Podcast 006 Buzzwords Part 1
In this episode, we give our take on five buzzwords that often overused as well as incorrectly: synergy, big data, unicorn, analytics, and influencer marketing.
Episode 006 Buzzwords Transcript
Heather McKee: Welcome to The Modern Polymath where we discuss topics in technology, economics, marketing, organizational behavior, market research, human resources, psychology, algorithms, higher education, cybersecurity.
Heather McKee: Hey, podcast universe. Thanks for tuning in. Quick message before we get started. To celebrate Cybersecurity Awareness Month in October, we’re going to have 2 episodes this month dedicated to the topic. Next week, we’ll give you some quick tips on how to be more secure online. And for those of you who are thinking, “Cybersecurity, not interested, don’t care,” or “Don’t understand enough to care,” we hope to change your mind. We’ll have a crash course on cyber security so you can better understand what it is, what it means, and why it’s important to be aware of the topic. But that’s later this month.
Heather McKee: On today’s episode of The Modern Polymath, we reflect on good old buzzwords. Don’t you just love when people clearly use them incorrectly or way too much? Yeah, neither do we. So from a list of a dozen or more commonly used buzzwords, we’ve selected five to unpack so we can all get on the same page. And, just for fun, we decided to give away a prize to the person who correctly counts the number of buzzwords used throughout the episode. To participate, simply leave your guess in the comment section of our LinkedIn or Instagram post regarding the prize. As always, we have Dr. Jon Christiansen, John-David McKee, Will Callaway, and I’m Heather McKee. Let’s get this podcast started.
John-David M.: All right, so synergy… How often do you hear people say, “We have to find synergy?” We have too… I mean, what is synergy truly? What do you really want to accomplish there? Because I obviously… It has a great connotation if used correctly.
Will C.: Synergy definition, the interaction or cooperation of two or more organizations, substances, or other agents to produce a combined effect greater than the sum of the separate part.
Dr. Jon C.: So in other words, you increase in an increasing rate. You’re… Instead of being one plus one equals two, because you have assets that make this great, and you have assets to make this great also, you become greater. So 1+1=5 because… If I have a marketing department that syncs really well-
John-David M.: [crosstalk 00:02:39] Right.
Dr. Jon C.: with an Ops department, and I can get product out the door, well… As long as we understand one core thing, like our only goal is to get product there in two days, Amazon, or our only goal is to get people there on time, Southwest, or whatever it might be. If that’s our goal, we just stick to that. That’s how synergy works.
John-David M.: It’s called a value proposition.
Dr. Jon C.: Yeah. I mean if you get good enough at it, yeah.
Will C.: [crosstalk 00:03:06]. Yeah, yeah.
Will C.: But that’s the thing. It’s under the assumption that you can cooperate between two agents to move towards a common goal.
John-David M.: So synergy is probably an output, and it’s something you look back on and say that you found. You can find commonalities. But the point that we were trying to make was, most of the time people say that and then what they really mean is a group think.
John-David M.: Legit. Just because the leader says so when we’re all there, it’s all, it’s synergy. We’re on the same page. All good. No it’s not.
Dr. Jon C.: No. What that is, is that’s a C level guy saying, “Here’s what we’re doing” and everybody is going to find a way to make that work. “Here’s how you’re going to be evaluated from now on.” Great, we’re going to play by those rules now.
Heather McKee: But that can be really dangerous, or that type of group think, can be really dangerous from a couple of different standpoints. I mean, one, if you have peers who are too scared to speak up and challenge each other, in a friendly way, about processes or policies, then the culture’s at risk of collapsing, as far as people feeling good about creating efficient workflows, because, if they’re afraid to speak up and challenge other’s thoughts, then you’re just going to be stifled the whole time.
Will C.: Right. And also it can kind of happen in a merger situation.
Heather McKee: Yeah, absolutely. A bigger parent company absorbing a smaller company, that smaller company could just fall into the trap of group think, the way that the bigger company goes, because they’re afraid to challenge those processes or speak up for fear of losing their jobs. But Jon, you’ve seen this a lot in your work.
Dr. Jon C.: Well, basically saying is you build a complex adaptive system which is, I put people in the right positions to be able to properly know what is happening in the environment, my role in it, what I need to know to effectively produce my tasks. But also, if it’s set up appropriately, I’m able to adapt, I’m able to be creative, and I have the flexibility to do both of those and I’m able to take pressure for what it is and… Ultimately because of that, the outcome is-
Dr. Jon C.: Really what… Where innovation stems from. [crosstalk 00:05:04]
John-David M.: Okay so, leadership and team building, right? And a vision of what you’re trying to do is what synergy becomes, but it’s an output.
Will C.: There are a bunch of variables that come into really good teamwork, synergies, all of that stuff. And one of those things, I think, that we often take for granted is responsibility and getting things done.
John-David M.: [crosstalk 00:05:28] Right.
Dr. Jon C.: Well, and it becomes… Because for the most part, when you have group think, it becomes diffusion of responsibility. “Hey, we know we got to do that. Someone will figure it out.”
Will C.: Yeah. Someone will do it-
Dr. Jon C.: Like, meetings, right? [crosstalk 00:05:38].
Will C.: Yeah, it’s just like that Tostitos commercial where there… All those guys in the corporate office and they’re eating the Tostitos, and there’s one guy working on the PowerPoint pitch deck, whatever it is, and they’re looking down at the construction workers. And there’s one construction worker doing all the work, and three of those guys looking around. It’s like, you know, to build effective teams, effective synergies, you have to have people who are willing to take on responsibility, perform, get things done, and work legitimately in a teamwork situation where everyone kind of appear, no matter what their title looks like.
Will C.: I think that goes right into getting granular with defining the problem and actually understanding what you need to do. Collaborate with different silos if that is how your business is run across sales, marketing, finance… You know to, again, add another buzzword, recognize how can we maximize our ROI.
Heather McKee: We talked a lot about synergy. Let’s talk about big data.
Dr. Jon C.: Big data became a thing because data warehousing became a thing, and then machine learning became a thing, and then-
Will C.: Cloud computing became a thing.
Dr. Jon C.: [crosstalk 00:06:45] Yeah and then heavy-
Will C.: Massive processing became a thing-
John-David M.: Well there’s nothing wrong with any of those things. [inaudible 00:06:50].
John-David M.: I mean, nothing wrong with the term big data other than it’s completely subjective, and it should be about big usable or big friendly data.
Dr. Jon C.: Well, and it goes back to my point, which is what small data?
Heather McKee: [crosstalk 00:07:00] Right? Yeah.
Dr. Jon C.: None? Some?
Heather McKee: [Crosstalk 00:07:03] Yeah.
Dr. Jon C.: 7 observations, 35 observations?
John-David M.: [crosstalk 00:07:07] Right.
Heather McKee: 100?
John-David M.: To be in-
John-David M.: An analytical field, but I mean, if you’re running analytics on this… There’s your buzzword, you need to have a number. Like, you have to have an observation that says, “This is now a big data,”-
Will C.: Let’s call it big queries, right? So you’re querying a massive amount of data rather than big… There’s, yeah, like you can have millions of lines of rows and observations and couple of hundred thousand variables and stuff, but it’s not about the big data-
Heather McKee: [crosstalk 00:07:37] Right.
Will C.: It’s about the purest, and the data that’s going to tell you the most of whatever you’re trying to figure out.
Dr. Jon C.: I see this government data. There will be one thing that’s a category and then a bunch of dummy variables about whether or not it is that, and then a bunch of repeat because Department A needs all this, Department B needs all this. And all they’re doing is saying the same thing over and over again. I’ve seen databases that are 7,000 columns, and you only need seven.
Will C.: [crosstalk 00:08:07] Yeah.
Dr. Jon C.: That’s it.
John-David M.: By the way, getting to the 7,000 of the 7 is one of the hardest things you can do.
John-David M.: I mean [inaudible 00:08:14] like simplicity is an extremely difficult thing so you’re not helping anybody by having more data, it’s not helping anything. It’s what is important.
Will C.: You know, it’s a dictology. It’s like, you don’t know what you don’t know.
Heather McKee: [crosstalk 00:08:25] Yeah.
Will C.: Right, and so you have to gather all of that.
Heather McKee: That’s going to become extremely difficult, especially with all the new rules that are coming in place like GDPR, the California rules that they have-
Will C.: I’d really like to get maximized all the information I can-
Dr. Jon C.: [crosstalk 00:08:37] Because you just don’t know. Target didn’t know… That whole story that came out in the Times or Wall Street Journal or wherever it was, and then Charlie put it out in his book about how to try to find moms right on the verge there and… Because they were trying to penetrate that more-
Heather McKee: [crosstalk 00:08:57] Expecting moms.
Dr. Jon C.: Yeah, expecting moms. So they didn’t know what it was they needed to find and it’s like… So that’s when they started identifying their trigger behaviors, right? And you don’t know what those are, but it goes back to the difference between predictive and explanatory modeling. Whereas certain things, that are better predictive of some outcome, are not very easily revealed as a surface level item.
Will C.: But we also know that humans don’t do random and don’t do that well-
Dr. Jon C.: Hence, yeah. Hence why, the most interesting thing, is the only criminals that really fully get away with it are the ones that do nothing with pattern. Everything is random.
Will C.: So next up we’re going to talk about unicorns, which is just a fancy word for tech companies that have reached a billion dollar evaluation because everyone seems to be obsessed with $1 billion. As we know, humans are obsessed with round numbers, and… Evaluation is a very subjective term.
Will C.: Are you pulling in that revenue, or are you just valued at that? Because if we’re going to just start talking about evaluations and we’re talking about VCs who are all just pumping their own chest and driving up your evaluation, even though you might not actually be worth what they are-
John-David M.: It’s not like they’re valuing these things on revenue generated, or profitability or, even a lot of cases, true intellectual property. The typical evaluation model has been for X revenue or 10 X profit, you know, something along those lines. But it’s much more difficult for the market, as a whole, and the collective people to be able to understand the underlying business model that they’re valuing whenever you’re dealing with things like number of users and engagement and not as hard of metrics as what we’re used to with profitability and things along those lines. And they also may not have access to the right data to be able to forecast a valuation so that they can understand what the company is going to do moving forward. It’s much more difficult because a lot of these unicorns were actually well in debt whenever they went public and the founders became billionaires. No revenue model is actually supporting it at that point.
Will C.: And that’s one of the things where even Uber loses money every month.
Heather McKee: [crosstalk 00:11:03] Right.
Will C.: Netflix loses money every month. So you know, is unicorn… Like you’re still using unicorn as something as a target and maybe don’t be a unicorn. Be somebody who gets sustainable business over the next-
John-David M.: [crosstalk 00:11:14] There’s a word.
Will C.: 10 years.
John-David M.: [crosstalk 00:11:15] Exactly.
John-David M.: That’s a buzz word that should never go away. Because is it a buzz word?
Heather McKee: [crosstalk 00:11:19] Yeah. It’s a good one.
John-David M.: But sustainable is probably the most important thing that you do or actually say-
Dr. Jon C.: Sustainable should be sustainable.
Heather McKee: So let’s say the unicorn is the antithesis to sustainable, which is what companies actually should be striving for, not to be the unicorn because your chances and probability of being that unicorn are slim to none, truthfully-
John-David M.: [crosstalk 00:11:40] Yeah, it’s an amazing thing that the marking is speculating on something based on users, which what you’re doing on this traditional user unicorn model in the last few years because you’re basing on number of eyeballs that are looking at something.
Will C.: [crosstalk 00:11:52] Yeah.
John-David M.: Versus the actual monetization of those users into something that looks like a sustainable business model, but nonetheless a unicorn is no different than viral.
Will C.: Yeah, and that’s the thing-
John-David M.: [crosstalk 00:12:03] That’s another buzzword.
Will C.: What is the value of viral?
Heather McKee: So I’m going to stop you there because we will have another episode about virality and randomness, which all of this we’ll do a deep dive into. Let’s get Jon riled up a little bit and talk about analytics. Would you like to tell us what your immediate reaction is?
John-David M.: Hold on. As the head of an analytics department at a very well known college-
Heather McKee: Yeah. Reputable person to be speaking about said buzzword-
Dr. Jon C.: As I look over the table, the one thing that comes to mind is, “No thanks.” But I do feel it’s necessary to… At least make some level of comment about this to… Because it’s so misconstrued because it doesn’t really mean anything. It’s a made up… It’s McLovin’. All these terms… The reason they have life is because they sound cool.
Heather McKee: [crosstalk 00:13:08] Yes. And they sound smart.
Dr. Jon C.: Right. So basically what you did was you combined analysis and metrics and you put them together. Analytics, analysis; metrics of analysis is what you’ve done. Problem is… What people that inquire about what this is all about is, they want a job in analytics and they don’t know what it is and it’s because nobody does. Analytics is a word that replaced statistics because statistics used to be… It’s kind of hard to say and-
John-David M.: [crosstalk 00:13:40] Stats isn’t.
Dr. Jon C.: We assume stats are just like what is on the back of a stat sheet. What we need to really figure out is, okay, so it’s analytics. Great. How many times have we gone into an environment where there is, “We need analytics.”
Dr. Jon C.: One of the first conversations I ever had in my career was people just saying, “We need analytics.” It’s okay, what do you… Are you trying to measure?
Heather McKee: [crosstalk 00:14:05] Right? What do you need them on?
Dr. Jon C.: Are you trying to predict? Are you trying to influence, what are you trying to do-
John-David M.: What you’re saying is, “I need to make sense of my data.”
Dr. Jon C.: But then you find out they don’t have any. So it’s like you don’t need analytics. You need data harvesting.
Heather McKee: [crosstalk 00:14:16] Yeah, yeah.
Dr. Jon C.: But isn’t that analytics?
Heather McKee: [crosstalk 00:14:18] Nope.
Dr. Jon C.: Well, yes or no? That’s at the point where the word becomes what everybody assumes it is.
John-David M.: [crosstalk 00:14:26] Exactly. Yeah.
Dr. Jon C.: And there is all these assumptions about what it is. The answer is, it’s all of them and none of them.
Heather McKee: [crosstalk 00:14:33] Yeah.
Dr. Jon C.: So it’s my favorite Uncle Pat Lencioni quote which is, “If you create a product for everybody, you’ve created a product for nobody.”
John-David M.: [crosstalk 00:14:42] Right, right.
Dr. Jon C.: And that’s pretty much… [Inaudible 00:14:44] This is pretty much what this has become. It means nothing. It’s either statistical analysis, it’s glorified reporting, which is just reporting summary statistics-
Heather McKee: [crosstalk 00:14:56] Right.
Dr. Jon C.: Or it’s some form of modeling.
John-David M.: [crosstalk 00:14:58] Yeah.
Heather McKee: Well the fact that you have to put some kind of real descriptor word around it, I think even goes to further that point that… You know, what type of analytics am I looking at? Am I looking at web, am I looking at product pricing? Am I looking at sales? Am I looking at visits to a store, you know, applications completed-
John-David M.: You know what’s amazing? The word has taken on its own meaning. At some point you have to embrace the fact that it’s there but know what you’re asking for.
Dr. Jon C.: So the only thing I’ve found that actually unifies everything we’re talking about is… It’s the one thing out there, depending on whatever you’re asking for, that is trying to inform something.
John-David M.: [crosstalk 00:15:45] Yeah.
Dr. Jon C.: At a minimum my knowledge about what is happening-
John-David M.: [crosstalk 00:15:47] Yeah, that’s exactly right.
Dr. Jon C.: How we’re performing-
Dr. Jon C.: [crosstalk 00:15:49] Okay.
Heather McKee: [crosstalk 00:15:49] Yeah.
Dr. Jon C.: Extend it further. My judgment about where we are, or where we should go, and even further than that, what decision we should make and, even more, so where we need to be going.
John-David M.: So that’s a perfect definition. Like what was hard is the best way to describe what we do for a living, is to say that we’re in analytics. And I know it’s not a real term but that would make more sense to most people.
Will C.: Analytics to us here at Ins and Outs looks a lot like data, statistics and data science methods and, based on the first 2, curating a strategy based on the insights to move forward with whatever company we’re consulting with. But that’s not necessarily the analytics standardized definition. It can look different across organizations.
John-David M.: Yeah, it’s a category term or categorical term that did not exist before. And that’s fine, because words are made up all the time. It just needs to actu ally mean something.
Dr. Jon C.: But it took off on a rail when Hal Varian, at Google, he… I think he was a chief economist or whatever. Funny enough, I read his textbook when I was in graduate school in Economics. A chief data scientist, or something of that effect, is going to be like, a statistician or whatever is, is going to be the sexiest thing ever in the next however many years. Yeah, you’re kind of right.
Heather McKee: [crosstalk 00:17:04] Yeah.
Dr. Jon C.: But the problem is, people hear an influencer say that and they react to it. So that is how you know the guy is a real influencer.
John-David M.: [crosstalk 00:17:12] Yeah.
Heather McKee: So going back to influencer… One of the reasons that I find the term influencer somewhat annoying and why I feel like we need to clarify it so that people begin using it correctly because it shouldn’t be annoying. It is a perfectly acceptable word to describe what it is. So let’s just set the definition straight. An influencer is definitely someone who a mass market of people look to for their opinion, their emotion on something, their reasoning for something because there is some characteristic or draw that they have to that person.
Heather McKee: But you also have to think about it in the chain of decision makers. If you’re… Think about it. Okay, that’s B2C, business to consumer. If you’re talking about business to business, your other influencers are the accountants who have to use the accounting software that your company is selling you. It’s their VP of Finance who has to sign off on that purchase order. It’s then the CEO… They’re all influencers and it’s identifying which one is an influencer or a decision maker or whatnot. But know the difference and don’t confuse the two when you’re saying, “Oh we need to do influencer marketing.” What do you really mean? Be clear about what you’re talking about and what your target.
John-David M.: Yeah. Well first of all, if you want to do influencer marketing in its truest sense, don’t have a bad product. Do what you say you’re doing like transparency is there.
Will C.: Okay so as an influencer, you’re actually trying to either push a product or you’re being paid to push a product. And in some circumstances, you do need to have some transparency if you’re an actual influencer. Like don’t push a bad product just because they paid you two hundred grand for one Instagram ad or one YouTube review or something like that. You’re damaging yourself. You’re damaging your own brand, and it’s very short term outlook for yourself.
John-David M.: Yeah, it means your credibility is your basis for being an influencer. So if you lose that, then you lose it.
Heather McKee: [crosstalk 00:19:28] Right.
John-David M.: But it actually has to matter. Your credibility has to matter to matter.
Will C.: And I think that’s where… When Heather was talking about when it goes to B2B, people who suggest a product that works for them, that’s their rep on the line. And they’re in the business world, their careers going to maybe go for another 20 years, regardless. They don’t have any social influencer or anything like that. They’re just saying-
Heather McKee: They don’t get a commission on that sale or product-
Will C.: They’re saying, “This works for us. This is what we do. This is what we sell. It might work for you, friend I went to college, business school, I met at a conference, whatever.” It might work.
Heather McKee: [crosstalk 00:20:03] Right. That’s a good point.
Will C.: That’s their… Not social influence, that’s kind of their currency is like, “Hey, I can suggest something to you that might help you.”
John-David M.: [crosstalk 00:20:12] Yeah.
Dr. Jon C.: So Jonah calls it social currency.
Heather McKee: Right, that would be Jonah Berger in his book, Contagious: Why Things Catch On. Great read, by the way. We totally recommend it.
Heather McKee: But that’s all the time we have for today. Don’t forget to submit your buzzword count guess in the comment section of our LinkedIn or Instagram post, and we’ll announce the winner later this week. You can subscribe to the podcast and be among the first to hear our recent episodes, or check our website, Insandouts.org, for more content. Thanks for tuning in. Catch you later.