The KellyAnne Conway School of Customer Service

It’s just a few weeks into a new year and unless you’ve been living in a cave, you’ve been exposed to interviews with White House Counselor KellyAnne Conway. She has masterfully demonstrated how to dodge questions, provide “alternate facts” and generally frustrate the media in their efforts to get to the truth. In a recent interaction with Samsung, I’m convinced that the customer service agent received training from KellyAnne, as I’ve never experienced such a roundabout set of back-and-forth email communications from any major brand — ever!

KellyAnne Conway[Editor’s note: Update — Today, White House officials told CNNMoney that Kellyanne Conway has been sidelined from TV appearances because her comments last week about former National Security Adviser Michael Flynn contradicted those of the White House. On Fox News, she denied being sidelined.]

It’s just a few weeks into a new year and unless you’ve been living in a cave, you’ve been exposed to interviews with White House Counselor KellyAnne Conway. She has masterfully demonstrated how to dodge questions, provide “alternate facts” and generally frustrate the media in their efforts to get to the truth.

In a recent interaction with Samsung, I’m convinced that the customer service agent received training from KellyAnne, as I’ve never experienced such a roundabout set of back-and-forth email communications from any major brand — ever!

Let me start with a little background: I don’t know about you, but I am not happy when it comes time to replace my mobile phone. Just as I get all my settings to work the way I want, and can flick screens, open apps and manipulate my device with minimal effort, the device inevitably starts to fail. First, it started shutting itself down when my power level fell below 50 percent, then it would freeze at the most inopportune moments, and finally, when it refused to hold any charge at all, I cried “Uncle!”

Okay, all you iPhone owners can start snickering now … because I own a Samsung Galaxy (and no, not the kind that self-ignites), and have done so since my Blackberry became a dangerously obsolete option (I still miss that qwerty keyboard!)

I braced myself for that ugly visit to the AT&T store. The one where no one seems to know how to import my contacts, or set up my email; true in keeping with my past experience, I was in the store for a full two hours and left with my old phone, a new phone and a promise to return in 24-hours after I had figured out how to set up my Exchange Server email myself. But that’s a story for another day.

The fun really started after I was upsold a Samsung tablet for $0.99 in the AT&T store. That probably should have been my first clue …

About 24-hours after my purchase, I received an email from Samsung congratulating me on my Tablet purchase and offering me 30 percent off on a tablet cover. Since I planned to carry my Tablet in my bag as a notepad, I figured a cover was a wise purchase decision. I copied the promotional code, and clicked the link.

The landing page presented me with a number of colorful Tablet cover options. I carefully looked at each one, compared the colors, the way they opened/closed, made my purchase selection, pasted the promotional code and checked out.

But when the Tablet cover arrived 10-days later, it was too big for my Tablet!

I immediately went to the Samsung customer service link and advised them of my plight. The customer service agent, Brian, started the conversation just like KellyAnne had taught him. Repeat the key word used in the question, but take your answer in another direction.

Even though I had clearly laid out the details of my transaction, Brian advised me that if my tablet type and the tablet cover purchased “matched” I would be offered a full refund. Since this was my first clue that there was a “tablet type” we all know where this is going … clearly they were not going to match because the cover didn’t fit!

After a very convoluted set of email exchanges, it turns out there are multiple tablet types, and even though Samsung knew what type of tablet I had purchased (it’s all about BIG data!), it never occurred to Samsung marketing people to send me to a landing page that presented tablet covers that would actually fit the device I had purchased. Instead, knowing I might own multiple tablets and want to purchase one for every tablet I owned, they presented me with all their tablet cover options. Never once did they point out “make sure you select a tablet cover that fits YOUR particular tablet type” or “Hey you idiot, there are multiple tablet types. Check your receipt to learn which tablet type you purchased and match it to the tablet cover.”

Call me dumb, but I honestly thought marketing would have linked their email to a landing page with covers that fit my device, and then offered a link to additional covers in case I owned additional devices. Now that would have made for a smooth customer journey.

Brian was not very helpful either. He ignored any facts relating to the email conversation I presented, he was dismissive of any data exchanges between AT&T and Samsung, and his reality was that I made a purchase error … and it was all my problem. Golly gee, KellyAnne trained you very well!

Now I can’t decide if I should pay to return the cover and get a new one, or simply sell the cover on e-Bay or sell the cover and the Tablet and call it a day. If you’re interested in any of these options, email me and I’m sure we can cut a deal that doesn’t involve Russia.

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

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.

A Popular (yet Ineffective) LinkedIn Tactic

Considering investing in LinkedIn automation software? Already using automated tactics? Beware: Automation is not helping social sellers start conversations. Don’t let your hopes or a LinkedIn “expert” (charlatan) tell you otherwise. This isn’t my opinion. I speak from experience — and that of my customers.

LinkedIn logoConsidering investing in LinkedIn automation software? Already using automated tactics? Beware: Automation is not helping social sellers start conversations.

Don’t let your hopes or a LinkedIn “expert” (charlatan) tell you otherwise.

This isn’t my opinion. I speak from experience — and that of my customers.

I don’t like to speak in absolutes. Nothing is certain in our world. But automating the gathering of lead data and sending messages to prospects wastes time, damages reputation and what’s worst is buyers see through it — instantly.

It’s spammy.

Also, LinkedIn is cracking down and suing service providers. It took a while but Microsoft has had enough.

Short-cuts rarely work in life. Buckle-down and do the work. And yes, I know you need to scale. Me too. Tech tools like LinkedIn help us scale time. But LinkedIn automation is ineffective.

Lately, it can also hurt you.

Automating Outreach and Scraping Contact Data

We need targets to call on: Companies, decision-makers and contact data. LinkedIn is a database. But gathering contact data is time-consuming. Plus, getting these contacts to connect with us (open the door to communication) takes time and effort.

Wouldn’t it be great to automate the data collection, connections and messaging? We could mass email messages to prospects — without much effort. We’ll reply to the responses, manage the leads.

Enter LinkedIn automation tools.

But beware of reality:

  1. Automated profile viewers and contact data scrapers are being sued by LinkedIn/Microsoft;
  2. Non-personalized (spammy) or “personalized” (fake personalization) messages aren’t helping sellers start conversations with buyers;
  3. Decision-makers are actually hiding from overzealous sellers and accepting fewer connection requests.

How Automation Software Works

You look up a group of contacts using a LinkedIn search. Boom. Software automatically:

  • Grabs those search results
  • Views each contact’s profile
  • Scrapes the screen (cuts-and-pastes name, company, title, etc. into a spreadsheet)

Software will also:

  • View profiles
  • Invite people with keywords or titles to connect
  • Automatically send them welcome messages when they accept
  • Automatically endorse them
  • Automatically send them congratulatory messages when they have a birthday, work anniversary or change jobs
  • Automatically send sales messages to large swaths of your connections

Sounds great. But let’s pretend you are Microsoft (LinkedIn’s new owner).

You just paid $26 billion for this data. How do you feel about people scraping it? How do you feel about automating all of these non-personalized functions (which are all trying to look personalized and social)?

That’s why LinkedIn is suing these service providers.

Automation tools are popular. But these are often “companies” that have no public contact data themselves! Companies that, in fact, aren’t companies … and have (for years now) operated in clear violation of LinkedIn’s Terms & Conditions.

LinkedIn prospecting expert, Bruce Johnston, is blunt:

“It is instructive that I went through my list and less than half of the companies I added 12 to 15 months ago still exist.”

Personalized Marketing: Past, Present and Future

Today, you can think of your printed materials the same as your digital materials (emails, digital ads, landing pages, etc.). That means you can personalize EVERY aspect of a printed piece, just as you do on a computer screen. Not just text, but also visuals, colors, layouts — every element on the printed page. Each piece coming off the press can be entirely different from the piece before it.

Personal.jpgIn 1996, I had this really cool idea to produce an invitation for WDMI (Women in Direct Marketing International) using 4-color variable data printing (VDP).

This was new printing technology that acted like a color copier on steroids. There wasn’t really any software to drive it, and few people knew what it was — but I was lucky. I’d always watched for new tech, and Cheryl Kahanec (who happened to be my cousin) had one of the presses producing 4-color VDP. Still, computing power was nothing like today, so we had to figure out how to create the design I’d come up with.

The concept was driven by the limited data we had for the club — their names, company and address. Next to the address was the line: “If this is your idea of personalization … ” which was followed by a headline on the inside that said: “Then you ain’t seen nothing yet!”

Below the headline was the recipient’s name in many different fonts, sizes and colors with the name overlapping and running off the page.

Ok, I’m still healing the scars from that project. We were way ahead of the curve. It took over seven days to rip the file (running the data into the file to create a printable file). It then took another seven days to print the 5,000 pieces. That’s right: over 14 days of ripping and printing.

It performed very well, even winning attention from a couple trade publications (it also gave my cousin and me many gray hairs).

Fast Forward…

Today, you can think of your printed materials the same as your digital materials (emails, digital ads, landing pages, etc.). That means you can personalize EVERY aspect of a printed piece, just as you do on a computer screen. Not just text, but also visuals, colors, layouts — every element on the printed page. Each piece coming off the press can be entirely different from the piece before it.

St. Joseph’s College wanted to encourage applicants who had been accepted to the college to commit to attend. So accepted applicants were invited to a special event at the college. To encourage their attendance, each applicant would receive an iTunes gift card when they clicked on the personal URL (pURL) to say they would attend, with a chance to win an iPad at the event.

Every image and text blurb on the piece was changed based on the degree program the applicant had indicated on the application. Their name was used throughout the piece, along with their pURL. This is the most dramatic element: The covers would feature a current student in their program of choice. A testimonial and photo of another student currently in the program was highlighted on the inside, with copy and photos regarding the program.

Each piece off the press was a one of a kind — exactly how your emails and digital marketing piece are on your recipient’s computer.

St. Joes VDPWhat’s in the Future

There’s no way to know the amazing tech the future will bring, but a more challenging element of the future is breaking down silos.

“Over the last 20+ years, variable data software and printing has come a long way. You can easily drive images and text complex business logic and embedded variables from multiple databases. Email, video and online variable data capabilities have become equally sophisticated. The challenge: They typically don’t work together. Adding to this struggle, many brands have agency’s that are digital- or print- only.

For multichannel/omnichannel and trigger programs to allow brands to have a conversation with their customers, all mediums must work together. There can no longer be silos.”

—Cheryl Kahanec, President, EarthColor, Marketing Solutions Group

As Cheryl describes above, the future will blend all communications, leaving no silos. Whether we read our screens, mail or any other marketing material, the blending of data and its capabilities is the future of marketing and communications. Who will get there first? Who’s on their way?

Why Buzzwords Suck

Let’s talk about why buzzwords are bad for the data and analytics business. I don’t entirely deny that there are some benefits of buzzwords. Sometimes buzzwords summarize a long list of complex concepts in one easy-to-understand phrase.

bees-44507_640In my previous column, “Don’t Hire Data Posers“, I wrote that one of the first signs of a poser is excessive use of buzzwords. This month, let’s talk about why such buzzwords are bad for the data and analytics business — besides the obvious annoyance of overuse.

I don’t deny that there are some benefits of buzzwords. Sometimes buzzwords summarize a long list of complex concepts in one easy-to-understand phrase. Big data, CRM (in the past), customer 360, personalizationcustomer experiencereal-time modeling or in-database scoring are some examples.

For instance, the term big data acts as an umbrella for many different ideas that not-so-technical people may not be familiar with. But by saying that magic term, we can cut to the chase much faster. Marketers and decision-makers often interpret the term as “all data and analytics activities that enable data-based decision-making processes,” regardless of the actual data sets and processes in question. So data players like me no longer have to take 15 minutes to explain what we do for a living, and data geeks have more succint voices in executive meetings nowadays.

Similarly, creation of a single customer view or a 360degree customer view may include many intricate steps, but who has time to list them all in a planning meeting? Just drop the term customer 360, and people will get the general idea.

But there are definite downsides to these over-simplifications. So, let’s list the harmful effects of abusing buzzwords:

  • Over-simplification in itself is bad already, as it undervalues the efforts. Just because it takes less than a second to say it, doesn’t mean the actual steps are just as quick and easy. Executors still have to sift through painstaking details to get anything done. I’ve seen marketers who actually thought that properly executing personalization would be simple and easy, when the reality of it is that even the very definition of the word deserves a lengthy consideration. Is it about content, delivery, data or analytics? The answer is all of the above, and one must plan for every aspect separately. Calling personalization simple is like saying, “Why don’t we make more movies like ‘Star Wars’ and make tons of money?” Well, can you make that lightsaber look real in someone’s hand?

The Big Problem With Sales Email Templates  

Spending time doing cold email outreach to new prospects? Trying to reignite smoldering discussions with existing customers? Then you’re probably using voicemail (the phone), LinkedIn’s InMail and email. Sales email templates are a big part of day-to-day life. The problem is they don’t work.

Nothing screams “impersonal” more than a templated email. Yet most sellers use templates.

EmailSpending time doing cold email outreach to new prospects? Trying to reignite smoldering discussions with existing customers? Then you’re probably using voicemail (the phone), LinkedIn’s InMail and email. Sales email templates are a big part of day-to-day life. The problem is they don’t work.

Nothing screams “impersonal” more than a templated email. Yet most sellers use templates.

Stop Using Templates, Now

Templates don’t work. Now, I know you know this. But you still use ’em. So allow me to issue you permission to stop. Right now — today.

Think about the last templated message you received. How quickly did you delete it? More importantly, how easy was it for you to spot?

Was it the subject line — the one that told you precisely what was inside the message? (A.K.A. a terrible pitch.)

Or did the subject line trick you into opening it — only to earn your immediate deletion because the first line was offensive?

After years of helping folks write sales email letters, I can tell you why this happens. The reason sales email templates rarely work is simple: Most use the same, “telling” communications format.

Are Your Emails Asking Questions?

One common reason potential buyers delete cold email templates is because they start with a question that causes them to roll their eyes: the kind that signals “terrible pitch ahead.” Most sales email templates rely on a lazy, transparent formula. They sabotage you.

Providing that these kinds of emails do get opened, the contents usually:

  • Ask a question known to be on the buyers’ mind.
  • Take longer than 30 seconds to read.
  • Present a solution, rather than provoking the buyer to hit reply and talk about their problem.

These are just a few characteristics. There are a half-dozen more. Today, I want to focus on the root cause of your cold email being deleted:

That silly question you are asking.

The one you are asking to try to appear relevant. Trouble is it’s a dead give-away. It’s lazy, and off the same cookie sheet as 95 percent of competitor emails pouring into your buyers’ inbox.

For example, one of my students was using, “Did you know that printing is typically the third highest office expense behind payroll and rent?” He sells managed print services to CEOs, COOs and IT managers at small and mid-sized businesses.

Opening with a question is always dangerous. If it is perceived as a “leading question”, you’re deleted. Because if your question feels like a setup to a sales pitch the message will fail to provoke response.

The prospect will think, “I know why you’re asking … ” — then roll his eyes and hit delete. You will have signaled the “sales pitch ahead” alarm, sabotaging your provocation.

If the only obvious answer to your question is “yes” or “no”, it may risk insulting the buyers’ intelligence.

“Did you know printing is expensive?” is an obvious yes.

This approach is risky as compared to a question that forces the buyer to introspect on a more complicated issue.

Why Personalization Matters in Direct Mail

Many times, companies shy away from complex personalization because they are worried about getting it wrong. Don’t let your fear stop you. Check out these stats from an InfoTrends 2016 study.

personasBefore we really get started, let’s define what personalization in direct mail is. It’s more than just putting the name and address on the piece.

In 2016, even just adding “Dear name” or just a name is no longer the best way to personalize your direct mail. Don’t get me wrong, you still need to have the name, but you can get so much more creative than that. So don’t just stop there, get more personal with offers, images, landing pages and more. Here are three reasons to get personal:

1. Recipients Like It

With all the technology out now, there’s no reason why you can’t personalize your direct mail, so just do it. Recipients have stated in multiple studies that they pay more attention to mail pieces that are personalized. It makes them feel special.

2. Better Targeting

With personalization, you are better able to target your message to each person. Using the information you have collected in your data base, you can create custom offers that appeal to the right people based on purchase history.

3. Better Response

Since your recipients like it and you can better target them, you are going to get a better response rate with personalization.

Many times companies shy away from complex personalization because they are worried about getting it wrong. Don’t let your fear stop you. Check out these stats from an InfoTrends 2016 study.

Survey Question: Does the customization or personalization of a direct mail piece make you more likely to open/read it? 29.2 percent said “Yes, Much more likely” and 55 percent said “Yes, a little more likely.” With numbers like that, you need to be personalizing.

Personalization in 2016 isn’t hard. There’s great software out there to help control the variable data and ensure that the correct information is on each piece. The key component is what you have in your data base. The more information you have, the better the personalization will be.

Top 3 Ways to Personalize Direct Mail

1. Images: The visual component of your direct mail sets the mood and draws the recipient into your message. Getting the images that will appeal most to each person is very important.

2. Offer: Using the data you have about each person to tailor the offer to their needs is very powerful. The right offer will get the response, the wrong one will not.

3. Copy: The copy is the obvious personalization to the recipient. It uses their name and other information from your data. Use the copy wisely to draw interest. Even though they like personalization, too much copy is a problem and they will not read it.

Are you convinced that personalization is the key to better ROI? I am. If you want to start smaller to take some of the fear out of it, you can do that. Just make sure you don’t get stuck in the mode of just using a name and calling that personalization. Build up your personalization with each mailing by incorporating more data and more complex variables. This gradual build up will give you the confidence to keep getting more personal. What ways have you used personalization?

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.

What Does Personalization Mean to You?

“Personalization” is the next big thing after “Big Data.” … And that is really too bad for the users of data, technology and analytics. Why? Because many users end up thinking that they are doing a good job at it, while in reality, they are only touching the surface.

We are still living through the aftermath of the Tower of Babel, though the main language of choice in the marketing, data and analytics industry remains English. Outside of the U.S., I speak at conferences and events in Korea, Brazil and the U.K. Even when I presented in Korean — with a PowerPoint presentation consisting entirely of English — I called data “Data,” though the pronunciation is more like “dei-tah” there. Korean business people love to say “Big Data” in English, though the meaning is quite different from what I am accustomed to. They use it with a much broader meaning than we do in America; they literally imply anything and everything related to data activities, small or big, raw or analyzed. Conversely, I have encountered groups of people in America who have a very narrow definition of it, whether it be about literal size, complexity or even specific platforms, such as Hadoop. I am sure each of you has a different notion of the word.

Recently, I participated in a retail conference in London regarding “Personalization.” I was a panelist, and I noticed they spelled the word “personalisation.” I didn’t want to argue about how funny that spelling looked among folks from a country where the English language was literally spawned, but what is the point of having the letter “z” in the alphabet if they are not using it for a clear “z” sound? In any case, they too seemed to be searching for the meaning of the word in marketing, as the very first question to the panel was “What does personalisation mean to you?” Not surprisingly, each panelist provided different answers.

Since then, I have been attending marketing and technology conferences quite diligently this season. While a great many panel discussions, industry tracks and keynote speeches were about personalization, I found that literally everyone meant different things by saying it. Unfortunately, some presenters were as confused as their audiences, and some were downright clueless (more on the subject of useless conference tracks in future articles). Yes, all of that popularity means “Personalization” is the next big thing after “Big Data,” and it truly reached the buzzword status. And that is really too bad for the users of data, technology and analytics.

Why? Because many users end up thinking that they are doing a good job at it, while in reality, they are only touching the surface. Such an attitude leads to investment in the wrong places, while other vital steps could be missed completely. It is not much different from patients in a placebo group thinking that they are taking the real trial drug. It is even worse than that in marketing, as users may have paid a good sum of money to check off that little box called “personalization.” The first blame should be on the service providers who overpromised the effectiveness of the toolset (as in “All your problems will be solved if you buy this!”), but the users must be more educated about it, too.

So, what does personalization mean to you? Allow me to list a few possible answers:

  • Addressing your customers by their first names?
  • Suggesting more of the same products that they just purchased through collaborative filtering?
  • Collecting explicitly expressed preferences and reacting to them?
  • Keeping in touch with your customers all of the time?
  • Customizing emails and landing pages based on customer preference?
  • Knowing when to contact them and through what channel?

I think we can safely agree that calling someone “Dear Jane” in an email isn’t the end of personalization. Suggesting more of the same products? Such practices, joined with “keeping in touch with customers all of the time,” often leads to “personally annoying your customers,” not necessarily personalization (refer to “Personalization Is About the Person”).

I happened to have caught a rather technical presentation (with a title that includes “personalization”) by a reputable provider of a personalization engine, and I was quite impressed with all of the complex and ingenious algorithms they applied to the effort. I am not a mathematician, and I do not mean to criticize those brilliant scientists about their efforts. But I must say that three out of four their steps were about products, not people, though they left a step for behavior-based segments. Presented segmentation methods and variable sets were not by any means at the level of as-good-as-it-gets, but adding behavioral segmentation is a very hopeful move, indeed.

Regardless of the complexity, stringing up related products together, using collaborative filtering, popularity hierarchy and/or clever methods to harness unstructured meta-data are still more about the product, not the consumers. People have an uncanny ability to smell machines, even through remote channels. Personalization definitely requires some human touches (or at least illusions of it), and that come from understanding the target’s current and past behaviors (refer to “Data Atrophy”).

So, what do marketers to do, if even the most advanced kind of personalization engines are still more about products, not people? We need to fill in the gaps with data and analytics. To get there, let’s first break down what personalization is made of:

  • Content
  • Delivery
  • Data
  • Analytics

I am a firm believer that every personalization (or any type of 1:1 messaging) must start with data. But for the purpose of being pragmatic, I reversed the order here.

Simply, if a marketer doesn’t have enough content that matches different types of customer demand and their personas, the effort will be pointless, even with an ample amount of data. Contents — literal and graphic — must be created with potential targets in mind, and they should be properly managed through DAM (Digital Asset Management) systems. We are talking about something far more organized than some memory sticks sitting in a desk drawer in a creative agency. For many marketers, this is “the” personalization effort, as content creation is an age-old marketing function, and effectively managing it is at the heart of digital marketing.

Then, the marketer needs to acquire the ability to show different contents to various types of customers. This is where all of those commercial solutions come into play. If it is about the website, is it modularized, so that various parts of the pages can be customized? If it is about email campaigns, can each email be tailored with different offers and feature products? If it is about offline campaigns, how flexible can versioning be? There are already supermarket chains that customize almost every coupon book with different binding sequences and contents. The ability to deliver customized messages to customers and prospects is a must-have, not an option, for any type of personalization initiative.

Next, are all of these efforts data-driven? What types of data are being used? Just product meta-data and product-level sales data? Or individual behavioral and demographic data? If so, are they just based on snapshot data of the present, or the person’s historical data, as well? Are product-, event- and transaction-level data summarized to an individual level for proper personalization?

That leads to analytics (and this “analytics” has many meanings, too). Are data converted to forms of segments or personas, or are the raw data still being plugged into the engine? The difference in effectiveness is huge, as even machines prefer clean and simple data. Further, even with ample amounts of transaction- or event-level data, we often find lots of huge holes in data when aligned around the person, as there is no way to know everything about everyone all the time. Such gaps should be filled with statistical models, while we often label those with different names, such as segments and personas. (This leaves yet more room for serious misunderstandings.)

Stephen H. Yu: 3-step approach to complete personalization

Illustrated is a three-step approach to personalization, starting with installation of a commercial personalization engine. Then test-run the engine with simple segments, based on available data. After all, reacting to immediate customer needs and displaying different versions of content based on known explicit data is not simple or easy. That would still be more like “personalize contents only sporadically for some people through some channels.” To reach the stage of “personalizing content constantly for everyone through all channels,” event- and product-level data must be realigned around target individuals, and personas must be built to fill in the gaps (refer to “No One Is One-Dimensional”).

Personalization is definitely the most popular buzzword these days, though it means different things to a lot of people. What does not change is that this movement is here to stay in the age of information overload, as marketers must stand out, for their survival, with relevant messages to ever-distracted consumers.

What we simply refer to as “personalization” is made of multiple components, and that is why many of us are confused by it. Therefore, we must aspire to reach a true personal level with our customers through a step-wise approach, not a single giant leap. Let us not make the mistake of calling the mere first few steps the whole thing, when more important data and analytics steps are not even in play yet.

Data Atrophy

Not all data are created equal. There are one-dimensional demographic and firmographic data, then there are more colorful behavioral data. The former is about how the targets look, and the latter is more about what they do, like what they click, browse, purchase and say.

Not all data are created equal. There are one-dimensional demographic and firmographic data, then there are more colorful behavioral data. The former is about how the targets look, and the latter is more about what they do, like what they click, browse, purchase and say. On top of these, if we are lucky, we may have access to attitudinal data, which are about what the target is thinking about. If we get to have all three types of data about the customers and prospects, prediction business will definitely get to the next level (refer to “Big Data Must Get Smaller”). But the reality is that it is very difficult to know everything about anyone, and that is why analytics is really about making the best of what we know. Predictive modeling is useful not only because it predicts the future, but also fills gaps in data. And even in the age of abundant data, there are many holes, as we will never have a complete set of information (refer to “Why Model?”).

Among these data types, some are more useful for prediction than others. Behavioral data definitely possess more predictive power than simple demographic data for sure. But alas, they are harder to come by. It could be that the target is new to the environment, so she may not have left much data behind at all. May be she just looked around and didn’t buy anything yet. Or she is very privacy-conscious and diligent about erasing her behavioral trails on the net or otherwise. Maybe she explicitly opted out of being traced at all, giving up much of the convenience factors of being known by the merchants. Then the data coverage comes into the equation, and that is why analysts rely on demographic and geo-demographic data for their readily available nature. Much of such data can easily be purchased and appended on a household or individual level, at least in the U.S. If we get to have some hint of identity of the target, there are ways to merge disparate data sets together.

What if we don’t get to know who are leaving data trails? Again, it could be about the privacy concerns of the target, or the manner by which the data are collected. Some data collectors avoid personally identifiable information, such as name, address or email, as they do not want to be seen as the Big Brother. Even if collectors get to have access to such PII, they do not share it with outsiders, to maintain dominance and to avoid the data privacy issue altogether. And there are many instances where that “who” part is completely out of reach. Movement data would be an example of that.

Weaving multiple types of data together is often the main source of trouble when it comes to predictive analytics. I have been talking about the importance of a 360-degree view of a customer for proper personalization and attribution, but the main show-stopper there is often the inability to merge data sources with confidence, not the lack of technology or statistical skills. That would be the horizontal challenge when dealing with multiple types of data.

Then there is the time factor. Like living organisms, data get old and wither away, too. Let’s call it the “data atrophy” challenge. Data players must be mindful about it, as outdated information is often worse than not having any at all for the decision-making or prediction business.

Now, not all data types deteriorate at the same rate. The shelf-life of demographic data are far longer than that of behavioral data. For example, people’s income levels or housing size do not change overnight, while usefulness of what we call “hotline” data evaporates much faster. If you get to know that someone is searching for a new car, how long will he be in the market? What if it is about a ticket or pay-per-view purchase for tonight’s ball game? Data that is extremely valuable this minute could be totally irrelevant within the next hour.