Need Prospects? 5 Direct Mailing List Types to Help You Find Them

Direct mail is a great way to reach targeted prospects to turn them into customers, but how do you select the right prospects? There are so many mailing list options, it can feel overwhelming. Let’s look at the various list options.

Direct mail is a great way to reach targeted prospects to turn them into customers, but how do you select the right prospects? There are so many mailing list options, it can feel overwhelming.

Let’s look at the various list options.

Prospect data is marketing data that has been collected and compiled for the purpose of new customer acquisition. This data is compiled from a variety of public record sources, including deed recordings, surveys, telephone directories, self-reported and more.

5 Prospect List Types

  1. Residential/Occupant: This list is compiled from USPS intelligence carrier route-level demographics, and you can segment businesses. The purpose of this type of list is to saturate an area and have names associated with it, along with census demographics, unlike EDDM. The advantage of this list is deep postal discounts. The disadvantages are the ability to only target to the ZIP-carrier route level, there are fewer options for personalization, and it uses only postal data.
  2. Consumer: This list can be selected by demographics, psychographics, life stage, lifestyles, behavioral, new mover, new homeowner, new borrower, new connect, pre-mover, mortgage/loan, and property data. The purpose of this type of list is to target consumers at their home addresses. The advantages of this list are: controlling who receives your offer; rich demographics selects for enhanced targeting, so you can use variable data for creative optimization; you can use look-alike targeting through the use of demographic profiles; and there are multi-channel opportunities.
  3. Business: This list can be selected by contact names, job titles, company size, ownership status, square footage, own vs. rent, years in business, business expenses, credit rating, SIC, and NAICS. The purpose of this list is finding businesses and/or business professionals. The advantage of this list is you can target specific types of businesses and key contacts within them.
  4. Specialty: This list can be selected by many things. Here are some of them: automotive, hospitals, doctors and nurses, education, government, voters, clubs/nonprofits, insurance agents, pilots, realtors, churches, or pool owners. The purpose of this list is to be able to target consumers or businesses based on specific niche attributes most commonly related to occupation/profession. The advantage of this list is a highly targeted audience.
  5. Managed: This list can be selected by niche marketing, RFM, subscriber files, specific purchases, past purchases, hotline buyers, multi-buyers, responders, or donors. The purpose of this type of list is the ability to identify consumers and businesses by their actions and affinities; to benefit from RFM (recency, frequency, and monetary value). The advantage of this type of list is significant targeting.

Keep in mind that the cost of prospecting lists goes up, the more targeted you get. However, by targeting correctly, you can send fewer mail pieces to more people who are most likely to buy from you. You can save money, send to the right segment of people, and increase your ROI when you mail to the right people.

There is also the option of profiling your current customers and then finding like people in your marketplace, which we will discuss in the next article. Are you ready to select your prospect list?

How B2B Marketing Can Make B2B Sales Easy

My headline isn’t going to win any friends across the aisle in the land of sales teams, and I’ll admit there’s a bit of attention-seeking there. But, even though I won’t suggest that sales is by definition easier than marketing, I do feel that strong marketing can have an outsize impact on sales results and sales efficiency.

My headline isn’t going to win any friends across the aisle in the land of sales teams, and I’ll admit there’s a bit of attention-seeking there. But, even though I won’t suggest that sales is by definition easier than marketing, I do feel that strong marketing can have an outsize impact on sales results and sales efficiency.

Marketing can only have that impact on sales when there is a progression of thoughtful activity from a firm’s earliest contact with a prospect to converting the sale, and beyond.

So the real headline should perhaps be, “Sales Is Easier When Marketing Is Done Well,” but that’s a mouthful. Let’s take a closer look at how marketing can make sales easier, if not truly easy.

B2B marketing

Who Really Wants a Super Bowl Ad?

Most of us in B2B sales and marketing are not seeking the mass audiences of, say, a Super Bowl ad. Our prospects can be much more tightly defined and more pointedly targeted. Rather than wading through a stadium full of people to find those few who might be interested in what we’re offering, we want to talk to the few hundred — maybe even few dozen — without all the additional noise. We want to connect with those who are likely to be a good fit for what we’re offering.

If our marketing can target our prospects tightly, we make the sales process more efficient; we don’t need to put 1,000 salespeople in the field, because we don’t have a stadium full of “prospects” to follow up with. A smaller team can communicate with the more select group who marketing has identified as qualified candidates.

Of course, if those candidates aren’t truly qualified — ever the sales team’s lament — the process breaks down. Which is why we need a strong marketing team to support the more focused sales team.

What Does Marketing Need to Do

Marketing then, needs to focus on content and other tools that appeal to the target audience and that are able to get a brand-relevant and useful message in front of them. When that happens, sales is a much more efficient task — less wasted time, fewer never-really-interested prospects, and a higher close rate. In other words, sales is easier because marketing is strong. (Thought, I’ll admit, sales is never easy, my headline notwithstanding.)

What About Branding?

It’s worth applying this concept to branding, as well, because the same things we can say about sales in relationship to marketing can be said about marketing in relationship to branding. Good branding makes marketing much, much more effective. Easier, even.

So now our headline should read: “Sales Is Easier When Marketing Is Done Well (And Marketing Is Easier When Branding Is Done Well)” Which is even more of a mouthful …

Of course, all of this well-planned activity will be for nothing if you don’t have a fantastic product to sell. And “fantastic” doesn’t have to mean a groundbreaking technological advancement. (Though clearly, the product has to provide a strong benefit to the client.) “Fantastic” means a product that is conceived and positioned to be better than any other available option for a particular audience segment.

So there is a bit of a circle here with product leading to positioning/branding, branding leading to marketing, and marketing leading to sales. There’s also a two-way connection between strategic thinking and tactical implementation that have to feed on one another. (Virtuously, we hope.)

All of this means that while sales isn’t really easier than marketing, when you do more of the hard work in the earlier steps, the later steps get easier. And because this is all quite circular, everything gets easier when you focus on strategy before tactics and seek ways to improve incrementally with each prospect interaction.

Data Geeks Must Learn to Speak to Clients

This piece is for aspiring data scientists, analysts or consultants (or any other cool title du jour in this data and analytics business). Then again, people who spend even a single dime on a data project must remember this, as well: “The main goal of any analytical endeavor is to make differences in business.”

This piece is for aspiring data scientists, analysts or consultants (or any other cool title du jour in this data and analytics business). Then again, people who spend even a single dime on a data project must remember this, as well: “The main goal of any analytical endeavor is to make differences in business.”

To this, some may say “Duh, keep stating the obvious.” But I am stating the obvious, as too many data initiatives are either for the sake of playing with data at hand, or for the “cool factor” among fellow data geeks. One may sustain such a position for a couple of years if he is lucky, but sooner or later, someone who is paying for all of the data stuff will ask where the money is going. In short, no one will pay for all of those servers, analytical tools and analysts’ salaries so that a bunch of geeks have some fun with data. If you just want the fun part, then maybe you should just stay in academia “paying” tuition for such an experience.

Not too long ago, I encountered a promising resume in a deep pile. Seemingly, this candidate had very impressive credentials. A PhD in statistics from a reputable school, hands-on analytics experience in multiple industries (so he claimed), knowledge in multiple types of statistical techniques, and proficiency in various computing languages and toolsets. But the interview couldn’t have gone worse.

When the candidate was going on and on about minute details of his mathematical journey for a rather ordinary modeling project, I interrupted and asked a very simple question: “Why did you build that model?” Unbelievably, he couldn’t answer that question, and kept resorting back to the methodology part. Unfortunately for him, I was not looking for a statistician, but an analytics consultant. There was just no way that I would put such a mechanical person in front of a client without risking losing the deal entirely.

When I interview to fill a client-facing position, I am not just looking for technical skills. What I am really looking for is an ability to break down business challenges into tangible analytics projects to meet tangible business goals.

In fact, in the near future, this will be all that is left for us humans to do: “To define the problem statement in the business context.” Machines will do all of the tedious data prep work and mathematical crunching after that. (Well, with some guidance from humans, but not requiring line-by-line instructions by many.) Now, if number-crunching is the only skill one is selling, well then, he is asking to be replaced by machines sooner than others.

From my experience, I see that the overlap between a business analyst and a statistical analyst is surprisingly small. Further, let me go on and say that most graduates with degrees in statistics are utterly ill-prepared for the real world challenges. Why?

Once I read an article somewhere (I do not recall the name of the publication or the author) that colleges are not really helping future data scientists in a practical manner, as (

  1. all of the datasets for school projects are completely clean and free of missing data, and
  2. professors set the goals and targets of modeling exercises.

I completely agree with this statement, as I have never seen a totally clean dataset since my school days (which was a long time ago in a galaxy far far away), and defining the target of any model is the most difficult challenge in any modeling project. In fact, for most hands-on analysts, data preparation and target definition are the work. If the target is hung on a wrong place, no amount of cool algorithms will save the day.

Yet, kids graduate schools thinking that they are ready to take on such challenges in the real world on Day One. Sorry to break it to them this way, but no, mathematical skills do not directly translate into ability to solve problems in the business world. Such training will definitely give them an upper hand in the job market, though, as no math-illiterate should be called an analyst.

Last summer, my team hired two promising interns, mainly to build a talent pool for the following year. Both were very bright kids, indeed, and we gave them two seemingly straightforward modeling projects. The first assignment was to build a model to proximate customer loyalty in a B2B setting. I don’t remember the second assignment, as they spent the entire summer searching for the definition of a “loyal customer” to go after. They couldn’t even begin the modeling part. So more senior members in the team had to do that fun part after they went back to school. (For more details about this project, refer to “The Secret Sauce for B2B Loyalty Marketing.”)

Of course, we as a team knew what we were doing all along, but I wanted to teach these youngsters how to approach a project from the very beginning, as no client will define the target for consultants and vendors. Technical specs? You’re supposed to write that spec from scratch.

In fact, determining if we even need a model to reach the business goal was a test in itself. Why build a model at all? Because it’s a cool thing on your resume? With what data? For what specific success metrics? If “selling more things by treating valuable customers properly” is the goal, then why not build a customer value model first? Why the loyalty model? Because clients just said so? Why not product propensity models, if there are specific products to push? Why not build multiple models and cover all bases while we’re at it? If so, will we build a one-size-fits-all model in one shot, or should we consider separating the universe for distinct segments in the footprint? If so, how would you determine such segments then? (Ah, that “segmentation of the universe” part was where the interns were stuck.)

Boy, did I wish schools spent more time doing these types of problem-solving exercises with their students. Yes, kids will be uncomfortable as these questions do NOT have clear yes or no answers to them. But in business, there rarely are clear answers to our questions. Converting such ambiguity into measurable and quantifiable answers (such as probability that a certain customer will respond to a certain offer, or sales projection of a particular product line for the next two quarters with limited data) is the required skill. Prescribing the right approach and methodology to solve long- and short-term challenges is the job, not just manipulating data and building algorithms.

In other words, mathematical elegance may be a differentiating factor between a mediocre and excellent analyst, but such is not the end goal. Then what should aspiring analysts keep in mind?

In the business world, the goals of data or analytical work are really clear-cut and simple. We work with the data to (1) increase revenue, (2) decrease cost (hence, maximizing profit), or minimize risks. That’s it.

From that point, a good analyst should:

  • Define clear problem statements (even when ambiguity is all around)
  • Set tangible and attainable goals employing a phased approach (i.e., a series of small successes leading to achievement of long-term goals)
  • Examine quality of available data, and get them ready for advanced analytics (as most datasets are NOT model-ready)
  • Consider specific methodologies best fit to solve goals in each phase (as assumptions and conditions may change drastically for each progression, and one brute-force methodology may not work well in the end)
  • Set the order of operation (as sequence of events does matter in any complex project)
  • Determine success metrics, and think about how to “sell” the results to sponsors of the project (even before any data or math work begins)
  • Go about modeling or any other statistical work (only if the project calls for it)
  • Share knowledge with others and make sure resultant model scores and other findings are available to users through their favorite toolsets (even if the users are non-believers of analytics)
  • Continuously monitor the results and re-fit the models for improvement

As you can see here, even in this simplified list, modeling is just an “optional” step in the whole process. No one should build models because they know how to do it. You’re not in school anymore, where the goal is to get an A at the end of the semester. In the real world (although using this term makes me sound like a geezer), data players are supposed to make money with data, with or without advanced techniques. Methodologies? They are just colors on a palette, and you don’t have to use all of them.

For the folks who are in position to hire math geeks to maximize the value of data, simply ask them “why they would do anything.” If the candidate actually pauses and tries to think from the business perspective, then she is actually displaying some potential to be a business partner in the future. If the candidate keeps dropping technical jargon to this simple question, cut the interview short — unless you have natural curiosity in the mechanics of models and analytics, and your department’s success is just measured in complexity and elegance of solutions. But I highly doubt that such a goal would be above increasing profit for the organization in the end.

Programmatic Advertising Is Running Amok

Having spent many years in the direct marketing business, I’m usually amused by examples of target marketing gone awry. My personal favorite happened when I was on Amazon purchasing a cell phone bracket for my bicycle.

Target stock imageHaving spent many years in the direct marketing business, I’m usually amused by examples of target marketing gone awry. My personal favorite happened when I was on Amazon purchasing a cell phone bracket for my bicycle. Amazon’s algorithm generated this suggestion:

Amazon wants Chuck to be a pirateNow I don’t know how frequently the pirate boots and the tri-corner hat are bought together with the cell phone mount, but I have to say that the combination was tempting for a few minutes.

The fact remains that direct marketing is not perfect. Many years ago, I made a donation to my alma mater, Rutgers College. The student on the phone asked if I wanted to designate my gift to a particular part of the University, and when I said, “No,” he said, “Well I’m in the Glee Club and we could sure use the money. Will you designate to the Glee Club?”

“Sure,” I said.

For decades now, I’ve been getting mail addressed, “Dear Glee Club Alumnus.” One day, I will attend a Glee Club reunion, certain that many people will remember my contribution to the tenor section.

While these harmless examples of imprecision are humorous, there’s nothing funny about the current exodus of major advertisers from the Google ad network and YouTube. Programmatic ad placement is a boon to target marketing, but like most direct marketing, it’s not perfect.

Major advertisers are in a tizzy over how to control where their ads appear … and the Google ad network is scrambling to get control over placement, as they should be. Advertisers need to protect their brands from appearing in an environment that can harm them.

Just a few examples: Ads for IHOP, Cinnamon Toast Crunch, “The Lego Batman Movie,” “Chips” and others have recently popped up among nude videos from everyday users or X-rated posts from porn-star influencers. Ad Age 3/6/17

A Nordstrom ad for Beyonce’s Ivy Park clothing line appeared on Breitbart next to this headline: NYTimes 3/26/17

Chuck's take on Nordstrom appearing on BreitbartHere’s a great attempt at an explanation for this juxtaposition:

“What we do is, we match ads and the content, but because we source the ads from everywhere, every once in a while somebody gets underneath the algorithm and they put in something that doesn’t match.  We’ve had to tighten our policies and actually increase our manual review time and so I think we’re going to be okay,” Schmidt told the FOX Business Network’s Maria Bartiromo. Fox News 3/23/17

Appearing next to hate speech is particularly problematic for brands:

Google-displayed ads for Macy’s and the genetics company 23andMe appeared on the website My Posting Career, which describes itself as a “white privilege zone,” next to a notice saying the site would offer a referral bonus for each member related to Adolf Hitler. Washington Post 3/24/17

The Wall Street Journal reported Coca-Cola, PepsiCo Inc., Wal-Mart Stores Inc. and Dish Network Corp. suspended spending on all Google advertising, except targeted search ads. Starbucks Corp. and General Motors Co. said they were pulling their ads from YouTube. FX Networks, part of 21st Century Fox Inc., said it was suspending all advertising spending on Google, including search ads and YouTube … Wal-Mart said: “The content with which we are being associated is appalling and completely against our company values.”
Ads for Coca-Cola, Starbucks, Toyota Motor Corp., Dish Network, Berkshire Hathaway Inc.’s Geico unit and Google’s own YouTube Red subscription service appeared on racist videos with the slur “n–” in the title. Wall Street Journal 3/24/17

And as difficult as it is for the ad networks to control, brands have their own challenges trying to protect themselves from undesirable placements. Different departments running different campaigns with different agencies cause ads to appear on corporate blacklisted sites. BMW of North America has encountered that issue because its marketing plan does not extend to dealerships. While the company does not buy ads on Breitbart, Phil DiIanni, a spokesman, noted that “dealerships are independent businesses and decide for themselves on their local advertising.” NYTimes 3/26/17

Clearly our technology’s ability to target has outstripped our ability to control it. And while it remains to be seen what controls will be put in place, it’s likely that, as always, target marketing won’t be perfect.

When Your Price Really Is Too High

When I get asked by sales professionals all around the county how they can overcome the “Your Price is Too High” objection, my response is you must first understand that in their operating reality, your prospect is right. Your price is too high. For now.

when-your-price-really-is-too-high[Editor’s note: Though this post talks about sales, it does get into issues marketers find vexing. It also provides solutions marketers may be able to use.]

When I get asked by sales professionals all around the county how they can overcome the “Your Price is Too High” objection, my response is you must first understand that in their operating reality, your prospect is right. Your price is too high. For now.

Your price is too high because you have not done one or both of the following:

  1. You have not uncovered a good and compelling reason for them to buy from you. Put simply, they have not recognized a need.
  2. You have a value problem. You have not established what your product or service will provide to them financially, operationally or personally and what problem you are solving for them.

You have choices when you hear that objection.

You can ask “Where do I need to be with my price in order to close this deal?” which many salespeople resort to. Selling on price, however, is always a losing proposition. You might win a deal, but you are left defenseless because someone can always come along with a cheaper price and take your client away. The other option is that you can take the time to uncover needs and sell value.

The most effective strategy against the price objection is preventing it in the first place.

What’s the Problem?

Let’s assume we have a great handle on all the features and benefits of our product/service. We also have a target set of clients that have been predetermined to likely need what we are selling. We might have even been trained on why they need what we sell. This combination can often be deadly — especially to the seasoned sales rep. We think we know the problem our client has (because we’ve seen it before) and so we charge in to solve it! Even if we are right, we set ourselves up for failure. Why? Because we didn’t take the time to ask about their situation, really listen to them and create something that will be meaningful to them personally. You must show that you care and that you want more than anything else to understand their operating reality and see if you can possibly make it better. If you do this, they will acknowledge a need for what you are selling. The only way to accomplish this is to use effective questioning skills and active listening skills.

So What?

True sales professionals concentrate on first understanding the client’s current challenges and identifying how your product or service will solve their problem. Think of it like this, no one buys the product or service you sell, they buy what it will do for them. 

WIIFM. What’s In It For Me. That is what they buy. Picture your prospect thinking to themselves with every sentence you utter about your product or service. “So What? So What does that mean to ME? What’s In It for ME?” If you can take the problem you uncovered and communicate the value you can deliver in those terms, you are well on your way.

Value = Benefits – Cost

Value has a price tag. And it varies depending on the buyer – not the product/service. Long before the price is ever mentioned, the sales professional must uncover what their prospect perceives as valuable and what the consequences of not buying are worth. With that in mind, they can position it so that the buyer feels as though the price was a great deal for them, regardless of the price. ROI! The equation Value = Benefits – Cost shows that we put a price on cost AND we put a price on the benefits. If in our mind, the benefits are greater, than there is value in making a purchase.

Let’s use buying a highly commoditized item as an example. A cotton, short-sleeved, T-shirt. These types of T-shirts can range in price from $5 to $100 or more. Things matter to buyers; color, sheen, logos, convenience of purchase, weight, etc. And, they often also appeal to emotions such as a souvenir of a great vacation, your favorite band, college, a show of super-fan for a favorite sports team. Personally, I wouldn’t pay much for a Mets T-shirt, but would spend plenty more on a Cubs T-shirt and even more still if I bought it at a game, where I had a great time watching them beat the Mets. But, that’s just me.

You can be prepared in advance to uncover the problem, position what you are selling in terms of what it means to them and in terms of their perception of value, AND help them justify their purchase when they state your price is too high. Or you can lower your price. It’s your choice.


Persona Marketing Tricks

How does a marketer go about creating the most effective set of personas? The first step is to create the 360-degree customer view out of available data. Personalization must be about the person, not about channel, product or even brand.

Personal.jpgHow does a marketer go about creating the most effective set of personas? The first step is to create the 360-degree customer view out of available data. Personalization must be about the person, not about channel, product or even brand.

For that, all event- and transaction-level data must be rearranged around the target individuals. Often, this data step turns out to be the first major hurdle for the marketers.

Then marketers, along with data scientists, should draw the list of required personas. After all, all analytical work must start with a clear definition of targets, and the targets must be set with clear business goals.

If you could ask for any personas for your marketing efforts, what would they be? Surely, the list would vary greatly depending on the lines of business that you are in. Obvious ones — such as “High-Value Customer,” “Frequent Shopper” or “Online Buyer” could be helpful for all types of retailers.

Going beyond that, marketers must expand their imaginations and think about the list from the customer’s point of view, while keeping a sight on the products and services that are to be offered to them. We must look at this as an ultimate “match-making” exercise between the buyers and the products, way more sophisticated than a rudimentary product-to-product level match (as in “If you purchased product A, you must also be interested in product B”).

The idea is to create personas imagining what you are going to do with them in marketing campaigns. “Frequent Flyer” maybe an obvious choice, but would you need a related but different one called “Frequent Business Traveler”? Would you extend the “Young Family” to “Avid Theme Park Visitors”? Why not both?

For B-to-B applications, we can think of many more along the lines of a “Consumable/Repeat Purchase” persona and “Big Ticket Items,” but the idea is to have both of them on the menu, as one may reveal both types of traits at the same time.

Similarly, if you are in a telecommunication business, what would be a good set of personas for broadband service? What type of personas can explain the “why” part of the equations? Simply for the sales of broadband, we can think of the following set as a starter:

  • Big Family
  • Home Office
  • High-Tech Professional
  • Avid Gamer
  • Avid Movie Downloader
  • Voice-over IP User
  • Frequent International Caller
  • Early Adopter
  • Etc., etc.

The key is matching the propensity of a customer and the product, and showing compelling reasons why they need to purchase a particular product. We all routinely consume all kinds of products and services, but each of us does it for different reasons. Personalizing the message based on known or inferred personal traits is the key to stand out in the age of over-communication.

Once we imagine the list, there are ways to build the personas. I can say that with conviction, as I’ve seen a persona called “NASCAR Fan” being used in an election season. So, don’t be shy and start being creative on your whiteboard today.

Creating Luxury Appeal for Any Brand

So why do many of us spend $55,000 and more on a luxury car that Consumer Reports says won’t perform as well as a much cheaper brand?

So why do many of us spend $55,000 and more on a luxury car that Consumer Reports says won’t perform as well as a much cheaper brand?

And what makes women buy that $40,000 Gucci crocodile handbag when, functionally, it does the very same thing as a $40 knock-off from Target?

According to my friend, Harlan Bratcher, who has been creating and defining luxury as a C-level executive for labels such as Calvin Klein, Armani and Reed Krakoff, it’s all about emotion.

“We don’t necessarily buy a luxury product because of how it’s made, or even its style, but more so because of how it makes us feel,” says Bratcher. “When you drive that $55,000 car, or carry an Hermès or Gucci handbag, consciously, but even more unconsciously, you feel you have achieved your aspirations, even if that aspiration is as simple as feeling good about yourself.”

As the lead personal shopper for Neiman Marcus in the early 1980s at the beginning of his fashion lifestyle career, Bratcher recalls helping women try on $15,000 gowns, watching them slumping as they looked in the mirror. After spending time getting to know them, and helping them feel beautiful inside and out, suddenly that $15,000 dress was worth even more.

If luxury is defined by how a product makes us feel, as suggested by Bratcher, then is it possible for any brand to become a “luxury,” or something for which consumers are willing to pay a premium?

According to the Merriam Webster dictionary, luxury means:

  • a condition or situation of great comfort, ease and wealth
  • something that is expensive and not necessary
  • something that is helpful or welcome and that is not usually or always available

Per the above definition, its seems a product or brand can call itself “luxury” if it makes consumers feel pampered, extravagant and exclusive.

Bratcher offers a different definition:

“A brand becomes a luxury when it becomes aspirational to the consumer. Aspiration can manifest in many ways, from elevated self-esteem, confidence and sense of self; to a personal statement you believe you deserve to make about yourself.”

While aspiration can traditionally be defined as our hopes, dreams and exquisite goals for life, its connection to luxury is taking on a new meaning in today’s consumer-driven climate. Luxury is not just about exclusive products that one in thousands might own. It is about the experience that elevates the perceived value of the product and brand.

“As CEO of Armani Exchange, my mission was to build a highly relevant experience for our customers that made them feel beautiful, energetic and happy, and in ways that helped them associate those feelings with our brand. One way we did this was to research our customers’ favorite music, and then play it loudly at each of our stores, creating that Friday night dance club feeling. Sales and customer loyalty soared.”

Beyond feeling young, urban and sexy from the purchases we make, today’s consumers are demanding a new sensation: altruism.

Research from both Cone Communications and Edelman shows that more than 80 percent of today’s consumers, from Gen Y to Baby Boomers, choose brands which can show the positive social impact they are having on the world. Aligning with social causes – not just fashion trends and glamorous living – is now becoming an essential part of branding for luxury brands in all categories – from designer apparel and vacation resorts to auction houses like Christie’s.

“Consumers today are seeking actualization in all they do, and they do this by finding purpose in their daily lives, from the deeds they do to the products they purchase, “ says Toby Usnik, Chief Social Responsibility Officer for Christie’s in New York City. “Luxury is now about a bigger brand statement than just the product itself. It’s about shared values, a higher purpose and a sustainable community.”

For Christie’s, Usnik has helped contemporize a 250-year old brand through new initiatives for giving back. This includes the creation of Bid to Save The Earth, a coalition charity auction on behalf of four leading environmental groups: Oceana, Natural Resources Defense Council (NRDC), Central Park Conservancy, and Conservation International. Over three years, this program earned several million dollars to support its causes, and substantially helped to further Christie’s profile as a luxury brand with far-reaching values.

While some might be tempted to up their price, bling their packaging and call their brand ‘Luxury,” the chances for successfully transforming a great brand to a luxury brand are greater if you follow these simple steps.

Create authentic experiences

  • Armani’s nightclub atmosphere was authentic and spot-on for creating a strong dose of the emotions that make us feel powerful, awesome and in a mood to shop.

Tap into feelings that matter

  • While feelings in a nightclub might be fleeting, especially when you wake up the next morning, the overall consistent feelings of belonging and self-esteem you can create with every shopping experience, service interaction, follow-up communications and events are what maintain a brand’s luxury status.

Preserve the Perception

  • Once you’ve broken out as a brand above the cluttered fray, its critical to maintain your sense of luxury. You can do this not just with exclusive experiences, and short product runs for really amazing items, but with your pricing strategies. As Bratcher and Usnik both suggest, lowering your price, or offering discounts, just reprograms the status of your brand and you may never get back the status you once had.

Engage Customers in Sincere Altruism

  • As Usnik says, long gone are the days when a company buys a table at a charity gala or donates here and there. Leading brands are putting a stake in the ground based on their values and communities. They have skin in the game — creating programs that support those values, having their employees volunteer for related non-profits, sharing their platforms with others committed to the same cause. Doing just that made Warby Parker a huge force in the eyewear industry, because its customers’ purchases give free glasses and vision to disadvantaged people globally.

Albeit trite and cliché to say, luxury is still in the eye of the beholder. But now more than ever, it’s in the heart, as well. Building a brand around authentic values and causes that make people feel they are one step closer to actualization, social and personal aspirations, will help elevate your brand in ways much more powerful than you can imagine.

What are the aspirations or hopes you can associate with your brand to secure loyalty and attract high-value customers? You don’t need to open up shop on Fifth Avenue in NYC to succeed. Instead, focus on the dreams, hopes and core values of your customers, and tell your story in a way that makes them want to be a part of it, and pass it on to others.

Bratcher sums it up:

“No one really needs luxury. It’s nonessential. That’s where the dream and mythology come in. And this is why my career has been about anthropology – making dreams for the moment – more than product lines.”

5 Data-Driven Marketing Catalysts for 2016 Growth

The new year tends to bring renewal, the promise of doing something new, better and smarter. I get a lot of calls looking for ideas and strategies to help improve the focus and performance of marketers’ plans and businesses. What most organizations are looking for is one or more actionable catalysts in their business.

The new year tends to bring renewal and the promise of doing something new, better and smarter. I get a lot of calls looking for ideas and strategies to help improve the focus and performance of marketers’ plans and businesses. What most organizations are looking for is one or more actionable marketing catalysts in their business.

To help you accelerate your thinking, here is a list of those catalysts that have something for everyone, some of which can be great food for thought as you tighten up plans. This year, you will do well if you resolve to do the following five things:

  • Build a Scalable Prospect Database Program. Achieving scale in your business is perhaps the greatest challenge we face as marketers. Those who achieve scale on their watch are the most sought-after marketing pros in their industries — because customer acquisition is far from cheap and competition grows more fiercely as the customer grows more demanding and promiscuous. A scientifically designed “Prospect Database Program” is one of the most effective ways great direct marketers can achieve scale — though not all prospecting databases and solutions are created equally.

A great prospecting database program requires creating a statistical advantage in targeting individuals who don’t already know your brand, or don’t already buy your brand. That advantage is critical if the program is to become cost-effective. Marketers who have engaged in structured prospecting know how challenging it is.

A prospect database program uses data about your very best existing customers: What they bought, when, how much and at what frequency. And it connects that transaction data to oceans of other data about those individuals. That data is then used to test which variables are, in fact, more predictive. They will come back in three categories: Those you might have “guessed” or “known,” those you guessed but proved less predictive than you might have thought, and those that are simply not predictive for your customer.

Repeated culling of that target is done through various statistical methods. What we’re left with is a target where we can begin to predict what the range of response looks like before we start. As the marketer, you can be more aggressive or conservative in the final target definition and have a good sense as to how well it will convert prospects in the target to new customers. This has a powerful effect on your ability to intelligently invest in customer acquisition, and is very effective — when done well — at achieving scale.

  • Methodically ID Your VIPs — and VVIPs to Distinguish Your ‘Gold’ Customers. It doesn’t matter what business you are in. Every business has “Gold” Customers — a surprisingly small percentage of customers that generate up to 80 percent of your revenue and profit.

With a smarter marketing database, you can easily identify these customers who are so crucial to your business. Once you have them, you can develop programs to retain and delight them. Here’s the “trick” though — don’t just personalize the website and emails to them. Don’t give them a nominally better offer. Instead, invest resources that you simply cannot afford to spend on all of your customers. When the level of investment in this special group begins to raise an eyebrow, you know for certain you are distinguishing that group, and wedding them to your brand.

Higher profits come from leveraging this target to retain the best customers, and motivating higher potential customers who aren’t “Gold” Customers yet to move up to higher “status” levels. A smart marketing database can make this actionable. One strategy we use is not only IDing the VIPs, but the VVIP’s (very, very important customers). Think about it, how would you feel being told you’re a “VVIP” by a brand that matters to you? You are now special to the brand — and customers who feel special tend not to shop with many other brands — a phenomenon also known as loyalty. So if you’d like more revenues from more loyal customers, resolve to use your data to ID which customers are worth investing in a more loyal relationship.

  • Target Customers Based on Their Next Most Likely Purchase. What if you knew when your customer was most likely to buy again? To determine the next most likely purchase, an analytics-optimized database is used to determine when customers in each segment usually buy and how often.

Once we have that purchase pattern calculated, we can ID customers who are not buying when the others who have acted (bought) similarly are buying. It is worth noting, there is a more strategic opportunity here to focus on these customers; as when they “miss” a purchase, this is usually because they are spending with a competitor. “Next Most Likely Purchase” models help you to target that spending before it’s “too late.”

The approach requires building a model that is statistically validated and then tested. Once that’s done, we have a capability that is consistently very powerful.

  • Target Customers Based on Their Next Most Likely Product or Category. We can determine the product a customer is most likely to buy “next.” An analytics-ready marketing database (not the same as a CRM or IT warehouse/database) is used to zero-in on the customers who bought a specific product or, more often, in a specific category or subcategory, by segment.

Similar to the “Next Most Likely Purchase” models, these models are used to find “gaps” in what was bought, as like-consumers tend to behave similarly when viewed in large enough numbers. When there is one of these gaps, it’s often because they bought the product from a competitor, or found an acceptable substitute — trading either up or down. When you target based upon what they are likely to buy at the right time, you can materially increase conversion across all consumers in your database.

  • Develop or Improve Your Customer Segmentation. Smart direct marketing database software is required to store all of the information and be able to support queries and actions that it will take to improve segmentation.

This is an important point, as databases tend to be purpose-specific. That is, a CRM database might be well-suited for individual communications and maintaining notes and histories about individual customers, but it’s probably not designed to perform the kind of queries required, or structure your data to do statistical target definition that is needed in effectively acquiring large numbers of new customers.

Successful segmentation must be done in a manner that helps you both understand your existing customers and their behaviors, lifestyles and most basic make up — and be able to help you acquire net-new customers, at scale. Success, of course, comes from creating useful segments, and developing customer marketing strategies for each segment.

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.

Data Deep Dive: The Art of Targeting

Even if you own a sniper rifle (and I’m not judging), if you aim at the wrong place, you will never hit the target. Obvious, right? But that happens all the time in the world of marketing, even when advanced analytics and predictive modeling techniques are routinely employed. How is that possible? Well, the marketing world is not like an Army shooting range where the silhouette of the target is conveniently hung at the predetermined location, but it is more like the “Twilight Zone,” where things are not what they seem. Marketers who failed to hit the real target often blame the guns, which in this case are targeting tools, such as models and segmentations. But let me ask, was the target properly defined in the first place?

Even if you own a sniper rifle (and I’m not judging), if you aim at the wrong place, you will never hit the target. Obvious, right? But that happens all the time in the world of marketing, even when advanced analytics and predictive modeling techniques are routinely employed. How is that possible? Well, the marketing world is not like an Army shooting range where the silhouette of the target is conveniently hung at the predetermined location, but it is more like the “Twilight Zone,” where things are not what they seem. Marketers who failed to hit the real target often blame the guns, which in this case are targeting tools, such as models and segmentations. But let me ask, was the target properly defined in the first place?

In my previous columns, I talked about the importance of predictive analytics in modern marketing (refer to “Why Model?”) for various reasons, such as targeting accuracy, consistency, deeper use of data, and most importantly in the age of Big Data, concise nature of model scores where tons of data are packed into ready-for-use formats. Now, even the marketers who bought into these ideas often make mistakes by relinquishing the important duty of target definition solely to analysts and statisticians, who do not necessarily possess the power to read the marketers’ minds. Targeting is often called “half-art and half-science.” And it should be looked at from multiple angles, starting with the marketer’s point of view. Therefore, even marketers who are slightly (or, in many cases, severely) allergic to mathematics should come one step closer to the world of analytics and modeling. Don’t be too scared, as I am not asking you to be a rifle designer or sniper here; I am only talking about hanging the target in the right place so that others can shoot at it.

Let us start by reviewing what statistical models are: A model is a mathematical expression of “differences” between dichotomous groups; which, in marketing, are often referred to as “targets” and “non-targets.” Let’s say a marketer wants to target “high-value customers.” To build a model to describe such targets, we also need to define “non-high-value customers,” as well. In marketing, popular targets are often expressed as “repeat buyers,” “responders to certain campaigns,” “big-time spenders,” “long-term, high-value customers,” “troubled customers,” etc. for specific products and channels. Now, for all those targets, we also need to define “bizarro” or “anti-” versions of them. One may think that they are just the “remainders” of the target. But, unfortunately, it is not that simple; the definition of the whole universe should be set first to even bring up the concept of the remainders. In many cases, defining “non-buyers” is much more difficult than defining “buyers,” because lack of purchase information does not guarantee that the individual in question is indeed a non-buyer. Maybe the data collection was never complete. Maybe he used a different channel to respond. Maybe his wife bought the item for him. Maybe you don’t have access to the entire pool of names that represent the “universe.”

Remember T, C, & M
That is why we need to examine the following three elements carefully when discussing statistical models with marketers who are not necessarily statisticians:

  1. Target,
  2. Comparison Universe, and
  3. Methodology.

I call them “TCM” in short, so that I don’t leave out any element in exploratory conversations. Defining proper target is the obvious first step. Defining and obtaining data for the comparison universe is equally important, but it could be challenging. But without it, you’d have nothing against which you compare the target. Again, a model is an algorithm that expresses differences between two non-overlapping groups. So, yes, you need both Superman and Bizarro-Superman (who always seems more elusive than his counterpart). And that one important variable that differentiates the target and non-target is called “Dependent Variable” in modeling.

The third element in our discussion is the methodology. I am sure you may have heard of terms like logistic regression, stepwise regression, neural net, decision trees, CHAID analysis, genetic algorithm, etc., etc. Here is my advice to marketers and end-users:

  • State your goals and usages cases clearly, and let the analyst pick proper methodology that suites your goals.
  • Don’t be a bad patient who walks into a doctor’s office demanding a specific prescription before the doctor even examines you.

Besides, for all intents and purposes, the methodology itself matters the least in comparison with an erroneously defined target and the comparison universes. Differences in methodologies are often measured in fractions. A combination of a wrong target and wrong universe definition ends up as a shotgun, if not an artillery barrage. That doesn’t sound so precise, does it? We should be talking about a sniper rifle here.

Clear Goals Leading to Definitions of Target and Comparison
So, let’s roll up our sleeves and dig deeper into defining targets. Allow me to use an example, as you will be able to picture the process better that way. Let’s just say that, for general marketing purposes, you want to build a model targeting “frequent flyers.” One may ask for business or for pleasure, but let’s just say that such data are hard to obtain at this moment. (Finding the “reasons” is always much more difficult than counting the number of transactions.) And it was collectively decided that it would be just beneficial to know who is more likely to be a frequent flyer, in general. Such knowledge could be very useful for many applications, not just for the travel industry, but for other affiliated services, such as credit cards or publications. Plus, analytics is about making the best of what you’ve got, not waiting for some perfect datasets.

Now, here is the first challenge:

  • When it comes to flying, how frequent is frequent enough for you? Five times a year, 10 times, 20 times or even more?
  • Over how many years?
  • Would you consider actual miles traveled, or just number of issued tickets?
  • How large are the audiences in those brackets?

If you decided that five times a year is a not-so-big or not-so-small target (yes, sizes do matter) that also fits the goal of the model (you don’t want to target only super-elites, as they could be too rare or too distinct, almost like outliers), to whom are they going to be compared? Everyone who flew less than five times last year? How about people who didn’t fly at all last year?

Actually, one option is to compare people who flew more than five times against people who didn’t fly at all last year, but wouldn’t that model be too much like a plain “flyer” model? Or, will that option provide more vivid distinction among the general population? Or, one analyst may raise her hand and say “to hell with all these breaks and let’s just build a model using the number of times flown last year as the continuous target.” The crazy part is this: None of these options are right or wrong, but each combination of target and comparison will certainly yield very different-looking models.

Then what should a marketer do in a situation like this? Again, clearly state the goal and what is more important to you. If this is for general travel-related merchandizing, then the goal should be more about distinguishing more likely frequent flyers out of the general population; therefore, comparing five-plus flyers against non-flyers—ignoring the one-to-four-time flyers—makes sense. If this project is for an airline to target potential gold or platinum members, using people who don’t even fly as comparison makes little or no sense. Of course, in a situation like this, the analyst in charge (or data scientist, the way we refer to them these days), must come halfway and prescribe exactly what target and comparison definitions would be most effective for that particular user. That requires lots of preliminary data exploration, and it is not all science, but half art.

Now, if I may provide a shortcut in defining the comparison universe, just draw the representable sample from “the pool of names that are eligible for your marketing efforts.” The key word is “eligible” here. For example, many businesses operate within certain areas with certain restrictions or predetermined targeting criteria. It would make no sense to use the U.S. population sample for models for supermarket chains, telecommunications, or utility companies with designated footprints. If the business in question is selling female apparel items, first eliminate the male population from the comparison universe (but I’d leave “unknown” genders in the mix, so that the model can work its magic in that shady ground). You must remember, however, that all this means you need different models when you change the prospecting universe, even if the target definition remains unchanged. Because the model algorithm is the expression of the difference between T and C, you need a new model if you swap out the C part, even if you left the T alone.

Multiple Targets
Sometimes it gets twisted the other way around, where the comparison universe is relatively stable (i.e., your prospecting universe is stable) but there could be multiple targets (i.e., multiple Ts, like T1, T2, etc.) in your customer base.

Let me elaborate with a real-life example. A while back, we were helping a company that sells expensive auto accessories for luxury cars. The client, following his intuition, casually told us that he only cares for big spenders whose average order sizes are more than $300. Now, the trouble with this statement is that:

  1. Such a universe could be too small to be used effectively as a target for models, and
  2. High spenders do not tend to purchase often, so we may end up leaving out the majority of the potential target buyers in the whole process.

This is exactly why some type of customer profiling must precede the actual target definition. A series of simple distribution reports clearly revealed that this particular client was dealing with a dual-universe situation, where the first group (or segment) is made of infrequent, but high-dollar spenders whose average orders were even greater than $300, and the second group is made of very frequent buyers whose average order sizes are well below the $100 mark. If we had ignored this finding, or worse, neglected to run preliminary reports and just relying on our client’s wishful thinking, we would have created a “phantom” target, which is just an average of these dual universes. A model designed for such a phantom target will yield phantom results. The solution? If you find two distinct targets (as in T1 and T2), just bite the bullet and develop two separate models (T1 vs. C and T2 vs. C).

Multi-step Approach
There are still other reasons why you may need multiple models. Let’s talk about the case of “target within a target.” Some may relate this idea to a “drill-down” concept, and it can be very useful when the prospecting universe is very large, and the marketer is trying to reach only the top 1 percent (which can be still very large, if the pool contains hundreds of millions of people). Correctly finding the top 5 percent in any universe is difficult enough. So what I suggest in this case is to build two models in sequence to get to the “Best of the Best” in a stepwise fashion.

  • The first model would be more like an “elimination” model, where obviously not-so-desirable prospects would be removed from the process, and
  • The second-step model would be designed to go after the best prospects among survivors of the first step.

Again, models are expressions of differences between targets and non-targets, so if the first model eliminated the bottom 80 percent to 90 percent of the universe and leaves the rest as the new comparison universe, you need a separate model—for sure. And lots of interesting things happen at the later stage, where new variables start to show up in algorithms or important variables in the first step lose steam in later steps. While a bit cumbersome during deployment, the multi-step approach ensures precision targeting, much like a sniper rifle at close range.

I also suggest this type of multi-step process when clients are attempting to use the result of segmentation analysis as a selection tool. Segmentation techniques are useful as descriptive analytics. But as a targeting tool, they are just too much like a shotgun approach. It is one thing to describe groups of people such as “young working mothers,” “up-and-coming,” and “empty-nesters with big savings” and use them as references when carving out messages tailored toward them. But it is quite another to target such large groups as if the population within a particular segment is completely homogeneous in terms of susceptibility to specific offers or products. Surely, the difference between a Mercedes buyer and a Lexus buyer ain’t income and age, which may have been the main differentiator for segmentation. So, in the interest of maintaining a common theme throughout the marketing campaigns, I’d say such segments are good first steps. But for further precision targeting, you may need a model or two within each segment, depending on the size, channel to be employed and nature of offers.

Another case where the multi-step approach is useful is when the marketing and sales processes are naturally broken down into multiple steps. For typical B-to-B marketing, one may start the campaign by mass mailing or email (I’d say that step also requires modeling). And when responses start coming in, the sales team can take over and start contacting responders through more personal channels to close the deal. Such sales efforts are obviously very time-consuming, so we may build a “value” model measuring the potential value of the mail or email responders and start contacting them in a hierarchical order. Again, as the available pool of prospects gets smaller and smaller, the nature of targeting changes as well, requiring different types of models.

This type of funnel approach is also very useful in online marketing, as the natural steps involved in email or banner marketing go through lifecycles, such as blasting, delivery, impression, clickthrough, browsing, shopping, investigation, shopping basket, checkout (Yeah! Conversion!) and repeat purchases. Obviously, not all steps require aggressive or precision targeting. But I’d say, at the minimum, initial blast, clickthrough and conversion should be looked at separately. For any lifetime value analysis, yes, the repeat purchase is a key step; which, unfortunately, is often neglected by many marketers and data collectors.

Inversely Related Targets
More complex cases are when some of these multiple response and conversion steps are “inversely” related. For example, many responders to invitation-to-apply type credit card offers are often people with not-so-great credit. Well, if one has a good credit score, would all these credit card companies have left them alone? So, in a case like that, it becomes very tricky to find good responders who are also credit-worthy in the vast pool of a prospect universe.

I wouldn’t go as far as saying that it is like finding a needle in a haystack, but it is certainly not easy. Now, I’ve met folks who go after the likely responders with potential to be approved as a single target. It really is a philosophical difference, but I much prefer building two separate models in a situation like this:

  • One model designed to measure responsiveness, and
  • Another to measure likelihood to be approved.

The major benefit for having separate models is that each model will be able employ different types and sources of data variables. A more practical benefit for the users is that the marketers will be able to pick and choose what is more important to them at the time of campaign execution. They will obviously go to the top corner bracket, where both scores are high (i.e., potential responders who are likely to be approved). But as they dial the selection down, they will be able to test responsiveness and credit-worthiness separately.

Mixing Multiple Model Scores
Even when multiple models are developed with completely different intentions, mixing them up will produce very interesting results. Imagine you have access to scores for “High-Value Customer Model” and “Attrition Model.” If you cross these scores in a simple 2×2 matrix, you can easily create a useful segment in one corner called “Valuable Vulnerable” (a term that my mentor created a long time ago). Yes, one score is predicting who is likely to drop your service, but who cares if that customer shows little or no value to your business? Take care of the valuable customers first.

This type of mixing and matching becomes really interesting if you have lots of pre-developed models. During my tenure at a large data compiling company, we built more than 120 models for all kinds of consumer characteristics for general use. I remember the real fun began when we started mixing multiple models, like combining a “NASCAR Fan” model with a “College Football Fan” model; a “Leaning Conservative” model with an “NRA Donor” model; an “Organic Food” one with a “Cook for Fun” model or a “Wine Enthusiast” model; a “Foreign Vacation” model with a “Luxury Hotel” model or a “Cruise” model; a “Safety and Security Conscious” model or a “Home Improvement” model with a “Homeowner” model, etc., etc.

You see, no one is one dimensional, and we proved it with mathematics.

No One is One-dimensional
Obviously, these examples are just excerpts from a long playbook for the art of targeting. My intention is to emphasize that marketers must consider target, comparison and methodologies separately; and a combination of these three elements yields the most fitting solutions for each challenge, way beyond what some popular toolsets or new statistical methodologies presented in some technical conferences can acomplish. In fact, when the marketers are able to define the target in a logical fashion with help from trained analysts and data scientists, the effectiveness of modeling and subsequent marketing campaigns increase dramatically. Creating and maintaining an analytics department or hiring an outsourcing analytics vendor aren’t enough.

One may be concerned about the idea of building multiple models so casually, but let me remind you that it is the reality in which we already reside, anyway. I am saying this, as I’ve seen too many marketers who try to fix everything with just one hammer, and the results weren’t ideal—to say the least.

It is a shame that we still treat people with one-dimensional tools, such segmentations and clusters, in this age of ubiquitous and abundant data. Nobody is one-dimensional, and we must embrace that reality sooner than later. That calls for rapid model development and deployment, using everything that we’ve got.

Arguing about how difficult it is to build one or two more models here and there is so last century.