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.
Sorry, but this industry needs 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 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. :)
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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.