Solving the Puzzle of a Medicare Age-in Strategy

As Medicare acquisition marketing gets harder, the next frontier will evolve. In this next stage, growth strategies are likely to swap to more of a robust age-in strategy. Getting the age-in year “right” relies not only on a tightly managed communication plan but also the content to support it.

As the Medicare marketing landscape has changed, we have seen the rate of Medicare eligible consumers who switch plans drop, and then plateau. In this next frontier, growth strategies are likely to swap to more of a robust age-in strategy, preparing those coming up on their Medicare eligibility for the process of selecting the best plan for them.

The journey that consumers take during their 64th year as they navigate their choices opens up some exciting possibilities. They’ll be choosing between Medicare Advantage, Original Medicare and other options. And healthcare marketers will be navigating:

• A wide open calendar – the Initial Enrollment period lasts seven months. A plan for communication to your age-in prospect pool as they age-in will be custom to their timeline.

A wide open media frontier – with an audience that engaged in a variety of media channels. In fact, in the U.S., there are more Facebook users from the 65 and above age group than those in the 13- to 17-year-old group.

• The potential for scale with age-in that comes with the influx of boomer population.

As exciting as the opportunity is, being there for the age-in “when” is also complex. But mainly from the perspective of doing so efficiently. Think about it, over the span of a year, consumers will choose their point of entry. Getting this “right” relies on a tightly managed communication plan and the content to support it.

The age-in timeline will need to support:

  • Awareness – Trying reaching out to the audience early, this way you’ll inspire confidence and make it easier to engage. Starting early will ensure you are part of the consideration set.
  • Engagement – Try to connect their planning process to your brand by engaging in a dialogue. And then tailor your approach. You’ll want to have the content available to satisfy the research needs of your audience.
  • Conversion – If you’ve done all of these things well, you’ll be in the position to support the really good stuff – the shopping!

That’s a lot! And determining the right amount of touches is daunting! That’s why we recommend investing in a data environment to help you make sense of the situation.

Your age-in data environment should enable:

  • Clarity – aggregate all relevant prospect and customer-level activities into one analyzable and consistent source.
  • Accurate reporting – drive accurate and timely reporting from your offline and online marketing spend and efforts
  • Deep insights – build a foundation for discovering marketing insights based on real world customer interactions.

A data environment will give you the confidence you need to make decisions and be in control of the situation. Then you’ll be able to set up all of the nifty content you need for success!

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.

Why Behavioral Science Is Critical to Marketing and Research

What if we could identify consumers’ underlying emotions or motivations to improve our understanding of whether they were actually going to purchase a product? Over the past few years, marketing and research has been digging into the “why” behind behaviors to get even deeper, below the surface of the insights we deliver. The goal is to help brands better understand the true drivers of consumers’ behavior — and it all starts with behavioral science.

What if we could identify consumers’ underlying emotions or motivations to improve our understanding of whether they were actually going to purchase a product? Over the past few years, marketing and research has been digging into the “why” behind behaviors to get even deeper, below the surface of the insights we deliver. The goal is to help brands better understand the true drivers of consumers’ behavior — and it all starts with behavioral science.

What Is Behavioral Science?

Behavioral science isn’t a new industry, but within the past few years is something of an emerging topic in marketing and research. At its core, behavioral science and the research that results from it, seeks to understand the many aspects related to someone’s habits or decision-making. Most importantly, as we noted, it helps to understand why people make certain decisions.

If you think of that in the context of our marketing and product strategies, it’s clear why behavioral science plays a role in market research. There are a variety of methods that can get close to truly understanding consumer behavior, but much of them can fail to capture empirical evidence — sensory information captured through observations and the documentation of behaviors through experimentation.

As a result, the importance and rise of behavioral science in marketing and research is no small subject. Just in the past year, there have already been numerous events discussing behavioral science specific to gathering and analyzing data to understand why consumers make decisions — but marketers and researchers, by and large, are still figuring out how to leverage it.

Leveraging Behavioral Data

Big data can be used as a possible solution for at least two reasons. First, it gives us access to more data than ever before, including data based on actual behavior from purchasing, web analytics, subscriptions, and more. As a result, big data can reduce the struggles we sometimes have with differences between stated versus observed behavior.

Second, there are big data sources that allow us to understand motivations of our consumers by examining the big 5 personality traits for millions and millions of people. By understanding different personalities, we can begin to realize if being “extroverted” or “conscientious” drives consumers’ purchasing. Some suggest that behavioral science and the resulting data on motivations behind decision making will be the new normal for market research. We agree that understanding what people don’t tell us in surveys is as important as what they do. Together, these two types of data give us a more well-rounded picture of consumer behavior, and with the right methodology, you can gain this knowledge quickly.

In a specific use case, a brand was looking to understand their target audience for a new product innovation. They had hypothesis’ about what this audience would look like, and likely could have gained that knowledge through standard quantitative research. However, by incorporating an approach that combines survey data and big data, they were able to understand who their audience was, but also what would motivate them to purchase this particular new product. The moral of the story? Consumers are more than just the people that buy your product.

Are Your Data Decisions Sending the Right Message?

The old way of “house-holding” is relevant when making household purchases, like cable service. But, it’s not relevant with making individual purchases, like shoes. Today even a 3-year-old child has a say in the type of shoes they wear. We like to think of the new way to view the household as the Intelligent Household where each person in the household is treated like an individual and each consumer journey matters. Separately. Individually.

How much thought have you given to the act of “house-holding” your data? Quite possibly a lot. Grouping customers together into “family units” is known as house-holding. This is done for at least one important reason — savings. After all, treating the individuals within a household uniquely seems pretty expensive. But, at what cost does this savings come? Understanding a customer’s household is a big part of your data driven marketing efforts.

The Data Driven Decision of Intelligent House-Holding

The old way of “house-holding” is relevant when making household purchases, like cable service. But, not with making individual purchases, like shoes. Today even a 3-year-old child has a say in the type of shoes they wear.

We like to think of the new way to view the household as the Intelligent Household where each person in the household is treated like an individual and each consumer journey matters. Separately. Individually.

Gender Identity Matters in Marketing and Data, Too

Here are two ways to consider – or reconsider – your approach to house-holding data for both digital and print impact.

1.) The impact of gender differences on response

With all of the brands dedicated to serving only-women (and there are even some few focused on only-men) it got us to thinking. Do men and women respond at the same rate to the same things (when the product and service applies equally to both)? After all, they are both people — and we are making our best creative appeals to people without any particular bias.

Surprise! There is a difference. And, at least for some of our work, the difference is not small. Certainly not small enough to overlook. It’s a whole new data-driven discovery.

When you consider the fact that it is far more common for women to be “house-holded” within the name of the man of the household and when you consider the fact that women often make the purchasing decisions, this does not yield an optimized approach.

Instead, why not seek to identify the ways you can make a creative appeal more to each gender and try it. For example, women are more hungry for information, background and rationale – and may be more skeptical of promises. There’s a lot of good data and research that supports this direction — and I’m abbreviating it here.

2.) The loss of a connection established

I struggled mightily for how to phrase this one. Here’s what I mean. I have a certain brand to which I am quite faithful. I make many purchases within this particular brand — spend more than my fair share on their products, and I own their credit card, solely in my name. My husband also favors the same brand. His name is not on my credit card, and he does not have one of his own. Yes, he buys things at the same place, but not with the same account and not with one of their store cards. Yet, when I go to MY page on their site, I have been house-holded — based on some fancy data work that they no doubt worked hard to manipulate. After all, we both do make purchases and we do live at the same address.

“Hello, Spyro!” it greets me cheerfully when I land on the profile page of my account, where he has never once made a purchase.

At what cost does this slick data manipulation come? Well, loyalty, I’m afraid. And reputation. I’m not exactly thrilled with this, and neither is he. He gets served up recommendations for teenage girl clothes and my identity is seemingly lost — or at the very best, hidden. Not ideal. And frankly, it doesn’t make them seem very smart.

As marketers we know it is important to value your clients and demonstrate that value in ways large and small. I sincerely doubt that this brand is even aware of the impact this (likely operationally-driven) act. Yet this simple mistake adds-up to ignoring people who spend money. Ouch.

This particular house-holding offers no advantage — there is no savings in the bill or statement sent, so I doubt due diligence was paid to the change. Once my page was mine. And now it’s not.

What ways have you seen gender differences impact your marketing efforts?

What Is a CDP and How Does It Differ From Other Customer Data Tools?

Customer data platforms (CDPs) are picking up steam: Gartner named them one of six marketing technologies to watch in 2018 and the category is expected to reach $1 billion in revenue by 2019.

Customer data platforms (CDPs) are picking up steam: Gartner named them one of six marketing technologies to watch in 2018 and the category is expected to reach $1 billion in revenue by 2019.

Publishers have actually been early adopters when it comes to CDPs, particularly because CDPs enable them to make greater use of first-party data (which publishers have a lot of) to drive content recommendations and individual-level ad targeting. Now more marketers are taking an interest in CDPs, which is expected to grow as the industry moves further toward permission-based marketing and is focused on providing more unified cross-platform experiences.

One of the first questions marketers may have about CDPs is what exactly they are and how they differ from other customer databases, notably data management platforms (DMPs). Let’s try to reduce that confusion.

Definitions first. The Customer Data Platform Institute, which I founded in 2016, defines a CDP as a “marketer-managed system that creates a persistent, unified customer database that is accessible to other systems”. That’s open to lot of interpretation. In more concrete terms, what CDPs do is:

Gather All Customer Data in One Place

That’s the “persistent, unified customer database” part of the definition. It distinguishes CDPs from systems that store data without linking items related to the same customer (like many “data lakes”), that gather customer data on the fly (like many behavior-based personalization products), or that hold just an anonymous cookie ID plus audience attributes (like a Data Management Platform). The CDP stores all details down to the level of web pageviews, email clicks, purchases, payments, and content selections. And it stores personal identifiers so data can be assembled into a single customer view and so messages can be directed at known individuals. One of the reasons that publishers have eagerly adopted CDPs is that detailed, personally identified data is exactly what they needed for content recommendations and individual-level ad targeting, two key profit drivers.

Allow Other Systems to Use the Assembled Data

This distinguishes CDPs from systems that build a single customer view for their own use, including many recommendation engines, personalization tools, and email platforms. Access can range from direct queries against a database table, to periodic file exports, to calls to an Application Program Interface (API). Sometimes the external systems read raw CDP data and sometimes they read data that’s pre-summarized and reformatted. Whatever the technical details, the key point is the CDP data isn’t locked away where no other system can use it.

Make Data Relatively Easy to Manage

“Relatively” is slippery weasel of a word, but bear with me. If anyone tells you they can build a persistent, unified customer database without any technical skills, just back away slowly, turn, and run for the nearest exit because they’re either dishonest or deluded. CDPs reduce the technical workload by using innovations such as “schema-less” data stores that ingest data without pre-defining its contents. They also contain prebuilt components, such as identity matching processes, that save time over building similar software from scratch. The net result is that CDPs can build and update customer databases much faster and with many fewer technical work-hours than conventional systems. This in turn means that marketers can request changes more freely because the cost of each change is so low. It’s hard to quantify the difference without looking at specific situations, but it’s quite realistic to expect changes that took weeks in a conventional system to take days or just hours with a CDP.

You’re probably skeptical of these claims. You should be. You’ll need some quality time with a CDP vendor to be convinced. But just suspend disbelief for a moment to imagine what you could do with a CDP if it actually performed as promised.

CDPs support two main applications. The first is analytics: by bringing together all customer data, cleaning it, creating a unified customer view, and making the result available to other systems, the CDP lets your analysts spend less time on wrangling data and more time creating value. This isn’t a little benefit: analysts spend as much as 80% of their time finding and preparing data.

The second application is execution support. CDPs can feed data to order management systems, websites, email engines, call centers, ad networks, and even data management platforms to support segmentation, personalization, campaign management, and content recommendations.

But there’s more. Quite a few CDP systems include their own analytical and execution applications. This further increases deployment speed and value provided.

In short, CDPs are different from your existing CRMs, data lakes, DMPs, and old-style marketing databases. They combine detailed data, low cost, and unprecedented flexibility to create value throughout a publishing operation. It may sound too good to be true. But it’s surely worth some time to find out.

Experience Design Benefits Greatly From Behavioral Data

Human-centered design thinking has influenced much of the way that companies think about user and customer experience, and for the better. Because customer experience is becoming an important vehicle through which brand propositions are communicated today, it is worth examining if the way we design customer experiences can be improved. Particularly, is there a way to better integrate data and analytics into design thinking?

Human-centered design thinking has influenced much of the way that companies think about user and customer experience, and for the better. Because customer experience is becoming an important vehicle through which brand propositions are communicated today, it is worth examining if the way we design customer experiences can be improved. Particularly, is there a way to better integrate data and analytics into design thinking?

A well-designed customer experience offers many benefits, such as:

  • increasing the productivity of users and service efficiency.
  • Making solutions easier to use and, therefore, reducing support costs
  • Increased accessibility and reducing discomfort and stress
  • Signature experiences that convey and re-enforce the brand proposition

In order to achieve these results, most experience design processes begin with deep empathy, which entails physically observing, interviewing and surveying customers to uncover unmet needs and pain points.

These methods often help uncover significant opportunities to improve the customer services. Just as often, however, they take companies down unprofitable journeys and fail to identify growth opportunities.

For example, Spirit airlines probably ignores every stated customer desire except price (in most cases), yet it has a very strong business model. Can you imagine the market research that says customers don’t care about on-time arrival, service or cabin comfort and want to be nickeled and dimed for every possible amenity? An examination of behavioral data, however, would show that there is a large market of travelers who consistently shop for the cheapest flight, regardless of service, brand and reputation, and Spirit has learned to cater to this segment very well.

In my view, most experience design projects fail to bring in behavioral data and resultingly miss the bigger opportunity. I have observed many customer experience projects that try desperately to empathize with the customer, but fail to examine if this is the customer they want and what their purchase and usage behaviors truly reveal.

Sometime back, my team and I were asked to identify key factors driving retention and renewal behavior among auto and home insurance customers. Certainly, survey-based feedback was helpful and identified areas of dissatisfaction, such as complicated billing, poor claims experiences and unexplained rate increases. Individual customer interviews yielded even more interesting satisfaction drivers, such as financial trust and need for honest advice. However, looking at behavioral data, such as the types of policies purchased, tenure of the policies and household makeup actually uncovered the deepest insights. Although this is now common knowledge in the insurance industry, customers who bundle auto and home policies are much less likely to switch. Therefore, most insurance carriers try to offer an Auto-Home discount. Other behaviorally observed factors, such as the level of coverage selected and signing up for auto pay are also significant predictors of retention. Surprisingly, none of these factors bubbled up directly in customer interviews or surveys. Furthermore, factors derived from the behavioral data explained 70 to 80 percent of the attrition in any given year.

Despite this example, it would be very wrong to assume that human-centered design principles do not work or that some of the methods employed to develop user/customer empathy are bunk. However, I would say that interviews and experience audits are only one source of customer insight; mining customer behavioral data is another powerful source of customer insights. A well-thought-out experience design should have the benefit of both.

Why Modeling Beats Rule-Based Segmentation

I have been talking about “employing all available data” for targeting and customer insights for some time now. So allow me to pick a different bone today. Let’s forget the data part, and talk about the methodology. When machines can build models super-fast, aversion to modeling only limits the users. After all, I am not asking any marketers to get a degree in statistics. I am just asking them to consider modeling techniques.

I cringe when I hear “rule-based” segments are sitting on top of so-called state-of-the-art campaign engines. This is year 2018 A.D. It’s the age of abundant data with an ample number of tools and options to harness their true powers. And marketers are still making up rules now? It’s time for marketers to embrace modeling.

I wonder what most of the rules marketers are using are made of. Recency? Certainly, but how recent is recent enough?

Frequency? Sure, why not? The more the merrier, right? But in what timeframe? Are you counting transactions, orders or items? Or just some “events”?

Monetary? Hmm, that’s tricky. Are we using an individual-level lifetime total amount, value of the last transaction, average spending per transaction, average spending amount per year, or what? Don’t tell me you don’t even have individual-level summary data. No customer is just a reflection of her last transaction.

Actually, if a company is using some RFM (Recency, Frequency, Monetary Value) data for targeting, that is not so bad. At least it’s taking a look at what actually happened in terms of monetary transactions, not just clicks and page views, along with basic demographic data.

I have been talking about “employing all available data” for targeting and customer insights for some time now. So allow me to pick a different bone today. Let’s forget the data part, and talk about the methodology. When machines can build models super-fast, aversion to modeling only limits the users. After all, I am not asking any marketers to get a degree in statistics. I am just asking them to consider modeling techniques, as this data industry has moved forward from the days when some basic RFM rule sets used to get a passing grade.

Let’s look at the specific reasons why marketers should consider modeling techniques more seriously and ditch rule-based segmentation.

Reason No. 1: Variable Selection

We are surrounded by data, as every move that anyone makes is digitized now. When you describe a buyer, you may need to evaluate hundreds, if not thousands, of data points. Even if you are just using simple set of demographic data without any behavioral data, we are talking about over 100 variables to consider out of the gate.

Let’s say you want to build a rule to find a good segment for the sale of luxury cruises. How would you pick the most predictable variable for that one purpose? Income and age? That is not a bad start, but that is like using just two colors out of a crayon box containing 80 colors.

Case in point: Do you really believe that the main difference between luxury cruisers and luxury car buyers is “income”? Guess what, those buyers are all rich. You must dig much deeper than that.

Marketers often choose variables that they can easily understand and visualize. Unfortunately, the goal of the targeting exercise should be effectiveness of targeting, not easy comprehension by the marketer.

We often find obscure variables in models. They may “seem” obscure, as a human being would never have instinctively picked them. But mathematics doesn’t care for our opinions. In modeling, variables are picked for their predictive power, nothing else. The bonus is that this is exactly how new patterns are discovered.

We hear tidbits such as “People who tend to watch more romantic comedies are more likely to rent cars over the weekend,” “Aggressive investors are less likely to visit family restaurants” or “High-value customers for a certain teenage apparel company are more likely to be seasonal buyers with high item counts per customer, but relatively lower transaction counts.”

These are the contributing factors found through vigorous mathematical exercises, not someone’s imagination or intuition. But they should always make sense in the end (unless of course, there were errors). Picking the right predictor is indeed the most important step in modeling.

Reason No. 2: Weight Factor

Let’s say that by chance, a user stumbled upon a set of useful predictors of certain customer behavior. Let’s go back to the last example of the teenage apparel company’s high-value customer model. In that one sentence, I listed: seasonality (expressed in number of transactions by month, regardless of year), number of item counts per customer (with time limits, such as past 36 months), and number of transactions per customer.

In real life, there would be a far greater number of variables that would pass the initial variable selection process. But for simplicity’s sake, let’s just review these three variables.

Now tell me, which one of these three variables is the most important predictor of this high-value customer model? (Please don’t say they are all equally important.) Model scores are made of selected variables multiplied by the weight of each, as not all predictors carry the same level of predictability. Some may even be “negatively” correlated to the ideal behavior that we are going after. In this example alone, we saw that the number of items was positively related to the high value, while the number of transactions are negatively related. When investigating further about this “strange” correlation, we found out that most of the high-value customers are trained by the marketer to wait for a big sale, and then buy lots of items in one transaction.

The main trouble with the “rule-based” segmentation or targeting exercise is that human beings put arbitrary weight (or importance) on each variable, even if “the right” variables were picked — mostly by chance — in the first place.

The modeling process reveals the actual balance among all important predictors, with the sole purpose of maximizing predictability. Conversely, I have never met a person who can “imagine” the dynamics of two or three variables, let alone 10 to 20 (the typical number of variables in models).

Forget about the recent emergence of machine learning; with or without human statisticians, modeling techniques have been beating rudimentary rules by end-users for decades. If solely left to humans, the No. 1 predictor of any human behavior would be the income of the target. But that is just a reflection of human perception and a one-dimensional way of looking at a complex composition of human behavior. You don’t believe you can explain the difference between a Lexus buyer and a Mercedes buyer with just income, do you?

Reason No. 3: Banding

Much of data are composed of numbers and figures. The rest of them are called categorical variables (i.e., data that cannot be added or subtracted, such as product category or channel description).

Let’s assume that income — not my first pick, as you can see — is found to be predictable for mid- to high-scale female accessory buyers. Surely, different ranges of income would behave differently in such models. If the income is too low, they won’t be able to afford such items. Too high, then the buyer may have moved on to even more expensive handbags. So, the middle ground may seem to be the ideal target. The trouble is that now you have to describe that middle group in terms of actual dollars. Exactly where does that ideal range begin and end? To make it even more complicated, what about regional biases in buying power? Can one set of banding explain the whole thing? We’ve gone way past any intuitive grouping.

Moving onto categorical variables, one of the most predictable variables in any B2B modeling is the SIC code. There are thousands of variations in any one field, and they are definitely not numbers (although they look like them). How would one go about putting them into ideal groups to describe the target (such as “loyal customers”)?

If you are selling expensive computer servers, one may put “Agricultural, Fishing and Mining” as a low priority group. Then, how about all those variations in huge groups, such as “Retail,” “Business Service” or “Manufacturing,” with hundreds of sub-categories? Let’s just say that I’ve never met a human being who went beyond the initial two-digit SIC code in their heads. Good luck creating an effective group with that one variable with rudimentary methods.

Grouping “values” that move together in terms of predictability is not simple. In fact, that is exactly why computers were invented. Don’t struggle with such jobs.

These are just a few reasons why we must rely on advanced modeling techniques to navigate through complex data. The benefits of modeling are plenty (refer to “Why Model?”). Compared to our gut feelings, statistical models are much more accurate and consistent. They also reveal previously unseen patterns in data. Because they are summarized answers to specific questions, users do not have to consider hundreds of factors, but just one model score at a time. In the current marketing environment, when things move at a light speed, who has time to consider hundreds of data points in real-time? Machine learning — leading to full automation — is just a natural extension of modeling.

Each model score is a summary of hundreds of contributing factors. “Responsiveness to email campaigns for a European cruise vacation” is a complex question to answer, especially when we all go through daily data overload. But if the answer is in the form of a simple score (say, one through 10), any user who understands “high is good, low is bad” can make a sound decision at the time of campaign execution.

Marketers already have ample amounts of data and advanced campaign tools. Running such machines with some man-made segmentation rules from the last century is a real shame. No one is asking marketers to become seasoned data scientists; they just need to be more open to advanced techniques. With firm commitments, we can always hire experts, or in the near future, machines that will do the mathematical jobs for us. But marketers must move out of old fashioned rule-based marketing first.

Crossing the Line Creates Cross Customers

It’s no new news that brands track our purchases and then send us coupons, promotions, special offers and “news” that fit our shopping patterns. That’s cool. Bring it on as, in most cases, we win with worthwhile discounts, loyalty rewards, and such that pay off in one way or another.

retargeting
“ad,” Creative Commons license. | Credit: Flickr by Eugene Peretz

It’s no new news that brands track our purchases and then send us coupons, promotions, special offers and “news” that fit our shopping patterns. That’s cool. Bring it on as, in most cases, we win with worthwhile discounts, loyalty rewards, and such that pay off in one way or another.

We expect this kind of personalized communications for simple products bought at Wal-Mart, Target, Amazon and so on. In most cases, we all know its happening, and its okay because its data that is not threatening. Who cares if Walmart knows I buy Newman’s spaghetti sauce, or that I have a fetish for glitter green nail polish? Right?

But, with all of the new technology available to track, monitor and influence consumers’ purchasing behavior in real-time, the game is changing.

We now are being listened to on our social sites so Facebook and others can serve us up ads for products we just browsed and might have left in our shopping cart, upping its profits if the social network can get us to go back and buy.

And we are being watched by big data users when we go to the store physically — not just online.

And for most — myself, included — this doesn’t feel so good.

Consider this: When out of town, shopping at a store where I don’t usually shop, I bought mouse traps as I unwittingly let one of these unpleasant creatures in my house. That night, while opening up the Solitaire app on my iPhone to help me find sleep, an ad for that very brand and type of mousetrap appeared on my phone. Odd, but I noted that someone was possibly tracking my purchases via my credit card and then appending that to my phone. Okay. Not what I signed up for, but I understood it — at least for this one purchase.

Then consider this: My husband went to the store and used his credit card to buy a little-known brand of gluten free bread — two uncommon variables, right there. Within the hour, an ad for that very brand and product showed up on MY phone, not his, but MY phone. Suddenly “watching” my purchases and those of my family is not okay with me any more, and it conjured up a lot of “what ifs.”

What if:

  • My husband had just bought me a 2-carat sapphire ring and wanted to keep it a surprise?
  • What if my husband had just bought medication for an illness he had not told me about yet?
  • And what if I were advertising my house on VRBO for holiday rentals and somehow my phone number on the listing was associated with that mousetrap purchase and all potential renters saw an ad in their side bar about mouse traps? That could conjure up a lot of yucky feelings, unconsciously, which could be unintentionally associated with my listing.

The list goes on … and so do:

The Questions All of Us Marketers Must Ask Ourselves

At what point does data tracking, customer profiling and targeted, automated marketing cross the line from “personalized customer service and care” to “creepy, stalkerish behavior” that makes consumers feel exposed, vulnerable and just downright uncomfortable?

How you answer this question and adapt your automated marketing messages and campaigns is critical. You might argue that our devices are anonymized and that brands really don’t know who goes with what IP address or device codes. But is this really accurate in terms of the possibility to pinpoint specifics about individuals? Consider the following example from an article posted on DeZyre.com.

An office supply store sent a customer a promotional letter and set up the personalization process to reference a personal detail or transaction on the envelope. In this case, that personalized envelope “teaser” was “Daughter killed in car crash.” That was not information he had opted to share with this office supply store, and clearly not information that was related to anything the store needed to know to offer him more laser pointers or copy paper at a discount. It is information that clearly was gleaned from other sources about his personal life and potentially legal or government records; which, clearly, he did not volunteer to a store for customer service purposes.

Be Honest — With Yourselves

Again, managers and servers of big data maintain that their promotional messages are sent to devices that are anonymized, so no secrets are revealed and consumers are not exposed. But at the end of the day, is it really? Any database that has customer transactions that also contains devices, IP addresses and names can be tracked back to an individual. Just ask the FBI, CIA, Mueller and any other investigative unit.

And is it really anonymized when social listening takes place? Track your conversations online and see what ads pop up shortly thereafter.

Beyond asking yourselves where you should cross the line, ask consumers how they feel about ads that “creep” up outside of personalized coupons you send via an opt-in program. I did just that on my Facebook page and here’s what came back from consumers:

  • “Scary and happening more frequently. Not okay.”
  • “It bothers me to no end. Once I started noticing it, I have become increasingly aware of it and it scares the $%^( out of me.”
  • “If I don’t sign up for it, it bothers me.”
  • “I always find it creepy when I’ve been looking/shopping for something and all of a sudden I get an ad for it.”
  • “Time to live off the grid and pay cash.”
  • “This is very scary.”
  • “No way!”

If consumers are scared of what you know about them, its time to rethink that proverbial line. Don’t cross it just because you can or because you’ve invested in the technology that automatically delivers those ads, so you have to use it fully to get your promised ROI. Think about how you can use this amazing data and technology for real-time marketing across devices and channels in ways that actually please customers vs. scare them, like inviting them to opt in like we have for so many other channels.

It’s not just a courtesy to involve customers in the decision to watch them in order to serve them really relevant timely ads, it’s critical to our future as an industry. How? Because if we don’t do it, we will likely increase more of those opt-outs and even legal regulations that will force us to stop communicating despite honest and good intentions we might have.

Consequences for Marketers

Think about it. Consumers have spoken up about getting harassed on the phone by opting into the “do not call” list. Consumers have shut down unwanted emails by advocating against spam and assuring they have a choice to opt out. Brands that spam are blacklisted and shut down by email servers as a result.

Just these two examples of consumer backlash have impacted the way we communicate with consumers and laws have been passed that we can’t get around. If we continue to serve “anonymized” ads to personal devices on apps that are personal, like my Solitaire game, are we setting ourselves up for more regulation — in addition to increased opt-outs for “permission” marketing from more angry, frustrated consumers who leave our brand to patronize one that doesn’t follow their every move?

As marketers, we have a big responsibility not to just do our jobs and fuel sales and lifetime value, but to consumers and our customers to preserve what matters most to them: anonymity, privacy and security.

Curious about your thoughts? Agree? Disagree? Please post your thoughts, suggestions and ideas for how we can continue to use the power of personalization, big data and automated marketing for the greater good? (The greater good for us and our happy, lifelong customers.)

Straightforward Steps to Achieving Empathy

Empathy is the ability to understand and share the feelings of another. In marketing, empathy is the code word for understanding your audience’s needs, desires and communication preferences so well that your marketing is tuned perfectly toward meeting those needs and desires, and inciting action. At least … that’s the goal.

Empathy is the ability to understand and share the feelings of another. In marketing, empathy is the code word for understanding your audience’s needs, desires and communication preferences so well that your marketing is tuned perfectly toward meeting those needs and desires, and inciting action.

At least … that’s the goal. In reality, marketers are challenged on a minimum of a three different levels:

  1. Do we truly have the capacity for empathy, or do we just like to say we have it?
  2. How can we best achieve empathy?
  3. If we’ve achieved empathy, are we actually expressing it? Are we providing value to our audience based on that common understanding? Or are we still pushing product and employing a couple of words to make it sound like we have empathy?

Let’s make the correct assumption that we should have empathy at the core of our marketing. So … how we do achieve empathy? And how should it shape our communications?

Empathy requires truly understanding our audience.

“You never really know a man until you understand things from his point of view, until you climb into his skin and walk around it.”
— Harper Lee. “To Kill A Mockingbird”

Certainly this wasn’t written with marketing on the brain. And Harper Lee’s words are not even the origin of the idea. But I’m going to terribly twist the thought to our ends and say it’s a great statement about what it takes to truly understand an audience. And currently, most marketers aren’t taking this tact when they say their gaining an understanding of audience.

Because, usually, the process marketers take (dubbed persona creation) involves gathering just about everyone into a room to talk about the audience…except members of the audience themselves! Which means marketers come together to discuss their biased beliefs of what an audience thinks, feels, wants and needs.

We’ve even gone so far as to try and talk ourselves into believing that’s the right way to do things by quoting other people — like Henry Ford (“If I had asked people what they wanted, they would have said faster horses.”) or Steve Jobs (“A lot of times, people don’t know what they want until you show it to them.”).

I would posit that those dudes were smart enough to know how important it is to know what people are asking for. And that, if the whole story is told, Henry would’ve heard “faster horses” and interpreted the thought as “a more rapid means of personal transportation.” Therefore he knew what his audience truly needed, even if it wasn’t in the form the audience thought it might come in. That’s understanding people far below the surface. That’s empathy. (I’ll give Steve the same kind of credit.)

If you’re going to truly understand your audience, then you have to spend time with your audience, and hear what they’re saying beyond just the words used.

How do you spend that time? Here’s three straightforward ways.

Straightforward Method 1: Observe

I guess you could call it stalking … but not the creepy “get yourself arrested” kind of stalking. As audiences are now creating plenty of profiles, content and commentary, those signals become the easiest entré into understanding who your audience really is, as individuals. Simply observing the language used (including shorthand like emojis), the commonalities of self-description and other surface cues can help you better understand the tendencies, needs and wants of your audience.

As an example, take a look at my actual Instagram profile. You’ll see several things that might be important to you, as a marketer. If you’re selling bourbon or beer, you’ve got the info straight from me that I’m a part of your audience. Likewise, if you’re selling marketing technology, I might be a good target, too. Now, that’s a bit too easy…especially if I’m already following your beer brand, this is just validation that I’m actually interested, but it’s not really new information.

If you go a bit farther, though, you’d find information that builds from that validation point, and gives you some interesting angles to work into valuable content for me (and others like me in your audience). I’ve been spending time at the pool … I play golf … I proudly promote my Raleigh community…so on and so forth. And I haven’t even delved into the photos I’ve liked from others – to start to build a picture of who I influence, and who influences me. Or followed myself (in this case) to other social networks to see what I’m posting.

One way to build empathy for your customers.

As a marketer, you can build some pretty amazing interest graphs of your audience that go far beyond demographics. And those interest graphs become the sparks of new content that is driven specifically by what I’m already engaging in. (Like: “Best IPAs To Drink Poolside.”) This is gaining an understanding of who I am, what I like, what I do and what I think. This is building empathy.

(A note on demographics: We marketers love the idea of personas. But I not-so-secretly hate personas. Because the commonly accepted version of personas are based on demographics. And empathy cannot be defined by demographics. One 44-year-old digital marketing expert is not just like another. But if you concentrate on demographics and don’t dig into the individuals behind the averages, that’s what you’ll be led to believe.)

The Positive Psychology of NO CHOICE

When asked why he always wore grey or blue suits, Barack Obama responded that he had enough other choices to make so this was a choice he could choose not to make. And per psychology studies, this was a smart choice.

choicesWhen asked why he always wore grey or blue suits, Barack Obama responded that he had enough other choices to make so this was a choice he could choose not to make. And per psychology studies, this was a smart choice.

Making choices actually depletes our brain energy and distracts our mental focus in ways that often lead to inertia, or procrastination of important events, and fatigue. In fact, several studies have shown that:

the more little decisions we make, the more it taxes our ability to make bigger decisions that are important to our advancement toward life’s bigger goals.

For example, a study conducted by University of Minnesota psychologist Kathleen Vohs and colleagues showed that participants who made several small choices while shopping were less likely to do well when asked to solve a simple algebra problem. This inability to go from a series of small choices to a more complex mental activity proved true with other tasks they conducted in this same study, which involved college students. Per the task studied involving students from prestigious universities, researchers found that students were more likely to put off studying for important tests if preoccupied with smaller decisions at the same time.

Vohs and her team conducted four different tasks associated with choice for their overall study and made some fascinating observations and conclusions:

  • Making choices can deplete the brain and body, creating mental and physical fatigue
  • Having to make choices is more depleting than just looking at options
  • Implementing choices imposed on you by somebody else is less draining
  • If you anticipate that making choices will be a fun and rewarding experience, the decision process is less depleting

These findings have substantial implications for anyone in marketing, whether B-to-B or B-to-C: If you want your customers to make quick decisions to purchase from you, and have an energizing vs. depleting experience, simplify the decision process by offering fewer choices.

Sounds counter-intuitive to some, but think about it. When you are faced with choosing from dozens of products on a shelf with lots of price and promise variations, you end up having to think more, analyze more, and it often results in muddled thinking and confusion. Per the above studies, you and many other consumers have likely made the choice to not choose when choosing becomes too time-consuming and exhausting. It happens when shopping for cars and even personal products at a grocery store. We get “depleted” mentally when trying to decide which product to purchase based upon our mental process to make sure we get the best deal, best value and don’t make decisions we might regret.

As a business, we need to do whatever we can to make choosing our products simple and energizing vs. depleting.

If you’re selling software as a service, such as a SaaS platform for CRM or some other business function, you likely have a big range of services people can choose from, and different price ranges for “packages” of those services. If you have three packages to choose from, your chances of getting sales quickly are likely going to be greater than if you gave them 10 packages to choose from or ask customers to create their own bundle out of dozens of services you offer.

And if you make that choice “safe,” by providing a generous cancellation or opt-out clause, you take the fear out of an easy choice. This is critical to the psychology of choice, as both of these activities take less energy from our mental capacities. And when we use less energy worrying or stressing or contemplating, we have more energy to anticipate the reward of that decision.

So ask yourself these key questions:

  • Do my offerings or sales model drain or sustain brain energy?
  • How can I simplify choices without making customers feel like they have none?
  • How can I make choices a replenishing, energizing experience that makes customers feel good about their decisions and my brand?

When you can build your sales offerings and marketing messages around the answers to those three questions you can transform your brand’s ability to close deals. And that can transform your bottom line and competitive advantage for a long time to come.