Why You Are Missing Out Without Conversion Tracking

Which digital marketing channels are driving the most leads and sales for your business? Are any channels just wasting your budget? Without properly set up conversion tracking, there’s no way to answer those two critical questions.

How can you tell if your new Google Ads campaign is improving your conversion rate? What percentage of visitors coming to your landing pages are there because of your Facebook Ads? You can’t get an accurate assessment of the ROI generated by your advertising efforts without implementing mechanisms to track visitor responses.

What Is Conversion Tracking?

Conversion tracking involves placing a piece of code on your website to track visitors and their actions. The data helps you understand their responses to various techniques used in your ad campaigns and different webpage designs. You can use conversion tracking for testing of keywords, redesigned landing pages, and new ad text.

Items You Should Be Tracking

  • Forms on your website (ex. quote requests, scheduled appointments, demo requests)
  • E-commerce sales
  • Coupon codes you give out as a way of encouraging people to visit physical locations
  • Phone calls

Here is what you gain by effectively tracking your digital marketing efforts.

1. Better ROI Tracking

You can add tracking codes to “Thank You” pages to monitor completed transactions by visitors and origination channels. That can tell you how many of those conversions came from visitors who clicked on specific advertisements. You can include tracking of signups, lead generation, or other items relevant to improving ROI.

2. Insights Into Campaign Successes

Some ads will perform better than others. Conversion tracking tells you how well specific keywords perform in attracting your target audience. You can also learn which ad campaigns to eliminate if they tend to draw visitors who quickly move away from your landing pages. Use information gained from your split testing efforts to tweak your keyword lists, ad copy, and landing pages for better performance.

3. Figuring Out What Content to Reuse

You want to stick with what works. Conversion tracking lets you know which content on your website attracted the most interest, or which campaigns helped drive higher-quality visitors. You want content that keeps visitors on your site who will eventually convert into a lead or sale.

4. Improved Audience Categorization

Segmenting audiences allows you to provide relevant content to those who visit your site or sign up to receive your email communications. Conversion tracking helps you figure out whether you have your contacts properly sorted for the type of information they receive.

Better categorization means your audiences aren’t sending your emails directly to the trash bin, or worse, clicking the “report spam” and/or “Unsubscribe” link. You also increase your chances of attracting the type of attention that leads to more conversions and better ROI.

5. Knowledge of Where to Direct Marketing Budget

Marketers running campaigns on a limited budget must maximize each dollar spent, while being cost-efficient. That allows you to create more effective campaigns that get the most for your money. You avoid dumping money into failing ad strategies and can direct those funds to higher-performing efforts.

Why Conversion Tracking?

Conversion tracking allows you to track and improve the ROI of your digital marketing campaigns by helping you identify your best-performing campaigns and eliminate those not delivering the desired conversion rates. It also helps you understand where you should be directing your budget across all the various marketing channels so you maximize every dollar invested.

Want more help tracking your marketing campaigns?  Click here to grab a copy of our “Ultimate Google Analytics Checklist.”

The Most Interesting Man in the World Is No Longer Interesting

In 2006, Dos Equis beer launched an ad campaign featuring “the world’s most interesting man” — a campaign that ran for 10 years and had an undeniable impact on sales, some reporting an increase of 22 percent while other imported beer sales fell 4 percent in the U.S.

Dos Equis Most Interesting Man in the WorldIn 2006, Dos Equis beer launched an ad campaign featuring “the world’s most interesting man” — a campaign that ran for 10 years, and it’s had an undeniable impact on sales with some reporting an increase of 22 percent while other imported beer sales fell 4 percent in the U.S.

It’s not surprising that the campaign resonated. It was clever, and the situations “the man” found himself in were outrageous, far-fetched and humorous. From surfing a killer whale, to slamming a revolving door, to “speaking French … in Russian,” to finding The Fountain of Youth but not taking a drink because “he wasn’t thirsty,” the campaign always elicited at least a smirk from the men in my household.

At the end of every TV spot, the most interesting man in the world would face the camera surrounded by several beautiful woman and comment, “I don’t always drink beer, but when I do, I prefer Dos Equis … Stay thirsty my friends.” The actor, Johnathan Goldsmith, was bearded, silver-haired and exuded sexual charm, despite being in his 70s. In fact it was his age that made his adventures believable!

But in the spring of 2016, Dos Equis announced that Goldsmith would be retiring from the role by sending him on a one-way trip to Mars. I was devastated. Did some focus group tell Dos Equis that Goldsmith didn’t resonate with Millennials? Were sales on the decline and the campaign was seen as no longer relevant?

That doesn’t seem to be true because Dos Equis has now launched a new campaign and has replaced Goldsmith with a younger version. But instead of being outrageous, far-fetched and humorous, the new TV spots are just plain dumb. But don’t take my word for it. My Millennial sons actually made the observation first.

The most recent spot, “The Most Interesting Man Spices Things Up,” has our hero in a competition of who can eat the spiciest pepper. After both take a bite, his competitor’s eyes bulge and sweat pours off his forehead; our hero simply smiles.

“Wow. They’ve missed the point!,” lamented my son. “We watch YouTube videos of people eating spicy things to see what happens to them … what’s interesting about a guy who has no reaction at all?”

Indeed.

A trip to YouTube shows a mere 323 views of this newest commercial, posted over 2 weeks ago. Another spot, posted 6 months ago, got 95,000 views. In comparison, one of the historical spots with my man Jonathan racked up 3.5 million views.

What is interesting is that Heineken’s share price (Dos Equis’ parent company) started dipping in the middle of 2016 … right around the time the old campaign ended. Another research report by YouGov shows that consideration of purchase among Millennials fell from 18 percent in November to 8 percent in December.

It’s just one more example of a brilliant marketing idea gone terribly awry. That’s the only interesting part of this story.

How It Should Have Ended: Your Marketing Campaign

I love a good, goofy YouTube series. Screen Rants, Crash Courses, Epic Rap Battles of History … it’s all good! One of my favorites is HISHE: How It Should Have Ended. And HISHE has a hidden lesson for all marketers.

I love a good, goofy YouTube series. Screen Rants, Crash Courses, Epic Rap Battles of History … it’s all good! One of my favorites is HISHE: How It Should Have Ended. And HISHE has a hidden lesson for all marketers.

HISHE is kind of a geeky idea. They make fun of movies by filling in some of the retrospective plot holes with stupid, stupid jokes, in cartoon form. Here’s one from a movie you’ve probably seen (so hopefully no spoilers):

(Sidenote: I never thought of Vader just force-catching Luke and dragging him back up, but now I can’t unsee it.)

Alright, that’s some dumb, cartoon, totally age-inappropriate humor. But it works because the creators look back and think about the movie, and think about what the characters should have done differently and “how it should have ended.”

Do you ever do that with your marketing campaigns? I’m sure you do your testing, make your decisions, execute the plan, and then look at the results and figure out how to do it better next time. … But do you ever look back at the actual process and results as they came in, and do a check for your own plot holes? Have you thought about how the campaign could have ended if you’d done a few things differently, for better or worse?

There’s a similar idea in publishing called the “Postmortem.” After you get the printed magazines in, usually a month or two after you finished working on them, everyone involved in the content and layout takes a copy and goes through it and marks it up with notes. You mark what you think worked or didn’t work, what you could have done better, catch any mistakes that got through proofing (which TOTALLY never happens in Target Marketing, of course).

It’s not always a comfortable exercise. There’s always some regrets. You spot some missed opportunities and the touches you wish you could’ve added that got lost in the rush against deadlines and closing dates. You get to relish some of the stuff you did well, though, too.

Through it all, you dial in your sense of what you should prioritize when you’re working under those tight deadlines. What touches are worth adding, and which ones are OK to let go.

You can do the same thing with your marketing, going back over the finished product of a marketing campaign (or a sample time period of your ongoing marketing processes), and see where your plot holes are, where fall off happened, where you forgot to use a force power or two.

The difference between media and marketing, is that once a movie or magazine is done, it’s over. You can’t fix that one, you can only hope to do the next one better. But in your marketing, you can always make adjustments on the fly or for the next deployment. You can fix your plotholes (and no one is going to start an Internet petition about who should’ve shot first).

When you think about How It Should Have Ended last time, you might be surprised at how much better you can make it end the next time.

What are some plot holes you’ve had that could’ve been an episode of HISHE: Your Marketing Campaign?

Are You Trapped in Groundhog Day?

Today’s the day … the day in which a 20-pound groundhog has informed us that we’ll see an early spring (atta boy, Punxsy Phil!). Yes, Punxsutawney Phil, America’s furriest weather reporter, has filed yet another report. I watched “Groundhog Day” for the first time recently, and it got me thinking about being trapped in a rut of nightmarish proportions.

Today’s the day … the day in which a 20-pound groundhog has informed us that we’ll see an early spring (atta boy, Punxsy Phil!). Yes, Punxsutawney Phil, America’s furriest weather reporter, has filed yet another report.


Courtesy of The Washington Post

I watched “Groundhog Day” for the first time recently (don’t judge me … I was 11 when it came out; my humor was not up to par), and it got me thinking about being trapped in a rut of nightmarish proportions, much like Bill Murray’s character, Phil Conors. It seems like it would go from bewildering, to amusing, to boring, to depressing, to … well, you’ve probably seen the movie and how Phil turns it around.

Groundhog DayNow, think about yourself. Are you trapped in a “marketing” Groundhog Day? Probably with fewer groundhogs (which is a bummer … they’re cute), and more tired campaigns, drab creative and design, frustrating workflows, and other things that make you feel like this:

Bill Murray Ground Hog Day Clock PunchOr maybe:

Groundhog Day Bill Murray Toaster in the BathOof. That got dark REAL quick.

But it’s happened to all of us. Sometimes we get trapped in doing the same old, same old because, well, it works. It makes money. It’s what our customers want. None of those are bad things.

That is, until it stops working. It stops making money. And either our customers don’t want it anymore, or we don’t want those kinds of customers.

When we get too comfortable, we get stale, unless what works is pushing ourselves out of our comfort zones constantly (If you’re doing that, can we chat? Because I would love some tips.).

As my Groundhog Day gift to you (Seriously Hallmark, where are the cards?), here are three “sassy” suggestions for breaking out of your personal Groundhog Day Marketing Hell and waking up to discover it’s Feb. 3 and life is good:

1. Question EVERYTHING. I might drive Thorin and other coworkers nuts with this at times, but when I’m free to comfortably question what we’re working on and why, I sometimes identify things we need to reconsider, change, etc. And then we have a moment of “Phew … good question!”

2. Say the Crazy Idea Out Loud. This is great for a couple of reasons. First, it gets it out there … off your mind and out of your mouth. I find thishelps me clear my mind to move on to better ideas. And secondly … sometimes the idea isn’t that crazy. And you know what, it could be really freaking good. But you have to embrace the totally out there in order to do this.

3. Determine What You Can Easily Test on the Cheap. Testing is great because it allows you to try things out; that said, it can get expensive, or become time consuming. But if you can figure out a few things to test easily and affordably, you might discover some really interesting things you can take action on.

Winnipeg Willow
Not only did Winnipeg Willow predict the weather in Canada, but she was a wildlife ambassador who taught children about the importance of eating veggies. I’m honestly sure she’ll be missed by those who cared for her. Photo from CBC.

And finally, in other sad weather-predicting rodent news, Canada’s own Winnipeg WIllow died this past Friday, unexpectedly. So I dedicate this post to you, Winni-Will … may you be munching on all the carrots and kale in the wildlife afterlife, and may it always be spring.

Stimulating Awe, Goosebumps and Chills in Copy

When your copy stimulates awe, your customer should experience a physiological reaction like goosebumps or chills. A physical reaction comes from stimulation of the mind. And the positive emotion of awe is more likely to move a person to action. Direct marketers and copywriters have the opportunity to create these physical sensations with awe-inspiring copy

When your copy stimulates awe, your customer should experience a physiological reaction like goosebumps or chills. A physical reaction comes from stimulation of the mind. And the positive emotion of awe is more likely to move a person to action. Direct marketers and copywriters have the opportunity to create these physical sensations with awe-inspiring copy.

The link between positive moods and the physiological reaction we get with goosebumps is proven. So if you give your prospects goosebumps, surely you can sell more.

Research at the University of California, Berkeley between emotions such as compassion, joy, and love, versus the levels of interleukin-6 (IL-6)—a secretion which causes inflammation in the body—finds that those who regularly have positive emotions have less IL-6. Researchers noticed the strongest reaction with one particular emotion:

Awe.

You may not think of creating awe and wonderment when writing copy, but you should. Dacher Keltner, a psychology professor and the senior author of the study, gave examples of awe by saying “Some people feel awe listening to music, others watching a sunset or attending a political rally or seeing kids play.”

So what is this emotion called “awe?” Look at a dictionary and you’ll be told it’s “an overwhelming feeling of reverence, admiration, and fear, produced by that which is grand, sublime, or extremely powerful.” It can also result in a subconscious release of adrenaline.

An adrenaline rush causes the contraction of skin muscles and other body reactions. Adrenaline is often released when you feel cold or afraid, but also if you are under stress and feel strong emotions, such as anger or excitement. Other signs of adrenaline release include tears, sweaty palms, trembling hands, an increase in blood pressure, a racing heart or the feeling of ‘butterflies’ in the stomach.

If you create a strong new memory in your message that reminds your audience of a significant event, with the adrenalin rush they may feel goosebumps or chills. Past awe emotions can resurface with the right triggers.

Most importantly, how do you spark awe in your direct marketing campaigns?

  • Stimulate emotions that recall a strong past positive memory
  • Use powerful visuals that accompany copy that paint a picture
  • Stir memory that resonates so strongly that it “feels” right

For your next marketing campaign, deliver that sense of awe so your customer feels goosebumps and chills. And there’s a chance you may feel them, too, as you look at your response rate.

Focus Group of One

If you’re sending your marketing campaigns without benefit of A/B or multi-variant testing—most companies admit to fewer than five tests per month—you are effectively acting as a focus group of one. You are assuming all of your constituents feel the same way about your campaign as you do. Big mistake.

If you’re sending your marketing campaigns without benefit of A/B or multi-variant testing—most companies admit to fewer than five tests per month—you are effectively acting as a focus group of one. You are assuming all of your constituents feel the same way about your campaign as you do. Big mistake.

Most of us have a least a bit of familiarity with A/B testing and have integrated it into some of our deployments. Testing subject line A against subject line B is likely the most common test, but with A/B testing you can go so much further—both simple and complex—for instance:

  • Best time of day for sending each of your email types (e.g., newsletter, offers)
  • Best day for sending each type of email
  • Frequency of sending each type of email
  • Length of subject line
  • Personalization within the subject line
  • Personalization within the message
  • Squeeze page vs. landing page
  • Conversion lift when video, demo or meeting booking are included
  • Diagnosing content errors
  • Challenging long-held behavior assumptions
  • Calls to action
  • Color
  • Format and design
  • Writing style (casual, conversational, sensational, business)
  • From name and email address (business vs. personal)

A/B and multi-variant testing enable you to learn what makes your prospects, leads, subscribers and customers tick. When you adopt a consistent testing process, your accumulative results will provide you with the knowledge to implement dramatic changes producing a measurable impact across campaigns, landing pages, websites and all other inbound and outbound initiatives.

We have a client whose singular call to action in every email is to discount their product, and each offer is more valuable than the last. When I asked how well this worked, they admitted, the bigger the discount, the more they sold. When pressed, however, they could not tell me the ROI of this approach. Sure, they sold more widgets, but at the discount level they offered, they also made far less profit.

I suggested an A/B-laden drip campaign offering no discounts, and instead providing links to testimonials, case studies, demos of their product, book-a-meeting links, and other inbound content. In this way, we were changing their position from asking for the business to earning the business. While I admit this usually lengthens the sales cycle, it also means money is not being left on the table unnecessarily.

For this client, the change in approach was simply too dramatic and they found they couldn’t stick with it long enough to gather the data needed to make long-term business decisions. The limited of data they were able to collect in the first few emails did show, however, an inbound approach deserved strong consideration by their organization.

Not all A/B testing need be this dramatic—we could have started them off with a less-committed approach. My takeaway was: You don’t have to learn it all now; A/B testing can be integrated in a small way. Whether you go all out or an occasional test, A/B data is useless if you do not set measurable goals. Measurable goals mean you will establish:

  • Required return on investment
  • Vehicle (email, direct mail, other)
  • What to test
  • Audience
  • Time frame
  • Testing protocol
  • How to integrate what you’ve learned into future campaigns

If your email application does not support A/B testing, you can use a more automated approach. Simply create two versions of your marketing campaign and divide your list randomly in half—unless, of course, what you’re testing is something within your list, such as gender or locale.

I often am in search of information well beyond opens, clicks and visits, so I turn to Email on Acid for email heat maps and Crazy Egg for landing page and website heat maps. While these are effective on live pages and campaigns, it’s not required you deploy A/B testing to a live audience. Testing can be just as effective with a small focus group, just be sure it’s not a focus group of one.

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.

3 Ways Rank-and-File Marketers Matter to the C-Suite in a Brave New Marketing World

A couple weeks ago in my post titled “Wanted: Data-Driven, Digital CMOs,” I wrote about the enormous pressure CMOs are finding themselves under as the world digitizes, requiring a new type of leader, one who understands and feels comfortable in the digital space. The result of this changing dynamic has been a dramatic shortening of your average CMO’s tenure. I’m not the first to observe this trend—it’s been covered in many places over the past few months, including this great article from Fast Company. In response to this post, however, many colleagues have asked me “What does this mean for the rank-and-file marketer?” I thought this was an excellent question; one I’ve not seen discussed elsewhere.

A couple weeks ago in my post titled “Wanted: Data-Driven, Digital CMOs,” I wrote about the enormous pressure CMOs are finding themselves under as the world digitizes, requiring a new type of leader, one who understands and feels comfortable in the digital space. The result of this changing dynamic has been a dramatic shortening of your average CMO’s tenure.

I’m not the first to observe this trend—it’s been covered in many places over the past few months, including this great article from Fast Company. In response to this post, however, many colleagues have asked me “What does this mean for the rank-and-file marketer?” I thought this was an excellent question; one I’ve not seen discussed elsewhere.

By any standard, it’s certainly not an easy time to be a marketer. Over the past decade, nearly everything we know has changed, as new technologies have arrived in a dizzying fashion, upending the established order. The result for most firms has ranged from confusion to clarity, from paralysis to paroxysm—very frequently all at the same time! Working in an environment like this is definitely no picnic, as firms flail around like a hurt animal trying to figure out what to do, reducing head count, hiring, outsourcing, in-sourcing, you name it.

It may not be an easy time to be a marketer, but I think it’s a good time. The reason why is that marketing has evolved in four very important ways:

1. Marketing has become data driven—in the digital age, information is power. Contemporary marketing requires learning about who your customers are, what they look like, what attributes and affinities they share, and so on. Success means becoming fluent in the new language of the digital age—understanding what terms like “impressions,” “clicks,” “likes” and “followers” mean. But that’s not all: Success requires a deep understanding of and familiarity with campaign analytics, what they mean and signify, and how to interpret and improve upon them.

2. Marketing is technology-focused—it’s no secret that a large portion of marketers’ budgets are now being allocated to digital. Anyone who’s worked in the digital marketing arena knows that success in the space means understanding the new technology ecosystem. The other major technology trend is the fragmentation of the IT infrastructure as the SaaS/Cloud model gains traction. In this new service model, it’s marketing that’s mostly responsible for buying, using and maintaining these new tools.

3. Marketing is highly operational in nature—unlike the brand strategists of yesteryear, today’s marketing department is almost entirely focused on operations, with a heavy emphasis being placed on creating, testing and launching, tracking and optimizing numerous marketing campaigns across various channels using different tools.

In this new environment, the DNA of the rank-and-file marketer has changed radically, morphing from that of a brand steward into, well, something else entirely. Any way you look at it, today’s marketers are highly trained and qualified specialists, possessing a wide range of skills and knowledge, which can take months, if not years, to master.

Moreover, success in any given marketing role requires a deep understanding of various marketing program details, familiarity with firm’s marketing technology, systems and tools, not to mention the prevailing corporate culture. All in all, it’s a tall order.

Over the years, I’ve consulted with dozens of large firms, and I can tell you firsthand that most marketing leadership stakeholders are not digital people. In other words, the only people in the firm who really “get” what the firm’s marketing department is actually doing are the marketers themselves. Interesting, huh?

So what does this all mean? Well, in coming years I foresee a shift in the balance of power as the old generation of marketers gives way to a new generation of younger digital specialists. Now, of course, one generation passing the mantle to the next is the natural order of things. But, based on what’s going on, I see this trend accelerating dramatically in coming months and years, as those who don’t get it are replaced by those who do.

If you’re a marketer, all if this is undoubtedly good news, meaning you’re not only much more important than you think, but your trip up the proverbial corporate ladder is that much shorter. So go forth, young man (or woman), it’s a brave new world!

Any questions or feedback? As usual, I’d love to hear it.

—Rio

Wanted: Data-Driven, Digital CMOs

There was a time, not so long ago, that the firm’s CMO basically acted as the chief brand steward, running a marketing department that focused on maintaining brand equity and making sure the company was sending out the right message to the masses. Data and analytics? They were usually scoffed at … That was the purview of the down-and-dirty world of the direct marketer, right? Direct marketers were the ones who obsessed over response rates, cost per order, lifetime value and so on.

There was a time, not so long ago, that the firm’s CMO basically acted as the chief brand steward, running a marketing department that focused on maintaining brand equity and making sure the company was sending out the right message to the masses. Data and analytics? They were usually scoffed at … That was the purview of the down-and-dirty world of the direct marketer, right? Direct marketers were the ones who obsessed over response rates, cost per order, lifetime value and so on.

Well, suffice it to say that those days are over—marketing in today’s multichannel environment is about much more than just cute creatives and killer copy. Today’s marketing is increasingly digital and data-centric. A recent article appearing in Ad Age explained that “real-time data-driven decisions, enabled by technology, have made the marketer’s job much more measureable and accountable.” Interestingly, the same article also points out that the average tenure of a CMO is a meager 28 months. No coincidence.

What it boils down to is that today’s CMO is expected, de rigueur, to be a pro when it comes to all things digital. We have two important trends to thank for this fact. The first one of these trends is the general transition to digital. Look, it’s no secret that over the past few years there’s been an incredible shift of marketing spend from traditional over to digital media. It’s the scale and speed of this transition that’s so breathtaking.

According to a June 2012 survey by RSW/U.S., 44 percent of marketers report that they are now spending at least half of their budgets on social and digital media. This represents a 42 percent increase from 2009 alone! And this is not the end of the process. I think it’s safe to say now that the proverbial tipping point has been reached—this trend will only accelerate in coming years.

Anyone who’s worked in the digital marketing arena knows that success in the space all really boils down to data: Impressions, clicks, conversions, opens—this is the vocabulary of the digital world. Well, guess what? Today’s CMO needs to have a deep understanding of these terms, what they mean and how the underlying technologies work—at least on a high level—and be generally comfortable playing in the digital space. Think about it: without a significant digital background, how on Earth can a CMO possibly be expected to run a marketing machine where at least half of the marketing dollars are being spent in the digital space? Not happening.

The other major trend is the inexorable fragmentation of the IT infrastructure within enterprise firms. Basically, what’s happening is that because technology has evolved radically over the past 10 years, it’s giving different stakeholders at companies the ability to purchase and use technology outside of their organization’s firewall, and often without IT’s involvement. Very often, in fact, IT is even without IT’s knowledge!

This is huge shift. Just a few short years ago, mind you, software was what you ran on your computer or on the company mainframe, and it was pretty much always purchased and managed by IT. Well, those days are most definitely over. What’s happened is that the emergence of the SaaS/Cloud model of software delivery has turned that world on its head.

Today, any marketer with a credit card can sign up for, say, a CRM tool or a marketing automation tool and be off to the races in seconds flat. Ask any marketer and they’ll explain how this has been a huge boon to their departments, liberating them forever from the clutches of IT.

Now, of course, a big reason for this excitement is the oftentimes frosty relationship between marketing and IT. Personality types side, in its essence this rocky relationship actually has a lot to do with conflicting mandates. It’s the IT department’s mandate to act as the stewards of the firm’s information and technology infrastructure. Essentially, it’s their job to keep internal systems running and make sure they’re secure. That’s about it. No, it’s not their job to build you a new landing page, or set up a new email campaign for this fall’s reactivation campaign.

Today’s marketing department, on the other hand, is much more focused on operations than anything else. Today marketing is about creating, testing and launching numerous marketing campaigns across various channels using different tools, and evaluating their performance using real-time analytics. And running an operationally focused marketing team requires the ability to build, dispatch and analyze lots of campaigns in rapid succession. Until recently, this heaped loads of pressure on the IT folks, who groaned under the strain. So you can see why marketers have cheered and embraced the emergence of Web-based SaaS marketing tools.

Okay, I got a little sidetracked there, so I’ll get back to the central point, which is that because marketing is rapidly becoming the de facto owners of their own IT infrastructure, this mean that they now control the technology itself and the data contained therein. It’s a big responsibility, requiring marketers to manage and safeguard this vital corporate infrastructure and information, taking on the dual roles of chief marketing technologist and data steward. But with this responsibility comes great power—to use these awesome tools and information to really, truly understand who customers and prospects are, and send out highly personalized and effective marketing campaigns with demonstrable ROI.

But evaluating performance in this environment means not only using new marketing tools and digging through mountains of data. Just as importantly, it also means understanding what it all means. In other words, just because you’re a CMO does not mean you don’t need to know how many opt-ins you have in your company database, or how many fans on Facebook.

And guess what? It’s hard to be comfortable with digital if you’ve never played in the space. But how many CMOs are also digital pros? Not too many. So not surprisingly, firms are finding that it’s incredibly difficult to find leaders with the hard-to-find combination of senior management leadership and digital marketing experience. Given this reality, it’s not too surprising to discover that many companies are running through CMOs in a conveyor belt-like fashion.

Do you know any data-driven digital pros with senior marketing leadership experience?? If so, bet your bottom dollar these executives will be cashing in big time in coming years.

—Rio