merit Direct) are three CEOs of data marketing organizations who have a few suggestions on where we can all go to look.

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.”