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.

Denny Hatch Takes on a Direct Brand With Direct Marketing

Harry’s is what’s now classified as a direct brand. But is traditional direct marketing more powerful? Politically correct or not, “It ain’t over till the fat lady sings” reminds us that the piece we write today may be chuck full of insight and wisdom now, but demands a fresh new look only a few milestones down the road.

Harry’s is what’s now classified as a direct brand. But is traditional direct marketing more powerful? Politically correct or not, “It ain’t over till the fat lady sings” reminds us that the piece we write today may be chuck full of insight and wisdom now, but demands a fresh new look only a few milestones down the road.

Denny Hatch’s name should not be an unfamiliar one here. Former Target Marketing editor, blogger and general gadfly, Hatch retains the mantle of data-driven marketing’s provocateur, par excellence, now sadly deprived of his joy at being able to limit his writings to twice the number of characters of the original Twitter. His new marketing blog is full of good stuff.

For his recent 700-character, “Getting Your Prospects to Say ‘Yes’ ” piece, he has turned his sights on Harry’s, the upstart direct-to-consumer razor company featured in this Maverick space almost a year ago. At that time I asked you, our readers:

Will the powerful copy and offer, the Harry’s against Goliath approach, go viral or sufficiently viral to extend the reach of the promotion well beyond the media that has been paid for? Will it bring the cost of trials and conversions down low enough to be “affordable,” attracting customers whose loyalty generates sufficient lifetime value to amortize the total marketing costs over that lifetime and let Harry’s end up with more than a sustainable profit?

direct brand Harry's
Credit: Peter J. Rosenwald

Although headlined, “Make Your Bet on Harry’s or Goliath,” readers were only asked whether they believed that the soft, brand-focused approach would be enough to build a loyal and profitable client base. This direct brand ad and similar treatments break all of the DM101 rules and, because they keep appearing, either they are driving a satisfactory response or, sooner or later, the remains of Harry will be marketing history.

The Denny Hatch traditional direct marketing answer to the “will you bet your money on Harry?” question is a snarling “no.” And he is willing to put his “cheek” (so to speak) where his money is, by offering Harry’s a Denny original, an ad designed to test the “on your face” Free Trial offer against the company’s editorial lede with the same Free Trial offer.

Hatch’s proposed direct marketing ad, seen here, is a classic old school mail-order: “FREE,” “GUARANTEED,” “No Cost,” “No Risk,” “No Obligation.” The call to action couldn’t be improved: “CLICK HERE FOR NO-RISK FREE TRIAL.” And the copy appears to be signed-off by a real person. It’s got everything.

direct brand vs. direct marketing ad
Credit: Denny Hatch’s Marketing Blog by Denny Hatch

But is “everything” what moves today’s consumer, or is the intriguing narrative about changing a $13 billion industry better attuned to today’s sensibilities? Problem is: Will we ever know the results? At this writing, Harry’s soft-focus direct brand ads are everywhere I seem to go on the web.

If Harry’s would run a valid split test of Hatch’s direct marketing ad against one of its regular ads, we would know which one had the better clickthrough. And if we waited long enough, we would know which would have the better lifetime value. (A parenthetical aside: The trouble with measuring lifetime value is that, theoretically, you have to wait until everyone is dead. That’s likely to be longer than you care to wait.) Hopefully, we’ll be able to get some data in this case and share it with you sometime in the future.

When there is more to come, journalists advise you to “watch this space”!

Wrestling the One-Time Buyer Syndrome

Marketers have different names for them. Some marketers call them “One-and-done” customers. Others call them by more innocuous “1-Time Buyers” phrases. The latter is the literal description of what they are. But I call them “problems” — potential or immediate.

Marketers have different names for them. Some marketers call them “One-and-done” customers. Others call them by more innocuous “1-Time Buyers” phrases. The latter is the literal description of what they are. But I call them “problems” — potential or immediate.

Considering how much marketers spend to acquire any new customer, those one-timers pose real challenges. In the metrics-governed marketing world where ROI means everything, they put marketers in the corner from the beginning.

“Great! Someone just walked in, bought and walked out with merchandise! But will we ever recover the acquisition cost from them? We gave them a fat 20% discount just for showing up!”

In the old days — not too long ago, though — marketers used to plan to break even on new customers on their second or third purchase. Now, no one seems to have that kind of patience in the fast lane, where “everything, all the time” is the norm and the consumers are distracted constantly by competing offers and messages. Hence, many retailers put out an ambitious goal of breaking even at “hello.”

The Customer Acquisition and Retention Challenge

That translates into good news for low-cost acquisition channels, like email or Facebook, and bad news for relatively expensive channels, like direct marketing or traditional media. Regardless of channel usage, however, marketers must be smart about both retention and acquisition. In other words, they must stop the bleeding and pump in new blood at the same time.

I often see that one-time buyers make up over 80% of the customer base of a retailer. Even when we go back four to five years and count every transaction, the lowest figure that I’ve seen hovers around 60% or so.

That means, even in an unusually decent case, more than half of new customers do not come back. Pretty scary stuff.

What Marketers Can Do to Retain Customers

If that figure goes over 80%, I recommend starting with a more refined acquisition strategy. Simply because without new blood coming in, there won’t be much to talk about in the near future.

The first thing that I would ask is how aggressive the marketers want to be in terms of channel usage. I’ve seen bold ones who go the multichannel route with varying degrees of cost-friendliness, and conservative ones who would stick only with cheap and measurable channels.

To Retain, Acquire Customers Intelligently

Regardless of the degree of aggressiveness, the first concern is if they have been targeting the “right” prospects.

Years of experience in data and analytics business taught me that not all customers are created equal. You may have multiple pockets (or segments) of vastly different types of customers in your base, starting with the most valuable customers to downright barnacles who are professional bargain-seekers with no chance of being a loyal customer.

Going after the right kind of customers during the acquisition stage will curb the one-time buyer problem.

Whether you want to toss a bunch target samples to Facebook, go to third-party data vendors or join a co-op for modeled prospects, I strongly suggest marketers define the ideal target for them first.

  • When you say “valuable,” what does that really mean?
  • In terms of frequency, is that measured by the number of transactions or days between transactions?
  • In terms of dollars, is that in total customer value or average spending level per transactions?

There are many ways to do it, and what I am suggesting is to try them as many as you can — when it comes to target definitions — and keep testing them. Targeting requires adjustment of the gunsight, with many rounds of practice shots.

One of the tricks I’ve learned while being a vendor all of my life is that you never try one method, one channel or one type of target. Because, if that “one” thing fails, you’ll be fired. Simple as that. But if you try three to four different combinations of target definitions and methodologies, then you have a better fighting chance to stay in the game, thanks to cumulative learning. After all, 1:1 marketing is all about learning from the past endeavors, isn’t it?

Here’s What I Recommend

So, I recommend trying different types of targeting (i.e., target definition of any “look-alike” modeling or simple selects) in different focus areas. For example:

  • Behavioral Targeting: Target after you your audience’s best behavior, however you define them. I would use separate measures, such as transaction frequency and dollar amount, as responsiveness is often inversely related to sheer value (e.g., an infrequent visitor who spends a lot in one transaction).
  • Demographic Targeting: What do those most valuable customers look like? What demographic clusters do they belong to, and what are their key demographic profiles? This type of targeting may not be as precise as behavioral targeting, but basic segmentation often provides a common language among disparate players in the acquisition play, including copywriters who would come up with relevant messages for each segment. Commonly defined clusters also open doors toward new target areas (e.g., targeting Millennials when an existing target base is mostly in older age segments).
  • Regional Targeting: It is not unusual to see a high concentration of customers around physical store locations, even for online traffic. Test in and out of traditional footprints for an effective expansion strategy by channel.
  • Product Targeting: Depending on the product lines, you may be dealing with vastly different customer profiles. Customer profile by high-level product category is important, as it is not a good idea to have a one-size-fits-all type of targeting when you carry distinct lines of products. The average of multiple types of customers is really nothing; there are no such things as “average” customers, when they are separated in dichotomous universes.

There are many ways to slice and dice this, but the important thing is to let the ideas fly within reason (i.e., don’t overdo it, either). And at some point, you will run out of options just using RFM segments, so plan to dive into look-alike models; many list vendors and social media publishers offer modeling, either in forms of traditional models or machine learning. But even the most cutting-edge targeting engines won’t work if the target is way off. Attracting barnacles is just one example.

Now Retain Those Customers

Then I would turn the attention to the retention side to curb this one-time buyer problem. But this time, I suggest marketers look at it not just from the segment/targeting point of view, but from the timeline point of view, as well.

4 Reasons Data Privacy Is Just Too Boring to Matter

Facebook was simply the poster boy for an uncomfortable data revolution well under way, but the hearings were very revealing. I am not sure how we will finally manage the complex issue of data privacy. However, it is clear what is not likely to happen in the near-term.

When I think about the current controversy around Facebook, personal data and the recently departed Cambridge Analytica, I am reminded of MAD Magazine. (Stay with me for a bit.) MAD was a rite of passage for Gen Xers such as myself. Irreverent and satirical of all things pop culture, the magazine was edgy (for that time) and a shock to polite sensibilities of the day. At a time when most people’s exposure to comedy was laugh-tracked sitcoms and Carson’s “Tonight Show,” MAD exposed the artificially flavored vanilla entertainment we were consuming for what it was, formulaic and fake.

It may seem that MAD magazine is tenuously relevant to today’s topic of data privacy, and I would agree except for one critical element. While parents and teachers could feel the sedition and revolution brewing in those pages they were comically inept at doing anything about it. Despite their frowns, despite all of their threats to censure, confiscate or ban the magazine, the magazine made its mark on my generation and contributed to a progression in brutally honest and sardonic comedy (“The Simpsons,” “Family Guy,” “Chappelle’s Show,” etc., etc.)

Fast forward to the congressional hearings where Mark Zuckerberg was grilled for hours on Facebook’s use of user data. Facebook was simply the poster boy for an uncomfortable data revolution well under way, but the hearings were very revealing. We saw Senators struggle, sometimes comically, to understand what really bothered them about this fiasco. Occasionally, they threw out threats to salvage their visibly worn-out veneer of authority. It was that familiar hapless authority figure trying to manage something ambiguously unnerving, while submitting to the inevitable change.

I am not sure how we will finally manage the complex issue of data privacy. However, it is clear what is not likely to happen in the near-term.

  1. There Will Not Be Any Effective Data Privacy Legislation. First, legislators don’t fully understand the intricacies, so they are rightfully hesitant to take strong action. Even more important, consumers are no longer naive about how their personal data could be accessed and used. There are even widely accepted conspiracies about murky information-gathering techniques, such as digital eavesdropping (“I swear XYZ is listening to my conversations because …”). Yet, every day and in very clear ways, consumers are giving permission by default when they post or view content and engage with apps. The lesson is that consumers care, but not enough to meaningfully change behavior. While a case can be made that data-driven services are designed for addiction and compel users to act in personally detrimental ways, like cigarettes, they are still a long way from becoming vilified products. For now, market demand will continue to drive lax data policies.
  2. Business Models Will Not Change Dramatically. When asked in the congressional hearing if there was a mass exodus of Facebook users since the Cambridge Analytica fiasco, Mark Zuckerberg said there was not. Furthermore, after the hearings concluded, Facebook stock rose 4.5% and has been on a major recovery trend since. If you believe in the wisdom of market forces, then this is a very strong vote for business as usual.
  3. Permission-Based Data Policies Will Provide Temporary Relief. These policies mean consumers decide where and how their data can be used. They will be ineffective, but will provide temporary cover until the next blow-up. These policies assume the consumer has time, ability and inclination to review the data policy of every platform they use. There will be companies who will enter into the personal data market, helping consumers monetize and manage their data, but their interest generally will not align with data privacy. The only one really interested in privacy is the consumer and most will not pay for it.
  4. No One Cares. Of all of my posts, the most informative was an article that discussed the wide landscape of consumer data. It is also the one that has had the fewest views, by a long shot. It is so dull, I rarely reference it and that should tell you all you need to know about the battle between sound data policy and data-driven consumerism.

Direct Marketing ‘Discovered,’ at Last

After years of being the poor relative to brand advertising, our direct discipline has finally been discovered by the big brand purveyors — all of those Mad Men who traditionally looked down their noses at any marketing efforts that demanded some form of response and were driven more by results than ego-polishing.

Perhaps we should all now breathe a welcome sigh of relief.

After years of being the poor relative to brand advertising, our direct discipline has finally been discovered by the big brand purveyors — all of those Mad Men who traditionally looked down their noses at any marketing efforts that demanded some form of response and were driven more by results than ego-polishing.

MediaPost’s’ editor Joe Mandese recently wrote an article with the intriguing, if slightly confusing title, “Excuse Me For Being Direct, But So Will You.”

“The most disruptive challenge to conventional media-based, brand-building advertising happened during the earliest days of Internet advertising, when agencies and brand marketers failed to define emerging digital platforms like the Internet — and ultimately, mobile — as a branding medium.

“Instead, direct-response marketers embraced the medium because of its real-time immediacy, access to data to track and ability to modify conversions and sales on-the-fly, and pure ROI efficiency.

“According to some experts, that trend is about to accelerate — as conventional brand marketers throw in the towel altogether and begin leveraging digital media to become direct sellers themselves.”

Conventional brand marketers throwing in the towel … becoming direct sellers themselves: That’s big news for those of us who have spent the better part of our careers trying to explain to those brand giants (and capturing some of all that money they seem to throw around) that while metrics like ‘reach’ and ‘frequency’ certainly have their value, nothing beats affordably capturing the business of new and returning customers and knowing their ROMI, the return on the marketing investment in each one of them. It is surprising they didn’t discover it years ago.

That expression “branding medium” suggests that those marketing initiatives which include a call to action and urge the consumer to “act now” do little or nothing to enhance the brand and often drive general agency art directors berserk, because those calls to action get in the way of their elegant designs.

Some years ago, before there was any significant “subscription” advertising in Brazil, where I now live, the small group who controlled newsstand distribution forbid publishers from advertising for subscriptions on pain of having their publications banned from the newsstands. They reasoned that this advertising would lure magazine buyers away. But when presented with the indisputable fact that offering subscriptions would allow a much greater advertising spend and in the best of all possible worlds, only 5 to 7 percent of the people who saw an ad would reply, while the rest would be positively exposed to the brand and many would purchase at the newsstand, they gave the publishers the go-ahead. Brand and subscription have gone hand-in-hand ever since, to their mutual benefit.

Quoting Publicis Groupe Chief Growth Officer, Rishad Tobaccowala on the reason for the “direct” discovery, Mandese wrote:

“… conventional brand-building media models aren’t working as well as they used to. It’s because big brands are realizing that the only way to have a relationship with and understand their consumers, is to cut out the middlemen and have a relationship with them directly.”

Wow! That’s a quantum leap from the historic paradigm that “direc”’ was, if not a strategy of last resort, well down the list of priorities. Working in big general agencies, how many of us have been asked to prepare 30-minute presentations to be an integral part of the same pitch with the agency’s brand campaign, only to see the time for it reduced to 20 and then 10 minutes or even — as time ran out — being asked to mention the “direct” recommendation while taking the client to the elevator?

Two important factors have principally changed the game:

  1. The emergence of vast amounts of data, the machines to process it and the ability of marketers to creatively use this data for their marketing initiatives;
  2. The growing understanding of CRM, the essential proactive relationship between brand and known customer.

Of course this hasn’t happened overnight. Data-driven marketing gurus have been planting and nourishing these seeds for decades and, as a result, the industry has grown and grown. Lester Wunderman said famously: “Data is an expense. Knowledge is a bargain.” As knowledge has grown and become more widely accessible, brand marketers are being increasingly drawn to it.

Poor relatives no more, “direct” practitioners have finally been “discovered” and have emerged from the shadows.

It feels great in the sunlight.

Security Is Part of the Customer Experience in Marketing

As companies work to define an exceptional customer experience, my guess is few of them think about the security of the customer and their personally identifiable information (PII). While consumers are willing to trade privacy for convenience, is it incumbent upon application providers to provide secure apps.

As companies work to define an exceptional customer experience, my guess is few of them think about the security of the customer and their personally identifiable information (PII). While consumers are willing to trade privacy for convenience, is it incumbent upon application providers to provide secure apps.

When we buy a product or service from a manufacturer, we do so with the assumption that the product will solve a problem. But what if it creates one with unforeseen circumstances?

Seventy-seven percent of applications have known vulnerabilities. Based on my interviews with hundreds of IT executives, they are not surprised. Organizations put much more emphasis on getting apps to market and monetizing them than ensuring they are secure.

Developers are rewarded for releasing applications as quickly as possible, without regard for the security of the application. Until consumers start worrying about the security of the apps they use and foregoing those apps that do not value the privacy of their information, we can expect more egregious breaches of B2B and B2C data.

While it’s not pleasant to think about, caveat emptor. The emoji keyboard that pops up on your phone has a vulnerability. The key fob to your car is easily replicated to steal your car. Hundreds of mobile websites and apps leak PII.

What’s a consumer to do? Ask questions about how the items they are buying are being secured. By asking questions, we begin to let manufacturers and solutions providers know that security matters and will be part of our purchase decision.

We know 55 percent of consumers are willing to pay more for a better customer experience. How many more are willing to pay for a better customer experience that’s also secure?

We’re in an ongoing battle with hackers to develop and deploy secure apps that protect our PII. It is incumbent upon us as consumers to hold suppliers accountable for the products and services we buy.

This goes for the security of our infrastructure, medical devices, as well as our cell phones. It’s a matter of making security part of the product requirements upfront and then employing security testing throughout the development process.

Playing the Amazon Game: Translating Big Data Into Big Dollars

Will 2018 be the year of Amazon (again)? The first week of the year is always filled with predictions, and there’s a good chance that most serious business predictions for 2018 will include some version of a call for businesses to respond to, react to, or create a new business model in order to compete in this age of Amazon.

Amazon boxesWill 2018 be the year of Amazon (again)? The first week of the year is always filled with predictions, and there’s a good chance that most serious business predictions for 2018 will include some version of a call for businesses to respond to, react to, or create a new business model in order to compete in this age of Amazon. Because the truth is, if you think that non-retail businesses are exempt from this challenge, you are wrong.

While Amazon may have started as an online bookseller, it is so much more than that now. It is, among many things, a cloud computing powerhouse, an award-winning original content producer and streaming content platform, a top-selling fashion house, a gamer’s paradise, the leader in AI and voice technology innovation, and the largest world marketplace for third-party sellers.

The company has innovated in pricing and subscription models, delivery systems and on-demand technologies and scared the heck out of those who previously thought their little corner of commerce was exempt from Amazon’s notice. No one is safe.

When Amazon enters a new industry or vertical — which the brand does with disquieting regularity — it changes the game for consumers and for businesses across segments and industries, challenging everyone and everything we thought we knew about consumer needs and how to sell stuff. Its impact is felt all along the business chain from suppliers and providers to adjacent businesses and directly to the consumer.

Amazon’s expansion plans and willingness to take risks, its consumer experience obsession, logistics expertise, consumer access and deep pocket investments have broad implications across categories. In 2017 alone, Amazon expanded through acquisition in non-retail directions including grocery stores (Whole Foods), cyber security (harvest.ai), gaming (GameSparks) and analytics presentation (Graphiq). And all these moves are strategically designed to strengthen its core offerings and consumer ties.

Amazon’s advantages also include a ubiquitous consumer presence in U.S. homes, (64 percent with Amazon Prime membership according to Forbes). This translates into tremendous data and insights into shopping patterns, price elasticity, promotion and offer value and critical consumer search patterns. And because it freely sells competing products, its marketplace supplies the company with nearly complete information on competitor strengths and weaknesses in not only sales data, but also consumer reviews.

This is in conjunction with the fact that it controls the marketplace and can therefore work the home-field advantage to highlight its own brands or those products that deliver the most value. In short, Amazon has a direct way to translate its big data into big dollars. This is increasingly important as Amazon aggressively expands its catalog of private label categories and products. Other key strengths include its forays into voice search, in-home electronics, alternate ordering methodologies and sheer operational excellence.

In terms of Amazon’s future endeavors, the brand has made recent investments, as well as public statements to include more acquisitions in AI and machine learning — maybe even in healthcare/genomics. And it’s probably safe to assume that we will see more proprietary devices like the Echo and Kindle that streamline consumer connections and reduce any friction in commerce while further building Amazon’s data advantage in the guise of consumer convenience and innovative experiences. Numerous patent applications in logistics, cyber security and cloud computing attest to its attention to the backbone that reliably delivers the Amazon experience.

Learning the Ways of Amazon

So how should marketers respond to such an intimidating competitor? I often think of Amazon as a wholly different planet filled with a lot of attractive consumers in active search mode for my products, but with its own set of customs, rules and laws. In order to commercially navigate on this planet, I have to familiarize myself with the environment and make some key adjustments.

  • My consumers may exist simultaneously in traditional sales channels and on planet Amazon as well as move frequently between the two. Therefore, I have to maintain a certain amount of consistency in experience and product as well as pricing unless I can distinguish an Amazon-only offering.
  • Amazon is built to provide consumers with easy access to a lot of competitive, comparative information. I better absolutely believe in the value and quality of my product before I enter this environment.
  • I must be ready to deliver at the potential scale and speed of the demand or otherwise risk a decrease in ratings and consequently, a downward sales spiral. This may require supply chain changes.
  • Planet Amazon competes directly with me and it has unfair advantages. I need to safeguard my margins to avoid giving them away.
  • The rules that helped me succeed in online marketing outside of Amazon may not help me succeed in optimizing search visibility or conversion rates within this proprietary world. I need to dedicate myself to learning the ad marketplaces, tools and options and be prepared for a dynamic environment that requires constant investment and learning.
  • I need to understand consumer expectations within this environment and work to achieve positive WOM and reviews/ratings to fuel sales.
  • I need to rethink my brand strategy within this saturated, pricing and ratings-driven marketplace.
  • I need to review my pricing strategy — including sales bundling — in light of the dense competitive field.
  • I need to carefully execute on CRM and other strategies I can control to build and develop sustainable direct connections with consumers outside of Amazon.

So by all means, plan your trip to planet Amazon, but do so carefully as it favors those that not only know its language and terrain, but also are willing to go at it with a full-fledged strategy.

Data’s $20B Role in Marketing

Right on cue. My last blog post happened to discuss Europe’s forthcoming “Data Freeze.” Enter a new U.S. study that articulates just how large the use of data for smarter marketing really is stateside — to the tune of $20 billion plus.

Third Party Data Study - Selected Chart
Credit: Data & Marketing Association by Winterberry Group

Right on cue. My last blog post happened to discuss Europe’s forthcoming “Data Freeze.”

Enter a new U.S. study that articulates just how large the use of data for smarter marketing really is stateside — to the tune of $20 billion plus.

The Data & Marketing Association and Interactive Advertising Bureau’s Data Center of Excellence commissioned the “State of Data 2017” study [available as a download], conducted by Winterberry Group. According to the foreword:

“…marketers and publishers looking to become ‘data centric’ have had little choice but to embark on that titanic change effort without the benefit of clear and complete intelligence; the inherent complexity of data and its myriad applications has previously made accurate reporting — on how users are investing in data, putting it to work and evolving their marketing approaches in turn — too challenging to accurately compile.

“This report represents the first industry-wide effort to address that gap. By providing credible, practitioner-informed insight, we hope to demystify how U.S. companies are investing in audience data (and its associated support functions), helping practitioners benchmark their own spending against industry norms and establish a firmer basis for future investments.”

If 2018 will be the year of third-party data quality, this study perhaps underscores why: Third-party audience data spending will top nearly $10.1 billion this year, in omnichannel ($3.5 billion), transactional ($3.0 billion), digital ($2.8 billion), specialty ($0.9 billion) and identity categories ($0.6 billion). Another $10.1 billion will be spent on various data “activation” solutions, from integration, processing and hygiene ($4.3 billion); to hosting and management ($4.2 billion); to analytics, modeling and segmentation ($1.6 billion).

In short, marketers are investing heavily on knowing prospects and customers better — and communicating intelligently with them to meet demands and expectations. For more and more brands and organizations in both consumer and business-to-business markets, third-party data is essential in this process — online, offline and omnichannel. But it’s indeed complex.

The scope of the study includes commercially licensable data and/or audience segments, as well as third-party data solutions that seek to activate or apply any combination of first-, second- or third-party data. It does not include data for “insourced” product development, aggregated data for market research, data for custom audiences that bundled inside “walled gardens” of social media platforms and other publishers, and enterprise data usage not related to advertising, marketing and media.

The study is helpful in providing benchmarks for companies as they evaluate their own third-party data dynamics in advertising, marketing and media planning — but I can’t help appreciate this snapshot on a wider economic basis. Responsible data collection for more relevant engagement with customers is a $20 billion business – a substantial and likely growing slice of all ad and marketing spend. [Early next month, Winterberry Group’s Bruce Biegel will present firsthand a “2018 Media Outlook” for direct, digital and data — and how they compare to overall media spending.]

If CMOs increasingly are judged on business effectiveness, on how advertising and marketing performs in this context, then gaining prowess with data — including third-party data — is fast becoming table stakes. Building out data-driven marketing capabilities will serve them well.

Third-party data and activation is indeed fuel for consumer engagement and business growth. This reality — documented in this study — needs to be understood, recognized and respected far beyond the C-suite. But let’s start with the C-suite.

The Art of Data Categorization

Machine-learning is getting better at recognition and categorization by leaps and bounds, for sure. My dog has a Facebook page — don’t ask why — and on his last birthday, Facebook correctly converted his age to dog years.

Do you know why some reports are unbearably long and filled with numbers that are irrelevant to decision-making? It is mostly because there are serious misalignments between the desired level of detail in reporting and actual data categorization. Raw data, with very few exceptions, are rarely ready for decision-making (through various reports) or statistical modeling (an important part of what we often call advanced analytics).

Machine-learning is getting better at recognition and categorization by leaps and bounds, for sure. My dog has a Facebook page — don’t ask why — and on his last birthday, Facebook correctly converted his age to dog years. I kept hearing that machines have a hard time separating dog and cat pictures, but apparently such an obstacle has been overcome (or do they just use dog years for cats, too?).

In any case, do machines understand the “purpose” of categorization and tagging, as well? Does it understand why it is even necessary to put my dog’s age in dog years? That is an entirely different matter, and whether the work is done by humans or machines, I have seen time and time again that categorization efforts with clear purposes result in improvement in analytics and prediction.

Let’s take an example of the hot topic of personalization. Folks who have read my previous articles may already know that I am not even nearly impressed with various marketing efforts under the banner of personalization today. Most are done on a product level, with raw product-level data, when the personalization must foremost be about the person.

Even at a basic level of personalization, consumers on the receiving end often suspect that some personalization engines don’t even consider categories of products, as a suggested product is often irrelevant, dubious or even stupid (as in, “Hey, I just bought that exact item! Why are they offering it to me again?”). I can think of many reasons why that happens (mostly around data and analytics), but the first wrong gear often is that data are not properly categorized.

Results of analytical efforts for personalization and other complex challenges certainly improve when clean data enters the system. The reasons why most analysts spend the majority of their valuable time in data preparation — or even give up to use some granular data — is mostly because input data are unclean, unstructured or uncategorized.

Allow me to share some categorization rules that I have developed based on countless trials and errors during my co-op database days, when we had to put tens of millions of SKUs from over 1,500 sources into one consistent list of categories, solely for the purpose of analytics for individual-level targeting. Whether the actual categorization is done by humans or machines is not the issue; they all have to “learn” what the proper category is to be assigned for each item, and that starts with a proper categorization framework.

The rules I am introducing here are for personal-level targeting and customization of messages; therefore, “customer-centric” at the core. You may need to develop separate frameworks, if the goals are different. Problem statements such as “What will be the most popular product next season?” for instance, would require product-centric categorization. Nonetheless, this framework will be useful when setting up your own, as well.

Without further ado, let’s dive into the list:

Categorize the Buyers, Not the Product

This may not sound intuitive, but it is the first item to remember when setting up a goal-oriented categorization framework. If it is for personalization, and if you are creating a 360-degree view of customers for that purpose, don’t stop there and convert the product-level information into descriptors of buyers. And categorizing items with this goal in mind results in a vastly different — and far more predictable — outcome.

For instance, some items in a nautical catalog, such as a wall-mounted weather station (displaying temperature, air pressure, humidity, etc. on a fancy panel), can also be purchased from an executive gift catalog or website. When assigning categories for items like that, think about the context of the purchase, not just SKU descriptions, to avoid cases where you end up sending nautical catalogs to casual gift buyers. When in doubt, imagine how many purposes baking soda serves; think about the context of the purchase to describe the buyer, depending on the specific purpose (e.g., baking, personal hygiene, deodorization of a refrigerator, domestic cleaning, etc.).

Also consider the price scale and purpose of the purchase, so that you do not end up putting a cheap, everyday lamp and a state-of-the-art home decor lamp in the same category, leading to seriously misaligned offers. You must look beyond simple product descriptions.

The More Specific, the Better

Basically, don’t be lazy and put a 4K TV under “Home Electronics” and call it a day. For apparel items, gender break is the easy part, but sub-categories are even more important for prediction. Most modern product categorization schema are multilevel, like Home Electronics>Home Theater>TV>4K TV. So use it fully.

I’m not saying that all the minute details are helpful for analytics; I’m just emphasizing that one can combine categories later in the process. But if things are lumped up to begin with, one cannot break them apart without resorting back to the source data.

You will be better off if this type of effort is performed as early in the process as possible. Don’t create some big homework for everyone — especially for the analysts — for later. 

Consistency Over Accuracy

This may sound weird as well, but consistently wrong data may be more predictable than “sometimes” accurate data. Assigning the same item to multiple categories creates all kinds of havoc in reporting and prediction downstream. We may argue forever if a certain type of luxury handbag belongs in a category, with no clear winner in the end. The key point is that one should not go back and forth with established categorization rules.

If you can’t settle the fight, then use multiple tags for an item (I don’t recommend it personally). In any case, to machines and algorithms, those categories are just a numeric representation of where they belong, without any judgement. Don’t spend too much energy on making human sense out of every assignment. We can always change the “label” at the reporting stage.

Categorize Only as Much as It Matters

When categorizing items for targeting and reporting, we do not have to create a new schema that covers the entire spectrum of items. If targeting is the end-goal, you don’t even have to touch the items that did not sell very well, as there are not many buyers behind them. Going further, it is alright to categorize the top 20 percent of the items in terms of popularity (i.e., number of transactions or revenue dollar amount), if it covers over 80 percent of the customer behaviors. Yes, I said don’t be lazy under No. 2, but there is no point in spending energy categorizing small items that may not even move analytical needles later. In other words, know when to stop and use the “All other” category for insignificant ones.

Cut Out the Noise

Not every little detail matters in analytics. For example, the “color” of an item may matter a great deal for inventory management (as in “Hey, we are running low on the toasters in Ferrari red!”). But unless you are thinking about targeting people who only purchase items in red, you may not need such details for customized communication and offers. Break down the elements that make up an item, and go only as far as your specific goal calls for. Consult with analysts when in doubt.

Be Inventive

Creating the category buckets is the first important step of categorization efforts. This is where one must “imagine” what type of category would be useful for reporting and prediction later. Simple food labels could lead to all kinds of interesting “behavioral” categories that may be extremely useful when personalizing offers (refer to “Freeform Data Are Not Exactly Free”). This may sound contradictory to No. 5, but hitting the right balance between “too much” and “too little” is indeed the human function — for now — that I was talking about.

Conclusion

Analytics, as we’ve been saying for a long time, is a “garbage-in-garbage-out” business. But in the age of abundant and ubiquitous data, some “seemingly” useless data can be truly predictable. If we don’t think about “data refinement” — of which categorization is a big part — analysts will end up beating down a few popular variables, or worse yet, push down the raw data through some analytical engine “hoping” for some good results.

If the current state of personalization is any indication, most available data must be refined in more systematic and rigorous fashion, whether done by machines or humans. And until the machine catches up with us in the area of creativity, intuition, as well as logical deduction, we will have to be the ones who set up the framework.

Data became too big and complex and customers became too demanding for marketers to leave anything to chance. Even your off-the-self personalization engine will run better with well-categorized data. So, commit to that step, set up proper frameworks and rules, and move onto automation once the organization is ready for it.

AI may take over the world soon, but different types of thinking machines will have to work together to make various marketing efforts truly fruitful. And categorization, along with predictive analytics, is an important component. That is, if you as a consumer believe that machine-driven personalization can use a “human touch.”