Do Buzzwords Get in the Way of Progress?

Have you read a column in the past week, month or year that’s void of buzzwords? Probably not. In the age of 5,000-plus choices of what partners, technologies or agencies to choose from, I find it uncanny how the marketplace is fraught with complex ways to explain simple things.

Have you read a column in the past week, month or year that’s void of buzzwords? Probably not. In the age of 5,000-plus choices of what partners, technologies or agencies to choose from, I find it uncanny how the marketplace is fraught with complex ways to explain simple things. Blame it on analysts who define industries? Blame it on a competitive marketplace and people trying to stand out with that killer phrase that describes what they do? Blame it on retailers striving to explain and justify what they do to their corporate leaders? Or startups striving to associate new ideas to mainstream challenges? Or blame it on consultants for making the simple complex and charging for it.

What it doesn’t help are retailers. In a perfect world, retailers live their brand. They look for simple ways to communicate with a broad spectrum of customers, and need creative yet practical approaches to words. You’re a merchandiser, an e-commerce company, and a lifestyle brand, and it can be a cultural challenge to balance buzzword frenzy with simple words the market needs to hear about your company. My main problem with buzzwords — and I’ve been as guilty as anyone in the use of them, just read a few of my columns — is using terms in loose context can minimize the impact of the term and make it actually more confusing. Therefore, in the spirit of no buzzwords, this column is just that: real talk for real retailers.

Lets start with a few buzzwords:

  • Disruptive technology: This begs the question of how disruptive your disruptive technology has to be for you to claim that it’s truly disruptive vs. just moderately irritating.
  • Ecosystem: This buzzword got big in mid-2014, 2015 as Luma Partners really promoted its Lumascape. Next thing you know every vendor is using it and every internal IT team began following suit to describe their “data lake strategies” and “technology road map.” I’m not sure I’ll ever get used to referring to my business interdependencies using the same terminology we use to talk about global warming and our attempt to save the planet.
  • Millennials: Are millennials really a buzzword? They might be. They’ve become more than just another generational grouping. As more millennials enter the workforce, replacing the retiring baby boomers, we will continue to spend a lot of time talking about the impact they’re having on the intersection between business, technology and our interpersonal lives. Maybe more importantly, we will continue to try to figure out why they break up with each other via text.
  • Thought leadership: This buzzword was prevalent for many years, and I still don’t really know what it means — or maybe I thought I did and really didn’t. I was awarded Thought Leader of the Year in 2016, and had trouble describing the award outside of … unfortunately, it seems to be entrenched and positioned to bother us for another year. I’ve been trying desperately to think of a new term that could supplant it, but question if I’m enough of a thought leader to make that happen.
  • Storytellling: I have to confess that I’ve coached and advised leaders to use stories to convey important things about their businesses because a good story resonates better than death by Powerpoint presentation. Now we’ve got storytelling classes, storytelling departments, and even storytelling gurus. Once gurus come into the picture, we’ve officially hit buzz status
  • Artificial intelligence/machine learning: These are likely the most overused, misunderstood and confusing buzzwords. How many times have you heard, “We have AI.” While this area of discipline and technology advances will reshape much of what we know today, any buzzword that conjures up impending doom of the human race isn’t helping in a dynamic business world.
  • Big data: I have trouble with anything that starts with “big” as a modifier of an industry trend. What’s big, and is there bigger? Much like the term disruptive, big data is an overused phrase that doesn’t serve many outside of its sellers. Google, Facebook, Amazon.com, Microsoft, Apple have big data. If you really want to understand big data in our society, there’s a great book: “The Human Face of Big Data.” Warning, this book is big, literally. In the end, the term does little to help you contextualize marketing problems or your own internal data challenges.

We’re in a world of endless information. Buzzwords in my opinion distort real talk and make complex concepts harder for the masses to address in situational marketing. Have fun with it by infusing a NO Buzzword culture or, better yet, force the offender to fully explain the term in the context of your business. And remember the goal of words is not to show how smart you are versus; they are a way to level set on complex ideas.

Make the complex simple!

Advice for the Digital Marketing Industry, Perhaps Too Late

I was recently asked what advice I would give my younger self to succeed in the digital marketing industry. The question, of course, is nonsense: No one has a time machine or could recreate the unique circumstances of these past decades.

I was recently asked what advice I would give my younger self to succeed in the digital marketing industry. The question, of course is nonsense: No one has a time machine or could recreate the unique circumstances of these past decades.

We now possess almost perfect information about the technology, business environment and leaps of faith and brilliance that created our digital world and a brand new industry — but we did not have that guidance back then. Still, after over 20 years in this “new” industry of online marketing it might be time to reflect on some of the challenges and choices that have shaped our current state.

Advice for the Industry: 20 Years Too Late

Stop being so defensive. It’s hard to imagine now but in the mid 90’s many were still calling the Internet a fad and were waiting for it to go away so they could return to “business as usual.” At that point many (even large) companies were still busy debating whether they even needed a website, some agencies and marketers were slow to learn or adopt digital skills and our educational institutions lagged behind in teaching students what they sorely needed to succeed. Much of this was pure defensiveness and a stubborn refusal to accept that the world was changing.

Stop creating buzzwords. We lacked the language or imagination to describe new concepts and capabilities effectively and the new buzzwords did nothing to add to our credibility. Buzzily named products, companies or approaches quickly became synonymous with something fleeting. They didn’t earn a place of respect even if the product deserved it.

Focus on the stuff that matters. Early technologies and efforts were often about what we could do and not what we should do. As the better technologists matured into businessmen and women who valued the metrics and results that mattered to a sustainable business and industry our bubbles were replaced with platforms and channels that have rewritten our world. Still, we suffered through too many shiny objects and useless toys that didn’t help the early credibility of the Internet as a business environment.

Make inclusiveness a priority. Make sure everybody and especially every young person in every neighborhood has access to the tools and training that will help them succeed. Expensive technology and slow moving infrastructure unfairly handicapped some populations in joining the web revolution. That slowed us down and limited our success as a whole.

Help industries and professions to evolve. Many found their jobs changing or disappearing (hello travel agents?) with no idea how to pivot their skills and services to stay relevant and effective. Disintermediation was a real thing. Some jobs and vocations became irrelevant or unrecognizable overnight and those affected were left to fend for themselves. We should be better than that.

Commit to standards that help everyone. Put a premium on cooperation over competition to set and keep common standards in data, ads and other elements. That consistency would have reduced a lot of the pain points and smoothed the learning and success curves for users all along the spectrum. Monopoly or first-to-market businesses, governing bodies and associations of professionals with vested interests kept us in chaos for far too long.

There are also a few things I wish I personally had known way back when that could have informed my own choices.

Advice for Myself: 20 Years Too Late

Get comfortable with change. Our industry is relentlessly dynamic. Get used to change as a constant and get prepared by committing early on to frameworks and processes that can absorb and integrate new approaches and opportunities without sacrificing the strategic core.

Get data smart. Marketers who understand how to collect and apply data are miles ahead of those who don’t in this day and age.

Write — a lot. Communications skills, especially written words, are at a premium as every person and business requires thoughtful content regularly generated in many formats. Strong and strategic writers, designers and communicators are critical in the digital economy.

And, just for good measure … find an exercise you love, stick to your diet, travel more and buy Apple stock.

Perhaps we can learn from our past to help us enrich our collective future.

What good advice would you give your past self?

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?

Don’t Hire Data Posers

There are data geeks and there are data scientists. Then there are data plumbers, and there are total posers. In this modern world where the line between “real” and “fake” is ever-blurrier, some may not even care for such differences.

data poserThere are data geeks and there are data scientists. Then there are data plumbers, and there are total posers. In this modern world where the line between “real” and “fake” is ever-blurrier, some may not even care for such differences.

Call me old-school, but at least in some fields, I believe that “the ability to do things” still matters. Analytics is one of those fields. When it comes to data and analytics, you either know how to do it, or you don’t know how to do it. The difference is as clear as a person who can play a musical instrument and one who is tone-deaf.

Unfortunately, there is no clear way to tell the difference in this data and analytics field. It’s not like we can line up contestants and ask them to sing and be judged here. Furthermore, “posers” often have louder voices — armed with fancy visuals and so-called automated toolsets.

I’ve been to many conferences and sat through countless presentations in my lifetime. It may sound harsh for me to criticize fellow data players and presenters, but let me just come out and say it: A great many presenters and panelists at conferences are posers.

How do I know that? Easy. I asked them. For example, when I stalked some panelists who preached about the best practices of personalization after the session, the answers were often “Well, it is not like we do all those things for real …” Sometimes I didn’t even have to ask the question, as I could tell something is seriously broken in their data and promotion chain by observing their marketing messages as a customer.

The bad news for the users of information — and for consumers, for that matter — is that it takes a long time to figure out things are not going fine. Conversely, we can all tell who is tone-deaf as soon as a singer opens her mouth. It is so hard to tell the difference between a data scientist (i.e., an analyst who provides insights and next steps out of mounds of data) and a data plumber (i.e., supposedly an analyst who moves big and small data around all day long and thinks that is his job), that I admit it sometimes takes a few months — generally after some near meltdowns — for me to figure it out.

LinkedIn for Stealth Job Seekers

Updating your LinkedIn profile without looking like you’re launching a job search is tricky. Colleagues, bosses and other connections often react to a profile update as, “Oh, she must be looking for a new job.” But when you take the right precautionary steps, it becomes much easier to take a proactive approach to your career. Today, we’re going to discuss just how to do this.

How to update your LinkedIn profile without looking like you’re launching a job search is somewhat tricky. Most often colleagues, bosses and other connections react to a profile update as, “Oh, did you see Mary’s new LinkedIn profile? She must be looking for a new job.”

Well, when you take the precautionary steps to limit or suppress announcements to changes, and frame your experience as a dedicated and loyal employee, it becomes much easier to take a proactive approach to your career. Today we’re going to discuss just how to do this.

Settings to be a Stealth Job Seeker on LinkedIn

LinkedIn recently gave their settings area a facelift so things are easier to find. I would suggest taking some time to familiarize yourself with all the settings so you know exactly what is being broadcast and how to get the most out of the platform.

The most important setting before making any changes to your profile is “Sharing Profile Edits.” You want to make sure this is switched to “No.” You can also do this right from your edit profile screen by clicking “No” in the “Notify Your Network?” box (found on the right-hand sidebar).

LinkedIn Sharing Profile EditsLinkedIn Notify Your NetworkNext you want to change “Who Can See Your Connections” to only you. This is so people aren’t notified of when you’re making new connections, especially helpful if you are connecting with a potential employer.

LinkedIn Who Can See Your ConnectionsThe last setting to change is “Profile View Options.” You probably want this to be in “Private Mode” or “Private Profile” characteristics, so when you’re researching potential employers on LinkedIn and viewing profiles, no one knows who you are. Beware of this setting, though, if you have the free version of LinkedIn. Being anonymous will erase your viewer history, and you also won’t be able to see who viewed you.

LinkedIn Profile Viewing OptionsHow to Write a Headline and Summary That Grabs Attention
(But Not Too Much Attention)

When you’re in stealth job seeking mode, you will have two audiences for your LinkedIn profile — your primary and secondary.

Road to Personalization

The marketing community loves buzzwords. One may say that some words just go viral. In the past, CRM was one. Server-migration (from mainframes) was another. Cloud computing – even among non-IT groups – has some magic power. Big Data has indeed been a big one the past few years, though

The marketing community loves buzzwords. One may say that some words just go viral. In the past, CRM was one. Server-migration (from mainframes) was another. Cloud computing – even among non-IT groups – has some magic power. Big Data has indeed been a big one the past few years, though it surely is losing its coolness, especially among data professionals. But, in some countries and communities, it is still gaining momentum. The latest one, I think, is “Personalization.”

Do you know how I get to find out how some words are becoming popular? The fastest way is to attend a conference and check out which session keywords are filling up the rooms. Attendance, like in the movie industry, is a sure way to measure the power of the keyword. We often see that some speeches and articles are not even remotely related to the word in question, but that doesn’t seem matter much. Everyone and their cousins start selling the word like it’s a magic potion that cures all. If you happen to come across a password to a goldmine, won’t you try it, too?

Once the word starts go viral, the power of magic starts to influence the real-life decision-making processes. Yes, I’ve been using every chance to debunk the mystery around Big Data, through this series and other opportunities. But I have to admit that those two words originally strung together for marketing purposes by software companies opened so many new doors to meetings and speaking engagements to which data geeks never dreamed of having access a mere four to five years ago. If you ask me what the best outcome of the Big Data movement is, my answer is that decision-makers, in general, became aware of the importance of analytics based on collected data. Analysts no longer have to spend a long time in meetings to justify the usage of data and analytics; we can simply dive right into the subject now.

Nevertheless, I still have a strong allergic reaction to buzzwords, like I do to syrupy pop songs of which I tire easily. The main reason, other than I just get sick of hearing them, is because buzzwords lead to kingdom-come-level promises. Overpromises lead to overinvestments, which lead to equally big disappointments (try showing decent ROI on overinvestments), which inevitably lead to finger-pointing and blame-games. That is why I, over time, tried to isolate the beneficial elements of Big Data, and attempted to put different spins and labels on it (refer to “Big Data Must Get Smaller” and “Smart Data, not Big Data”). After all, I am a believer in data and analytics for real-life (i.e., not theoretical) applications, and I want decision-makers and marketers to succeed. I want to find ways to make money with data, whatever you name that activity.

Now I see that the word “Personalization” is becoming the hot topic in conference circuits and the blogosphere. More and more, that word is uttered even during the first encounter with a potential client. Signs are everywhere that it is about to be “the” buzzword in the marketing community.

And I welcome it. Through this series, I have been repeating that the key goals of analytical activities for marketers, regardless of employed channels, should be:

  • Knowing whom to contact, and
  • Knowing what to offer through what channel, if a customer or prospect is indeed to be contacted.

An amazing amount of data that became available to marketers led to over-communication to an “everyone, all the time” level, and the response rate of any marketing endeavor cannot be sustained that way. Out of desperation, some marketers actually “increase” the contact frequency to maintain the revenue level, and some already have reached a “six times a week, per target” level. What are they going to do after reaching seven times per week? What then? Invent a new day, like Ringo Starr blurted out with “8 days a week”? Spamming more surely isn’t the way out.

Some of my colleagues ask me if we should just take a leap of faith that personalization is the key to the future of marketing, as there aren’t many – there are only few – good success stories about it yet. My answer is to look at all these marketing messages from the consumer’s point of view. Aren’t you completely sick of this barrage of irrelevant pushes, even from so-called reputable retailers? Wouldn’t you pay more attention to something that is more relevant to you, that resonates with you over countless inept and, at times, completely annoying messages? When we show a group photo to anyone, most people check themselves in the picture first. How do “I” look in it? Let’s face it, everyone cares about themselves first, and we are conditioned to pick out anything about us through all kinds of noises.

That is why I believe that this personalization is the future of marketing. In the age of information overload, it is the customers who are picking and choosing messages that are relevant to them, not the other way around. Everyone is exposed to at least five to six types of screens every day. And with new inventions, the noise level will certainly increase. We are no longer living in the world where marketers can just push the products and services according to their priorities. Instead, consumers are ranking products and services. Traditional “push”-type endeavors still have their place in marketing. But in the future, “every” channel will be a 1-to-1 medium, and the consumers will be in full control, choosing what they want to see and mercilessly ignoring irrelevant messages. Marketers must try their best to comply to that demand and show consumers what they may like to see, using all available data and statistical techniques. And the marketers do that right will move ahead. But only if they do it right (refer to “Personalization is about the Person”).

The road to proper personalization is a long and winding one. It starts with the data, of course, as we need to decide “who gets what message” based on them. Various technologies must be employed to display different versions of messages through multiple channels individually, still maintaining consistency. Multiple versions of copies should be written and new stack of creatives must be prepared. Collected data should be refined to be used in such personalization engines, as raw data can only do so much, even with very expensive toolsets. If required data are not explicit enough, or worse, not available at all, we will need to calculate the propensity of certain desired behaviors or consumer characteristics – as in, “not sure if the target is a health-conscious young parent for certain, but he surely looks like one.” As I stated in my previous columns, explicit data are hard to come by, even in the age of Big Data, and we all must make the most of what we get to have. No customer will wait until you have the perfect set of data.

Like in any field, may it be a musical field or martial arts, there are virtuosos (or “virtuosi”?) and grand-masters, then there are mediocre talents and complete novices. In data and analytics such levels exist, as well. Not all analysts or data scientists are on the same level, though I often argue that an unexceptional statistical model is still better than someone’s gut feeling. For end-to-end marketing executions, things get more complicated, as many different types of technologies and skills, as well as overall vision, must work harmoniously to achieve goals. Unfortunately, I often see marketers who still don’t believe in the effectiveness of advanced analytics because they “think” that they had a bad experience with it. But is it fair to dismiss time-tested methods, when many other factors could have gone wrong?

In the interest of not killing the idea of “Personalization” due to unfavorable results from rudimentary trials, allow me to share the “10 stages of personalization efforts” from a data, analytics and technology point of view (i.e., marketing creative is not considered here):

  • Not even considering personalization yet. They still think that spraying the same HTML to everyone is alright, as long as the process runs smoothly.
  • Personalization is considered, but they do not know where to start.
  • Identified basic steps toward personalization, but they do not have specific data or a technology roadmap.
  • Created the data roadmap, but they did not start thorough data inventory.
  • Identified required data sources, but datasets are not cleaned up or consolidated for 360-degree view of customers (a must-have in personalization).
  • Datasets are ready for personalization, but only with “known” (or explicit) data; statistical modeling to fill in the gaps is not considered yet.
  • Tested personalization engines through major marketing channels of choice, employing collected “known” (or explicit) data.
  • Creating “personas” (or implicit data) using statistical techniques with available data, filling in the gaps with statistical models (an ongoing effort).
  • Personalizing most messages and offers through every touchpoint, employing explicit data (known data) and implicit/inferred data (in forms of personas).
  • Collecting and utilizing results data to enhance model-based personas and personalization engines continuously, leading to automation.

So, at what stage is your organization? Are supporting datasets previously locked in channel silos merged together to form a customer-centric view? Or are you just plugging transaction or event-level data into some personalization software with a fancy name and a high price tag? Are you personalizing only sometimes through some channels to some people who happened to volunteer – explicitly or implicitly – some of their information to you, or are you doing it for most people, most times, through most channels? The differences are huge. Unfortunately, too many marketers are just personally annoying customers in the name of personalization, and most don’t even do that consistently.

I understand that not all marketing organizations have to achieve ninth-degree black belts in personalization, as from company to company, business models, channel usage, success metrics, budget limitations and available data are undeniably different. Nevertheless, I dare to say that personalization will be more important for the survival of most businesses, as companies that are better at it are visibly leaping ahead. Look at the ways that some big name retailers are doing it from a consumer’s perspective; they are clearly not operating under the old paradigm of “marketers push, consumers respond.” Even when committed to the concept, before any organization gets into the thick of things, decision-makers must set the data and technology roadmap first. The order of operation is important here, and it would be easier to prove the worthiness of the endeavor in baby steps, too. Dismissing the whole idea after trying a few rudimentary steps out of order would be a real shame.

Like any guru would say, awareness is the first step toward improvement. Understanding how far one must go is at the core of any learning process. Isn’t that what Master Yoda tried to teach a young Jedi named Luke Skywalker on that swampy planet of Dagobah?