Albert asks, "What is the best way to sell the idea of marketing data science to executives that don't see its value, without making them lose face or look bad?"
Generally speaking, most executives want the same things - save money, save time, make money. Without pointing out specific failures, look for opportunities where a campaign has gone off the rails because decisions were made that went in the face of data, and share how proper use of the scientific method could have saved the campaign or prevented the mistakes in the first place.
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What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.
In today's episode and Albert asks, What is the best way to sell the idea of marketing data science to executives that don't see it's value without making them lose face or look bad? So data science is a practice, right? There's nothing really to buy.
It is a methodology using the scientific method, and technical, mathematical and business skills.
And so it blends in pretty well with pretty much anything that's going on in an organization that has data and wants to get more value out of it.
Generally speaking, is is not always true for all organizations and people but generally speaking, most executives want the same things right.
They want get their bonus, they don't want to look bad, they don't want get fired.
And fundamentally, it comes down to wanting to save time.
Save money or make money, right? Those are kind of the three big levers that most executives want to be able to have at their disposal.
How can I make this go faster? How can I think make things more efficient? How can I make things more productive? And this is true across, you know, almost every industry and discipline.
So there's a couple different ways you can sell in the idea of data science to an organization first is, you know, without pointing out specific failures, unless they're pretty high profile, and everyone knows it, or ideally, pointed out competitors, failures, people like that a whole lot more.
look for opportunities where campaign went off the rails because people made decisions that went in the face of data went in the face of proper analysis.
You can see when a competitor has a misstep, and you're like, you know, if only they had actually done some research and And not put out an ad or campaign or creative or an email that was so tone deaf or out of step with a marketplace.
That's how you can point out the value of data science to somebody who doesn't necessarily understand it.
A lot of the times you don't get buy in for a few reasons, right you one is they don't understand what it is they're buying.
Two is they feel there's too much risk.
And three, there's not enough upside.
If they feel there's too much risk, it's because they don't understand it is because they don't understand in this case, a discipline way of thinking right there's you're not selling somebody a piece of software.
Yes, there are software costs or agency costs or whatever.
Once you make a commitment to go down that route, but the principles of data science the discipline of data scientists That's that its way of thinking.
So when you look at a competitor that's made a misstep and made a decision that flies in the face of data, share how proper use of the scientific method could have saved that campaign, it could have prevented the mistakes in the first place.
Oh, you put out a an advertisement for a certain thing on a day that was culturally insensitive, right, that's a clearly doing something in in the face of known data.
And it's a way to say to somebody Look, this is if you just done a little bit of work, and run a small test to an to an audience to prove the value or to prove effectiveness, then you can will have a big one.
That's the scientific method, right? You test with a hypothesis, proven true or false, refine it, and repeat over and over again until you figure out what really works.
That's the essence of data science.
It's just the scientific method using your data.
So for buy in Focus on the competitors.
Find the lever that works for the executive.
And ultimately, show them how you would have done things differently.
And when it comes to launching your first data science initiatives within the company, look for small, easy things to start with.
Right? Don't try and build a massive, massive infrastructure.
say let's I want to use the scientific method properly, to AB test, a call to action on the website, right? So that way you can use existing tools like Google Optimize, that can handle the test, collect and analyze phases of the scientific method.
I say I'm just looking to try and improve conversion rates on that button there by 2%.
It's very, it would be very difficult for an executive to say okay, you want to improve conversion rates on one little thing for a short period of time, with no investment.
What do they got to lose? Right, using industry proven tools, find that pilot and if you happen to deliver more than 2%, great.
If you don't, they're not out anything and you can say, yeah, we ran an experiment, we proved that there is no value in changing the color of the call to action.
Right? That's easy.
That is understandable.
People can wrap their brains around that and go, Okay, we're just going to run a test to try and improve conversion a little bit here.
Right? Run AV test in email, and look at your calendar, look at your organization and say, Okay, we're going to do this at the least busy time, so that we're not you know, don't do it.
Like if you're a retailer and you're and you want to embrace data science, don't do it in the middle of the holiday season, right? Do it after the holidays, do it when it's quiet and you're still getting some traffic and stuff, but your whole company's future is not hindering on it.
That way, people again, you're trying to mitigate risk.
You're trying to reassure people that this is a relatively risk free way of thinking about using your data.
So there's a lot of different ways to approach this.
It depends ultimately on your company's culture, and their appetite for risk and your ability to present a low or no risk set of use cases that are small in size that will let them see some benefit but not terrified people.
Good question, Albert.
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