How to Maximize Your Lead Volume Within Your Allowable Cost per Lead

Many times marketers running lead generation programs shortchange their lead volume in order to maintain tight controls on their cost per lead. Their fear is that if they rollout media that tested at a cost per lead (CPL) that’s just equal to or slightly below their target CPL that a variation in response might put their overall CPL over the top. As a result, they roll out only those media properties that are performing below their target CPL.

Many times marketers running lead generation programs shortchange their lead volume in order to maintain tight controls on their cost per lead. Their fear is that if they roll out media that tested at a cost per lead (CPL) that’s just equal to or slightly below their target CPL that a variation in response might put their overall CPL over the top. As a result, they roll out only those media properties that are performing below their target CPL.

This conservative strategy ends up cheating you out of volume that could significantly increase your program’s total revenue and positively impact your ROI. The fact is that every well-constructed media test has its big winners as well as its big losers. The trick is to leverage the big winners in a way that allows you to include the “little losers” in the mix and still meet your overall target cost per lead.

With a few simple spreadsheet tricks, you can maximize your lead volume and still hit your target CPL by including media that actually generate higher lead costs than your target CPL! Think about it this way. If your target cost per lead is $15, for every $10 lead you get from a “big winner” media, you can accept a $20 lead from a “little loser.”

Let’s walk through the simple spreadsheet manipulations you need to manage this process.

Start out with your basic results spreadsheet like Table A that shows your media cost, responses, and cost per response for each media. For this example, we’ll look at a 500,000 impressions test (10 properties,
50,000 impressions each, with a roll-out potential of 15 million. The target CPL is $15.

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As you can see, the test yielded 700 responses at a cost of $11,425 or a total CPL of $16.32. But there are 7 out of 10 properties that are performing worse than the target CPL of $15.

The first thing you need to do is rank the results in ascending order of CPL using the Data Sort function, and you end up with Table B below. (Make sure you don’t include the total line in your sort).

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Here we see that properties H, B, and C are below the target of $15 per lead while all the others are higher. The combined roll-out quantity of these three properties is a disappointing 4,050,000 impressions out of the total potential roll-out quantity of 15 million. But let’s look at what the actual roll-out potential is when we leverage the “big winners” against the “little losers.”

To the spreadsheet that you sorted by ascending CPL, add columns for cumulative responses, cumulative cost and cumulative CPL. Table C, shows the formulas for calculating those.

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Looking at the results of this calculation in Table D, we get a better picture of the potential roll-out universe.

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If you look at the cumulative cost per lead column, you can see that taken together, 8 out of 10 media properties produce an aggregate cost per lead under $15. That leaves only properties E and F with their high CPLs out of the mix, creating a potential rollout of 12,250,000 impressions. (Note: If you decide to re-sort this spreadsheet do not include the cumulative results columns in the sort).

Now, some words of caution. Don’t roll all these marginal media out before retesting them in a larger quantity, say 250,000 impressions to make sure that you’re going to repeat your results. A test quantity of 50,000 impressions generating less than 100 responses does not create a high level of statistical confidence. So be especially careful with properties like A and I that have higher CPLs. You’ll also want to retest your “big winner” properties with a greater number of impressions to make sure the test results are not an aberration.

At Your Service! Really!

I had to meet a friend unexpectedly at the hospital the other day. As you would expect, my mind was racing with all sorts of “what ifs.” I was wondering where to park when I pulled into the main entrance, and several kind people positioned at the door offered to valet my car and escort me to where I needed to go. This level of service reminiscent of a fine hotel, not a hospital, pleasantly surprised me. Genuine helpfulness and sincere caring. (And, thankfully, all turned out well for my friend.)

I had to meet a friend unexpectedly at the hospital the other day. As you would expect, my mind was racing with all sorts of “what ifs.” I was wondering where to park when I pulled into the main entrance, and several kind people positioned at the door offered to valet my car and escort me to where I needed to go. This level of service reminiscent of a fine hotel, not a hospital, pleasantly surprised me. Genuine helpfulness and sincere caring. (And, thankfully, all turned out well for my friend.)

As a brand strategist and a customer of many brands, I am in tune to the many ways companies tout their customer service. If your experiences are akin to mine, actual meaningful and truly excellent service still seems to be a rarity. Customer service gets lots of talk time (the one true brand differentiator!) these days, but is it time to double check and see if your brand is paying more than lip service to this important customer-centric activity?

Do you know if your service level is actually accomplishing what matters most to your customers? Would customers consider it a concierge experience? Take a peek at these examples and see how a few companies pay more than lip service to this important function:

Focus: Target Audience
Bed Bath & Beyond knows that the back-to-school season is almost akin to Christmas-in-August for its brand. With thousands of new freshmen heading to campuses nationwide in need of all things dorm related, Bed Bath & Beyond has truly gone beyond in creating an amazingly useful college-prepping brand experience. The website is chockfull of helpful advice about pertinent things top-of-mind for new college students. Take a peek at the topics covered in their online College Checklist:

  • Storing Your Stuff
  • Making Your Bed Better
  • Climate Control
  • An Inspiring Work Area
  • Resolving Technical Difficulties
  • Keeping Your Room Clean
  • Doing Laundry
  • Surviving a Shared Bathroom

After perusing both a printed checklist, a succinct magalog and an online version, students can enter their colleges in the company’s website and see if there are convenient Bed Bath & Beyond locations near their dorms so they don’t have to haul all this new merchandise from home. This concierge-esque brand takes it even a step further and has prepared lists of what the specific colleges and universities have already provided, what they want students to bring and what is not allowed. There’s even a college registry available, all set for family members who may want to gift the new freshmen upon high school graduation with these dorm life must haves.

And, once those students are settled in and living their particular collegiate lives, Bed Bath & Beyond continues to develop its student relationships with a “Grade My Space” program described as follows:

Grade My Space is a new interactive site where you’ll get an inside look at college living spaces and residence halls. Students connect and share ideas, designs, comments and provide the inside scoop on campus living and more.

How might your brand borrow brilliantly from Bed Bath & Beyond and put this usefulness in action for one of your specific customer segments?

Focus: Product Category
Target’s “guest-centric” brand attitude has always hit the bull’s eye, but the company is building on this experience in one particular category in a more nuanced way across 300 of its stores—Beauty. According to a recent press release:

Participating stores are staffed with a Target Beauty Concierge, a highly-trained, brand agnostic beauty enthusiast who is available to answer guests’ questions in-store. Serving as a trusted expert, the Beauty Concierge provides guests with personalized, detailed and unbiased information about beauty and personal care products offered at Target and acts as a knowledgeable source of advice in what can sometimes be an intimidating department. Beauty Concierges are located in the beauty aisles at Target wearing a distinct black apron. No appointment is necessary.

In addition to Target doing this with beauty, Lands End has done this with swimwear … a troublesome category for many women. Might there be a department or category within your brand that customers would welcome some one-on-one consultation? How might you enhance your service level in a key product category to generate not only more sales, but a more customer-centric experience?

Company-Wide Focus
Nordstrom has long wowed its customers with service that goes the extra mile. Today, its website reminds customers that unlike some other department stores, working with Nordstrom personal stylists is “fast, fun, free and zero pressure!” They’ll even prep your dressing room for you in advance of your visit.

“We’ll be there the whole time to offer new suggestions and honest advice—even if you are only looking to research, not to buy.” My girlfriend utilized this service in helping outfit her son, a new college graduate preparing for an international job opportunity. Not only was the time saved important, but now this stylist has all his measurements and style/color preferences recorded to make future shopping needs a breeze.

Office supply multichanneler, Staples, also is promising a company-wide concierge experience to back up its brand promise of “EASY”! Under its “Need Help?” tab is a listing for Product Concierge. Here’s what Staples says:

Can’t find what you’re looking for? We’re here to help! If you need help tracking down an item, we’ll search for it for you-even if it’s something we don’t currently have on our site. Tell us a bit more about the product and we’ll do our best to find it. There’s no obligation to buy.

Might your brand be able to promote this kind of across-the-board expectation? If not, what might have to change to do so?

Truly serving your customers concierge-style takes a full commitment from each and every brand ambassador within your company. It requires active listening and keen observation. It requires a servant heart and a willingness to sweat the small stuff to provide an excellent and memorable experience that will not only delight your customers once but keep them coming back for more … and raving about your brand to others.

Why Model?

Why model? Uh, because someone is ridiculously good looking, like Derek Zoolander? No, seriously, why model when we have so much data around? The short answer is because we will never know the whole truth. That would be the philosophical answer. Physicists construct models to make new quantum field theories more attractive theoretically and more testable physically. If a scientist already knows the secrets of the universe, well, then that person is on a first-name basis with God Almighty, and he or she doesn’t need any models to describe things like particles or strings. And the rest of us should just hope the scientist isn’t one of those evil beings in “Star Trek.”

Why model? Uh, because someone is ridiculously good looking, like Derek Zoolander? No, seriously, why model when we have so much data around?

The short answer is because we will never know the whole truth. That would be the philosophical answer. Physicists construct models to make new quantum field theories more attractive theoretically and more testable physically. If a scientist already knows the secrets of the universe, well, then that person is on a first-name basis with God Almighty, and he or she doesn’t need any models to describe things like particles or strings. And the rest of us should just hope the scientist isn’t one of those evil beings in “Star Trek.”

Another answer to “why model?” is because we don’t really know the future, not even the immediate future. If some object is moving toward a certain direction at a certain velocity, we can safely guess where it will end up in one hour. Then again, nothing in this universe is just one-dimensional like that, and there could be a snowstorm brewing up on its path, messing up the whole trajectory. And that weather “forecast” that predicted the snowstorm is a result of some serious modeling, isn’t it?

What does all this mean for the marketers who are not necessarily masters of mathematics, statistics or theoretical physics? Plenty, actually. And the use of models in marketing goes way back to the days of punch cards and mainframes. If you are too young to know what those things are, well, congratulations on your youth, and let’s just say that it was around the time when humans first stepped on the moon using a crude rocket ship equipped with less computing power than an inexpensive passenger car of the modern days.

Anyhow, in that ancient time, some smart folks in the publishing industry figured that they would save tons of money if they could correctly “guess” who the potential buyers were “before” they dropped any expensive mail pieces. Even with basic regression models—and they only had one or two chances to get it right with glacially slow tools before the all-too-important Christmas season came around every year—they could safely cut the mail quantity by 80 percent to 90 percent. The savings added up really fast by not talking to everyone.

Fast-forward to the 21st Century. There is still a beauty of knowing who the potential buyers are before we start engaging anyone. As I wrote in my previous columns, analytics should answer:

1. To whom you should be talking; and
2. What you should offer once you’ve decided to engage someone.

At least the first part will be taken care of by knowing who is more likely to respond to you.

But in the days when the cost of contacting a person through various channels is dropping rapidly, deciding to whom to talk can’t be the only reason for all this statistical work. Of course not. There are plenty more reasons why being a statistician (or a data scientist, nowadays) is one of the best career choices in this century.

Here is a quick list of benefits of employing statistical models in marketing. Basically, models are constructed to:

  • Reduce cost by contacting prospects more wisely
  • Increase targeting accuracy
  • Maintain consistent results
  • Reveal hidden patterns in data
  • Automate marketing procedures by being more repeatable
  • Expand the prospect universe while minimizing the risk
  • Fill in the gaps and summarize complex data into an easy-to-use format—A must in the age of Big Data
  • Stay relevant to your customers and prospects

We talked enough about the first point, so let’s jump to the second one. It is hard to argue about the “targeting accuracy” part, though there still are plenty of non-believers in this day and age. Why are statistical models more accurate than someone’s gut feeling or sheer guesswork? Let’s just say that in my years of dealing with lots of smart people, I have not met anyone who can think about more than two to three variables at the same time, not to mention potential interactions among them. Maybe some are very experienced in using RFM and demographic data. Maybe they have been reasonably successful with choices of variables handed down to them by their predecessors. But can they really go head-to-head against carefully constructed statistical models?

What is a statistical model, and how is it built? In short, a model is a mathematical expression of “differences” between dichotomous groups. Too much of a mouthful? Just imagine two groups of people who do not overlap. They may be buyers vs. non-buyers; responders vs. non-responders; credit-worthy vs. not-credit-worthy; loyal customers vs. attrition-bound, etc. The first step in modeling is to define the target, and that is the most important step of all. If the target is hanging in the wrong place, you will be shooting at the wrong place, no matter how good your rifle is.

And the target should be expressed in mathematical terms, as computers can’t read our minds, not just yet. Defining the target is a job in itself:

  • If you’re going after frequent flyers, how frequent is frequent enough for you? Five times a year or 10 times a year? Or somewhere in between? Or should it remain continuous?
  • What if the target is too small or too large? What then?
  • If you are looking for more valuable prospects, how would you express that? In terms of average spending, lifetime spending or sheer number of transactions?
  • What if there is an inverse relationship between frequency and dollar spending (i.e., high spenders shopping infrequently)?
  • And what would be the borderline number to be “valuable” in all this?

Once the target is set, after much pondering, then the job is to select the variables that describe the “differences” between the two groups. For example, I know how much marketers love to use income variables in various situations. But if that popular variable does not explain the differences between the two groups (target and non-target), the mathematics will mercilessly throw it out. This rigorous exercise of examining hundreds or even thousands of variables is one of the most critical steps, during which many variables go through various types of transformations. Statisticians have different preferences in terms of ideal numbers of variables in a model, while non-statisticians like us don’t need to be too concerned, as long as the resultant model works. Who cares if a cat is white or black, as long as it catches mice?

Not all selected variables are equally important in model algorithms, either. More powerful variables will be assigned with higher weight, and the sum of these weighted values is what we call model score. Now, non-statisticians who have been slightly allergic to math since the third grade only need to know that the higher the score, the more likely the record in question is to be like the target. To make the matter even simpler, let’s just say that you want higher scores over lower scores. If you are a salesperson, just call the high-score prospects first. And would you care how many variables are packed into that score, for as long as you get the good “Glengarry Glen Ross” leads on top?

So, let me ask again. Does this sound like something a rudimentary selection rule with two to three variables can beat when it comes to identifying the right target? Maybe someone can get lucky once or twice, but not consistently.

That leads to the next point, “consistency.” Because models do not rely on a few popular variables, they are far less volatile than simple selection rules or queries. In this age of Big Data, there are more transaction and behavioral data in the mix than ever, and they are far more volatile than demographic and geo-demographic data. Put simply, people’s purchasing behavior and preferences change much faster than family composition or their income, and that volatility factor calls for more statistical work. Plus, all facets of marketing are now more about measurable results (ah, that dreaded ROI, or “Roy,” the way I call it), and the businesses call for consistent hitters over one-hit wonders.

“Revealing hidden patterns in data” is my favorite. When marketers are presented with thousands of variables, I see a majority of them just sticking to a few popular ones all the time. Some basic recency and frequency data are there, and among hundreds of demographic variables, the list often stops after income, age, gender, presence of children, and some regional variables. But seriously, do you think that the difference between a luxury car buyer and an SUV buyer is just income and age? You see, these variables are just the ones that human minds are accustomed to. Mathematics do not have such preconceived notions. Sticking to a few popular variables is like children repeatedly using three favorite colors out of a whole box of crayons.

I once saw a neighborhood-level U.S. Census variable called “% Households with Septic Tanks” in a model built for a high-end furniture catalog. Really, the variable was “percentage of houses with septic tanks in the neighborhood.” Then I realized it made a lot of sense. That variable was revealing how far away that neighborhood was located in comparison to populous city centers. As the percentage of septic tanks increased, the further away the residents were from the city center. And maybe those folks who live in scarcely populated areas were more likely to shop for furniture through catalogs than the folks who live closer to commercial areas.

This is where we all have that “aha” moment. But you and I will never pick that variable in anything that we do, not in million years, no matter how effective it may be in finding the target prospects. The word “septic” may scare some people off at “hello.” In any case, modeling procedures reveal hidden connections like that all of the time, and that is a very important function in data-rich environments. Otherwise, we will not know what to throw out without fear, and the databases will continuously become larger and more unusable.

Moving on to the next points, “Repeatable” and “Expandable” are somewhat related. Let’s say a marketer has been using a very innovative selection logic that she came across almost by accident. In pursuing special types of wealthy people, she stumbled upon a piece of data called “owner of swimming pool.” Now, she may have even had a few good runs with it, too. But eventually, that success will lead to the question of:

1. Having to repeat that success again and again; and
2. Having to expand that universe, when the “known” universe of swimming pool owners become depleted or saturated.

Ah, the chagrin of a one-hit-wonder begins.

Use of statistical models, with help of multiple variables and scalable scoring, would avoid all of those issues. You want to expand the prospect universe? No trouble. Just dial down the scores on the scale a little further. We can even measure the risk of reaching into the lower-scoring groups. And you don’t have to worry about coverage issues related to a few variables, as those won’t be the only ones in the model. Want to automate the selection process? No problem there, as using a score, which is a summary of key predictors, is far simpler than having to carry a long list of data variables into any automated system.

Now, that leads to the next point, “Filling in the gaps and summarizing the complex data into an easy-to-use format.” In the age of ubiquitous and “Big” data, this is the single-most important point, way beyond the previous examples for traditional 1-to-1 marketing applications. We are definitely going through massive data overloads everywhere, and someone better refine the data and provide some usable answers.

As I mentioned earlier, we build models because we will never know the whole truth. I believe that the Big Data movement should be all about:

1. Filtering the noise from valuable information; and
2. Filling the gaps.

“Gaps,” you say? Believe me, there are plenty of gaps in any dataset, big or small.

When information continues to get piled on, the resultant database may look big. And they are physically large. But in marketing, as I repeatedly emphasized in my previous columns, the data must be realigned to “buyer-centric” formats, with every data point describing each individual, as marketing is all about people.

Sure, you may have tons of mobile phone-related data. In fact, it could be quite huge in size. But let me turn that upside down for you (more like sideways-up, in practice). Now, try to describe everyone in your footprint in terms of certain activities. Say, “every smart phone owner who used more than 80 percent of his or her monthly data allowance on the average for the past 12 months, regardless of the carrier.” Hey, don’t blame me for asking these questions just because it’s inconvenient for data handlers to answer them. Some marketers would certainly benefit from information like that, and no one cares about just bits and pieces of data, other than for some interesting tidbits at a party.

Here’s the main trouble when you start asking buyer-related questions like that. Once we try to look at the world from the “buyer-centric” point of view, we will realize there are tons of missing data (i.e., a whole bunch of people with not much information). It may be that you will never get this kind of data from all carriers. Maybe not everyone is tracked this way. In terms of individuals, you may end up with less than 10 percent in the database with mobile information attached to them. In fact, many interesting variables may have less than 1 percent coverage. Holes are everywhere in so-called Big Data.

Models can fill in those blanks for you. For all those data compilers who sell age and income data for every household in the country, do you believe that they really “know” everyone’s age and income? A good majority of the information is based on carefully constructed models. And there is nothing wrong with that.

If you don’t get to “know” something, we can get to a “likelihood” score—of “being like” that something. And in that world, every measurement is on a scale, with no missing values. For example, the higher the score of a model built for a telecommunication company, the more likely that the prospect is going to use a high-speed data plan, or the international long distance services, depending on the purpose of the model. Or the more likely the person will buy sports packages via cable or satellite. Or the person is more likely to subscribe to premium movie channels. Etc., etc. With scores like these, a marketer can initiate the conversation with—not just talking to—a particular prospect with customized product packages in his hand.

And that leads us to the final point in all this, “Staying relevant to your customers and prospects.” That is what Big Data should be all about—at least for us marketers. We know plenty about a lot of people. And they are asking us why we are still so random about marketing messages. With all these data that are literally floating around, marketers can do so much better. But not without statistical models that fill in the gaps and turn pieces of data into marketing-ready answers.

So, why model? Because a big pile of information doesn’t provide answers on its own, and that pile has more holes than Swiss cheese if you look closely. That’s my final answer.

New Developments in B-to-B List Acquisition

To reach cold prospects among business audiences, sales and marketing teams often begin by developing a list of prospective targets. Marketers can find just about every target company, title and job function they need from traditional list suppliers. Plus, the Internet has made possible the introduction of some excellent new opportunities for identifying prospects at various stages of the buying cycle. Let’s look at what’s new in B-to-B lists these days

To reach cold prospects among business audiences, sales and marketing teams often begin by developing a list of prospective targets. Marketers can find just about every target company, title and job function they need from traditional list suppliers. Plus, the Internet has made possible the introduction of some excellent new opportunities for identifying prospects at various stages of the buying cycle. Let’s look at what’s new in B-to-B lists these days.

Traditionally, the first step in list development has been working with a list broker who has experience in your target audience category. There are more than 40,000 business lists available for rent in the U.S., plus numerous databases and online data enhancement services to choose from.

Business lists can be divided into four general types:

  1. Compiled files assembled from directories, the Internet or other public and private sources, by such suppliers as D&B, InfoGroup, Data.com, NetProspex and ZoomInfo. In recent years, many compilers have been making their data available for rent via an online interface, vastly enhancing the speed and flexibility of ordering.
  2. Response files created as a by-product of other businesses, like catalog/e-commerce sales, seminars, trade organization memberships, or magazine and newsletter subscriptions. Response files tend to be more current and accurate than compiled files.
  3. Cooperative databases from multiple list owners, offered in either open format, where you pay for what you use (examples being MeritDirect’s MeritBase, InfoGroup’s b2bdatawarehouse and Mardev DM2’s Decisionmaker database), or closed format, where only members who put customer names in can take prospect names out (examples include Epsilon Abacus Cooperative and the b2bBase, a joint venture of MeritDirect and Experian).
  4. Internal databases populated from billing systems, lead management systems, and website registration systems. Many companies today use their marketing automation or CRM systems as their marketing databases, and populate them from a variety of internal and external sources.

A New Direction in B-to-B Lists
The B-to-B list industry has changed considerably in the last decade, with the proliferation of social networks. But the big new development today is the trend away from static name/address lists, to dynamic sourcing of prospect names complete with valuable indicators of buying readiness culled from their actual behavior online. Companies such as InsideView and Leadspace are developing solutions in this area.

Leadspace, created by a team of former Israeli intelligence officers, is a leader in targeted, real-time prospecting data for business marketers. Their process begins with constructing an ideal buyer persona by analyzing the client’s best customers, which can be executed by uploading a few hundred records of name, company name and email address. Then, Leadspace scours the Internet, social networks and scores of contact databases for look-alikes and immediately delivers prospect names, fresh contact information and additional data about their professional activities.

How LevelEleven Took its Prospecting to the Next Level
LevelEleven provides a cloud-based platform where sales managers can create fresh and compelling sales contests within Salesforce.com. For example, the Detroit Pistons recently used LevelEleven to organize a sales contest for skyboxes at their arena, and drove sales of over half a million dollars. In other words, 50 percent of the skybox annual sales target was closed in a mere six weeks.

LevelEleven’s target prospect is a sales manager or sales operations manager in any company that uses Salesforce.com as its CRM system. Today, LevelEleven’s sales team gets leads from four sources:

  1. The Salesforce.com AppExchange, where other Salesforce users search for partners.
  2. Conferences and trade shows, like Dreamforce.
  3. Registrations from content downloads at the LevelEleven website.
  4. Rented lists of prospects.

LevelEleven has tried a variety of list sources over the years, with mixed results. In the first half of 2012, the prospecting sources produced zero in closed sales. In June 2012, they began experimenting with Leadspace. In the second half of 2012, a full 30 percent of LevelEleven closed deals came from this source.

According to Bob Marsh, CEO, the power of Leadspace for LevelEleven is its close targeting based on the LevelEleven customer profile. “Leadspace helps us infer pretty accurately whether a prospect is using the Salesforce platform,” he says. “And they deliver to us a short list of highly likely contacts in the account, like the Salesforce administrator or the sales operations manager. Everyone on our sales team has a Leadspace license, and it is performing for us.”

It’s a good thing that the B-to-B list business is continuing to evolve in new directions. What new developments are you seeing?

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

Testing for B-to-B Marketers: How Hard is It?

B-to-B marketers are often guilty of laziness when it comes to testing their communications, whether it’s testing the copy approach, the layout, the offer or the target audience. Well, to call it laziness may not be entirely fair. It’s a fact that the typical B-to-B campaign targets universes that are too small to support a split test. If you’re selling specialized machine tools, you’re lucky if you have 10,000 potential customers worldwide.

B-to-B marketers are often guilty of laziness when it comes to testing their communications, whether it’s testing the copy approach, the layout, the offer or the target audience. Well, to call it laziness may not be entirely fair. It’s a fact that the typical B-to-B campaign targets universes that are too small to support a split test. If you’re selling specialized machine tools, you’re lucky if you have 10,000 potential customers worldwide.

I work with a company that offers employee benefits programs, and markets to HR professionals. We are planning a campaign to take the service into the Boston area, targeting firms with more than 100 employees, which number about 6,000 sites. At two HR contacts per site, using direct mail, we would have a mail plan of 12,000. With an estimated response rate of 1 percent, we’re looking at only 120 inquiries-clearly not enough to conduct a test of the two good offer ideas we are kicking around. Which is a shame, because we really have no idea which motivational offer is going to work better with this audience.

But in the digital world, B-to-B marketers have a lot more options for testing. Split tests are easy to set up, and applicable to any communications vehicle that drives a response-whether it be an email, a landing page, a banner ad, Adwords copy, anything, using free tools like Google Website Optimizer or scores of other SaaS or enterprise software tools.

Plus, there are abundant resources out there now to guide and inspire business marketers. Have a look at Which Test Won, a weekly comparison of two B-to-B live test versions-usually landing pages-where visitors are invited to go with their guts, and pick a winner. Then, you can view the actual winner and participate in a lively discussion of possible reasons why. This brilliant site was the brain child of Anne Holland, the founder of Marketing Sherpa.

So my client would like to conduct an offer test through digital channels, and we are exploring various options. It’s still not easy with a small prospect universe in a limited geography. There are not enough targeted banner media available to reach HR professionals in the Boston-only area. Email to entirely cold prospects is too spammy to generate leads at a reasonable cost-and still doesn’t solve the universe size problem that we face with direct mail. We considered Google AdWords with location targeting, but it’s going to be hard to sell the offer properly within the AdWords copy limits. Not to mention questions about how long it would take to get enough clicks to call the results. So our search continues, and we’d welcome ideas from Target Marketing readers on this one.

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