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Posts Tagged ‘snake oil’

You can actually do the work, or you can fake it and try to make an easy buck. It doesn’t matter what industry or profession you’re in. Athletes cheat. Accountant cut corners. Political consultants adjust poll numbers. Teachers hire surrogates to take their certifications for them. And yes, social media gurus make up magic equations that promise to measure everything from ROI to the value of a like.

We are surrounded by people who have chosen to make bullshit their vehicle of “success.”

Why? Because it’s easier than doing the work. Because it’s a faster path to revenue. Because for every executive or fan or client who sees bullshit or bad science for what they are, there are two or three who won’t know any better and will gladly pay for the next “big” thing.

Selling bullshit isn’t any different from selling anything else: at its core, it’s just a numbers game. You don’t have to sell to everyone. You won’t. You just have to sell to enough people who don’t know better and you will make a living. If you care more about positive cash flow than your reputation, about your next bonus or potential book deal than professional responsibility, about appearing to build value than actually providing any, then you can do pretty well selling complete crap.

Welcome to the world of gurus, of cult leaders, of chief tribe strategists.

About once or twice a year, I run into an example of social media bullshit that I find worthy of sharing with you on this blog. Sometimes, it’s a egregious money-making scheme whose sole intent is to prey on desperate, gullible, underemployed would-be “consultants” looking for an easy in to the “social media expert” space. Sometimes, it’s just bad science – a lousy equation or even a poorly conceived (insert acronym here) “calculator” whose authors didn’t really take the time to test and submit to any kind of legitimate peer review. Assumptions were made. Corners were cut. The whole thing was rushed.

I want to stress that not all social media gurus and self-professed digital experts are out to rip you off or sneak a sordid scheme past your bullshit detector. Many are just scam artists, but many are not. Sometimes, bad science just happens. Bad math, silly equations, erroneous reporting and made-up acronyms don’t get chucked into the FAIL pile because their author didn’t really know any better. Because they didn’t take the time to really put their own work to the test. They weren’t diligent with the proofing and peer review part of their experiment. Whether it’s laziness, incompetence, distraction, convenience or denial is for you to decide. All I know is that regardless of intent or reason, bad math is still bad math, and bad science is still bad science, and none of that ads net positive outcomes for those of us trying to make things work better in the social business space.

Today’s example illustrates how easily this sort of thing can happen. And before I get into the meat of it, let me just say that this post is in no way meant to be a bashing of Dan Zarrella. I’m sure he is very knowledgeable and supremely competent in a number of areas. I don’t know Dan. We’ve never worked on a project together. I have no idea who he is or what he does other than that he works for HubSpot. So what I am sharing here today isn’t meant as an attack on his character or competence or on whatever HubSpot is selling with this VOAL “model.” I just want to show you how easily business measurement nonsense can become “legitimized” by leveraging and combining personal brands, trusted publishing channels, market confusion, and the absence of a legitimate academic peer review process in the publishing of mathematical and measurement models anymore.

So before some of you jump on me for criticizing your best bud, stop. Breathe. Get some perspective. I’m not trying to hurt Dan or Hubspot. I am doing what someone around them should have done before this equation was published. This isn’t me bitching or making noise because I like the attention. This is me explaining something important and making sure that unsuspecting executives and decision-makers don’t fall for the latest flavor of bad social business measurement “science.” We’re never going to get out of this vicious cycle of “hey look at me, I invented a whole new social media equation” bullshit unless we have these kinds of discussions. We need to have them, even when they aren’t pleasant.

This industry is in desperate need of a serious dose of reality.  And if that sometimes comes with a swift kick to the balls, then sorry but that’s just what needs to happen.

An overview of the VOAL Equation:

This week, Dan Zarrella published a piece in the Harvard Business Review blog titled “How To Calculate The Value of a Like.” In it, he attempts to loosely equate the value of a like (VOAL) to ROI, then offers the following equation to calculate this so-called “value”:

The beauty of an equation like this is that virtually no one is going to take the time to try and make sense of it. Most marketing execs looking for a simple and easy way to calculate the ROI of their activities in digital channels will simply assume that the person behind the mathematical model is qualified and smart and competent. In fact, this was one of the argument provided by Dan on twitter yesterday when I questioned the equation.

For sport, we could dig into the equation itself. We could look at all of its components and determine whether they can be thrown into a bucket together, and through the alchemy of selective math, be twisted and bent into a legitimate measure of the value of a like. here’s how it breaks down:

L (Total Likes): The total number of audience members connected to your social media account. On Facebook, these are Likes of your page, and on Twitter, these are followers.

UpM (Unlikes-per-Month): The average number of fans who “unlike” your social network account each month. On Facebook, this is an “unlike,” and on Twitter, this is an “unfollow.”

LpD (Links-per-Day): The average number of times you’re posting links, and potentially converting links driven from your social media account. On Facebook, this is the number of posts you’re making, per day, that lead to a page on your website. On Twitter, this is the number of times, per day, you’re Tweeting these kinds of links.

C (Average Clicks): The average number of clicks on the links to your site you’re posting on your social media accounts.

CR (Conversion Rate): The average conversion rate of your website, from visit to sale or visit to lead. This can be an overall average, but for increased accuracy, use the conversion rate measured from traffic coming from the social network you’re calculating.

ACV (Average Conversion Value): The average value of each “conversion.” In this context, a “conversion” is the action you’ve used to measure CR for. It could be average sale price or average lead value. For increased accuracy, use the average conversion value of traffic coming from the specific social network.

If you went through the process of actually making sense of the equation, you would realize fairly quickly that because the ACV is a subjective value that can be pretty much anything you want it to be, the math can be bent to deliver any kind of “value” you want it to. You might also notice that for whatever reason, “unlikes” are measured monthly but likes are measured along an indeterminate timeline. You might also be driven to ask yourself why LpD (links per day) even needs to be part of this equation or why it is multiplied by 30 (days per month) when the clicks and likes are not.

Let me pause here. The point is that, already, the logic behind equation is already a mess.

What is wrong with this VOAL “model” (first sweep):

1. Its bits and pieces don’t make a whole lot of sense.  We have “total likes” up against “average clicks.” If we have total likes, why not also have total clicks? As an aside, what does one even have to do with the other? (Which brings me to item number 2…)

2. The relationship between the bits and pieces doesn’t make a lot of sense. Why are we multiplying net likes by links per day x30, then again by clicks divided by likes, then again by the conversion rate, and then again by (an admittedly subjective) conversion value? That’s a lot of multiplication. A x B x C x D  = LV? Really? That’s the model?

3. The cost of any of these activities is not taken into account anywhere. Tip: It’s hard to calculate the value of anything without factoring the cost somewhere in the equation. That’s a problem.

4. C = Average Clicks. Okay. Per day? Per month? Per day x 30? What am I even plugging into the equation? Not clear.

5. In what currency is the “value” of a like measured? Is this value a monthly value? An average value? An average monthly value? Is it even a $ value? Not clear. (Again.) What about offline transactions? What about transactions that can’t be measured by a last-click-attribution model? Are they divorced from the “value” of a like?

6. I see no metric for shares or comments. Another major oversight given the importance of sharing and commenting in regards to attention and propensity to click on a link or consider a purchase.

What else is wrong with this VOAL “model” (second pass, caffeinated this time):

For what little time we just wasted on this pointless exercise, we haven’t even touched on the more relevant aspects of why this equation fails to deliver a mathematical solution to the question of like value. Seven of them in particular:

1. A Facebook fan’s value (now called a like) is not the same as the cost of that fan’s acquisition. I bring this up because measuring the value of a like without taking into account the cost of that like makes the process null and void.

Also, give some thought to the difference between page likes (fans) and update/content (likes). What likes are we measuring again? Oh wait… here it is:

L (Total Likes): The total number of audience members connected to your social media account. On Facebook, these are Likes of your page, and on Twitter, these are followers.

So… the equation doesn’t measure those daily “little” likes. The ones that are attached to content and updates. To measure that kind of engagement on a Facebook page, the equation instead looks at clicks on posted links. But for some reason, it looks at average clicks, not net clicks.

????…

(Why? Your guess is as good as mine.)

No details on whether those are average daily clicks or average monthly clicks either. Could they be average hourly clicks x 24 x 30 x 12? No idea.

2. Since “likes” really stand for fans of a page, let’s talk about that: A Facebook fan’s value is relative to his or her purchasing habits (and/or influence on others’ purchasing habits). A like/fan is worth absolutely $0 unless that individual actually purchases something. Let’s start there.

If your intent is to measure fans/likes to transaction dollars attributable to your Facebook page, no need for a complicated VOAL equation. Save yourself the trouble and just measure inbound traffic from Facebook against online sales $. It will only speak to a last-click attribution model (a pretty limited way to measure the impact of a channel on sales if you ask me) but at least it will be much easier to measure and far more accurate than a bullshit equation that makes no sense at all. Then just divide your online sales from Facebook links by the number of fans/likes on your page, and voila. Done. It’s still a crap way to measure the average “value” of your Facebook fans/likes, but at least your math won’t be wrong.

3. Determining the average value of a fan may be interesting as a baseline for other measurements, but give some thought to the fact that each Facebook fan’s value is unique. One fan may engage with your content in a measurable way 300x per month but never spend a penny on your products. Another may engage with your content only on occasion but spend $3K per month on your products. Averaging your fans “value” won’t only give you a false sense of the relationship between likes and transactions, it will also obscure genuine lead generation and customer relationship development opportunities in a space that begs to be social. What’s the value to your business of averaging out net lead generation values again? None. If this is what you spend your time on, you might as well stop wasting your time on social channels.

4. A Facebook fan’s value is also likely to be very elastic. Some customers just have erratic purchasing habits. They might spend $3K with you one month and not buy from you again for a year. Depending on the size of your community and your type of business, this elasticity’s effect on that equation will drive you nuts and won’t help you make sense of what is going on with your Facebook strategy.

5. There is little correlation between a Facebook like and an actual transaction in the real world. (Maybe I should have started with that.)

6. Likes can be bought and/or manufactured, and often are, rendering this kind of equation (even if it made any sense at all) completely worthless. If you have no idea how many fake followers/fans/likes you have and try to measure VOAL you’re basically screwed. Have fun with that.

7. Once again, what about offline transactions? (What about any and all transaction behaviors that don’t neatly fall into a last-click-attribution model, for that matter?) The equation seems to completely ignore the relationship between Facebook fans/likes and offline sales. For most businesses, that’s going to be a tough pill to swallow.

And since I haven’t yet mentioned proxy sales structures (distribution channels, like Ford dealerships vs Ford’s brand pages, or Best Buy vs. HP for instance), maybe this is a good time to bring them up, because this “model” doesn’t address that either. At all. If I ask my local VW dealer to measure his page’s likes against his monthly car sales using Zarrella’s VOAL & digital conversion model, somebody is going to walk out of that discussion with serious hypertension, and a social media manager somewhere is going to be out of a job.

(If you still need convincing, click here for a more in depth discussion.)

Bad Math in Action: Try the VOAL Equation for yourself.

If you can’t make heads or tails of Zarrella’s equation or my explanation, don’t worry. He has built a nice little website for you where you can just fill in the blanks and go see how it works for yourself. Here it is: www.valueofalike.com. Try it. I plugged in several of my clients’ numbers and according to the tool, the average value of their fans/likes seems to hover around $73,736.25.

Yes, you read that right: According to the site’s math, every additional 14 fans/likes I bring to their respective pages amounts to over $1,000,000.00 in value/potential revenue. (Over how long, nobody knows, though evidently, the average fan-customer spending $25/month with them has an lifespan of about 245 years.) My clients will be thrilled to hear all about that. Maybe I should start charging more for my services.

In the meantime, check your numbers against the math and see if you get more accurate results than I did. Maybe I did it wrong. I’ve been known to be wrong before, so it’s possible. Or maybe the calculator is off somehow. That’s possible too. Or am I just missing something? Was I supposed to move a decimal point over at some point?  I’ll try to do this using the long form of the equation later, just to see if I can make it work. Or maybe not. I don’t really care anymore. This whole thing is so stupid, pointless and overly complicated that it’s giving me a genuine headache.

We get it. It doesn’t work. Now what?

Let me share four final things with you and we can all get back to work:

1. If all you are looking to do is determine the average value of a fan/like in the context of a last-click attribution model (linking a transaction to the last link someone clicked on to get to your site before pressing “buy”), then just add up sales $ resulting from inbound traffic from Facebook and divide that by the number of fans/likes on your page. That will tell you the average value of a fan/like – which is to say it won’t really tell you a whole lot but at least you’ll be done in under a minute instead of spending ten minutes filling the blanks of Zarrella’s VOAL equation, and then another week trying to figure out why your numbers look so weird. Bonus: It will be just as useless, but it’ll be so quick that you’ll have more time to get back to doing real work.

Also, if you want to measure the ROI of your Facebook activity, you’ll have to work a little harder at it, but item 3 on this list ought to give you a few simple guidelines that will get you on the right track. What’s nice about it is that my example focuses mostly on linking offline (brick and mortar) transactions to channel activity, and that’s actually harder than linking digital activity to digital transactions. So have fun with it and I’ll be glad to answer any questions.

2. Because Zarrella’s article was published via the Harvard Business Review’s blog, scores of people won’t think to question it. The fact that he works for Hubspot (a reputable company) makes the equation seem that much more legitimate. And because it looks complicated as hell, who is going to take the time to figure out if it actually works (or how)? Nobody.

In other words, the assumption of competence on the part of the author (a) the perceived complexity of the equation itself (b) and the assumption of an editorial review process on the side of the publisher (c) will combine to ease readers into assuming that the contents of that article are solid. This is why we can’t have nice things.

Too many assumptions, not enough fact-checking. Again.

Shame on HBR for not making sure that what they publish has been verified, by the way. It isn’t the first time something like this has slipped through their editorial review process (assuming there even is one). Remember this gem?

Tip: Next time someone tells you they’ve invented a metric, run. Seriously. Turn around and start hoofing it.

3. I spent a little time explaining to Dan on Twitter how to actually measure the value of channels as they relate to actual sales, so you might want to check that out. (Feel free to skip the initial petty bickering and scroll straight to the process I outline in the example.) There are two versions of that exchange for you to pick from:

Rick Stillwell’s capture (go say hello) and Paul Shapiro’s capture (both unfortunately miss a few of our wittier exchanges, but that’s okay. The process part of it is far more important.) That method can be replicated by small and mid-sized businesses with little to no access to social media management tools like Radian 6, by the way. It takes a little work, but it’s simple. And yes, simple works. if you need more details on it, I talk about it in Social Media ROI.

4. Dan and HubSpot: Let me extend the following invitation. If you are serious about building a channel and fan/follower measurement model that actually works online and offline and will bring value to organizations you work with, I will gladly help. I can show you how to do this and how not to do it too. Get in touch if you want to. Or don’t. Totally your call.

For everyone else, also check out this piece by Zachary Chastain on Thought Labs. He gets to the point a lot faster than I do, and with far less bite. And also Sean Golliher’s brilliant piece, which outlines further problems with Zarrella’s VOAL model.

And if you’ve noticed that my writing has been scarce here lately, it’s because I have been writing about digital command centers and real-time social business intelligence over on the Tickr blog. No worries, I’m still here, but I have to split my time between both blogs right now. New project with exciting developments coming very soon, so stay tuned. (And go check it out.)

Until next time, have a great day. 🙂

*          *          *

Not to take advantage of bad science to sell books, but since I go over real measurement methodology vs. bogus social media “measurement” in  Social Media R.O.I.: Managing and Measuring Social Media Efforts in Your Organization, it’s worth a mention. If you are tired of bullshit and just want straight answers to real questions about value, process, planning, measurement, management and reporting in the social business space, pick up a copy. The book is 300 pages of facts and proven best practices. You can read a free chapter and decide for yourself if it’s worth the money (go to smroi.net).

And if English isn’t your first language, you can even get it in Spanish, Japanese, German, Korean and Italian now, with more international editions on the way.

CEO-Read  –  Amazon.com  –  www.smroi.net  –  Barnes & Noble  –  Que

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Snake-oil 2

Part 1: Return on Incompetence

Here’s a flash of obvious: When most of us don’t know how to do something, we typically know that we don’t know how to do it.

Let me illustrate: As much as I would love to be an F-18 pilot, I don’t know how to fly an F-18 (or any aircraft, for that matter). As a result, you don’t see me walking around in a flight suit  pretending that I am an F-18 pilot.

More to the point, you don’t see me advertising my services as an F-18 flight instructor.

For the exact same reason, you don’t see me trying to sell services as an ice-carving, cat wrestling or underwear-modeling instructor. Why? because when most of us don’t know how to do something, we have a) enough sense and personal integrity not to pretend that we do, and b)  enough professional acumen not to pretend that we are qualified to teach it.

Yet this line of logic (and basic sense of professional ethics) seems to escape a disturbingly large group of people who evidently have latched-on to Social Media as an easy meal ticket  – one to be earned, in many cases, on the backs of people and companies who don’t know any better.

To add injury to insult, most of these would-be Social Media “experts” aren’t even good at masking the fact that they have no clue what they are talking about. You would think that they would at the very least grasp the most basic building blocks of social media… like the fact that a Twitter follower, a Facebook fan and a YouTube subscriber are essentially the same thing, but evidently, even that simple of a concept escapes some of these folks. In far too many instances, they don’t seem smart enough to realize how little they understand about a discipline they claim to be experts in.

Yet these are the people who increasingly find themselves advising companies around the US as to how to build, integrate, manage and measure social media programs.

Now, those of us who actually do this for a living and take it seriously can spot these posers a mile away, but how are unsuspecting executives supposed to? To the uninitiated, anyone with a neat sounding social media measurement formula and a whiteboard presentation can sound like they know what they’re talking about. (If you had never seen what gold looks like and a self-proclaimed expert came along with a bag of yellow dust and told you it was gold, why wouldn’t you believe them?)

More often than not, the otherwise innocent combination of inexperience, ambition and lack of professional accountability are to blame. But in far too many cases, an unhealthy blend of raw opportunism and… economic conditions  have given rise to a mob of social media snake oil salesmen. Whatever the causes may be, it worries me to see how quickly both dangerously inexperienced amateurs and deliberate hacks alike have taken over the “social” management business with their special brand of complete nonsense.

Perhaps more troubling: Very few among those of us working hard to build up this discipline are speaking out against bad practices and obnoxious BS from this unscrupulous crowd.

Look, leadership isn’t just about thought leadership. It isn’t just about being pioneers or evangelists or mavens. It certainly isn’t just about getting the accolades and the followers and the invitations to speak at conferences.  Leadership is also about responsibility. And we have a responsibility to keep our community and this field as ethical, professional, and free of BS as we can.

If leaders in this field don’t stand up for its integrity, who will?

28-tweets-later

Return on Inaction:

The reality is that the hijacking of Social Media by hack jobs isn’t happening in a vacuum: Every instance of an agency, firm or consultant promising results and delivering a goose egg makes it that much less likely that their  clients will put their trust in a social media adviser again, genuine or not. And rightly so.

To outsiders, there is no difference between a Chris Penn (who knows his shiznit) and a Joe Shmoe (who couldn’t find his way out of a paper bag if Twitter drew him a map), and that is a big problem: Anyone with a little bit of SEO savvy can have his/her bogus methodology pop up at the top of Google searches. And if no one calls him/her on the BS, there will be no indication anywhere that what s/he is selling is complete nonsense.

Case in point: How many companies have already fallen for misguided methodologies like these? (I use the term misguided since as far as I can tell, their authors seem to have developed these methods in good faith.)

Social Media ROI Calculator no.1. (Go here for the analysis.)

Social Media ROI Calculator no. 2.

Social Media ROI Calculator no. 3.

Bogus Social Media ROI measurement methodology no. 327.

And then there’s  this conversation on LinkedIn. Count how many of these folks are consultants. Tell me that level of widespread confusion and ignorance about the mechanics of Social Media doesn’t open the door for abuse and nonsense on a large scale.

We can do better.

If you believe that the hacks and misguided amateurs will eventually go away on their own, you’re wrong. Why would they? The money isn’t bad, the opportunities are growing and there is still virtually zero accountability in this line of work. Every other company is looking for a social media expert to teach them how to either develop, integrate, manage or measure social media, yet the vast majority of business execs couldn’t tell a true professional from an agency flunkie with a Facebook account. You do the math.

We’re going to be on the wrong side of that growth curve for while unless we start establishing standards for the industry. And I mean sooner rather than later.

How Not To Measure Social Media – Part 2

Here is Part 1.

Below is another example of social media nonsense passing for expertise. I don’t want to make any assumptions about anyone’s motives in this specific case, so to be fair, it’s probable that whomever put this together genuinely thought that the thinking behind the equation and methodology was sound. There is no reason to think that anyone at Digital Royalty was trying to make a quick buck off unsuspecting clients when they developed this. (My guess is that they mean well.) But the fact remains that the people behind this thing don’t understand either Social Media or program measurement well enough to teach either. And that’s the danger: Regardless of people’s intentions and motives, bad methodology is bad methodology no matter how you look at it. And bad methodology quickly turns into bad business for everyone involved.

But don’t take my word for it: Watch the video and make up your own mind. (Watch carefully because I’ll have questions for you afterwards.)

And here is the “equation” referenced in that video:

DigitalRoyaltymetrics

If the video doesn’t play for you, go watch it here.

Rather than listing out all of the flaws in this methodology and equation, let me ask a few questions that outline some of my key concerns. You can try to answer them yourselves or go straight to the answer/comment. You choice. (These were questions I asked the author on her blog post. They remain unanswered.)

1) Why do FB fans and Twitter followers fall into the volume column but YouTube subscribers fall into the engagement column?

Twitter followers, Facebook fans & friends and YouTube subscribers fall into the same category. They should be in the same column. (The “reach” column – not featured in this methodology.) Reach is neither hot nor cold. It’s a hard metric.

Demonstrating a lack of understanding about something as basic as this throws up a bright red flag right off the bat.

2) Why do frequency and reach apply to volume but not engagement or conversions? (Warning: This one may hurt your brain, so feel free to skip ahead.)

Assuming that Volume and Engagement are relevant categories/columns, (and that’s a very big if) Frequency and Reach would apply to both.

Let’s take a step back and look at the definitions of Frequency and Reach:

Frequency is a measure of how often something happens. (An activity, a transaction, etc.)

Reach is a measure of how many people you can touch. To use Amy’s own ecosystem terminology, reach is the number of people who live in your ecosystem.

A subset of reach is how many people within that ecosystem you actually touched or engaged with for a particular campaign.

Once you realize that, you start to see how the concept of a volume column is a bit shaky: You would have to include “reach” as an element of the “volume” column even though they are basically two words describing the same thing. See how this makes no sense?

But I digress.

Based on this model, Frequency (of interactions) should also show up in the engagement column: (How often do you engage?) Likewise, Reach would manifest itself in the engagement column in terms of how many people were touched/reached through… engagement.

If a Conversion column exists, then Frequency also applies to it: Frequency of conversions = how often conversions happen. (Or transactions, for that matter.)

Regardless of how flawed your method may be, this is simple, basic, common sense stuff that can be plugged in properly IF you understand it.

The lack of basic understanding of reach and frequency raises another red flag.

3) What does “actual activity and action” mean?

Transactions? Website visits? Who knows? Knowing what you’re talking about matters. Letting other people what you’re talking about matters too. “Actual activity and action” doesn’t mean anything.

Another red flag.

4) The size of your “ecosystem” = Reach. How can reach fall into both the cold and warm columns?

The size of an ecosystem is the definition of reach.  Calling something two different things doesn’t actually make it two different things. (This doesn’t happen when you know what you’re talking about.)

Having the same element appear twice in the same equation is bad math.

The red flags keep popping up.

5) Return on Influence = Adding 3 web analytics values to sentiment x reach (again)? Seriously? That’s the equation?

Before we dive deeper into this, the equation needs more work, starting with the fundamental flaws I already outlined.

Once you’ve revamped the basic elements of the method and equation, you have to then understand how the pieces fit mathematically (assuming they even do). Throwing a bunch of unrelated values together does not constitute serious social media measurement.

6) What unit of measure do you use? If it’s a “return,” what are we talking about? What does the number mean? What is it in reference to?

If the equation is supposed to create an influence index, then it doesn’t need a unit of measure. Just call it a Social Influence Index (SII) or whatever you want, and you’ll be fine. Whatever number the equation spits out is your “score”. Fair enough. There is nothing scientific about it, but you can probably get away with it.

But if you are going to call it “return on X,” you need a unit of measure. In real Return on Investment, you distill the investment in time, human capital and other resources to $$$. The “return” on that investment therefore is calculated in $$$ as well. Money is the common unit of measure.  The ROI equation gives you a ratio of money invested to money earned. The same rules apply here: If this is to be a “return on influence,” what unit of measure is used to calculate that ratio of return?

Without a clear unit of measure, you cannot calculate “return” on something. Basic 101 stuff. Learn it.

7) If “warm” data is “intangible and hard to measure,” why is it part of the equation to begin with?

Either it’s soft data and thus anecdotal or it is hard data and it can be used here. You have to pick one. You can’t claim that “warm” data is intangible/hard to measure and then add it to your “equation”. This is not a gray area.

Data isn’t cold, warm or hot. There’s only reliable data and unreliable data.

8) (Just added) Why does the equation on the board not match the equation in the graphic?

I knew something else about that equation was bugging me, but I couldn’t quite put my finger on it. Yet it was under my nose the whole time:

In the video, the “cold” half of the equation is interpreted as (Page Views x Visits) / Time Spent

In the graphic, the “cold” half of the equation is interpreted as (Page Views x Visits) + Time Spent

Which is it? Do we divide by Time Spent or do we add Time Spent?

(Thanks to @AnnaObrien for pointing it out.)

Incidentally, Frequency is not defined either in net page views or net unique visits either…

… And most of the KPIs explained early in the video seem to completely vanish…

… but by this point, who cares.

I could go on and on and on, but you get the picture: At first glance, the equation and the methodology look solid to the average person: Clean graphics, columns on a whiteboard, buzzwords we’ve all heard before, the promise of a digital agency backing it up, etc. Because the video and equation has already been posted on a variety of sites that fail to ask basic stink test questions by people who don’t know what flaws to look for, it makes its way to the top of search queries. Before you know it, complete nonsense goes mainstream and passes for real methodology because of three things: Decent packaging, Search, and confirmation of validity by other non-experts who barely skimmed the material.

Ca-ching.

This is how real Social Media expertise gets hijacked, and how Social Media management – as a discipline – loses credibility in the business world before it even gets a chance to prove its worth.

Last words:

This stuff isn’t just going to go away on its own. It’s great to see some folks in the Social Media management community speak out against this kind of BS, but they are the exception rather than the rule. At some point, the incessant retweeting and arse-kissing has to start making room for more relevant and responsible behavior: Call a cat a cat. When you see BS, don’t just look the other way. Call it out. Ask real questions. Put people to the test. That’s what leaders do. That’s what professionals do. We can’t keep allowing remedial BS to invalidate the real work being done in this space by those of us who actually care about it.

Rant over.

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