Don’t Blame Personalization After Messing It Up

In late 2019, Gartner predicted “80% of marketers who have invested in personalization efforts will abandon them by 2025 because of lack of ROI, the peril of customer data, or both.” But before giving up because the first few rounds didn’t pay off, shouldn’t marketers stop and think about what could have gone wrong?

In late 2019, Gartner predicted “80% of marketers who have invested in personalization efforts will abandon them by 2025 because of lack of ROI, the peril of customer data, or both.” Interesting that I started my last article quoting only about 20% of analytics works are properly applied to businesses. What is this, some 80/20 hell for marketers?

Nonetheless, the stat that I shared here begs for further questioning, especially the ROI part. Why do so many marketers think that ROI isn’t there? Simply, ROI doesn’t look good when:

  1. You invested too much money (the denominator of the ROI equation), and
  2. The investment didn’t pay off (the numerator of the same).

Many companies must have spent large sums of money on teams of specialists and service providers, data platforms featuring customer 360, personalization software (on the delivery side), analytics work for developing segments and personas, third-party data, plus the maintenance cost of it all. To justify the cost, some marginal improvements here and there wouldn’t cut it.

Then, there are attribution challenges even when there are returns. Allocating credit among all the things that marketers do isn’t very simple, especially in multichannel environments. To knock CEOs and CFOs off their chairs – basically the bottom-line people, not math or data geeks – the “credited” results should look pretty darn good. Nothing succeeds like success.

After all, isn’t that why marketers jumped onto this personalization bandwagon in the first place? For some big payoff? Wasn’t it routinely quoted that, when done right, 1:1 personalization efforts could pay off 20 times over the investment?

Alas, the key phrase here was “when done right,” while most were fixated on the dollar signs. Furthermore, personalization is a team sport, and it’s a long-term game.  You will never see that 20x return just because you bought some personalization engine and turned the default setting on.

If history taught us anything, any game that could pay off so well can’t be that simple. There are lots of in-between steps that could go wrong. Too bad that yet another buzzword is about to go down as a failure, when marketers didn’t play the game right and the word was heavily abused.

But before giving it all up just because the first few rounds didn’t pay off so well, shouldn’t marketers stop and think about what could have gone so wrong with their personalization efforts?

Most Personalization Efforts Are Reactive

If you look at so-called “personalized” messages from the customer’s point of view, most of them are just annoying. You’d say, “Are they trying to annoy me personally?”

Unfortunately, successful personalization efforts of the present day is more about pushing products to customers, as in “If you bought this, you must want that too!” When you treat your customers as mere extensions of their last purchase, it doesn’t look very personal, does it?

Ok, I know that I coveted some expensive electric guitars last time I visited a site, but must I get reminded of that visit every little turn I make on the web, even “outside” the site in question?

I am the sum of many other behaviors and interests – and you have all the clues in your database – not a hollow representation of the last click or the last purchase.  In my opinion, such one-dimensional personalization efforts ruined the term.

Personalization must be about the person, not product, brands, or channels.

Personalization Tactics Are Often Done Sporadically, Not Consistently

Reactive personalization can only be done when there is a trigger, such as someone visiting a site, browsing an item for a while, putting it in a basket without checking out, clicking some link, etc. Other than the annoyance factor I’ve already mentioned, such reactive personalization is quite limited in scale. Basically, you can’t do a damn thing if there is no trigger data coming in.

The result? You end up annoying the heck out of the poor souls who left any trail – not the vast majority for sure – and leave the rest outside the personalization universe.

Now, a 1:1 marketing effort is a number’s game. If you don’t have a large base to reach, you cannot make significant differences even with a great response rate.

So, how would you get out of that “known-data-only” trap? Venture into the worlds of “unknowns,” and convert them into “high potential opportunities” using modeling techniques. We may not know for sure if a particular target is interested in purchasing high-end home electronics, but we can certainly calculate the probability of it using all the data that we have on him.

This practice alone will increase the target base from a few percentage points to 100% coverage, as model scores can be put on every record. Now you can consistently personalize messages at a much larger scale. That will certainly help with your bottom-line, as more will see your personalized messages in the first place.

But It’s Too Creepy

Privacy concerns are for real. Many consumers are scared of know-it-all marketers, on top of being annoyed by incessant bombardments of impersonal messages; yet another undesirable side effect of heavy reliance on “known” data. Because to know for sure, you have to monitor every breath they take and every move they make.

Now, there is another added bonus of sharing data in the form of model scores. Even the most aggressive users (i.e., marketers) wouldn’t act like they actually “know” the target when all they have is a probability. When the information is given to them, like “This target is 70% likely to be interested in children’s education products,” no one would come out and say “I know you are interested in children’s education products. So, buy this!”

The key in modern day marketing is a gentle nudge, not a hard sell. Build many personas – because consumers are interested in many different things – and kindly usher them to categories that they are “highly likely” to be interested in.

Too Many Initiatives Are Set on Auto-Pilot

People can smell machines from miles away. I think humans will be able to smell the coldness of a machine even when most AIs will have passed the famous Turing Test (Definition: a test of a machine’s ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human).

In the present day, detecting a machine pushing particular products is even easier than detecting a call-center operator sitting in a foreign country (not that there is anything wrong about that).

On top of that, machines are only as versatile as we set them up to be. So, don’t fall for some sales pitch that a machine can automatically personalize every message utilizing all available data. You may end up with some rudimentary personalization efforts barely superior to basic collaborative filtering, mindlessly listing all related products to what the target just clicked, viewed, or purchased.

Such efforts, of course, would be better than nothing.  For some time.  But remember that the goal is to “wow” your target customers and your bosses. Do not settle for some default settings of campaign or analytics toolsets.

Important Factors Are Ignored

When most investments are sunk in platforms, engines, and toolsets, only a little are left for tweaking, maintenance, and expansion. As all businesses are unique (even in similar industries), the last mile effort for custom fitting often makes or breaks the project. At times, unfortunately, even big items such as analytics and content libraries for digital asset management get to be ignored.

Even through a state-of-the-art AI engine, refined data works better than raw data. Your personalization efforts will fail if there aren’t enough digital assets to rotate through, even with a long list of personas and segments for everyone in the database. Basically, can you show different contents for different personas at different occasions through different media?

Data, analytics, contents, and display technologies must work harmoniously for high level personalization to work.

So What Now?

It would be a real shame if marketers hastily move away from personalization efforts when sophistication level is still elementary for the most.

Maybe we need a new word to describe the effort to pamper customers with suitable products, services and offers. Regardless of what we would call it, staying relevant to your customer is not just an option anymore. Because if you don’t, your message will categorically be dismissed as yet another annoying marketing message.


Emotions Matter — Why Your B2B Marketing Must Connect Before It Can Convert

Have you ever walked into a store or restaurant and thought to yourself, “Yes! This just feels right.” If so, then the rest of this article won’t come as any surprise to you; though if you’re like many marketers, your B2B marketing may be overlooking the value and importance of that “it just feels right” moment.

Have you ever walked into a store or restaurant and thought to yourself, “Yes! This just feels right.”

If so, then the rest of this article won’t come as any surprise to you; though if you’re like many marketers, your B2B marketing may be overlooking the value and importance of that “it just feels right” moment.

We’re Not All Coolly Rational Consumers

We may like to think that B2B prospects are all like Mr. Spock — coolly rational and unswayed by their emotions, but research and our own experience disproves that at nearly every turn.

Like a Rock, Best-in-Class, or Ram Tough

Credit: Wikimedia Commons by Colin

Lets look for a moment at pickup trucks. There is a large group of buyers who would never consider a Ford pickup truck. And a similarly large group who wouldn’t be caught dead in a truck sporting a Chevrolet or GMC nameplate.

They can’t both be right about the superiority of their chosen brand; which, setting aside functional differences — like towing capacity being more important than torque or vice versa — leaves only the emotional component of the brand.

(My choice of pickups as an example isn’t random. Truck buyers are reputed to be among the most brand-loyal consumers on the planet, though there is some evidence that this is changing.)

Connecting Without Smothering

Back to B2B marketers: For us, the trick is in making an emotional connection without making your case emotionally. We can’t “chew the scenery,” so to speak. We simply don’t have an audience that is as passionate about our services as consumers are about trucks or chocolate or puppies and kittens in need of forever homes … But we do need to make sure we’re connecting with our audience on a level other than “just the facts, ma/am.”

Even with the necessity of a more restrained approach, we do need to create opportunities for our prospects to feel their decision rather than just think it. How do we do this?

Well, there are a lot of tools that can work. Developing personas for your buyers and doing market research into their needs can help you understand motivation and pain points around which emotional connections can be built. Also important are things like testimonials from existing clients and case studies about success stories from people “just like me” who have used your service to profitable effect.

Whose Language Are You Speaking?

Perhaps most importantly, it requires language and presentation that is comfortable to the prospect. Are you speaking their language? Have you met them where they live?

At some point, prospects will want to hear you geek out on the minutiae of your offering — the details and features that make it a better choice. But first, they want to feel the benefits. How does this benefit me? How does this reduce my risk? How is this preferable to doing nothing?

This isn’t an easy goal to achieve consistently, but one worth striving for. Because if you can bring that ever-so-subtle smile to your prospect’s face that says, “Yeah, this is going to work,” you’ve got a winning formula.

Use People-Oriented Marketing: Because Products Change, But People Rarely Do

In 1:1 marketing, product-level targeting is “almost” taken for granted. I say almost, because most so-called personalized messages are product-based, rarely people-oriented marketing. Even from mighty Amazon, we see rudimentary product recommendations as soon as we buy something. As in: “Oh, you just bought a yoga mat! We will send you absolutely everything that is related to yoga on a weekly basis until you opt out of email promotions completely. Because we won’t quit first.”

In 1:1 marketing, product-level targeting is “almost” taken for granted. I say almost, because most so-called personalized messages are product-based, rarely people-oriented marketing. Even from mighty Amazon, we see rudimentary product recommendations as soon as we buy something. As in: “Oh, you just bought a yoga mat! We will send you absolutely everything that is related to yoga on a weekly basis until you opt out of email promotions completely. Because we won’t quit first.”

How nice of them. Taking care of my needs so thoroughly.

Annoying as they may be, both marketers and consumers tolerate such practices. For marketers, the money talks. Even rudimentary product recommendations — all in the name of personalization — work much better than no targeting at all. Ain’t the bar really low here, in the age of abundant data and technologies? Yes, such a product recommendation is a hit-or-miss, but who cares? Those “hits” will still generate revenue.

For consumers, aren’t we all well-trained to ignore annoying commercials when we want to? And who knows? I may end up buying a decent set of yoga mat cleaners with a touch of lavender scent because of such emails. Though we all know purchase of that item will start a whole new series of product offerings.

Now, marketers may want to call this type of collaborative filtering an active form of personalization, but it isn’t. It is still a very reactive form of marketing, at the tail end of another purchase. It may not be as passive as waiting for someone to type in keywords, but product recommendations are mixture of reactive and active (because you may send out a series of emails) forms of marketing.

And I’m not devaluing such endeavors, either. After all, it works, and it generates revenue. All I am saying is that marketers should recognize that a reactive product recommendation is only a part of personalization efforts.

As I have been writing for five years now, 1:1 marketing is about effectively deciding:

  1. whom to contact, and
  2. what to offer.

Part One is good old targeting for outbound efforts, and there are a wide variety of techniques for it, starting with rules that marketers made up, basic segmentation, and all of the way to sophisticated modeling.

The second part is a little tricky; not because we don’t know how to list relevant products based on past purchases, but because it is not easy to support multiple versions of creatives when there is no immediate shopping basket to copy (like cases for recent purchases or abandoned carts).

In between unlimited product choices and relevant offers, we must walk the fine lines among:

  1. dynamic display technology,
  2. content and creative library,
  3. data (hopefully clean and refined), and
  4. analytics in forms of segments, models or personas (refer to “Key Elements of Complete Personalization”).

If specific product categories are not available (i.e., a real indicator that a buyer is interested in certain items), we must get the category correct at the minimum, using modeling techniques. I call it personas, and some may call it architypes. (But they are NOT segments. Refer to “Segments vs. Personas”).

Using the personas, it is not too difficult to map proper products to potential buyers. In fact, marketers are free to use their imaginations when they do such mapping. Plus, while inferred, these model scores are never missing, unlike those hard-to-get “real” data. No need to worry about targeting only a small part of potential buyers.

What should a marketer offer to fashionistas? To trendsetters? To bargain seekers? To active, on-the-go types? To seasonal buyers? To big spenders? Even for a niche brand, we can create 10 to 20 personas that represent key product categories and behavioral types, and the deployment of personalized messages become much simpler.

And it gets better. Imagine a situation where you have to launch a new product or a product line. It gets tricky for the fashion industry, and even trickier for tech companies that are bold enough to launch something that didn’t exist before, such as a new line of really expensive smartphones. Who among the fans of cutting-edge technologies would actually shell out over a grand for a “phone”? This kind of question applies not just to manufacturers, but every merchant who sells peripherals for such phones.

Let’s imagine that a marketer would go with an old marketing plan for “similar” products that were introduced in the past. They could be similar in terms of “newness” and some basic features, but what if they differ in terms of specific functionality, look-and-feel, price point and even the way users would use them? Trying to copy some old targeting methods may lead to big misses, as even consumers hear about them from time to time.

Such mishaps happen because marketers see consumers as simple extensions of products. Pulling out old tricks may work in some cases, but even if just a small bit of product attributes are different, it won’t work.

Luckily for geeks like us, an individual’s behavior does not change so fast. Sure, we all age a bit every year; but in comparison to products in the market, humans do not transform so suddenly. Simply, early adapters will remain early adapters, and bargain seekers will continue to be bargain seekers. Spending level on certain product categories won’t change drastically, either.

Our interests and hobbies do change; but again, not so fast. It took me about two to three years to turn from an avid golfer to a non-golfer. And all golf retailers caught up with my inactivity and stopped sending golf offers.

So, if marketers set up personas that “they” need to push their products, and update them periodically (say once a year), they can gain tremendous momentum in reaching out to customers and prospects more proactively. If they just rely on specific product purchases to trigger a series of product recommendations, outreach programs will remain at the level of general promotions.

Further, even inbound visits can be personalized better (granted that you identified the visitor) using the personas and set of rules in terms of what product goes well with what persona.

The reason why models work well — man-made or machine-built — is because human behavior is predictable with reasonable consistency. We are all extensions of our past behaviors to a greater degree than the evolution rate of products and technologies.

Years ago, we’ve had a heated internal discussion about whether we should create a new series of product categories from VHS to DVD. I argued that such new formats would not change human behavior that much. In fact, genres matter more than video format for the prediction of future purchases. “Godfather” fans will buy the movie again on DVD, and then again in Blu-ray. Now some type of ultra-high-definition download from some cloud somewhere. Through all of this, movie collectors remain movie collectors for their favorite types of movies. In other words, products changed, but not human attributes.

That was what I argued then, and I still stand by it. So, all the analytical efforts must be geared toward humans, not products. In coming days, that may be the shortest path to fake human friendliness using AI and machine-made models.


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.


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.

How to Formulate Your 2018 Content Marketing Strategy

Carolyn, a director of demand generation in the hospitality industry, shared that “It takes too much work to develop the wrong content.” In this month’s step of the revenue marketing journey, we are going to cover content marketing strategy and the steps to developing the best content editorial calendar.

Carolyn, a director of demand generation in the hospitality industry, shared that “It takes too much work to develop the wrong content.” Sadly, many organizations use a “spray and pray” methodology for content development and discover too late that much of their effort was wasted on the wrong content. Carolyn is not going that route and in this month’s article. In this month’s step of the revenue marketing journey, we cover content marketing strategy and the steps to developing the best content editorial calendar.

Step 1: Know What Content Is Valuable for Your Clients

Seems like a simple concept, right? When was the last time you surveyed your customers to find out what content topics they like, what channels they like, or their preferred content medium? In a recent interview, Michael Brenner, CEO of Marketing Insider Group and co-author of “The Content Formula,” shared that companies are only just now learning “how to utilize content to effectively meet the needs of their audience as opposed to meeting the needs of their business.” If the primary guide for your content decisions is the download reports from your website you are not on solid ground for planning your content calendar. So conduct a customer engagement survey, find out what content they like. Get free subscriptions to Buzzsumo and Grapevine6 and learn:

  • Which audience is interested in what topics
  • What type of content they are sharing
  • What sources of information are they using
  • Which influencers are most important

Step 2: Document Your Personas (5 to 7 Max)

Buyer personas are examples of real people who make up your customers and clients. They can also include individuals who may influence the buying decision in some way. A persona goes deeper than demographics. Personas are developed by asking questions about a buyer’s motivation and learning what holds the buyer back from making a purchasing decision. By taking the time to document and understand your customer in this way, your content team will develop content that resonates and engages, moving leads through the buyer’s journey to conversion.

Step 3: Document the Full Customer Journey Map

Marketing engages with prospect and customer not just when they are in the funnel for the first time, but throughout their lifecycle including adoption, value realization, loyalty and advocacy. This means that we need content suitable for every stage of the customer journey map.

Your customer journey map should inform your content marketing strategy.
Your customer journey map should inform your content marketing strategy.

Step 4: Audit Your Current Content

Now that you have the customer journey map and the personas, audit your content based on which personas suit what pieces of content and in which stages of the customer journey map can it be effective. Some additional criteria you might consider in the audit include content type, medium, consume-ability, centricity (product, company, or customer), level of engagement achieved, product/service served, industry, gated/ungated, purpose (reach, engagement, conversion, retention) etc. Build the audit in such a way that it can be used as an ongoing inventory of content and so new entries are added to it as they are developed. With the audit in hand, you should be able to see the gaps where more content is needed, but we’re not done yet.

Creating a Persona Menu (for You)

Personas are like menu items, each representing key characteristics of target customers that marketers need to know to push their products.

“93H,” Public Domain license. | Credit: Flickr by saul saulete

I have been writing about the importance of using modeling techniques for personalization for some time now (refer to “Personalization Is About the Person” and “Segments vs. Personas”). If I may summarize the whole idea down to a 15-second pitch:

  • We need modeling because we will never know everything about everybody, and;
  • Selfishly for marketers, it is much simpler to assign personas to product groups and related contents than to have to deal with an obscene amount of customer data and a long list of content details at the same time.

Simply, personas are like menu items, each representing key characteristics of target customers that marketers need to know to push their products.

One may say, “Hey, I just put in SKU-level data into some personalization engine!” To which, I must ask, “Do you also put in unrefined oil into your beloved automobile?” I didn’t think so. Not that ruining some personalization engine will break anyone’s heart. But it may annoy the heck out of your customers by treating them as extensions of their immediate purchases, not as living, breathing human beings.

I’ve actually met someone from a software company at a conference who claimed to be able to create hundreds of thousands of combinations of SKU-level transaction data and content data. If you have a few hundred thousand SKUs and tens of thousands of pictures and creative items, well, the number of combinations will be quite large. Not exactly the number of stars in the universe, but quite unmanageable, enough for marketers to just “let go” and leave it all to the machine on a default setting. So, even if someone automated the process of combining such data (with some built-in rules, I’m sure), how would any marketer – and recipients of messages – make sense out of it all?

That type of shotgun approach is the mother of all of those annoying “personalizations,” like offers of the very same items that you just purchased. For such rudimentary methods, it might actually be a great achievement to offer a yoga mat to someone who just bought a yoga mat. Hey, they are in the same category after all, categorically speaking, right?

The key to humanization of marketing messages is to make them about the customers, not about marketers, products or channels. And that kind of high-level personalization requires, well, a real human touch. That means, each block of information must be bite-sized so that human beings – i.e., marketers – can process and consume it easily.

When I first came to America (a long time ago), it wasn’t so easy to go through menu items in a typical diner. Too many items! How can I pick just “one” of those items that matches my appetite and mood of the day? Now imagine a menu that goes on for hundreds of thousands of lines. And you have to act fast on it, too.

Personas, or architypes as some may call them, are the bridges between obscene amounts of data points and yet another large set of pictures and content. The idea is to have a manageable number of personas to make it easier for us to match the right content to the right target.

I bet most content libraries are not crazy big, but large enough. But on that side, it is what it is. You will not cut out some valuable digital assets just because the inventory got big. So, we have to make the personal data – especially behavioral and transactional data – more compact to facilitate easy assignment, as in “Show this picture of a glass of red wine next to a juicy steak” to a persona called “Wine Enthusiast” or “Fine Dining.” The assignment itself would be as simple as saving a room for persona designation in the content library (if you don’t even have a content library, we need to talk).

Then, how would you come up with the right list of personas for “you”? Having done this a few times for many companies in various industries on a national level, I have some tips to share.

  1. Be Product-Centric: Anyone who has been reading my articles about personalization will be surprised by this one, as I have been screaming “customer-centric marketing” all along. But, in the end, we are doing all of this to sell more of our products to customers. Think about the products you want to push, then think about the types of characteristics that you would love to know about customers to push those products in a relevant way.

Trying to sell cutting-edge products? Then you may need personas such as “Early adopter.” Selling value-based items? You may want “Bargain-seekers.” Pushing travel items? Try “Frequent business traveler” or “Family vacation” personas. Dealing with high net-worth people? Well, go beyond simple income-select and try “Globetrotter,” “Luxury car,” “Heavy stock investor,” etc., depending on what you are selling. By the way, these luxury personas may or may not be related to one another, as human beings are much more complex than their income levels.

  1. Be Creative: Models can be built if you have data for “some” people who have actually behaved in a certain way to be used as targets. That limitation aside, you can be as creative you want to be.

For example, if you are in the telecommunications industry, expand the typical triple-play offering, and dig deeper into “why” people would need broadband service. Is it because someone is an “Avid gamer,” “Heavy VOIP user,” “Frequent international caller,” part of a “Big family,” “Home office worker” and/or “On-demand movie watcher”? If you can differentiate these traits, you don’t have to push broadband Internet services with brute force. You can now show reasons why they need over 100 megabits per second service.

If you are dealing with mostly female customers (who are, by the way, responsible for the bulk of economic activities on a national level), one can imagine categories that start with various health and beauty items, going all of the way to yoga and fitness personas. In between those, add any persona that is an ideal target for the products you are trying to sell, be it “Fashion enthusiast,” “Children’s interests,” “Gardening enthusiast,” “Organic food,” “Weight watchers,” Gourmet Cooking,” “Family entertainment,” etc., etc. The keys is to describe the buyer, not the product.

  1. Start Small, but be bolder as the list grows: In the beginning, you may have to prove that personalization using model-based personas really works. Yes, building a persona is as simple as building a propensity model (in essence, they are exactly those), but that doesn’t mean that you start the effort with 50 persons. Pick the product that you really want to push, or characteristics that you need to know in order to resonate with your core customers, and build a few personas as a starter (say five to 10). You may find some data limitations along the way, but as you go through the list, your team (or analytics partners) will definitely gain momentum.

Then you can be bold. I’ve seen retailers who routinely maintain over 100 personas for just one major product category. And I’ll bet that list didn’t grow that big overnight, either.

Also, when you are in an expansion mode, just add items when in doubt. Think about the users of those personas, not mathematical differences among models. Do you know the difference between Kung Pao Chicken and Diced Chicken with Hot Peppers? Just peanuts on top. But restaurants have them both because customers expect to see them.

Similarly, there may be only slight differences between “Conservative Investor” and “Annuity Investor” personas. But the users of those personas may grab one or the other because of their targeting need at the moment. Or whatever inspired their marketing spirit. Think in terms of user-friendliness, not mathematical purity.

  1. Do Not Go Out of Control: When I was leading a product development team in a prominent data compiling company in the U.S., our team developed about 140 personas covering the entire country for various behavioral categories, including investment, travel, sports (both active participation and being a fan of), telecomm, donation, politics, etc. One of our competitors tried to copy that idea, and failed miserably. Why? It had built too many models.

For instance, if you are building personas for the cruise industry in general, you may need just “Luxury cruise” and “Family cruise” for starters. Those are good enough for initial prospecting. Then, if you must get deeper into cross-selling for coveted “onboard spending,” then you may get into “Adventure-seeker,” “Family entertainment,” “Gourmet,” “Wine enthusiast,” “Shopping expedition,” “Luxury entertainment,” “Silver years,” “Young parents,” etc., for customization of offers.

My old copycats with too many models had developed separate models for “each” cruise fleet and brand. How were they going to use all of that? One brand at a time, with one company as a user group? Why not build a custom model as needed, then? Surely that would be more effective if the model is to target a specific brand or fleet. Anyway, my competitors ended up building a few thousand models, for any known brand out there in every industry, seriously limiting the chance those personas would be used by marketers.

As I mentioned in the beginning, this is about matching offers (or content) to the right people at the right time. If you go out of control, it will be very difficult to do that kind of match-making. If your persona list is just big for the sake of being big, well, how is that any different from using the raw data? You’ve got to know when to stop, too. The key is “not too small, and not too big,” for humans and machines alike.

  1. Update Periodically: Like any menu, persona lists go out of date. Some items may not have been used actively. Some may become obsolete as business models and core product lines go through changes. And models do go stale, as well. You may not have to review this all of the time, and there will be staple menu items, like spaghetti with meatballs in a restaurant. But it will be prudent to go through the menu once in awhile. If not because of the product, then because of people’s attitudes about it changing.
  2. Evangelize: It would be a shame if the data and analytics people did all of this work and marketers didn’t use it fully. These personas are in essence mathematical summaries of “lots of” data in compact forms. They can be used in targeting (for selecting the right target for specific product offers), and for personalization of offers and messages based on dominant characteristic of the target (e.g., show different pictures to “Adventure-seeker” and “Family entertainment” personas, even if they are about to board the same ship). Continuously educate your fellow marketers that using personas is as easy as using any other type of data, except that they are compressed model scores with no missing values.

The personalization game is complex. It may look easy if you just buy an off-the-shelf personalization engine, set up some rules with unrefined data and let it run. While it’s better than sending uniform message to everyone, that kind of rudimentary approach is far less than ideal, not to mention the annoyance factor.

To maximize the power of all available data and the personalization engine itself, we must compress the data in forms of personas. Resultant messaging will be far more relevant to your target audience as, for one, a persona is a built-in mechanism for the personal touch. If you set the menu up as a bridge between data and people, that is.

Personalization Framework

In the age of constant bombardment with marketing messages, staying relevant to prospects and customers is not just good practice in the manual; it is a matter of survival.

personalizationIn the age of constant bombardment with marketing messages, staying relevant to prospects and customers is not just good practice in the manual; it is a matter of survival.

Recipients of marketing messages are more immune to generic offers than ever, and a relentless series of emails and we-will-follow-you-to-the-end-of-your-journey attitude literally trained them to ignore anything that even resembles commercial messages.

You want to stand out in this world of omnichannel marketing? Try to stand out by making it about “them,” not about “you.”


Personalization is not just another buzzword that came after the Big Data hype. It actually is something that marketers must care about.

According to Gartner Research, “By 2018, organizations that have fully invested in all types of online personalization will outsell companies that have not by more than 30 percent.”

I am not sure how they boldly put such a numeric prediction out. But in this case, I honestly think that the gap could end up being even larger, because the winners in this zero-sum game are moving at light-speed, while others still stubbornly carry that “If you keep reaching out to them, they will respond” attitude.

Being Clueless

I’ve actually met marketers who asked me how many more emails they should send out each week to compensate for an increasing number of non-responders.

They actually asked me if they can poke their customer base even more frequently. (They were sending uniform messages to everyone more than six times a week.) That means they had been diligently training the customers to ignore their emails.

I bluntly told them they just can’t mail their way out of that trouble. They should think about contacting their targets less frequently, and staying relevant as much as possible.

Do Unto Others

It is not difficult to sell the concept of personalization to marketers. They, too, are recipients of irrelevant marketing messages, and I bet that they mercilessly purge them out of their personal inboxes on a daily basis.

Surely, there are enough conference tracks, webinars, whitepapers and articles about this subject. But how are they supposed go about it? Do we even agree what that word means? (Refer to “What Does Personalization Mean to You?”)

Based on all of the client meetings that I’ve been to, the answer unfortunately is a hard “no.” And that conclusion was not solely drawn from some rudimentary practices being conducted by many marketers in the name of personalization, either. Because of available data and in different stages of customer relationship development, we do need to differentiate various types of activities under that all-inclusive personalization banner.

We Can Get There From Here

There are many personalization frameworks out there, listing various endeavors, such as collaborative filtering (as in “if you bought that item, you must be interested in these products as well”). Then there’s customer segmentation, and personas development based on predictive modeling techniques, usually in that sequence. If you add technical elements in terms of ability to show different things to different people, multiplied by content generation and content management pieces, things get complicated quite fast.

In any case, I do not agree with such sequential framework, as that is like saying the patient cannot be admitted to the operating room unless the doctor’s exhausted all of the simpler forms of treatments. Needless to say, some patients need surgery right away.

Likewise, when it comes to maximizing the value of data assets for personalization, marketers should not avoid predictive modeling by habit, just because it sounds complicated. That shouldn’t be the way in this age. If you want to be sophisticated about personalization, you’ve got to get serious about analytics without resorting back to simper, often ready-made, options. Unless of course, you as a consumer think that seeing offers for similar (or the same) products that you’ve just purchased for next couple of months is an acceptable form of personalization. (I don’t.)

Nuts and Bolts

Then, what should be the not-so-sequential data framework for personalization? Allow me to introduce one based on activity type and data availability, as no marketer can be free from data scarcity issues at different stages of customer relationship development.

2016: What Did I Know?

Very early this year, I set down a series of predictions for what we’d see in 2016. Now that the run of the year is mostly behind us, it’s time to find out: What did I know?

Very early this year, I set down a series of predictions for what we’d see in 2016. Now that the run of the year is mostly behind us, it’s time to find out: What did I know?

1. Social media advertising is going to get bigger and bigger. I’m not saying that just because of the size of the networks or the time Americans spend on them. The real tipping point factor here is the ability to target your message to a small audience, and deliver it pretty accurately just to them.

Tribalism is one of the more important factors influencing all media today: People want to see only things they want and/or agree with, and the ability to build a custom social circle that filters news and conversations they’re exposed to reinforces this. To maximize the effectiveness of ads, and minimize the chance for a faux pas turns into a major PR disaster (I’m looking at you, Bloomingdale’s “spiked eggnog” ad), advertisers should be trying to capitalize on those same mechanisms.

The social networks, with their in-platform targeting options, are going to benefit from that development.

Frankly, i think predicting that social media advertising was going to get bigger was basically cheating. Of course it was going to get bigger.

socialmediaadvertisingBut I think I tuned a bit more into my inner Nostradamus with the bit about Tribalism. That played out like a Ocean’s 11 bank heist throughout the course of the 2016 election. It got so bad that fake news out-performed real news across Facebook this year.

Tribalism is a powerful force. People care about reinforcing their beliefs so much that it far outweighs facts or proof. With that in mind, I’m starting to wonder what marketing could look like in what you might call a “post-truth world.”

2. More marketers are going to use personas, they’re going to use more of them, and they’re going to get more sophisticated. Again, this is about targeting and understanding your audience. As marketers move further away from campaign-based strategies and deeper into personalized, ongoing marketing, the ability to optimize ads, offers, landing pages and whole websites to a segment of your audience is essential to successful execution.

The growth of individual-level data for targeting and personalization isn’t going to replace the need to do a lot of strategizing and optimization at a segment level (i.e., personas). The ability to build useful personas, include more factors in them (especially behavioral factors), and use those insights to boost ROI is going to be a major factor in the success of online marketing.

I think I might have been behind the state of the art on this one. Personas are important to marketing, but I feel like the growth area has really been on moving beyond personas and using machine learning to do things like find look-alikes or identify buying behaviors.

3. Google updates are going to cause less chaos. Google’s aim in refining its algorithms has become pretty clear: Google wants to give searchers what they want. If you deliver web pages that satisfy the person who entered that search query, you’re likely to continue to do well with Google. If you’re manipulating your site to get more SEO traction, you’re likely to take a hit at some point in the future.

Don’t aim for where Google is today, aim for where it’s going: Make search visitors happy.

I haven’t had to describe what i mean by the term “Google Ball” all year (a reference to “Calvin Ball” from Calvin and Hobbes, where Calvin changes the rules every time to suit him), so I think this one worked out pretty well. AMP is a big deal, of course, and page load speed in general has been emphasized, but I don’t think we’ve seen anything as disruptive as Panda, Penguin and Hummingbird.

Google is big business now, and unpredictability is bad for big businesses. I think Google is trying to shed its reputation for volatile rules changes and give website owners a more stable rules set they can count on.

4. You’re going to see more brand marketing in online direct marketing spaces. This ties into No. 1 a little bit, too. From banner ads to email and content marketing, a lot of online marketing evolved around direct marketing tactics and the call to action. I think you’re going to see more of that online marketing done as a way to promote brand content that in the past would have become a TV ad spot. The Ford In Focus videos Melissa talked about yesterday are a part of this trend. So is Red Bull’s content marketing.

This is a recognition of the content marketing fact that you need to earn time with your audience by giving them something they want to watch instead of constantly interrupting them. These types of content could have smaller audiences online, but they’re getting much more attention from the audiences they do attract. And the content can be targeted to those audiences can be targeted more effectively.

In essence, target marketing is becoming more important, even if it’s a little less direct than it used to be.

Every year, it gets harder to draw a line where direct marketing ends and brand marketing begins. But I don’t think the branding role has significantly moved online or displaced CTA-focused online ads.

The exception to that doesn’t come in the online ad space, but in the continued growth in content marketing and targeted distribution of that content.

So there’s my moment of accountability for 2016! How do you think I did? Are those predictions pretty much in line with what you saw? Are they what you expect to see in 2017? Let me know in the comments.

Killer Content Strategy in 2 Hours

To efficiently get your team to a killer content strategy you need a common framework that can be applied to all your content decisions, as well as a simplified planning process that connects your approach to your audience and business goals.

MeetingTo efficiently get your team to a killer content strategy you need a common framework that can be applied to all your content decisions, as well as a simplified planning process that connects your approach to your audience and business goals.

The Conversation Framework

We often talk about digital content as a storytelling medium, but that assumes a one-sided relationship with one storyteller and one or many listeners.

I prefer to think of it as a conversation that may include stories. In a best case scenario, your content resembles an ongoing dialogue with your audiences that you can learn from over time, just as a good conversation requires listening and thoughtful reaction.

If you think about content planning in this context of a natural dialogue you will find there are certain elements that impact the direction and elements of the varied kinds of conversation that we all engage in day to day:

  • Depth of relationship: You talk about different things and in a different cadence and tone with strangers or new friends than with those you know well.
  • Frequency of touch point: Catching up with a long lost friend takes on a different flavor than conversing with another friend that you see more regularly.
  • Passion point: If you have something in common with someone that can often become the central theme of your interactions.
  • Attention: Is it a passing opportunity to chat or do you have uninterrupted hours to spend together?
  • One-to-one or one-to-many: Are you addressing a group or having a private conversation?
  • Utility: Is the focus on getting something specific accomplished?
  • Conversation initiation: Are you initiating the conversation? If so, you carry the burden of the setting the clear direction, pace and tone.
  • Intent: Are you trying to persuade? Entertain? Educate? All require different approaches and info.
  • Channel conventions: What’s accepted and commonplace in some channels may not be in others.
  • Format: Content can take many forms including visual, audio, interactive, etc… and the format will influence the structure and flow of the conversation.
  • Language or tone varies based on norms for the intended audience: Certainly age and other demographics but also take into account regional flavor, language preferences or degree of formality.
  • Investment: Depending on how important the interaction is to your goals you may invest your time or other resources more or less liberally, including using paid media to maximize reach.
  • Content authorship: Are you using your own stories and content or sharing something that someone else created?

You can quickly see how these and many other subtleties impact the flavor and flow of our conversations and how they could also influence your content choices. Once you have that conversational framework in mind you can get through the actual planning pretty swiftly.

Simplified Content Planning Process

Now to break down the two-hour planning process into managable 30-minute chunks.

Segments vs. Personas

Personalization may mean different things to marketers, but we may break it down to, one, reacting to what you specifically know about the target and, two, proactively personalizing messages and offers based on both explicit and implicit data.

Tina ThrillseekerPersonalization may mean different things to marketers, but we may break it down to, one, reacting to what you specifically know about the target and, two, proactively personalizing messages and offers based on both explicit and implicit data.

The first one is more like “OK, the target prospect is clicking a whole a lot in the hiking gear section, so show him more related products right now.” This type of activity requires technical know-how regarding Web and mobile display techniques, and there are lots of big and small companies that specialize in that arena. Simply put, what good is all this talk about data and analytics, if one doesn’t know how to display personalized messages to the target customer? If you “know” that the customer is looking for hiking gear, by any means, usher him to the proper section. There are plenty of commercial versions of “product-to-product” matching algorithms available, too. We can dissect the data trail that the consumer left behind later.

All those transaction data trails become integral parts of the “Customer-360” (yet another buzzword of the day). Once that type of customer-centric view (a must for proper personalization) becomes a reality, however, marketers often realize “Oh jeez, we really do not know everything about everyone.” That is when the analytics must get into a higher gear, as we need to project what is known to us to the unknown territory, effectively filling in the gaps in the data. I’d say that is the single most important function of statistical modeling in the age of abundant, but never complete data — a state of omnipotence that we will never reach.

Then the next natural question is how we are going to fill in such gaps? In such situations, many marketers jump into an autopilot mode to use what we have been calling “segmentation” since the ’70s and ’80s (depending on how advanced one was back then). But is it still a desirable behavior in this day and age?

As “data-driven” personalization goes, no, using a segmentation technique is not a bad thing at all. It is heck of a lot more effective than using raw data for customized messaging. As a consumer, we all laugh at some ridiculous product suggestions, even by so-called reputable merchants, and that happens because they often enter raw SKU-level data into some commercial personalization engines.

If we get to have access to segments called “rich and comfortable retirees” or “young and upcoming professionals,” why not make the most of them? We can certainly use such information to personalize our offers and messages. It is just that we can do a lot better than that now.

The traditional segmentation technique has its limitations, as it tends to pin the target into one segment at a time. Surely, we all somewhat look like our neighbors, but are we so predictably uniform? Why should anyone be pigeonholed into one segment, and be labeled along with millions of others in that group? Even for rich and prestigious-sounding segments, it may be insulting to treat every member equally, as if they all enjoy the same type of luxury travel and put their money into the same investment vehicles. Simply put, in the real world, they do not.

Every individual possesses multiple dominant characteristics. For that reason alone, it is much more prudent to develop multiple personas and line them around the target consumer. The idea is the opposite of “group them first, and label them later”-type segmentation. It is more like “Build separate personas for all relevant behaviors, then find dominant characteristics for one person at a time.” With modeling techniques and modern computing power, we can certainly do that. There already are retailers who routinely use more than 100 personas for personalized campaigns and treatments.

The following chart compares traditional clustering/segmentation techniques to model-based personas:

Screen Shot 2016-06-08 at 11.16.08 AM

This segment vs. persona question comes up every time I talk about analytics-based personalization. It is understandable, as segmentation is an age-old technique with long mileage. Marketers feel comfortable around the concept, as segments have been the common language among creative types, IT folks and geeky analytical kinds. But I must point out that the segments are primarily designed for “general” message groups, not for individual-level personalization with wider varieties.

Plus, as I described in the chart, personas are more updatable, as they are much more agile than a clunky segmentation tool. I’ve seen segmentation tools that boast of more than 70 to 90 segments. But the more specific they become, the harder it is to update all of those with any consistency.

Conversely, personas are built for one behavior/propensity at a time, so it is much easier to update and maintain them. If the model scores seem to be drifting away from the original validation, just update the problematic ones, not the whole menu.

In the end, the personalization game is about which message and product offer resonates with the customers better. Without even talking about technical details, we know that more agile and flexible tools would have advantages in that game. And as I mentioned many times in this series, matching the right product and offer to the right person is a job anyone can do without a degree in mathematics. Just bring your common sense and let your imagination fly. After all, that is how copywriters imagine their target; by looking at the segment descriptions. That part isn’t any different from looking at the descriptions of personas instead; you will just have more flexibility in that matchmaking business.