‘Too Much’ Is a Relative Term for Promotional Marketing

If a marketer sends you 20 promotional emails in a month, is that too much? You may say “yes” without even thinking about it. Then why did you not opt out of Amazon email programs when they send far more promotional stuff to you every month?

If a marketer sends you 20 promotional emails in a month, is that too much? You may say “yes” without even thinking about it. Then why did you not opt out of Amazon email programs when they send far more promotional stuff to you every month? Just because it’s a huge brand? I bet it’s because “some” of its promotions are indeed relevant to your needs.

Marketers are often obsessed with KPIs, such as email delivery, open, and clickthrough rates. Some companies reward their employees based on the sheer number of successful email campaign deployments and deliveries. Inevitably, such a practice leads to “over-promotions.” But does every recipient see it that way?

If a customer responds (opens, clicks, or converts, where the conversion is king) multiple times to those 20 emails, maybe that particular customer is NOT over-promoted. Maybe it is okay for you to send more promotional stuff to that customer, granted that the offers are relevant and beneficial to her. But not if she doesn’t open a single email for some time, that’s the very definition of “over-promotion,” leading to an opt-out.

As you can see, the sheer number of emails (or any other channel promotion) to a person should not be the sole barometer. Every customer is different, and recognition of such differences is the first step toward proper personalization. In other words, before worrying about customizing offers and products for a target individual, figure out her personal threshold for over-promotion. How much is too much for everyone?

Figuring out the magic number for each customer is a daunting task, so start with three basic tiers:

  1. Over-promoted,
  2. Adequately promoted, and
  3. Under-promoted.

To get to that, you must merge promotional history data (not just for emails, but for every channel) and response history data (which includes open, clickthrough, browse, and conversion data) on an individual level.

Sounds simple? But marketing organizations rarely get into such practices. Most attributions are done on a channel level, and many do not even have all required data in the same pool. Worse, many don’t have any proper match keys and rules that govern necessary matching steps (i.e., individual-level attribution).

The issue is further compounded by inconsistent rules and data availability among channels (e.g., totally different practices for online and offline channels). So much for the coveted “360-Degree Customer View.” Most organizations fail at “hello” when it comes to marrying promotion and response history data, even for the most recent month.

But is it really that difficult of an operation? After all, any respectful direct marketers are accustomed to good old “match-back” routines, complete with resolutions for fractional allocations. For instance, if the target received multiple promotions in the given study period, which one should be attributed to the conversion? The last one? The first one? Or some credit distribution, based on allocation rules? This is where the rule book comes in.

Now, all online marketers are familiar with reporting tools provided by reputable players, like Google or Adobe. Yes, it is relatively simple to navigate through them. But if the goal is to determine who is over-promoted or adequately promoted, how would you go about it? The best way, of course, is to do the match-back on an individual level, like the old days of direct marketing. But thanks to the sheer volume of online activity data and complexity of match-back, due to the frequent nature of online promotions, you’d be lucky if you could just get past basic “last-click” attribution on an individual level for merely the last quarter.

I sympathize with all of the dilemmas associated with individual-level attributions, so allow me to introduce a simpler way (i.e., a cheat) to get to the individual-level statistics of over- and under-promotion.

Step 1: Count the Basic Elements

Set up the study period of one or two years, and make sure to include full calendar years (such as rolling 12 months, 24 months, etc.). You don’t want to skew the figures by introducing the seasonality factor. Then add up all of the conversions (or transactions) for each individual. While at it, count the opens and clicks, if you have extracted data from toolsets. On the promotional side, count the number of emails and direct mails to each individual. You only have to worry about the outbound channels, as the goal is to curb promotional frequency in the end.

Step 2: Once You Have These Basic Figures, Divide ‘Number of Conversions’ by ‘Number of Promotions’

Perform separate calculations for each channel. For now, don’t worry about the overlaps among channels (i.e., double credit of conversions among channels). We are only looking for directional guidelines for each individual, not comprehensive channel attribution, at this point. For example, email responsiveness would be expressed as “Number of Conversions” divided by “Number of Email Promotions” for each individual in the given study period.

Step 3: Now That You Have Basic ‘Response Rates’

These response rates are for each channel and you must group them into good, bad, and ugly categories.

Examine the distribution curve of response rates, and break them into three segments of one.

  1. Under-promoted (the top part, in terms of response rate),
  2. Adequately Promoted (middle part of the curve),
  3. Over-promote (the bottom part, in terms of response rate).

Consult with a statistician, but when in hurry, start with one standard deviation (or one Z-score) from the top and the bottom. If the distribution is in a classic bell-curve shape (in many cases, it may not be), that will give roughly 17% each for over- and under-promoted segments, and conservatively leave about 2/3 of the target population in the middle. But of course, you can be more aggressive with cutoff lines, and one size will not fit all cases.

In any case, if you keep updating these figures at least once a month, they will automatically be adjusted, based on new data. In other words, if a customer stops responding to your promotions, she will consequently move toward the lower segments (in terms of responsiveness) without any manual intervention.

Putting It All Together

Now you have at least three basic segments grouped by their responsiveness to channel promotions. So, how would you use it?

Start with the “Over-promoted” group, and please decrease the promotional volume for them immediately. You are basically training them to ignore your messages by pushing them too far.

For the “Adequately Promoted” segment, start doing some personalization, in terms of products and offers, to increase response and value. Status quo doesn’t mean that you just repeat what you have been doing all along.

For “Under-promoted” customers, show some care. That does NOT mean you just increase the mail volume to them. They look under-promoted because they are repeat customers. Treat them with special offers and exclusive invitations. Do not ever take them for granted just because they tolerated bombardments of promotions from you. Figure out what “they” are about, and constantly pamper them.

Find Your Strategy

Why do I bother to share this much detail? Because as a consumer, I am so sick of mindless over-promotions. I wouldn’t even ask for sophisticated personalization from every marketer. Let’s start with doing away with carpet bombing to all. That begins with figuring out who is being over-promoted.

And by the way, if you are sending two emails a day to everyone, don’t bother with any of this data work. “Everyone” in your database is pretty much over-promoted. So please curb your enthusiasm, and give them a break.

Sometimes less is more.

Healthcare Marketers Live in Multiple Worlds — Leverage That Insight

As healthcare marketers, you live in multiple worlds. Of course you are a professional. But every time you go to the doctor, you’re a healthcare consumer. And while your employer provides care to tens of thousands of people each year, it’s also one of the largest purchasers of health insurance coverage in your market.

As healthcare marketers, you live in multiple worlds. Of course you are a professional. But every time you go to the doctor, you’re a healthcare consumer. And while your employer provides care to tens of thousands of people each year, it’s also one of the largest purchasers of health insurance coverage in your market.

These multiple perspectives can be a strength as you build bridges among your audiences. Or they may frustrate you, because it adds nuance and complexity to the task at hand.

Let’s take a look at the duality of being both a provider of healthcare and a consumer of health insurance, with all of its rules and paperwork.

Hospitals are one of the largest employers in most communities. A hospital of 200 beds may employ as many as 1,400 full- and part-time benefit-eligible employees, while large facilities can top 5,000. Workforces of that size are diverse, with many roles that impact patient experience but don’t require familiarity with the intricacies of health insurance. But, hopefully, all of those employees are eligible for insurance and made their selections last fall for the 2020 coverage year.

Likewise, consumers who may have changed insurance or their doctor are beginning their patient experience journey. Perhaps, as a consumer yourself, you’ve taken one of your kids to a new doctor and experienced a little disorientation. What would have helped?

This is all to say that more often than you think, you have opportunities to see things through more than one lens. That recognition of the friction points can lead to real improvement in communications and brand experience.

Bring those insights to the table.

SEOs: Should You Seek Continuing Education or Certification, Teach Yourself, or Hire Someone?

SEO is an integral part of online marketing and is now included in most marketing curricula offered at colleges. Additionally, there are numerous certification courses offered by tool vendors and various organizations. There is also many SEOs who either learned on the job or are self-taught.

SEO is an integral part of online marketing and is now included in most marketing curricula offered at colleges. Additionally, there are numerous certification courses offered by tool vendors and various organizations. There is also many SEOs who either learned on the job or are self-taught.

Because almost every resume for an online marketer includes a reference to SEO proficiency, the question remains how to evaluate the depth of learning and level of competency of these candidates. Many very experienced SEOs have never studied SEO as part of a curriculum of study.

When I first started working in search, there were just a few online guides and some excellent forums for those wanting to discuss and solve problems. The entire industry was new and evolving. Most SEOs learned through the proverbial school of hard knocks — success, failure. Their colleagues/peers were the teachers, conferences provided extremely valuable learning opportunities.

In today’s business environment, I would not want to trust a key portion of my marketing to an amateur using trial and error. But that was the way it was. Schools are reopening, so I’d like to use this opportunity to provide a few tips for those who are hiring SEOs for their projects.

Don’t Judge a Book by Its Cover

Technical SEO is a mixture of both left and right brain skills. A single semester course, certification course, or module will not provide the depth of skill needed to helm a large SEO project.

Not everyone learns SEO through official training. Many SEOs with technical/marketing experience will not have the academic coursework, but they often have learned SEO on the job.

On the other hand, coursework can provide a new hire the necessary knowledge to execute tactical steps on even a very large project.

A marketer who does not have a passing knowledge of code (can read and understand what the code instructions say) and how sites are architected must rely on programmers and other more technically proficient personnel. This is not ideal, for the technical team. The SEO must work collaboratively and in tandem to solve problems and achieve business results. It has been years since I personally wrote code, but I have found it a valuable skill to be able to read, understand and critique what the programmers have created.

Tools Are Just Tools

SEO is the home of the online tool junkie. There are literally dozens of toolsets available for almost every task — from keyword selection to analyzing the finished product. Some of the tools have a steep learning curve, others are very easy to learn and are almost intuitive. If your business has an already defined toolset used for SEO, then it makes sense to search for a candidate who is familiar with your chosen toolset.

To help deal with the need for measuring proficiency, some tool providers offer certifications (for example, Google Analytics and Adobe Analytics). A certified candidate offers the hiring managers a measure of confidence in the candidates’ competency.

However, tools are just tools. A candidate with lots of valuable skills, and who’s maybe even certified on a different toolset, but unfamiliar with your toolset, may still be the best candidate.

Tools are constantly changing, and SEOs must adapt to a fluid tool environment.

Hire the Lifelong Learner

The candidate, whether for an in-house SEO job or from an agency pitching for your business, who claims to know everything about SEO is waving a bright red flag. If a candidate does not have a bottomless curiosity and a rich set of sources of information to consult for continuous learning, their skills will quickly stale and become outdated and obsolete.

Hire the lifelong learner with a broad portfolio of skills for your technical SEO, and you will not go wrong.

How to Lose Your Audience’s Trust With Your Marketing

Marketing works best when it helps you reduce perceived risk and gain your prospects’ trust. That’s the most effective way to help a prospect move through the buying process.

If one of marketing’s main goals is, as I touched on a few weeks back, to reduce perceived risk for your prospects, then it’s worth discussing ways you can lose your audience’s trust and how to avoid the mistakes that are likely to lead to that loss.

First, it’s worth remembering that your prospects’ expectations have changed. In both the B2B and B2C worlds, buyers are less likely to be won over by “bullhorn” marketing (“Here we are! Buy from us now, now NOW!”) and more  likely to respond to an approach that focuses on their needs.

Delivering Promotion Where You’re Promised Resources

That shift to prospect needs typically means that the most effective way to connect with prospects is to provide information of value to them as they’re deliberating their choice.

You still need to make the case for your solution, of course, but you’ll more often be doing this indirectly. Or, at least, you’ll be focusing on the prospects’ needs first, and following up with marketing that focuses on what differentiates your offering.

For example, an agency with a focus on email marketing might attract new clients by publishing information on designing emails that are compliant with accessibility guidelines. Or they might talk about ways to maximize the deliverability of your email marketing messages.

If that agency promotes helpful materials like that, but then delivers marketing content that illustrates those points by going heavy on examples of how great their solution is, they’ll lose their prospects’ attention and trust immediately.

So be sure to separate company news and promotional materials from educational materials and broader information. There’s a place in your marketing for both, but if you promise resources, deliver resources.

Case Studies Where the Case Is You

On a similar note, think about how you use case studies. We’ve read too many case studies where the focus is on the marketer and their solution, rather than on the outcomes and the benefits that the client experienced.

Make sure your case studies are a reflection on you, not about you.

One special consideration: process. Process can be a key differentiator for a lot of marketers. If this is the case for you, once again be sure that you highlight how the process benefits the buyer rather than simply illustrating how great the process is.

Don’t Ask If You Don’t Need, and Don’t Ask Twice

Use technology to help you grow your prospect relationships. Tools like progressive profiling and integrations between your CRM, email marketing functionality, and your website can help you build a prospect profile without redundancy.

On first contact, don’t ask for more than an email address. When you reach out to offer the next logical piece of content, prepopulate that email address and ask for another useful data point.

The trick here is for the data point to be useful to you and to the prospect.

For example, if you ask them to tell you the size of their firm, you should be offering them information that’s tailored to firms of that size. Otherwise, trust erodes as they wonder what you’re doing with that information. Why should they let you try to get to know them if they don’t feel like you’re really even trying?

The goal is to strike a balance between what you give and what you get so that you really can learn enough about a prospect’s needs to establish a relationship, build trust, and win their business.

Don’t Settle for Last-Touch Attribution in Marketing

Last month, I talked about factors marketers should consider for attribution rules. Here, I would like to get a little deeper and discuss last-touch attribtuion, as just talking about contributing factors won’t get you anywhere. As in all data-related subjects, the devil hides in the details.

Last month, I talked about factors marketers should consider for attribution rules. Here, I would like to get a little deeper and discuss last-touch attribution, as just talking about contributing factors won’t get you anywhere. As in all data-related subjects, the devil hides in the details. How to collect the data, what to consider, how to manipulate clean and dirty data, and in what order one must execute different steps.

I wonder sometimes why last-touch attribution is such a popular industry, with all of the flaws embedded in that methodology. Without even getting into geeky programming details, let’s think about the limitation of last-touch attribution, in a logical sense.

First off, by giving all of the credit for a conversion to “one” medium of the last touch, you would be ignoring all of the previous efforts done by your own company. If you are the lucky channel manager of the last touch, you wouldn’t mind that at all. But is that fair? C-level executives should not accept such flaws in the name of efficiency or programming convenience.

Why You Shouldn’t Settle for Last-Touch Attribution

Let’s use my own experience as a buyer to illustrate a typical customer journey in a multichannel marketing environment. Like any man who shaves daily, I’ve always felt that most quality brand blades were way overpriced. And I found it quite inconvenient that I had to visit a physical store to buy them, when I knew that I would need new blades at a regular interval. All of that changed when a few blade delivery services popped up in that lucrative men’s grooming market a few years ago.

I was one of the early adopters who signed on with one of the programs. But after cutting my face a few times with defective blades, I just canceled the delivery service, and went back to my favorite brand of my adult life, knowing that it would cost more. I considered that to be an affordable luxury.

Then one day, I saw an ad on Facebook, that my favorite brand now offers home delivery service, at a significantly lower price point in comparison to store purchases. Call that my first touch before conversion (to the newly offered service). But I didn’t sign up for it at that time, even though I clicked-through to the landing page of its website. I was probably on my mobile phone, and I also wanted to examine options regarding types of blades and delivery intervals further when I had more time.

That means, I visited the site multiple times before I committed to the subscription model. I remember using Google to get to the site at least once; and later, I hit on a bookmark with its URL a few more times. Let’s say that Touch No. 2 would be labeled as “Organic Search,” and touches No. 3 and No. 4 would be considered “Direct-to-Site.”

If you employ last-touch attribution, then Facebook and organic search would get zero credit for the transaction here. That type of false intel may lead to a budget cut for the wrong channel, for sure. But as a consumer, I “know” that it was the Facebook where I first learned about the new service from the brand.

Imagine if you, as a marketer, had a toggle switch between Last Touch and First Touch rules. When thousands, if not millions, of touch data points are aggregated in an attribution report, even a simple concept, such as “the most important acquisition channel,” will have a different look depending on the attribution rules. In one, Social Media may look like the most effective channel. In another, Organic Search may take the cake. The important lesson is that one should never settle for last-touch attribution, just because that is how it’s been done within the organization (or by the analytics vendors).

There Are a Few More Attribution Methods

The Last and First Touch rules are the easy ones — if you have access to all touch data on an individual level (because you’d have to line touchpoints up for each buyer, in sequence). As I briefly introduced last month, there are a few more methods to consider. Let’s dig a little deeper this time:

  • Last Touch: Although there could have been many touchpoints before conversion, this method would just give all of the credit to the last one. As flawed as it may be, there are some merits. For one, last touch would be the one that is directly attributable (i.e., connected) to the transaction (and the session that led to it) without any assumptions or rules. I suspect that the simplicity of it all is the main reason for its popularity.
  • First Touch: This would be useful for the study of acquisition sources. Timeline is an important consideration here, as effectiveness of channel or offer may decay at different rates, depending on product and channels in question. A consumer may have researched for a washing machine four months ago. And saw a newspaper insert about it three weeks ago. And then got an email about it a week ago. How far back can we go with this? A catalog that was mailed six months ago? Maybe, as we are dealing with a big-ticket item here. And are we sure that we have any digital touch data that go back that far? Let’s not forget that the word “Big Data” was coined to describe click-level data to begin with.
  • Double Credit: If a person was exposed to and engaged in multiple channels before the purchase, why not credit all involved channels? Overkill? Maybe. But we use this type of reporting method when dealing with store-level reports. There is no law that one customer can visit only one store. If one visits multiple stores, why not count that person multiple times for store-level reports? So, with the same reasoning, if a transaction is attributable to multiple channels, then count the transaction multiple times for the channel report. Each channel manager would be able to examine the effectiveness of her channel in an isolation mode (well, sort of).
  • Equal Credit: This would be the opposite of Double Credit. If there are multiple channels that are attributable to a transaction, create a discount factor for each channel. If one is exposed to four channels (identified via various tags and tracking mechanisms), each would get ¼ of the transaction credit. When such discounted numbers are aggregated (instead of transactions, as a whole number), there will be no double-counting in the end (i.e., the total would add up to a known number of transactions).
  • Proportional Credit: Some channel managers may think that even Equal Split is not a fair methodology. What if there were eight emails, two organic searches, three paid searches and a link on a Facebook page that was clicked once? Shouldn’t we give more weight to the email channel for multiple exposures? One simple way to compromise (I chose this word carefully) in a situation like this would be to create a factor based on the number of total touches for each channel, divided by the total number of touches before conversion.
  • Weighted Value: An organization may have time-tested — or politically prevailing — attribution percentages for each employed channel. I would not even argue why one would boldly put down 50 percent for direct marketing, or 35 percent for organic search. Like I said last month, it is best for analysts to stay away from politics. Or should we?
  • Modeled Weighted Value: Modeling is, of course, a mathematical way to derive factors and scores, considering multiple variables at once. It would assign a weighted factor to each channel based on empirical data, so one might argue that it is the most unbiased and neutral method. The only downside of the modeling is that it would require statistically trained analysts, and that spells extra cost for many organizations. In any case, if an organization is committed to it, there are multiple modeling methods (such as the Shapley Value Method, based on cooperative game theory — to name one) to assign proper weight to each channel.

I must point out that no one method would paint the whole picture. Choosing a “right” attribution method in an organization with vastly different interests among teams is more about “finding the least wrong answer” for all involved parties. And that may be more like Tony Soprano mediating turf disputes among his Capos than sheer mathematics spitting out answers. That means the logically sound answer may not void all of the arguments. When it comes to protecting one’s job, there won’t be enough “logical” answers as to why one must give credit for the sale to someone else.

While all of this has much to do with executive decisions, people who sit between an ample amount of data and decision-makers must consider all possible options. So, having multiple methods of attribution will help the situation. For one, it is definitely better than just following the Last Touch.

Start With Proper Data Collection

In any case, none of these attribution methods will mean anything, if we don’t have any decent data to play with. Touch data starts with those little pixels on web pages in the digital world. Pages must be carefully tagged, and if you want to find out “what worked,” then, well, you must put in tracking requests properly for all channels.

A simple example. In a UTM tag, we see Medium coded with values such as Paid Social. A good start. Then we go to Source, we would see entries like Facebook, Instagram, Twitter, Pinterest, etc. So far, so good. But the goal is to figure out how much one must spend on “paid” social media. Without differentiating (1) Company’s own social media page, (2) Paid ads on social media sites, and (3) Referrals by users on social media (on their Facebook Wall, for example), we won’t be able to figure out the value of “Paid Social.” That means, all of the differentiation must be done at the time of data collection.

And while at it, please keep the data consistent, too. I’ve seen at least 10 different ways to say Facebook, start with “fb.”

Further, let’s not stop at traditional digital tags, either. There are too many attribution projects that completely block out offline efforts, like direct mail. If we need to understand where the marketing dollars must go, why settle with one type of tracking mechanism? Any old marketer would know that there is a master mail file behind every direct mailing campaign. With all those pieces of PII in it, we can convert them into yet another type of touch data — easily.

Yes, collecting such touch data for general media won’t be easy; but that doesn’t mean that we keep the wall up between online and offline worlds indefinitely. Let’s start with all of the known contact lists, online or offline.

Attribution Should Be Done in Multiple Steps

Attribution is difficult enough when we try to assign credit to “1” transaction, when there could be multiple touchpoints before the conversion. Now let’s go one step further, and try to call a buyer a “Social Media” responder, when we “know” that she must have been exposed to the brand at least 20 times through multiple media channels including Facebook, Instagram, paid search through Google, organic search through some default search engine on a phone, a series of banner ads on various websites, campaign emails and even a postcard. Now imagine she purchased multiple times from the brand — each time as a result of a different series of inbound and outbound exposures. What is she really? Just a buyer from Facebook?

We often get requests to produce customer value — present and future — by each channel. To do that, we should be able to assign a person to a channel. But must we? Why not apply the attribution options for transaction to buyers, as I listed in this article?

That means we must think about attribution in steps. In terms of programming, it may not exactly be like that, but for us to determine the optimal way to assign channels to an individual, we need to think about it in steps.

Conclusion

Now, if you are just settling for last-touch attribution, you may save some headaches that come with all of these attribution methods. But I hope that I intrigued you enough that you won’t settle so easily.

Marketing Metrics Aren’t Baseball Scores

Lester Wunderman is called “the Father of Direct Marketing” — not because he was the first one to put marketing offers in the mail, but because he is the one who started measuring results of direct channel efforts in more methodical ways. His marketing metrics are the predecessors of today’s measurements.

Lester Wunderman is called “the Father of Direct Marketing” — not because he was the first one to put marketing offers in the mail, but because he is the one who started measuring results of direct channel efforts in more methodical ways. His marketing metrics are the predecessors of today’s measurements.

Now, we use terms like 1:1 marketing or digital marketing. But, in essence, data-based marketing is supposed to be looped around with learnings from results of live or test campaigns. In other words, playing with data is an endless series of learning and relearning. Otherwise, why bother with all this data? Just do what you gut tells you to do.

Even in the very beginning of the marketer’s journey, there needs to a step for learning. Maybe not from the results from past campaigns, but something about customer profiles and their behaviors. With that knowledge, smart marketers would target better, by segmenting the universe or building look-alike or affinity models with multiple variables. Then a targeted campaign with the “right” message and offers would follow. Then what? Data players must figure out “what worked” (or what didn’t work). And the data journey continues.

So, this much is clear; if you do not measure your results, you are really not a data player.

But that doesn’t mean that you’re supposed to get lost in an endless series of metrics, either. I sometimes see what is commonly called “Death by KPI” in analytically driven organizations. That is a case where marketers are too busy chasing down a few of their favorite metrics and actually miss the big boat. Analytics is a game of balance, as well. It should not be too granular or tactical all of the time, and not too high in the sky in the name of strategy, either.

For one, in digital marketing, open and clickthrough rates are definitely “must-have” metrics. But those shouldn’t be the most important ones for all, just because all of the digital analytics toolsets prominently feature them. I am not at all disputing the value of those metrics, by the way. I’m just pointing out that they are just directional guidance toward success, where the real success is expressed in dollars, pounds and shillings. Clicks lead to conversions, but they are still a few steps away from generating cash.

Indeed, picking the right success metrics isn’t easy; not because of the math part, but because of political aspects of them, too. Surely, aggressive organizations would put more weight onto metrics related to the size of footprints and the rate of expansion. More established and stable companies would put more weight on profitability and various efficiency measures. Folks on the supply side would have different ways to measure their success in comparison to sales and marketing teams that must move merchandise in the most efficient ways. If someone is dedicated to a media channel, she would care for “her” channel first, without a doubt. In fact, she might even be in direct conflicts with fellow marketers who are in charge of “other” channels. Who gets the credit for “a” sale in a multi-channel environment? That is not an analytical decision, but a business decision.

Even after an organization settles on the key metrics that they would collectively follow, there lies another challenge. How would you declare winners and losers in this numbers game?

As the title of this article indicates, you are not supposed to conclude one version of creative beat the other one in an A/B test, just because the open rate was higher for one by less than 1%. This is not some ballgame where a team becomes a winner with a walk-away homerun at the bottom of the 11th inning.

Differences in metrics should have some statistical significance to bear any meaning. When we compare heights of a classroom full of boys, will we care for differences measured in 1/10 of a millimeter? If you are building a spaceship, such differences would matter, but not when we measure the height of human beings. Conversion rates, often expressed with two decimal places, are like that, too.

I won’t get too technical about it here, but even casual decision-makers without any mathematical training should be aware of factors that determine statistical significance when it comes to marketing-related metrics.

  • Expected and Observed Measurements: If it is about open, clickthrough and conversion rates, for example, what are “typical” figures that you have observed in the past? Are they in the 10%to 20% range, or something that is measured in fractions? And of course, for the final measure, what are the actual figures of opens, clicks and conversions for A and B segments in test campaigns? And what kind of differences are we measuring here? Differences expressed in fractions or whole numbers? (Think about the height example above.)
  • Sample Size: Too often, sample sizes are too small to provide any meaningful conclusions. Marketers often hesitate to put a large number of target names in the no-contact control group, for instance, as they think that those would be missed revenue-generating opportunities (and they are, if the campaign is supposed to work). Even after committing to such tests, if the size of the control group is too small, it may not be enough to measure “small” differences in results. Size definitely matters in testing.
  • Confidence Level: How confident would you want to be: 95% or 90%? Or would an 80% confidence level be good enough for the test? Just remember that the higher the confidence level that you want, the bigger the test size must be.

If you know these basic factors, there are many online tools where you can enter some numbers and see if the result is statistically significant or not (just Google “Statistical Significance Calculator”). Most tools will ask for test and control cell sizes, conversion counts for both and minimum confidence level. The answer comes out as bluntly as: “The result is not significant and cannot be trusted.”

If you get an answer like that, please do not commit to a decision with any long-term effects. If you want to just declare a winner and finish up a campaign as soon as possible, sure, treat the result like a baseball score of a pitchers’ duel. But at least be aware that the test margin was very thin. (Tell others, too.)

Here’s some advice related to marketing success metrics:

  • Always Consider Statistical Significance and do not make any quick conclusions with insufficient test quantities, as they may not mean much. The key message here is that you should not skip the significance test step.
  • Do Not Make Tests Too Complicated. Even with just 2-dimensional tests (e.g., test of multiple segments and various creatives and subject lines), the combination of these factors may result in very small control cell sizes, in the end. You may end up making a decision based on less than five conversions in any given cell. Add other factors, such as offer or region, to the mix? You may be dealing with insignificant test sizes, even before the game starts.
  • Examine One Factor at a Time in Real-Life Situations. There are many things that may have strong influences on results, and such is life. Instead of looking at all possible combinations of segments and creatives, for example, evaluate segments and creatives separately. Ceteris paribus (“all other factors held constant,” which would never happen in reality, by the way), which segment would be the winner, when examined from one angle?
  • Test, Learn and Repeat. Like any scientific experiments, one should not jump to conclusions after one or two tests. Again, data-based marketing is a continuous loop. It should be treated as a long-term commitment, not some one-night stand.

Today’s marketers are much more fortunate in comparison to marketers of the past. We now have blazingly fast computers, data for every move that customers and prospects make, ample storage space for data, affordable analytical toolsets (often for free), and in general, more opportunities for marketers to learn about new technologies.

But even in the machine-driven world, where almost everything can be automated, please remember that it will be humans who make the final decisions. And if you repeatedly make decisions based on statistically insignificant figures, I must say that good or bad consequences are all on you.

Is Your Content Marketing the Right Length to Touch the Ground?

The content marketing debate revolving around length makes me think of a story. A curious little girl is said to have asked Abraham Lincoln how long one’s legs should be. After a moment’s reflection, the tall and lanky president responded wisely, “just long enough to touch the ground.”

The content marketing debate revolving around length makes me think of a story. A curious little girl is said to have asked Abraham Lincoln how long one’s legs should be. After a moment’s reflection, the tall and lanky president responded wisely, “just long enough to touch the ground.”

He certainly could not have realized that he was creating an unassailable template used endlessly ever since to provide dimensions for just how short or long any form of communication should be. Thorin McGee, Target Marketing editor-in-chief, recently explored how to find the right length for your content here and concluded — rightly, I would suggest — that the right length was as long as you can keep your audience engaged. Because when they become bored, they leave.

“Think like a reality TV editor,” he writes, referencing popular media for couch potatoes. He might have found a better frame of reference in the novels of Dickens or Victor Hugo’s ‘Les Miserables’, originally published in weekly installments in the popular press. To be certain readers would come back and buy the next installment, each had to end with a cliffhanger — would the hero/heroine fall off of the proverbial cliff or be saved, just in the nick of time, to continue the story?

There is no question that if the copy is engaging or compelling, if it makes promises and poses questions you feel you must have the answers to, length isn’t a primary consideration. Guru Frank Johnson’s classic rule is:

Tell them what you are going to tell them.

Tell them.

Tell them what you told them and what to do about it.

It never fails. And whether you do that in 100 or thousands of words depends only on the type of product, the medium but — most of all — on the ability of the writer to increase the attention and interest of the reader as the narrative continues, never letting him get bored. Johnson liked to remind us that great copy “tracks” — like a train going to the next station, it has to stay on the track or you have a fatal derailment.

Try this from TheDogTrainingSecret.com:

Hi Peter,

It gets me every time …

You see a homeless guy on the streets, a dog cuddled at his side.

Life has clearly not been kind to the gentleman, he’s wearing the rattiest, dirtiest jacket you’ve ever seen and shoes so old, there’s no way his feet could be dry.

His life’s belongings are gathered at his side, in a small duffle bag and maybe a weathered grocery bag.

He’s collecting change in a paper coffee cup.

Maybe $1.25 so far today.

Not much.

And as a result of hard living, he’s painfully thin. Much too thin, for a man living on the streets. And life is bleak.

Except for the one obvious ray of sunshine in his life:

That misfit dog, cuddled up at his side.

A dog with nothing but love, admiration and adoration for his master, pouring from his heart and eyes.

Has YOUR dog ever looked at you like that?

Like you’re the center of his world, the only thing that matters, the only person he trusts, his rock and the one person who’s worth 100% of his love and attention?

I don’t know about you …

… But that look of love you get from a dog?

I tell you, it’s a gut check for me every time.

And it’s this feeling that inspired the next designer T-shirt in our line-up:

Be The Person Your Dog Thinks You Are.

Because wouldn’t the world be a better place if we all stepped up and lived this way? And loved this way?

This T-shirt comes in 3 styles … kids, women’s and unisex.

In a variety of stylish colors.

Check them out …

It’s just 275 words. Is it too long, too short or just right? Can you possibly get bored as the story unfolds?

OK, not everyone loves dogs or will buy the T-shirt, but I’d bet many do. (Disclaimer: I bought one.)

So what is the bottom line of the long or short content length issue?

To this maverick marketer, it is simply that every commercial communication must have an objective supported by a narrative engaging and compelling enough to take you by the hand and lead you to the call to action and to the action itself. All of the theorizing about generational differences in attention spans and similar research pales against one simple thing: Does the story accomplish the objective; is it the right length to touch the ground?

Personas, Be Gone: 1:1 Marketing Revisited

Soccer moms, coffee house professionals, gears-and-gadget guys — in the world of data marketing, the audience personas available to select from enterprising data vendors go on and on and on. Tailoring and targeting based on personas — with hundreds of variables and data elements — dominate the business rules that direct billions in media spending and gazillions of business rules built inside customer journey mapping.

Millennials are not the only ones who eschew labels.

Soccer moms, coffee house professionals, gears-and-gadget guys — in the world of data marketing, the audience personas available to select from enterprising data vendors go on and on and on. Tailoring and targeting based on personas — with hundreds of variables and data elements — dominate the business rules that direct billions in media spending and gazillions of business rules built inside customer journey mapping. Practically every retailer, every brand, has a best customer look-alike model — and segments to that model.

But ask most consumers — they say they don’t want it that way.

An international survey released last week by Selligent Marketing Cloud, reported by Marketing Charts, says that 77 percent of U.S. consumers want to be marketed to as individuals, rather than as part of a larger segment.

Credit: MarketingCharts.com

The take-away seems to be that personalization at a 1:1 level should be any brand’s consumer engagement mantra. Throw out those data segments to which you may think I, the consumer, belong. “Pay attention to what I’m doing!”

That Darn Privacy Paradox … Again

Yet there’s a paradox here. “Paying attention to what I’m doing” raises the creep factor. The same survey shows that nearly eight in 10 consumers have at least some concerns about having their digital behaviors tracked, findings that seem to echo greater societal concerns about technology and business, with real branding impact.

Part of the addressable media conundrum comes down to intimacy. My mailbox is outside my door. I have no issues with personalization there, and I expect it. But pop “into” my laptop and now you’re getting closer to how I spend my days and nights — moving between work, play and life. That gets even more pronounced on the most intimate media of all, my smartphone. (I suppose a VR headpiece might be the “what’s-next” level of intimacy — or an embedded chip in my forehead.)

Conflicted as a marketer? Which path does my brand follow?

Revisiting Moments of Truth

One might argue that going from mass marketing to 1:1 marketing is an easier step than going from database marketing to 1:1. I’m reminded of Procter & Gamble’s moments of truth, freshly updated. A brand doesn’t need to know everything I do all day long in order to recognize the critical moments when purchase consideration comes into play. Less in-your-face, more in-the-right moment.

“Delighted, table for one.”

Whether database or 1:1 (or some combination of both), I cannot think of a smarter marketing scenario — one that engages the consumer — that does not depend on data, analysis, insight and action. Even the beefs that consumers have with marketing — remarketing when the product is already bought, not being recognized from one screen to another, for example — are cured by more data (transaction data, graph data, respectively here), not less, and such data being applied in a meaningful way.

“I’ll order the sausage, please. It’s delicious.” (Just don’t tell me how it’s made.)

In this age of transparency, we can no longer hide behind veils of ad tech and algorithms. We must explain what we’re doing with data in plain English. Based on the Selligent Marketing Cloud survey, for most consumers, it seems the path is to tell exactly how data are collected and to serve each as individuals. And we need to be smarter when, where and how ads are deployed even ad professionals are blocking ads today.

As for vital audience data, maybe we should re-think how we explain segmentation to consumers — less about finding “lookalikes” and more about serving “you,” the individual.

Consumer Engagement, But Not Yet Marriage

How many times have we been asked (or asked ourselves) to come up with a valuation of a minute of a prospect’s time and attention, AKA consumer engagement? Almost all advertising is bought and sold using some version of the metric (cost per person, mostly expressed as CPM) and yet no one seems to have nailed an equation that can reliably be used as a baseline.

How many times have we been asked (or asked ourselves) to come up with a valuation of a minute of a prospect’s time and attention, AKA consumer engagement? Almost all advertising is bought and sold using some version of the metric (cost per person, mostly expressed as CPM) and yet no one seems to have nailed an equation that can reliably be used as a baseline.

It’s not that marketers haven’t tried. The most recent expression was reported in Media Daily News at the end of July. Advertisers and agency executives were researched to determine what they “considered” (perhaps better described as their “best guesses”) on the per-minute value of engaged consumer attention and they came up with $1.81. They even produced a bar graph to add verisimilitude.

consumer engagement chart
Credit: Peter J. Rosenwald

This didn’t impress one skeptical reader who commented wryly: “With a sample of 300 people AND no hard guidelines as to how anyone in the survey determined ‘value’ other than for a very narrowly-defined universe, this is just cocktail party fodder.”

Even after a couple of martinis, it would be hard to derive much value from this yardstick of consumer attention. As so-called “opt-in” and “rewarded” advertising models — which let the prospect have some free content before “opting-in” through a paywall or some other device to more content — are becoming increasingly fashionable, it is not surprising that marketers are trying to put some metrics in place to value them.

This illuminates the fact that in today’s multimedia marketplace the “value” of a minute or some other measure of someone’s time, and perhaps even more importantly, attention, depends on a basket of variables that will be unique to each prospect or cluster of prospects. If we can discover which ones are critical to the purchasing process and at what point they influence the customer journey, we may have the beginning of metrics which will intelligently inform our marketing actions. The question is how we get there and the answer remains elusive.

First we need to know what we mean by “engaged consumer”? We all have lots of experience with commercial messages (Wendy’s “Where’s the beef,” for example) which can be described as highly “engaging,” because the creative brilliance attracts the attention of viewers. But that attention has no value whatever for say, vegetarians.

How much the marketer would be willing to pay for an engaged customer, someone who has demonstrated interest in the marketed category and hopefully has the resources to purchase, is more to the point? The Lamborghini dealer should be willing to pay quite a bit more for that engaged minute than the corner taco vendor.

In a September column addressing marketing metrics and suggesting that we stop chasing our tails, I tried to put a figure on the real cost of reaching the target audience for an advertiser like Pampers. Using a $25 CPM cost of a TV spot reaching only women and, after eliminating all women who were neither in the last trimester of pregnancy nor had children under two years old, I came up with a ballpark figure of $208 per thousand. In fact, with a normal average viewing frequency of five times, capturing the engagement of each one of those thousand women for 30 seconds should be worth about $1 ((208*5)/1000), twice that for 60 seconds of attention, not far off of that $1.81 guess.

But will the “engagement” lead to a committed relationship, a marriage if you will, of consumer and brand? Certainly, if the prospect can opt-in or be rewarded with truly relevant and valuable content by clicking to visit the advertiser’s website, and the website can elevate interest to purchase, and the product satisfies and stimulates repeat purchase, the investment in getting that initial 60 seconds of attention will have a quantifiable value.

But putting a figure on that value is as likely to be correct as predicting the length and quality of the marriage.

As a friend of mine says, instead of trying to figure it all out in advance, just start dating.

3 Tips for Dealing With the Stress of MarTech-Driven Marketing

As a marketer in today’s data-driven world, it is very hard to keep your head on straight. With thousands of martech solutions in the market vying for your attention, combined with the pressure to make data-driven decisions and justify expenses, it is easy to become overwhelmed by martech-driven marketing.

As a marketer in today’s data-driven world, it is very hard to keep your head on straight. With thousands of martech solutions in the market vying for your attention, combined with the pressure to make data-driven decisions and justify expenses, it is easy to become overwhelmed by martech-driven marketing.

The result is a constant feeling that you are falling further and further behind. While that may be, it is also likely that you are in good company as this is a common anxiety among most marketers.

Here are three tips for dealing with the anxiety from tech-driven marketing.

Understand and Acknowledge the MarTech-Driven Marketing Landscape Is Needlessly Complex

It’s not your job to sort it out. There are thousands of martech solutions out there and you can’t/shouldn’t keep up with all of them.

If you did, you would hardly have time for your day job. It is better that you understand the technologies as broad capabilities (such as marketing automation, CRM, content management systems, etc.) then focus on determining if you need that capability and why.

Then carefully select vendors with that capability to work with on specific solutions.

Ignore the Noise and Get Back to Marketing Strategy

Too often, marketers are letting the marketing technology world dictate how strategy should be run.

For example, when discussing lead development strategy, I had a client tell me that their marketing automation vendor was looking into it. This is akin to having your building materials provider design your dream home. Some may offer basic design services, but the result is likely to be staid and semi-custom, at best.

Similarly, most martech companies do not want to be in the business of developing your marketing strategy, but they oftentimes are forced to do so in order to get you comfortable with leveraging their technology.

No one wins in this scenario, and what often results is a generic marketing strategy.

The key is to understand what broad martech capabilities are relevant for you and to build a custom go-to-market strategy that reflects your brand’s vision and purpose.

Then incorporate data-driven capabilities — and lastly, evaluate a specific solution.

Don’t Be a Slave to Your Data

I often hear marketers ask, “How can we better leverage all this data?”

This is like starting your holiday shopping by asking, “How can I leverage all of the available retailers out there?”

The more sensible questions should be: “What do I want to achieve and how can data help me get there?”

Then, look into your own data to determine if the relevant data is there. If it isn’t, don’t fret. Many times, the relevant data is cheap to generate, and you should begin to understand what it is you specifically need and how best to generate it.

Concluding Thoughts About Tech-Driven Marketing

After many years in consulting with Fortune 500 companies on marketing data and technology strategy, I can confidently tell you that the vast majority of marketers feel overwhelmed and not in control.

What I can also say is that most marketers do not struggle with what to do; rather, they struggle with what not to do.

With a torrent of marketing solutions available today, it is easy to lose focus. Successful marketers understand that martech solutions affect how you think about marketing and customer strategy execution. However, they also understand that smart, brand-centric strategies drive solution selection — not the other way around.