Monina asks, “What is data science and why does it matter in content marketing?”
This is part 1 of a 7 part series. Data science is the practice and professional of extracting meaningful insights from data using the scientific method. Watch the video for more details.
Can’t see anything? Watch it on YouTube here.
Listen to the audio here:
- Got a question for You Ask, I’ll Answer? Submit it here!
- Subscribe to my weekly newsletter for more useful marketing tips.
- Find older episodes of You Ask, I Answer on my YouTube channel.
- Need help with your company’s data and analytics? Let me know!
- Join my free Slack group for marketers interested in analytics!
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 menina asks, What is data science? And why does it matter in marketing and content marketing specifically, but this is part one of a seven part series from the content Tech Summit that I’m doing in April.
So if you have not had a chance to stop by there, and hopefully see you in April, and you should register to attend that show.
Data Science is the practice and profession.
It’s a profession of deriving meaningful insights from data using scientific methods.
Now, that’s really important statement because a lot of what people think is data science tends not to be it tends to be something related analytics, for example, or statistics or coding.
Those are all components of what data science is, but are not data science by themselves.
analytics for examples extraction of the analysis of data, what happened? What does the data tell us? It’s important, right? We’re not saying any of the things are unimportant, they are very, very important.
Reporting is the communication of data to somebody else engineering data engineering is the profession of trying to make data, easy to work with, easy to store, easy to access.
Statistics, of course, is the mathematical field.
It’s an entire field on its own, that you use in data science.
But it is itself not data science and artificial intelligence and machine learning again, use some of the very similar concepts, statistics, coding, etc.
But that is for the development of software, AI and machine learning.
The fundamental output is software, data science, the fundamental output of data science is scientific inquiry.
It is it is new knowledge that you’ve created from an investigation.
And so data science has four key components.
Number one is business skills and acumen, you have to be in tune with business to understand what it is that a business is trying to solve, what kinds of problems that you’re trying to solve, and it doesn’t have to be in marketing.
It can be in finance, it can be in operations if you’re a scientific company, it can be about the science itself.
You know, if you’re doing biotech, a data scientist can operate in any profession it is in any industry, it is a horizontal if you will.
The second is statistical mathematical skills, you have to know statistics you have to know probability you probably need to know some linear algebra, but you need those mathematical skills as a data scientist because that understanding of the mathematics particularly of statistics, is vital to knowing whether or not your scientific experiments are valid or not.
Are they reproducible? Are they other error rates, correct things like that.
The third area is the technology and the coding skills.
This is being able to work in a statistical language, our Python SPSS, take your pick.
But there are a number of different technologies that you need to be comfortable with as a data scientist because you’re doing science on data.
So there’s all these technological things that are part of being a data scientist, but the most important the most important are scientific skills, understanding and applying the scientific method and is this which sets apart data science from everything else.
It’s literally in the name as I said, science on data.
And science is all about discovering new things right? It’s about proving new things.
The scientific method is all about asking questions establishing a hypothesis, which is a provably true or false statement.
And then the system of inquiry that goes around proving a hypothesis true or false.
So, a real simple example would be this would not be data says, Hey, our conversion rate optimization is lower.
Let’s Let’s improve our conversion rate optimization.
How do we improve it? Okay, that’s that’s a very open ended question that is not a provably true or false statement.
On the other hand, if you went into your website and said, based on the analytics based on our exploration of the data, we believe passive voice of the problem.
The hypothesis would be converting the website from passive voice to active voice should improve conversion rates by at least 1%.
Right? That would be our hybrid This is statement and then you would conduct experiments.
To prove that true or false, you would convert some pages not convert other pages or use optimization software like Google Optimize, to show different versions of a page with different language to people and measure the conversion rates.
And that is that science that scientific background that makes data science different than analytics or reporting or statistics or AI even that hypothesis you can prove true or false.
And once you’ve proven it true or false in a reproducible, rigorous way, you now have a working theory and that we use that word very carefully because it is the scientific meaning not the general public being a general public meaning is a theory literally means to most people, I guess, right, which is not what it is.
A theory in science is.
This has been proven, it is open to interpretation, so is open to further experimentation with a refined hypothesis, but we have a working model of how we think The thing works.
So if you did that experiment and you built a website that used active voice only and you saw 7% increase in conversion, your theory, your model of website conversion language would be that active voice improves conversion rates by 7%.
That would be your model your scientific theory that you would put forth.
And then again, you can repeatedly test it, you can test it on different websites and different industries and things to see if that model that theory holds up.
This is what is sets apart data science from everything else.
It is that rigorous testing to prove or disprove something.
And so that’s why it matters in content marketing.
That’s why it matters in any kind of marketing.
If you’re using the scientific method along with the stats, the technology of the business document, you’re developing models of how customers behave, how marketing behaves in a way that allows you To, to reproduce and build on your knowledge base and to continue refining in a reproducible, replicable way you can do the experiment over and over again, you keep getting the same results.
That is why data science matters.
So, so much.
A lot of marketing, people could just kind of guess, right, they just kind of trying things randomly.
And that’s not the way to build a scientifically reliable method of doing your marketing.
So really important, you can tell I could go on for hours and hours about this, but really important.
That’s why the scientific method is so critical to what is data science, right? You have to be using the scientific method.
And there’s, again, many, many more rat holes, you can dive down but keep that in mind.
It’s so important.
Leave your questions in the comments box below.
Subscribe to my YouTube channel and the newsletter and if you’re in Interested in this stuff? Join my slack group go to Trust insights.ai slash analytics for marketers.
We do talk about all things analytics and data science and statistics there and stop on violence.
A lot of fun.
If you like nerdy math stuff if you don’t like that it’s no fun at all.
Thanks for watching.
I’ll talk to you soon.
What helps solving your company’s data analytics and digital marketing problems, visit Trust insights.ai today and listen to how we can help you
You might also enjoy:
- Retiring Old Email Marketing Strategies
- Almost Timely News, 17 October 2021: Content Creation Hacks, Vanity Metrics, NFTs
- Understand the Meaning of Metrics
- Transformer les personnes, les processus et la technologie - Christopher S. Penn - Conférencier principal sur la science des données marketing
- What Content Marketing Analytics Really Measures
Want to read more like this from Christopher Penn? Get updates here:
Get your copy of AI For Marketers