The Legends of B2B Marketing March 5, 2024 Selim Maalouf 7 min read
Would you look at that? I have a LinkedIn Newsletter Now. I can finally write one article a week and post it on LinkedIn... Oh, wait. I have been doing that since the start of 2020.
Snarky intro aside, I have found a new vigor to write and share my thoughts again on LinkedIn. Less about trying to grow an audience, and more about getting back into the swing of content creation.
But why now? Why am I writing articles again after having strayed away from them after 7 months of weekly posts?
In July 2020, I did a deep dive into the metrics of my content. The numbers showed that if I'm trying to grow an audience, my time was more effectively spent chasing the newest formats that LinkedIn was building and pushing (Native Video, Sliders, Polls, etc) instead of sticking to long-form articles hidden away from the main feed behind a click.
An extensive, well-thought-out article was being seen 90% less on average than a 1200-character text post. The data was clear.
Or maybe it wasn't. Maybe I was looking for an excuse to stop writing my thoughts into long-form articles. More on that later.
4 months ago, creator mode came crashing onto the LinkedIn creator scene, and with it came free access to LinkedIn Newsletters. I wrote about the sudden deluge of invitations to subscribe to all kinds of newsletters and how everybody and their mother suddenly wanted to send us their content directly into our notification tray.
And here I am, doing the same thing.
Is it because I want to start making content again? Why am I not doing video instead? Is it because LinkedIn is pushing this new feature? (They're not, their clubhouse copycat is the new shiny toy)
It is because of recent conversations I've been having about data.
Data is Objective. Data is Useless.
In the age of the internet and hyperconnectivity, we have witnessed the power shift from those who control the resources (Oil and Gas) to those who control data.
Every single venture out there is based on or enabled by the trading of data. Data is coveted by everyone, especially advertisers. For them, the value of data is straightforward: They use it to deliver ads in exchange for money.
Data brokers are similar, their product is the data itself.
But what about everyone else? All the "data-driven" companies out there, how are you benefiting from data?
The answer is simple: you're not. You're being data-driven off a cliff.
If you're reading this right now and feeling attacked, I urge you to look at the proverbial mirror. Are you truly data-driven, or is it a buzzword that you threw around one too many times and you now believe the lie that you told your customers and your stakeholders?
Let me know if this sounds familiar: you spent a long time making business decisions based on your past experiences when they are relevant and based on your gut feeling when breaching new territory. You woke up one day and realized that this is not a sustainable way to grow a business.
The latest thought tracking pixel made by Facebook (I refuse to call them Meta!) recognized your thought pattern and served you this ad while you were scrolling through pictures of your niece's dance recital.
Admit it, you even tried to click this image because you are already bought into being data-driven.
However, acquiring the data, be it through your own internal tracking or through various data brokers is not enough. Data is objective, and that is true. It is not affected by your emotions, aspirations, and mental struggles. Cold hard facts.
But this data is as useful as the treadmill you bought 6 years ago when you decided you were going to become healthy, so you threw money at the problem.
Scratch that, at least the treadmill could function as an overpriced coat hanger. All data can do is sit there and waste your digital storage and your money.
You need to find the story in the data
If you've ever seen the inside of the cockpit of a rally car, you've seen and probably heard the co-driver shouting cryptic ciphers and weird code words. You've asked yourself if the driver is actually listening and understanding what the co-driver is blabbering on about.
When you're going up to 70 miles an hour on average on twisty and unfamiliar gravel roads, the difference between crossing the finish line or falling off the cliff is a simple lapse of judgment. While it falls on the driver to steer the vehicle, the co-driver suggests how to approach each section of the course using their pace notes. They can tell the driver what is coming before the driver can see it.
If this metaphor is confusing, don't worry, it's not because I fell in love with the pun in the title. It's because you need someone to explain the metaphor for you, the same way you need someone who can look at the data and find the story for you.
Look around your team, and ask yourself: Is there someone that you can trust to look at the data and find the story that the data is trying to tell?
If that person exists, are they immune to the influence of the stakeholders' plans and desires? Can they be objective in figuring out the best insights that the data is trying to show instead of having a goal in mind and diving into the data trying to find how to get there?
The armor doesn’t go where the bullet holes are. It goes where the bullet holes aren’t. - Abraham Wald - SRG
A commonly referenced incident when talking about bias in data analysis is the research around the design of US fighter planes during the Second World War.
The Statistical Research Group at the University of Columbia was provided with some interesting data by the military. The data showed non-uniform bullet hole patterns on returning fighter planes.
The military suggested that there was an opportunity to optimize the distribution of the plane's armor. Their conclusion? Concentrate the armor where the planes are getting hit the most.
Abraham Wald, a statistician at the research group, disagreed. He had realized what everyone else missed because their nose was too deep in the data.
The planes were also getting shot in the empty spots but were not making it home and as a result, did not show up in the data.
The data did not mislead them, they were eager to be misled. They saw the story that fit their aspirations. They had a goal in mind and looked at the data to back up their decision. But someone who had no stakes managed to find the right story.
In the business world, that person is your data analyst. If you don't have one, get one. You won't regret it.
What if the data is lying
The data never lies. It can, however, be bad data.
Be it through malice or incompetence, the harvesting and the maintenance of said data was bad.
Either the data was made up, the tools used to collect it were used incorrectly, the humans who entered it were lazy, or the storage of this data was corrupted.
All of these cases result in data that might look innocuous on the surface, but deep down, will potentially cause irreparable damage to your business.
Jumping off a cliff sounds like a calculated risk when you grabbed a parachute and someone taught you how to use one before. But if that parachute you grabbed was a backpack filled with blankets and pieces of string, that calculated risk starts to look more like a suicide.
Data Stewardship is one of the most important roles under the data governance functions in big organizations. But what about you, the medium and small business? You want to hoard data and use it to inform your decisions, but you cannot afford a dedicated data steward.
Do you forge on and trust that your data harvesting mechanisms will never break down, your data entry is immaculate and your databases will never be corrupt? What happens if your data stabs your business in the back and causes insurmountable losses? Could you afford a data steward in that case?
Without the pit crew, the co-driver cannot save a rally car from failing during a race and causing a fatal car crash. Would you drive your own car on the highway without regular maintenance?
I wouldn't. Neither should you and your data-driven companies.
Or else, your company will be data-driven off a cliff.
