Death of the Salesman

There’s no question that the Willy Lomans of this world have been dying a slow, agonizing death—only instead of losing the fight to travel exhaustion, the opponent is the Internet … And marketing

There’s no question that the Willy Lomans of this world have been dying a slow, agonizing death—only instead of losing the fight to travel exhaustion, the opponent is the Internet.

According to a recent CEB article in the Harvard Business Review, 57 percent of purchase decisions are made before a customer ever talks to a supplier, and Gartner Research predicts that by 2020, customers will manage 85 percent of their relationship with an enterprise without interacting with a human. That shouldn’t surprise anyone since we spend much of our days tapping on keyboards or flicking our fingers across tiny screens.

In Willy’s day, the lead generation process would have consisted of making a phone call, setting up an appointment, hopping a plane to the prospect’s office, and dragging a sample case through the airport. In the 1980’s, that sample case turned into an overhead projector, then a slide projector and a laptop, and finally a mini projector linked to a mobile device or thumb drive. In 2014, salespeople are lucky if they can connect to a prospect on a video conferencing call.

Clearly the days of gathering in a conference room for the sales pitch are long gone. We’ve always known that sales people talk too much and buyers, who’ve never had the patience to listen, now have the tools to avoid them altogether: websites, whitepapers, case studies, videos, LinkedIn groups, webcasts—virtually anything and everything to avoid talking to sales.

As a result, the sales function has now been placed squarely in the hands of the content strategists and creators. And yes, that means that the sales function is now in the hands of marketing.

Now a different problem exists. Most marketing folks don’t know how to help the buyer along their journey because that’s not how they’ve been trained. They have no idea how different types of buyers think, or how they search for information, or make decisions, so they don’t know how to create nor position content in a meaningful and relevant way—and that’s long been the complaint of sales. In their opinion, all marketing does is churn out “fluff” that is irrelevant to a serious buyer.

Now marketers must step up and really understand how to optimize marketing tools in order to help that buyer reach the right brand decision at the end of their journey. That’s really why content has become the marketing buzz word.

And just like we despised the salesman who talked too much, potential buyers despise content that is full of sales-speak. While a product brochure has a purpose, it is not strategic content. Similarly, a webinar in which most of the supporting slides are simply advertising for the product, turns off participants who quickly express their displeasure via online chat tools to the host and by logging out of the event.

Great content should seek to:

  • Be authentic: What you say needs to sound genuine and ring true—no one believes you are the only solution to a problem. On the contrary, the discovery process is all about evaluating your options (the pros and the cons). Avoiding a question because your answer may reveal the flaws of your product or service only shines a spotlight on the issue. Honesty is always the best policy.
  • Be relevant: Share insightful information that leverages your expertise and experience; help the buyer connect the dots. “How to” articles are popular, as are comparison charts—if you’re not going to do it, the prospect will be doing it for themselves anyway, so why not help by pointing out comparison points (that benefit your product) they might not have previously considered?
  • Be timely: To get a leg up in the marketplace, you need to be prepared to add value when the timing is ripe. It’s highly unlikely that your marketplace hasn’t changed in the last 50 years. Help show buyers how your product/service is relevant in today’s marketplace—how it deals with challenges you know they’re facing or are going to face tomorrow.

Smart marketers have a lead nurturing strategy in place—an organized and logical method of sharing relevant content along the buy cycle. And that content is well written and segmented by type of decision maker. The CFO has a different set of evaluation criteria from the CEO and the CTO. Business owners look at purchase decisions through a completely different lens than a corporate manager.

Depending on the industry, business buyers have different problems they’re trying to solve, so generic content has less relevance than content that addresses specific issues in an industry segment. Those in healthcare, for example, perceive a problem from a different perspective than those in transportation.

The new name of the selling game is “Educate the Buyer—but in a helpful and relevant way.” And while Willy Loman may continue to sit at his desk making cold calls or sending out prospecting emails, the reality is nobody has the patience or interest to listen to his sales pitch any more. So marketers need to step up and accept responsibility for lead generation, lead nurturing and, in many instances, closing the sale.

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.

Which Costs More: Video or Direct Mail?

What are the economics of producing and distributing a direct marketing video? And, how does it line up with costs for direct mail? If you’re a traditional direct marketer who has lived and breathed marketing costs, then running the numbers should come naturally. For this discussion, we’ll use direct mail as the comparison because historically it’s the distribution channel of choice

What are the economics of producing and distributing a direct marketing video? And, how does it line up with costs for direct mail? If you’re a traditional direct marketer who has lived and breathed marketing costs, then running the numbers should come naturally. For this discussion, we’ll use direct mail as the comparison because historically it’s the distribution channel of choice for direct marketers.

We’ve created a “Video Budget Checklist” that helps you itemize cost comparisons of creative, production and distribution between video and direct mail. If you’d like a copy, email me using the link in the left column. It’s free for our readers.

(If the video isn’t just above this line, click here to view it)

Direct mail can come in all sorts of configurations. Low-cost postcards. A simple package of a letter and flyer inside an envelope. Or more expensive with multiple enclosures such as a letter, fold-out four-color brochure, lift note, order form, reply envelope and outer envelope. Sometimes the outer envelope is a custom size or has an oversize window, or there are expensive die-cuts on cards or tip-on elements that are outside of typical print configuration.

The fixed costs to create each of these packages by employees, agencies or freelance creative teams are pretty broad, from several hundred dollars to well into the five-figures when using proven, top-flight direct response creative professionals.

A wide range of configurations can apply to video production, just as it can to direct mail.

You can pop out a 45-second video using your Webcam or flip-camera and post it on YouTube. You just have to ask yourself if the poorly lit, distracting background, muffled or echoey sound of that presentation exemplifies your organization. Alternatively, the video could be purely voice-over with words scrolling along on the screen. Or you can make it visually more alive with photography images or stock video footage. At a more costly level, you might shoot testimonials or interviews in a studio or shoot on location to demonstrate your product. Of course, length impacts cost (just as the number of components impacts cost in direct mail). There are a lot of variables that go into video production, just as there are for direct mail.

The point is this: Start with a budget you’re comfortable with, talk with writers (ideally writers experienced in both direct response print, online and video), develop a video script and storyboard, and work with a skilled video editor. Don’t just be wowed by special effects on someone’s demo reel. Dig in and learn what results were produced from some samples or case studies. You might just want voice-over with images on screen. (See our last blog post for an example of a 3-minute video and details of how we adapted it from a direct mail package.)

If your personality is a draw, you can record yourself on a small camera that can fit in a pocket with a lav microphone for under $200, total. Make sure you have good lighting and background. Or spring $500 or so and get a green screen and lights. That’s the equipment we use to shoot our video for this blog. Be aware, assembling the right equipment and editing software is the easy part. Knowing how to use it all to your best advantage comes from training and practice—or hiring a pro.

Distribution Costs
For direct mail, you have list costs if you’re renting names, data processing, printing, lettershop and postage. The cost can range widely. If you’re testing in small quantities, you’ll pay more per piece.

Knowing the volume of prospects or prior customers to mail, the marketer calculates how many responses are needed to make a specific profit (or break-even) objective. Translate that number into a required response rate to meet your objectives—your allowable marketing cost—and presto, you can use the test of reasonableness to see if the numbers pan out.

For video, your distribution cost is driving viewers to your landing page. You might email your customer file, or rent a list, and give the reader a compelling reason to click to your landing page to watch the video, possibly opt-in for more information, or attempt to convert to a buyer then. You will need to include the cost to set-up the landing page and related items.

We suggest you begin with a budget where your objective is to create a video for the amount of money it would cost to produce a moderate to elaborate direct mail package (although video production on the cheap is possible—and might work).

Then compare the cost to print and mail a direct mail package versus that of emailing (whether it’s to customers at a low cost to email, or rent an email list at a higher cost). And add in the cost for developing your landing page. Chances are your cost per contact will be less for email and the landing page, but as we all know, it all comes down to the cost per sale or lead so bring your focus back to this metric.

One example worth mentioning is that of the Dollar Shave Club. Perhaps you’ve read about it. A big success for a 1:34 video that reportedly cost $4,500 and after a few days generated over 12,000 orders. The video has now been viewed over 4.6 million times.

Bottom line: just as you’d run the numbers to see if it makes financial sense to use direct mail, you need to run the numbers for video, too. And you just might be surprised how favorable the numbers look to reach out and explore video.

P.S.: Just out: comScore has released its April 2012 online video rankings data with a few notable metrics:

  1. 181 million U.S. Internet users watched nearly 37 billion online content videos in April.
  2. 85.5 percent of U.S. Internet audience viewed online video.
  3. The duration of the average online content video was 6.4 minutes.

Stephanie Miller’s Engagement Matters: Email Storytelling Sells

Combat the fatigue from crowded inboxes by embracing the role of storyteller. Telling a story, rather than just announcing a fact or blasting out an announcement, is a more engaging way to share information. The storytelling approach weaves a relationship through a cadence of touchpoints. Any nurturing or loyalty program is built on the same concept, and many B-to-B marketers are very good at telling stories to move prospects through a buying process.

Gone are the days of the passive email subscriber. Consumers and business professionals tire easily when publishers and marketers broadcast to them. It’s the online equivalent of shouting. Your customers and readers want meaningful conversations — and they know they have other options if you don’t deliver.

Combat the fatigue from crowded inboxes by embracing the role of storyteller. Telling a story, rather than just announcing a fact or blasting out an announcement, is a more engaging way to share information. The storytelling approach weaves a relationship through a cadence of touchpoints. This isn’t complex. Any nurturing or loyalty program is built on the same concept, and many B-to-B marketers are very good at telling stories to move prospects through a buying process.

It’s simply a series of stories about use cases, cool new features and real-life implementation of your editorial, products and services. So invite your subscribers to the proverbial campfire and build their anticipation with a question, “How can I help you today?” Email marketing is great for providing the answer.

Invite subscribers on a story journey
Instead of sending a generic newsletter or “special offers,” invite website visitors to accept a two to five message email series on a particular topic. Make it about how your products, services or content will help them: “Five ways to be beautiful this summer,” “Three strategies for impressing your boss,” “Doctor’s advice on buying contact lenses online,” “Ten things your CEO wants you to know,” “Five great summer games for kids under 10.”

Make it easy to sign up by putting invitations in prominent locations on pages that have related content. And be sure permission is clear. If the offer is just for two to five email messages over the same number of weeks or days, then say so. You’ll likely find a higher sign-up rate and higher response and engagement because the content is so targeted. If you’re also signing them up for your ongoing e-newsletter, be clear about that. There’s no reason you can’t encourage a further subscription after you’ve delivered the series, too. Earn their trust first, then sell. Consider the following strategies:

  • Make your story interactive.
  • Tap the socially connected nature of today’s digital experience.
  • Integrate opportunities for subscribers to share with their social networks or forward to others.
  • Invite subscribers to take a poll or survey or give you feedback.
  • Offer a page where subscribers can upload their own stories or photos, and then share that user-generated content back to the group in your series.
  • Ensure your customer service team monitors these pages so that you can quickly respond to any questions or direct prospects to your sales team or e-commerce site.

Why does it work? An email series strategy is based on a fundamental truth of marketing: Provide something of value and customers will continue to engage. A series makes it easy for you to customize messages to the interests of subscribers at that moment. The topic is top of mind for them, and that creates selling and relationship opportunities for you.

Another benefit is that when your email messages are more relevant, you won’t have as many people clicking the “Report Spam” button, which registers as a complaint at internet service providers like Yahoo or Gmail. Even a small number of complaints can result in a poor sender reputation and a block on all your messages. Make even some of your messages more relevant, and the response rates for all your messages will go up and complaints will go down.

For content, consider the following four options:

1. Make it easy to learn more. Offer website visitors a two- to three-part email series rather than a whitepaper. Most downloaded content never actually gets opened or read. Once a whitepaper is downloaded and saved, it’s out of mind. An email series forces marketers to package up content in bite-sized pieces (you can always link to more detail on your website), and gives them several opportunities over a few weeks to engage. Advertising CPMs for these targeted messages can be at a premium, as well.

2. Comparison shopping. Advertisers know that readers are researching and want publishers to help them shorten sales cycles. Use a series of email messages to help subscribers compare competitive sets — the more honest/nonadvertorial you are, the longer they stay on your site! — find testimonials and bloggers, and make a strong business case.

3. Move free-trial subscribers to paid circulation. A series can give prospects confidence in your content or technology. Help them actually use your service during the trial — help them find the best reviews or product feature comparisons, or let them download tools that help them forecast productivity, revenue or cost savings as a result of making a decision to buy. Test if increasing incentives as prospects move through the cycle helps or hurts your conversion (and margin).

4. Educate. Send one great idea each week, and include ways to practice or implement. The next week, ask for input or a story about how that idea worked or didn’t work. Then, the next day, send the next idea. This interactive cadence will build value for subscribers and let them engage repeatedly over time.

Storytelling lets you retain control over the content while giving subscribers the freedom, choice and interactivity they crave. Successful email marketing is built on a very simple concept: Give subscribers what they want, and they’ll give you what you want. Subscribers want you to help them. When you do, they’ll reward you with higher response and sales, positive buzz and sharing, and stronger brand loyalty.

Let me know what you think by sharing any ideas or comments below.