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
Nice article. And you are right, skip to
#5 There is little correlation between a Facebook like and an actual transaction in the real world.
I’d toss one more nugget into this big rich candy bar.
Over the summer, I read a book about how to accurately measure the value of your *customers*. One of the people that gets overlooked when trying to calculate such a value is your small-time buyer/big-time referrer. They might buy once, or buy a lot of little things, but have such a great experience that they tell lots of people about you.
Where is this more true than on a Facebook page? A lot of the magic (so the legend says) of social media is not necessarily the people whom you get to buy from you directly (which often is not very many people). The real magic is the person who invites people to learn more about you. Who joins your page and shares your status updates without you asking or begging or without a prompt like, “Like this if you remember “My Little Pony.” In attempting to narrow the “value of a like” into a calculator you can type values into, a company can miss this kind of intricacy pretty easily. That’s not to say one facet is more important than the other, but let’s face it – someone who talks pretty about your company in an open public stream is pretty darn valuable, even if they don’t spend more than $5 a month with you.
I hope people read this analysis carefully. Sometimes, if something seems too neat, especially in the marketing world, it’s truly too simple to actually work.
Aside from the maths being absurd, the notion that the value of these channels can be found by (a) automating a value process (even if it’s a measurement process) and (b) averaging customer data into a uniform baseline betrays a thorough misunderstanding of how social channels are meant to be used.
The beauty of channels like facebook pages and twitter accounts is that companies can identify loyal customers from other customers, non-transacting fans from transacting fans, big spenders from thrifty spenders. The data should strive to be more granular, more specific, more actionable. Crap like this (again, in rare cases where the maths are even right) strive to do the very opposite. This doesn’t make data more actionable, it makes it more vague and obscure. It creates a layer of opacity between the company and its individual customers (and non-transacting fans).
The people behind this must be web analytics guys with very little practical understanding of consumer insights management, business development, social business mechanics, marketing, data analysis, maths… oh, my head is hurting again.
I guess I should buy you a lifetime supply of your favorite headache killer. This seems to be a new talent of mine 🙂
Really well done my friend. And very objective. And since 98% of our lives take place in real life off line or social networks it is really hard to know anything concrete. It is why Klout drives me crazy. We forget that during the dot.com crash high flying stock analysts like Henry Blodgett were saying BUY a stock publicly (so in that 2% of online communication) but rivately were laughing at anyone who buys the stock.
We have many tools and lots of data and people should not look for the golden formula but find the one that fits their own reality for their brand and products. There are many luxury brands on facebook where the fans will never buy the product like Ferrari and I bet if you own a Ferrari or could afford one you aren’t spending much time on facebook.
Yep. The math is wrong, the formula is nonsense, and the very notion of VOAL as a means of measuring a meaningful business-relevant benchmark is pretty absurd if you don’t take things like cost and offline transactions into account.
Without a cost input, the whole premise is flawed. Never Mind the Maths…
Yep. And without a connection to offline transaction, you’re essentially measuring nothing.
I absolutely love this post, Olivier.
I like to fancy myself a “non-charlatan” digital media strategist and I cannot tell you how refreshing it is to see an article like this. Sometimes I get the feeling that everyone has started drinking their own kool-aid and have started to wait for the spaceships.
To make it ridiculously simple if you had to focus on one thing when it comes to Facebook it would be shares. And even with shares it’s hard to come up with a hard equation that determines their worth.
A good social media/web consultant knows the value of measuring results to determine real return on investment or worth.
Again, thanks for this. Great read.
You’re very welcome. 🙂
There’s a “when numbers go too far” analogy from the world of baseball: an award that is voted on (thus subjective) gets awarded by a group of writers, not math analysts. So, this year, a guy named Miguel Cabrera won the Triple Crown (leading the league in Batting Average, Home Runs and RBIs), something that had not been done in 45 years.
He won the MVP, the above-referenced subjective award, because his value to his team (which went to the World Series) and the game was judged to be higher than that of a young, strapping Mike Trout.
Here’s where the math comes in: when you ask the people who now run teams (Sabermetricians, who figure prominently in the movie “Moneyball”), Mike Trout was deemed to be mathematically more valuable – through a whole host of metrics (Wins Above Replacement, for instance – how many wins he added to his team when compared to an “average” replacement player).
The point, and I do have one: Sometimes you can over-analyze for the sake of over-analysis…and you create metrics that, in spite of your best efforts to legitimize them, just aren’t going to be the things that make sense to your business.
Ask the Detroit Tigers, for whom Mr. Cabrera plays, whether or not a guy who leads their team to the World Series in part because he led the league in three prominent offensive categories is the Most Valuable Player in the League…the answer is clear cut.
Ask the Los Angeles Angels of Anaheim (that’s their name), for whom Mr. Trout plays, whether or not their team even made the playoffs this year (they didn’t), and how many of those three offensive categories Mr. Trout led the league in.
Metrics for the sake of metrics sometimes don’t make sense.
But… book deals, man. There can be only one Nate Silver. so what’s everyone else with dreams of writing a social media book or becoming the Seth Godin of digital analytics supposed to do? Actually spend years experimenting, failing and trying again? In the immortal words of Sweet Brown, “Ain’t nobody got time for that!”
And you know, if someone who sucks at math wants to rebrand himself as a data scientist on the twitternets, who are we to stand in the way of his personal branding’s self-actualization? Every boy should have t right to call himself an astronaut on his own blog if he wants to. Just like every little kid gets a winner trophy after the game, even if their team lost. It’s the same thing. We’re all winners, man. We’re all geniuses and experts and gurus and Navy SEALs if we want to be.
We’re all the warm little centers of the universe too, no matter how badly we suck at the jobs we’ve invented for ourselves. Because nobody checks and nobody cares. They’ve already moved on to the next thing.
It’s the new way, Mr. Jones. It’s the social media way. 2 + 2 equals whatever the fuck you want, and if you can afford my retainer, I’ll show you what I can do with 2 x 2.
The ironic thing is, this is all more complicated than figuring out how much we made from the effort after we’ve subtracted how much we spent on the effort in the first place.
Seems like it’s all rooted in the old school media blast methodology. Flood the market, see what sticks. One way. Shut up and buy, stupid. Sooner we get to 100% permission-based marketing, where the only way anyone sees the message is if they expressly opt in (vs. paid interruption), the better.
Excellent point, Brian. It would be very simple to calculate how much you spent on building your audience (man hours, ad spend, etc) and then use a method like Olivier suggested in his discussion on Twitter with Dan to understand how much revenue you actually gained from reaching that audience through each channel.
That’s much closer to calculating the value of a like than this silly formula. 🙂
Very well said, Olivier! Every time I look at this equation I see another reason it won’t work, an important metric left out, substituted for something which doesn’t even belong in an equation trying to answer this question.
Also, thanks for the shout-out and the link to my post! 🙂
I’m glad peeps like you are on the ball, man. Well done. 🙂
Honestly, I’m not convinced it is possible to have one formula for every company to use. Different business models demand different measurement tools and I think we are all too often seduced by the need to simplify and create one size fits all solutions. Having said that, I understand the point you were trying to make here Olivier, but there is a difference between respectfully debating Dan’s point and giving him a “kick in the balls” as you call it.
Great deconstruction. I would also add that online, especially with really big brand marketers, there is a negative correlation between clicks and sales. In fact, it is actually impressions, delivered at the optimized reach-and-frequency, that are more predictive of ROI, particularly sales. The fact that this was so direct-response driven, and limited to online, made it seem worthless from the get-go.
Basically, yeah. If you look at my latest comments, you’ll find a simpler equation that actually does what Zarrella was trying to accomplish. But even though it works, it still suffers from the same problem: It’s limited not only to digital transactions, but to a last-click attribution model. So… the value of a fan/like isn’t being measured properly because our measurement is limited to a fraction of the full spectrum of transaction behaviors, online and offline.
A couple of points:
— I’m not bothered so much by the fact that the “cost of fan acquisition” isn’t calculated here, because that’s variable. If you *could* indeed reduce the equation to figure “value of a like,” then you have a valuable data point. Simply, it tells you the ceiling you should spend on acquisition, or gives you a number you can plug into something else. If the value of a fan is less than $.15, then I can leverage that against my time and expertise and the like…
— I *am* bothered by the sloppiness of the formula. The very first thing that jumped out at me is that we had (Likes) in the numerator and (Likes) in the denominator. Those would cancel completely.
Now, if you want to calculate the VOAL, then the number you presently have is irrelevant to that equation. But why leave additional numbers in there? Why not talk this up as something that is eliminated from the formula?
You know, if I introduced a very great new earth-shattering formula that explains the relationship between matter and energy —->
(60 E / 10) = 2M x (3 x C x C)
I would be laughed out of the room.
Sure, but since we’re trying to calculate an average $ value per fan/like the cost of fan/like acquisition metric could be averaged as well. It would be easy to plug that into a much simpler formula. Like this:
(RFF / # of fans) – ACA = VOAL
RFF = Total revenue from facebook inbound links
ACA = Average Cost of Fan Acquisition
VOAL = Average $value of a fan
That’s mathematically sensible and it calculates actual average value much more effectively (and quickly) than Zarrella’s equation.
Unfortunately, it’s still useless. Here are a few key reasons why:
1. It doesn’t take into account offline transactions.
2. It only takes into account digital transactions resulting from facebook links.
3. It doesn’t take into account % or net inactive fans.
4. It doesn’t take into account deltas in net new fans vs existing fans (this is relevant to deltas in transaction volume from period to period AND the amortization of ACA along a timeline (which is in line with your “variable cost of acquisition argument).
So even with an equation that actually makes sense mathematically, we still fall way short of actually measuring VOAL because we’re only measuring a fraction of the impact of Facebook on consumer behaviors and their transactions.
Without me having to do math, I just thought of another problem with this or any other formula.
Let’s say you are a brand new social media maven for a company and you want to share a special deal with your friends and family on your own page. People could click to the website (ostensibly) without ever even visiting your page. The inbound link would still come from Facebook but it would not indicate the value of a fan, per se.
Um… yeah. The entire premise is wrong. We’ve established that. 😀
I’m just trying to apply an equation to it that works, even if it’s all for naught anyway.
I thought your post as well as Dan’s were great. Note that I can still take value from Dan’s and yours, even if I don’t agree with each 100%. Nevertheless, it got me (“continued me”?!) to think, and that’ probably the goal. The Twitter exchange was good, too. One other note: Above your write “Because Zarrella’s article was published via the Harvard Business Review’s blog, scores of people won’t think to question it.” I’m not sure when you last looked at the post, but the comments trend mostly to your viewpoint. As in, “A for effort, but” so to speak.
Alan, as you know the percentage of people who comment is much lower than the number who print it out and stick it on the bulletin board, or on a subordinate’s desk.
I am very fortunate that my superiors trust my judgment on these things, but I fear that I am an outlier in that regard. For every Ike, there are a dozen social media strategists who get a memo from a VP that says “New Metric for Your Reports. It’s SCIENCE.”
Hi, most of the intuitions about the formula are correct here. I did actually take a little time to analyze this fake formula yesterday. I saw it on Harvard Business Review and it caught my attention because it looked incorrect immediately. The number of likes cancels from the equation. Which caused me to keep reducing. Also, it’s not written in a form we are used to seeing in mathematics. HBR wouldn’t publish my comments pointing out the errors. Anyway, It actually reduces to one variable. To see this you can go to http://www.valueofalike.com. Now, move the likes button at the top and watch the calculated value at the very bottom. It doesn’t change value. This means you can get rid of likes in the equation since it doesn’t change the calculated value. Which is seen in the equation by inspection. So, the average “value of a like” doesn’t depend on likes is the claim. I am surprised HBR would publish an advertorial promoting fake science. A quick analysis of the equation is on my blog if you’re interested in seeing how it reduces to one single variable. I had to put likes back in to get an average since they don’t have likes in the equation: http://www.seangolliher.com/2012/social-media/your-follower-count-doesnt-matter-hubspots-incorrect-value-of-a-like-voal-formula-published-by-harvard-business-review/
Fascinating. I hadn’t even noticed that.
I just read your article, by the way. Well done. I’m going to add a link to it.
Shit I thought some of the Big 4 Accounting Ratios and Formulas made no sense and got away with being complex for the same reason! Interesting how people just take what people give them before taking it for a test drive.
I completely agree that they should have analyzed this a bit more! Great recap!
Can someone please agree with me that Zarrella’s self-portrail as a martyr is really annoying as well…
A martyr? How so? Did he write something about this?
Just during his conversation with you he was tweeting things like “You can recognize a pioneer by the arrows in his back,” and “Do you create anything, or just criticize others work and belittle their motivations? -Steve Jobs”
Seriously? 😀 Wow…
Steve Jobs, huh? Why not Jesus or Leonardo da Vinci while he’s at it?
I won’t sugarcoat it Olivier, you need to re-take grade 8 math.
Can you elaborate? That’s a little vague.
I was on the receiving end of one of these kinds of posts several years ago when I wrote about the hypocrisy of real-time ROI on my blog and you, um, responded to me.
I’m not commenting on the math here. You and others have done that. Dan and I have debated social media math too and we released it as a video.
I do agree with your main point that people should be “doing the work.” Not sure how much work you did to understand what Dan (and HubSpot) are up to since you didn’t even spell “HubSpot” correctly. Either that or you purposely spelt it incorrectly in which case your statement “this post is in no way meant to be a bashing of Dan Zarrella” is false.
Disclosure: I am on the board of advisors of HubSpot.
So your point for commenting was to tell me I misspelled HubSpot? Or… was it to tell me about the awesome social media math video that you and Dan did together? (Don’t feel bad if I don’t waste my time on what I am sure is a fascinating discussion.) Or was it to remind everyone of your role on HubSpot’s advisory board, even? 😀
Look, I’m sorry that you’re still sore about being “on the receiving end” of an honest critique. I am. Not having thick skin must be tough when you’ve become a “personal brand” and whatnot. We all grow into adulthood at our own pace, and I understand that. But it’s probably time to get over it now, don’t you think? You’re going to get called out on bullshit a lot, David. And as a growing number of qualified, experienced, no-nonsense pros come to weigh the realities of the new digital space against the kind of lazy theory and bad science that used to sell books five years ago, it’s only going to get worse for guys like you and Dan. If you aren’t willing to either work at actually being the most knowledgeable guys in the room (not just pretending to be) or the ones with the thickest skin (or both), it’s going to be very a rough road. People’s tolerance for bullshit is growing thin, man. And acting like scorned little children every time someone tells you that you’re wrong (and shows you why) isn’t helping either.
At any rate, thanks for taking the time to add value to this discussion. I’ll go correct the spelling of HubSpot immediately. Feel free to come back and actually discuss the math anytime.
I’m not sure that an accidental misspelling merits saying that someone doesn’t know what they’re talking about. I think it’s great to have someone involved with HubSpot in the discussion, but if you are going to challenge Olivier’s analysis, I’d rather see actual reasons why you feel he’s wrong and hasn’t tried hard enough to understand the math, rather than “he misspelled the company’s name, so he must not have stared at numbers long enough to understand what they mean.” Where’s the logic in that?
Thanks for your great post and blog!
I have a question regarding your calculation of average value of a fan for last click distribution. I know you don´t recommend it either, but my question is regarding the ¨sales $ resulting from inbound traffic on Facebook.¨ Is that number broken out in Google Analytics? If it´s not and you only have the number of web visitors coming from Facebook, how could you be sure which online sales are directly attributable to those Facebook visits?
Great question. The easiest way to do that is to create a tagged hyperlink that you can track using some 3rd party app. Basically, you plug in your Google Analytics account info into the app and create tracking codes to follow user behaviors between Facebook and your checkout. Here are some pretty straightforward explanations of how it works, using several different scenarios:
3rd party app: http://www.bigcommerce.com/ecommerce-blog/how-to-track-visitors-and-sales-from-facebook-with-google-analytics/
Not linking directly to a product page: http://www.linkedin.com/answers/marketing-sales/advertising-promotion/internet-marketing/MAR_ADP_INM/985909-6429876
Basic tagging breakdown: http://www.trafficgenerationcafe.com/campaign-tracking-links/ (the site is pretty MLM-salesy, but the piece is pretty good)
And there’s also this: http://www.themoneytimes.com/featured/20121117/facebook-comes-novel-sales-tracking-tool-e-retailers-id-1701712434.html
Let’s also consider the Pareto Principle which can really be applied in any industry. For the most part 20% of your customers are responsible for 80% of your business. The same could be said for social media. Your (rabid) fans are the ones who are always going to be the ones sharing your content and pushing your brand and/or its products. No matter what the size of the fan base the actual amount of interaction and engagement is very low (I believe research recently came up with the number of less than 1% for most brands). uh huh
An anagram of ‘social media guru’ is ‘a ludicrous image’….go figure! 🙂
Thanks for writing this. Unfortunately I sat down and did the work on this supposed VOAL and was completely stumped by it’s arcane-nonesensity. I wasted an hour going over it again and again. And the site uses different data points! I would also recommend reading this post that breaks down the math (or lack thereof) a little more: http://www.seangolliher.com/2012/uncategorized/your-follower-count-doesnt-matter-hubspots-incorrect-value-of-a-like-voal-formula-published-by-harvard-business-review/#respond
Although I’m 100% with you on the formula, Oliver, I’m not sure why you had to stretch this out so painfully for poor Zarrella.
A simple post putting it right by saying that virtually all of the data points have interdependencies (which clearly makes it nonsense from the outset) would have been enough, with a little example, right?
I honestly don’t think any marketer worth their salt would throw this at their MD or Sales department and have it stick. Or … brace yourself for stock phrase #27 … am I missing something?
First, Zarrella needs to learn this stuff. I needed to explain it in detail. Second, because everyone else can benefit from it too. Had he not been so obtuse on Twitter when I tried to explain it the first time, I probably wouldn’t have had to be so thorough.
Also bear in mind that it isn’t just a random guy spewing nonsense. It’s also Hubspot and the Harvard Business Review. And for every pro like you and me who gets it, there are twenty marketing managers out there who buy this crap hook, line and sinker.
Cheers, Steve. Thanks for the comment.
Love this article, I’m tired of the over emphasis on social media!
Well said Olivier… it’s high time someone put some sense in these so called social media gurus…Great post
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